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Abstract

Service firms are encouraged by historic evidence that loyal customers are less price sensitive. Yet, some research has challenged the assertion while others have demonstrated considerable heterogeneity within loyal segments. Aiming to reconcile this debate, we investigate the relationship between customers’ behavioral loyalty and the importance they place on price relative to two managerially relevant service attributes: rewards and convenience. We also assess the moderating role of attitudinal loyalty resulting from superior service experience. Results from a longitudinal survey and transaction data from an airline carrier show that as customers’ behavioral loyalty increases, they place more importance on price and less importance on rewards and convenience, revealing that behavioral loyalty causes a shift in emphasis toward price. As a result, behaviorally loyal customers spend less and revenue decreases. However, by improving attitudinal loyalty, firms achieve the desired outcome of reducing price sensitivity and increasing revenue. Specifically, after experiencing better service, behaviorally loyal customers focus less on price and instead shift their focus toward rewards and convenience, and this results in revenue gains for the firm. Overall, attitudinal loyalty from better service experience acts as a key mitigator of the positive link between behavioral loyalty and price sensitivity.
ORIGINAL EMPIRICAL RESEARCH
Do loyal customers really pay more for services?
Nita Umashankar
1
&Yashoda Bhagwat
2
&V. Kumar
3,4,5,6
Received: 24 March 2016 /Accepted: 13 July 2016
#Academy of Marketing Science 2016
Abstract Service firms are encouraged by historic evidence
that loyal customers are less price sensitive. Yet, some re-
search has challenged the assertion while others have demon-
strated considerable heterogeneity within loyal segments.
Aiming to reconcile this debate, we investigate the relation-
ship between customersbehavioral loyalty and the impor-
tance they place on price relative to two managerially relevant
service attributes: rewards and convenience. We also assess
the moderating role of attitudinal loyalty resulting from supe-
rior service experience. Results from a longitudinal survey
and transaction data from an airline carrier show that as cus-
tomersbehavioral loyalty increases, they place more impor-
tance on price and less importance on rewards and conve-
nience, revealing that behavioral loyalty causes a shift in
emphasis toward price. As a result, behaviorally loyal cus-
tomers spend less and revenue decreases. However, by im-
proving attitudinal loyalty, firms achieve the desired outcome
of reducing price sensitivity and increasing revenue.
Specifically, after experiencing better service, behaviorally
loyal customers focus less on price and instead shift their
focus toward rewards and convenience, and this results in
revenue gains for the firm. Overall, attitudinal loyalty from
better service experience acts as a key mitigator of the positive
link between behavioral loyalty and price sensitivity.
Keywords Service experience .Attribute importance .Price
sensitivity .Behavioralloyalty .Attitudinalloyalty .Rewards .
Convenience
Service firms want to know which customers can be charged
more for their services. They are encouraged by evidence that
the most loyal customers are the least sensitive to changes in
price (Krishnamurthi and Raj 1991;Melaetal.1997;
Srinivasan et al. 2002). For example, car insurers are infamous
for price optimization practices in which they raise the pre-
miums for customers who have been with them the longest.
Similarly, telecommunication companies typically entice new
customers with lower prices and incentives but end up elimi-
nating such perks as the relationship continues.
However, many have questioned the idea that loyal cus-
tomers are less price sensitive and can therefore be charged
more. In particular, research has shown that there exists con-
siderable heterogeneity in price sensitivity within loyal seg-
ments (e.g., Guadagni and Little 2008; Krishnamurthi and Raj
1991;KrishnamurthiandPapatla2003). Further, many have
found that loyal customers have a narrower latitude of price
acceptance, spend less and at a lower margin, and negotiate
harder for discounts (e.g., Kalyanaram and Little 1994;
Kalwani and Narayandas 1995; Reinartz and Kumar 2000,
Dhruv Grewal served as Area Editor for this article.
*V. Kumar
vk@gsu.edu
Nita Umashankar
numashankar@gsu.edu
Yash oda B h a g w a t
y.bhagwat@tcu.edu
1
J. Mack Robinson College of Business, Georgia State University,
Atlanta, GA, USA
2
Neeley School of Business, Texas Christian University,
Fort Worth, TX, USA
3
Richard and Susan Lenny Distinguished Chair, Center for Excellence
in Brand and Customer Management, J. Mack Robinson College of
Business, Georgia State University, Atlanta, GA, USA
4
Huazhong University of Science and Technology, Wuhan, China
5
Singapore Management University, Singapore, Singapore
6
Indian School of Business, Hyderabad, India
J. of the Acad. Mark. Sci.
DOI 10.1007/s11747-016-0491-8
2002; Wieseke et al. 2014). Thus, the widespread notion that
service firms will benefit from charging their most loyal cus-
tomers more due to the perceived insensitivity to price is ques-
tionable and possibly perilous. For example, in 2011 Netflix
gravely suffered from introducing a price hike to its existing
customers and runs the risk of doing the same this year with a
planned $2 monthly increase to active subscriptions users
(Pevos 2016). Aiming to reconcile these competing findings
on the relationship between loyalty and price sensitivity, we
examine whether customersbehavioral loyalty, defined as
their cumulative spending with the firm, influences the impor-
tance they place on price, and if so, in which direction.
Customers are relentlessly sensitized to changes in price. In
fact, many service firms change their prices daily, heavily
advertise price promotions, and compete with other firms on
the basis of price. It is reasonable to expect that such a heavy
focus on price can come at a great cost to the firm, warranting
the need for firms to find other ways to provide value to
customers. Many service firms design their offerings to pro-
vide value in the form of rewards and convenience. For ex-
ample, if customers are willing to pay more for an airline
ticket, then they can earn more rewards and fly at a more
convenient time for a shorter duration. Similarly, centrally-
located hotels typically command higher prices, but as a result
customers gain more convenience and rewards. Interviews
with managers at hotel and airline firms confirm that implicit
in the design of service offerings is the hope that service re-
wards and convenience will provide enough value to cus-
tomers to shift their focus away from price. In this paper, we
examine whether behavioral loyalty affects customersrela-
tive price importance, or the tradeoffs they make between
price and service rewards and convenience.
Examining the effect of behavioral loyalty on relative price
importance may not shed adequate light on the multi-faceted
nature of loyalty (Watson et al. 2015). In particular, although
repeat patronage is desirable from a financial perspective, it is
not optimal to take behavioral loyalty at face value (Liu-
Thompkins and Tam 2013), because customersrepeat pur-
chases may be driven by other reasons (Dick and Basu 1994).
Indeed, customersemotional and psychological commitments
to a brand, or their attitudinal loyalty (Oliver 1999), also drive
their sensitivity to various actions by the firm (Umashankar et al.
2016). We examine how customersattitudinal loyalty moder-
ates the effect of their behavioral loyalty on the importance that
they place on price relative to rewards and convenience.
Using a rich dataset of time-series survey responses and
transaction data from an international airline firm, we address
the following research questions:
1. Should service managers pay attention to the importance
that customers place on various service attributes?
Specifically, does the importance of price, rewards, and
convenience impact firm outcomes, such as revenue?
2. If so, then what drives customersattribute importance?
Specifically, does behavioral loyalty influence tradeoffs
between price and rewards and convenience?
3. Does attitudinal loyalty moderate the effect of behavioral
loyalty on relative price importance?
4. If so, then what strategic actions should the firm take to
improve attitudinal loyalty?
Our findings contribute to the literature in several ways.
First, we demonstrate that as the importance of price increases
to customers, they spend less, whereas as the importance for
convenience increases, they spend more. Further, despite the
fact that on average, customers rate service rewards programs
as important, they are unwilling to spend more for these re-
wards. This finding aligns with past work on designing loyalty
programs (e.g., Liu 2007;YiandJeon2003)bysuggesting
that service firms might not be offering customers the Bright^
types of rewards. Importantly, we find that attribute impor-
tance varies across purchase occasions and across customers
and is not uniformly important to all customers at all times.
This suggeststhat service managers should pay close attention
to individual customersattribute importance over time be-
cause doing so will give them a sense of revenue potential.
Second, we identify factors that drive customersattribute
importance, with a special focus on price due to its ubiquity
and relevance to both firms and customers (Rahinel and
Ahluwalia 2015). We confirm a plausibly unintended conse-
quence of building behavioral loyalty: customers focus more,
and not less, on price, and as a result, spend less. We contend
that this occurs because behaviorally loyal customers define
value differently than less loyal customers do, and they tend to
focus on what they have already given to the firm (in sales). To
balance the value-alignment equation (Kumar and Reinartz
2016), behaviorally loyal customers expect the firm to give
up something in returnnamely, in the form of lower prices.
Third, past research has stated that loyal customers derive
value from the service relationship and the many benefits it
offers (Aurier and NGoala 2010). However, this may not be
true in all cases. We find that compared to non-loyal cus-
tomers, the firms most loyal customers shift their focus to-
ward price and away from rewards and convenience by 8%.
This results in a net decrease of 33% in per unit revenue (e.g.,
the price of an airline ticket). This adds to past work on loyalty
programs (Yi and Jeon 2003) and service convenience
(Seiders et al. 2007) by empirically demonstrating that while
these service attributes are generally important to customers,
the importance of price prevails.
Fourth, this work contributes to the growing literature on
service experience (Berry et al. 2002a,b;Cole2015;Kumar
et al. 2014). We uncover a new angle on the benefits of im-
proving service experience and show that attitudinal loyalty
softens behaviorally loyal customersfocus on price and in-
stead shifts it toward rewards four-fold and toward
J. of the Acad. Mark. Sci.
convenience more than three-fold. This results in an improve-
ment in per unit revenue of 43%. In contrast, we find that
attitudinal loyalty has little to no ability to reduce the price
sensitivity of customers exhibiting low levels of behavioral
loyalty. Thus, managers should consider focusing their service
experience investments on behaviorally loyal customers. By
doing so, they should see a lift in revenue resulting from
attribute tradeoffs that favor revenue-generating attributes
and deemphasize revenue-detracting attributes.
Finally, we provide service managers with directions on
how to improve attitudinal loyalty. A follow-up analysis re-
veals that investments in core (vs. peripheral) service features
drive attitudinal loyalty upward, which has positive implica-
tions for reducing price sensitivity in behaviorally loyal cus-
tomers and generating revenue. Core service features include
frontline employee friendliness, orderliness of procedures,
and physical comfort.
In what follows, we review the relevant literature and pro-
pose several hypotheses. We follow with a description of our
data, the method we used, and the results of our model esti-
mations. We proceed with a discussion of the implications of
this work and conclude with the limitations of this research
and directions for future research.
Literature review and hypotheses
The notion of value creation has been vastly explored in the
literature (e.g., Anderson et al. 1993, Anderson and Narus
1998; Monroe 1971;Wilson1995; Zeithaml 1988). Value has
been defined as Ba tradeoff between the quality of benefits they
perceive in the product relative to the sacrifice they perceive by
paying the price^(Monroe 1990). More recent work (e.g.,
Kumar and Reinartz 2016; Vargo and Lusch 2004) has expand-
ed the definition of value to include non-price costs to the cus-
tomer, including transaction, maintenance, and learning costs.
Sources of perceived value are many. Customers derive
value from minimizing costs (e.g., monetary, search, transac-
tion costs) and maximizing benefits (e.g., privacy, status,
perks, convenience, functionality) (Kumar and Reinartz
2016). Still, service firms tend to focus on a few key service
attributes that influence the desirability of a service offer.
Interviews with senior managers at an airline and a hotel
helped us determine that the price of the offering, how conve-
nient it is (e.g., location, availability, duration), and the re-
wards program dictate much of the perceived value from the
offering. Accordingly, these managers design their service of-
ferings with the hope that offering greater rewards and conve-
nience will allow them to command higher prices in the mar-
ketplace. Our review of airline and hotel offerings confirms
this. For example, a higher priced flight offers more rewards
and is available at more desirable times. Further, customers
must use a greater number of rewards to redeem a more
desirable flight (e.g., shorter duration, ideal timing), and al-
though they dont incur any monetary cost, they forgo the
opportunity to earn more rewards on the purchase. Similarly,
a search for a room on any major hotel website reveals that
customers are encouraged to make their purchase decisions
based on hotel location and room availability, rewards re-
demption and accrual, and the price of the room. In fact, the
search function on the websites emphasize these attributes
over other attributes. Thus, while several service attributes
likely affect customerspurchase decisions, we argue that in
competitive service industries in which customersswitching
costs are relatively low, service firms largely compete on
price, rewards, and convenience.
Sources of value: price, rewards and convenience
Price The role of price in customersproduct evaluations has
attracted considerable attention in the marketing literature
(Bornemann and Homburg 2011). The interest in price stems
from its prevalence in the marketplace and its inherent ambi-
guity (Lichtenstein et al. 1993). Importantly, the dynamic na-
ture of price setting generally affords customers substantial
opportunity to learn about and focus on changes in price
(Rahinel and Ahluwalia 2015). According to classical eco-
nomic theory, a higher price increases perceptions of monetary
sacrifice, resulting in a negative relationship between price
level and purchase probability. At the same time, for many
services, a higher price increases perceptions of quality, and
thus positively affects purchase probability (Rao and Monroe
1988). Indeed, customersperceptions of price are highly sub-
jective and susceptible to contextual influences (Alba et al.
1999; Krishna 1991; Krishna et al. 2002; Zeithaml 1988).
As a result, customers differ in how much they are willing to
pay for a given service. The more that managers know about
customer evaluations of and reactions to price, the more suc-
cessful they should be in meeting revenue goals. Thus, the
concept of price importance, which points to the relative
weight of price as a decision making factor in the buying
decision process (Kujala and Johnson 1993), is of great use
to marketing management (Goldsmith and Newell 1997).
Rewards Across many service industries including airlines,
hotels, banks, car rental companies, department stores, and
health clubs, firms attempt to formalize their relationships with
customers through loyalty programs (Daryanto et al. 2010).
Loyalty programs offer customers the opportunity to accumulate
free rewards when they make repeated purchases (Liu 2007).
Such rewards are considered as value-sharing instruments and
can enhance customersperceptions of what a firm has to offer
(Bolton et al. 2000; Woodruff 1997;YiandJeon2003).
Loyalty programs provide value to consumers in two
stages. In the first stage, program points are issued to con-
sumers at the time of purchase. Although these points have
J. of the Acad. Mark. Sci.
no practical value until they are redeemed, they are psycho-
logically meaningful to customers (Hsee et al. 2003;Van
Osselaer et al. 2004). Point accumulation creates an anticipa-
tion of positive future events, which increases their likelihood
of staying in the relationship (Lemon et al. 2002). In the re-
demption stage, customers receive both psychological and
economic benefits from a loyalty program. The free reward
functions as a positive reinforcement of customerspurchase
behavior and conditions them to continue doing business with
the firm (Sheth and Parvatiyar 1995). Thus, how important
rewards become to a customer is likely to be a function of
how invested in the service relationship he or she is.
Convenience Past work has readily acknowledged customers
interest in conserving time and effort (Gross and Sheth 1989;
Kelley 1958; Nickols and Fox 1983). This phenomenon has
encouraged the development of convenience goods and ser-
vices, increased advertiserspromotion of the time-oriented ben-
efits of their products, and motivated customers to use conve-
nience as a basis for making purchase decisions (Gross and
Sheth 1989). The continuous rise in consumer demand for con-
venience has been attributed to socioeconomic change, techno-
logical progress, more competitive business environments, and
opportunity costs that have risen with incomes (Seiders et al.
2000). In practice, firms devote greater resources to provide
convenience as part of a strategic shift to more effective custom-
er management. Researchers also are increasingly interested in
understanding the effects of convenience on consumer behavior,
and recent empirical studies indicate that convenience influ-
ences critical marketing consequences, including customer eval-
uation and purchase behavior (Rust et al. 2004; Seiders et al.
2005). Overall, perceived convenience is valued by customers
but is also under the direct purview of management, making it
an attribute worthy of further investigation.
Next, we formally establish the significance of customers
attribute importance, as it relates to price, rewards, and con-
venience, in driving firm revenue. Doing so allows us to ar-
gue, and empirically test, whether managers should indeed
pay attention to the importance that customers place on these
service attributes. Subsequently, we argue what drives cus-
tomersfocus on these attributes.
Does customer attribute importance affect firm revenue?
In many product categories, perceived value is positively in-
fluenced by benefits and negatively influenced by price
(Dodds et al. 1991; Hamilton and Srivastava 2008;Zeithaml
1988). An implicit assumption is that, all else equal, customers
are willing to pay more for services that provide higher per-
ceived benefits (Bolton and Drew 1991; Monroe 1990;
Zeithaml 1988). We extend this reasoning to propose that
the more important that a benefit becomes to a customer, the
more that he or she will be willing to pay for it, and as a result,
he or she will spend more to consume that service. Because
loyalty programs reward customers for their repeated patron-
age, customers tend to focus their purchases in one program to
maximize the benefits they receive (Sharp and Sharp 1997).
Studies find general support for such programs in terms of
increased spending levels (Drèze and Hoch 1998;Liu2007;
Ver h oe f 2003; Watson et al. 2015)astheycreateanexpectan-
cy of positive outcomes associated with making a purchase
(Vroom 1964). Thus, we can expect that as rewards increase in
importance to customers, so will their spending.
Similarly, we posit that customers will be willing to pay more
for convenience. The marketing literature has emphasized the
importance of customersdesire for convenience and the value
of time (Berry et al. 2002a,b). Customersperception of conve-
nience has a positive influence on their satisfaction with the
service and assessment of the firm (Berry et al. 2002a,b).
Related is the notion that service convenience can be purchased,
and as a result, should have an effect on revenue. For example,
by paying more, a customer can purchase a direct flight instead
of a one-stop flight. Further, a movie at a more desirable time
(e.g., weekends and weeknights) costs more, and if a desired
time is important to the customer, then he or she will spend more
to obtain that convenience. Anecdotal evidence supports the
notion that customers spend more for loyalty perks and conve-
nient service timings (Nagy 2010).
In contrast, it is reasonable to expect that the more that price
becomes important to a customer, the less he or she will be
willing to pay to consume a product. In service settings in
particular, the very same service can bepriced very differently.
For example, sophisticated yield management systems render
the exact same seat on a flight or hotel room at very different
prices, depending on who the customer is, what the market
conditions are, and even the day of the week (McGraw-
Herdeg 2014). Under such circumstances, the allocative
(monetary cost) role of price will dominate its informative role
(Rao and Sattler 2003). In other words, higher price increases
perceptions of monetary sacrifice, resulting in a negative rela-
tionship between price level and purchase probability
(Bornemann and Homburg 2011). Based on this rationale, it
follows that an increase in the importance of price to a cus-
tomer will fare poorly for the firm and will decrease revenue.
In sum, we hypothesize the following:
H1: As the importance of (a) price increases, revenue will
decrease whereas as the importance of (b) rewards and
(c) convenience increases, revenue will increase.
Customer loyalty and attribute importance
Given the proposed significance of customersattribute im-
portance to the firm, we next examine its antecedents, with a
particular focus on what drives the importance of price relative
J. of the Acad. Mark. Sci.
to rewards and convenience. Price is the baseline of inquiry
because of its ubiquity and significance to firms and cus-
tomers alike (Rahinel and Ahluwalia 2015). In fact, the entire
value proposition of firms such as Walmart, Southwest
Airlines, Costco, and Ikea is centered on lower prices.
Further, even luxury retailers such as Coach and Michael
Kors and flash-sale sites such as Gilt.com, MyHabit, and
Rue La La discount heavily to generate revenue from a
luxury-seeking audience. A key concern for such firms that
arises over time is ways in which they can shift customers
focus away from price.
We posit that customersloyalty to the service relationship
will directly influence how important price is to their purchase
decisions and whether the service attributes of rewards and
convenience can detract their attention from price. The litera-
ture has widely emphasized the importance of a loyal custom-
er base to any business (Reichheld and Teal 2001). Customer
loyalty is typically associated with customer retention, repeat
business, and positive word of mouth (Anderson and Mittal
2000; Mittal and Kamakura 2001; Morgan and Rego 2006),
all of which can lead to an increase in revenue.
Loyalty can roughly be divided into two categories: behav-
ioral and attitudinal (Baloglu 2002; Kumar et al. 2006).
Customers display behavioral loyalty when they repeatedly fre-
quent a business, often to the exclusion of competition.
Indicators of behavioral loyalty include, among others, the total
amount spent by customers, frequency of purchases, and share
of wallet (Kumar and Shah 2004; Liu-Thompkins and Tam
2013). Although such repeat purchases are desirable from a
financial perspective, behavioral loyalty alone may not be an
adequate indication of relationship strength (Liu-Thompkins
and Tam 2013), because customersrepeat purchases may be
driven by different reasons, such as favorable attitude, switching
barriers, and sunk costs (Dick and Basu 1994).
Attitudinal loyalty addresses the psychological component
of a customerscommitment to a brand and encompasses be-
liefs of service superiority as well as positive and accessible
reactions toward the brand (Oliver 1999). Even if customers
fail to make a repeat purchase, their recommendation of the
service to other customers provides evidence of their attitudi-
nal loyalty (Kursunluoglu 2011). Importantly, although attitu-
dinal loyalty can lead to repeat patronage, not all repeat pur-
chases are the result of attitudinal loyalty (Liu-Thompkinsand
Tam 2013). Furthermore, while customers may psychologi-
cally be committed to a brand, they may not purchase from
the brand as frequently as other customers. Thus, the con-
structs of behavioral loyalty and attitudinal loyalty are unique.
From a value-alignment perspective, our primary interest is
in the influence of behavioral loyalty on the importance of
price relative to rewards and convenience. Behavioral loyalty
comes at a cost to the customer because he or she has
expended considerable monetary resources to maintain the
service relationship. As a result, behaviorally loyal customers
are likely to view the value-alignment equation, or how much
they have given up versus how much the firm has given up
(Kumar and Reinartz 2016), very differently than are less be-
haviorally loyal customers. Differences in value-alignment
perceptions should directly influence which service attributes
rise in prominence and which decline. Thus, we subsequently
argue the effect of behavioral loyalty on the importance of
price, rewards, and convenience and generate arguments
about the relative importance of price. Further, it is conceiv-
able that customersattitudinal loyalty will influence the de-
gree to which behaviorally loyal customers pay attention to
price (or other service attributes) on account of what theyhave
already given to the firm (e.g., monetary resources as a result
of being behaviorally loyal). We follow with an examination
of the moderating effect on attitudinal loyalty on the link be-
tween behavioral loyalty and relative price importance.
The unintended consequence of behavioral loyalty
While customers are increasingly more demanding of both
higher service quality and lower prices (Slater 1997), they rec-
ognize that tradeoffs have to be made (Monroe 1990).
Customers are faced with multiple options, with each option
having a competitive advantage on some attributes but a disad-
vantage on others (Sun et al. 2012). Many service firms design
their offerings to provide value to customers in the form of
loyalty perks, or rewards, and convenience (Frei 2008). We ex-
amine how behavioral loyalty affects the importance customers
place on price, rewards, and convenience with the intention of
uncovering customersrelative price importance, or the tradeoffs
they make between price and service rewards and convenience.
The effect of behavioral on price sensitivity is decidedly mixed
(see Table 1). Some have argued for a negative effect wherein
behaviorally loyal customers are less price sensitive than are less
loyal customers (e.g., Krishnamurthi and Raj 1991;Melaetal.
1997; Srinivasan et al. 2002). Others have qualified this finding
by demonstrating that the strength of the negative relationship
varies within loyal segments and over time (e.g., Guadagni and
Little 2008; Krishnamurthi and Raj 1991; Krishnamurthi and
Papatla 2003). Another set of researchers have found that behav-
iorally loyal customers are in fact more price sensitive (e.g.,
Kalyanaram and Little 1994; Kalwani and Narayandas 1995;
Reinartz and Kumar 2000,2002; Wieseke et al. 2014).
We adopt the perspective of the latter group in which the
relationship between behavioral loyalty and price sensitivity is
thought to be positive. Specifically, we expect that in the con-
text of highly competitive service industries in which cus-
tomers are courted with lower prices and have the ability to
switch firms relatively easily, behaviorally loyal customers
will care more, and not less, about price. In other words, cus-
tomersbehavioral loyalty comes at a cost to the firm.
Customers who have committed more monetary resources
over time to the service relationship are likely to be more
J. of the Acad. Mark. Sci.
Tab l e 1 Literature review of loyalty and price articles
Reference Setting Measures of loyalty and price Finding Relationship between
loyalty and price sensitivity
Krishnamurthi and Raj (1991) BURKE brand data and
IRI coffee data
Loyalty: proportion of times brand
is purchased Price: price paid
and price faced
Loyal customers are less price
sensitive in choice decision
butaremoresensitivein
quantity decision.
/+
Kalyanaram and Little (1994) Scanner databases (sweetened
and unsweetened drinks)
Loyalty: interpurchase time Price:
latitude of price acceptance
Consumers with a higher frequency
of purchase have a narrower
latitude of price acceptance.
+
Kalwani and Narayandas (1995) Compustat data in business-to-business
industries
Loyalty: long-term relationships
Price: gross margin
Firms in long-term relationships
face lower gross margins over time.
+
Mela et al. (1997) Consumer packaged goods data Loyalty: brands share in the last
four purchases of a household
Price: price paid and price
promotion redemption
Non-loyal consumers are more price
and promotion sensitive than are
loyal consumers.
Reinartz and Kumar (2000) Retailer catalog households Loyalty: tenure Price:
transaction amount
Long-life customers do not pay
higher prices.
+
Reinartz and Kumar (2002) Corporate service provider,
grocery retailing, mail-order,
direct brokerage firms
Loyalty: longevity Price:
willingness to pay
A loyal customer (corporate or
consumer) is more price sensitive
than an occasional customer.
+
Srinivasan et al. (2002) Online customers maintained
by a market research firm
Loyalty: survey measures of loyal
behavior Price: survey measures
of willingness to pay more
E-loyalty has positive impact on
willingness to pay more.
Krishnamurthi and Papatla (2003) ERIM scanner panel (liquid
detergent, margarine,
ketchup, yogurt)
Loyalty: whether brand was
purchased on purchase
occasion t Price: net price paid
The loyaltyprice sensitivity
relationship is dynamic and
heterogeneous across customers.
- (to varying degrees)
Palmatier et al. (2007) Industrial buyers, salespeople,
and rep firms
Loyalty: survey measures of loyalty
to firm and salesperson Price:
average price premium
Both salesperson-owned loyalty
and firm loyalty increase the
customers willingness to pay
a price premium.
Guadagni and Little (2008)Ground coffee store and panel
records from four supermarkets
Loyalty: brand repurchase Price:
brand price and promotion
Groups of loyal customers who are
relatively insensitive to marketing
actions and a pool of switchers who
are quite sensitive.
/+
Wieseke et al. (2014) Jewelry retail chain transaction
and survey data
Loyalty: number of store visits
with a transaction Price:
price concessions
Loyal customers receive deeper discounts. +
This paper Airline customerstransaction
and survey data
Loyalty: cumulative purchase
amount ($) Price: survey
measure of price importance
Behavioral loyalty increases importance
of price relative to rewards and convenience.
+
J. of the Acad. Mark. Sci.
demanding of the relationship (Slater 1997), due to feelings of
entitlement arising from what they have Bgiven^to the firm.
For perceptions of value to be mutually aligned between be-
haviorally loyal customers and the firm, the firm should be
willing to give up something in return for their behavioral
loyalty. One mechanism by which the firm can do this is in
the form of lower prices. Further, customers who have spent
more in the past are more familiar with the firm and its tactics
(Kalyanaram and Little 1994). As a result of learning over
time, behaviorally loyal customers are more likely to notice,
and continue to monitor, one of the most fundamental things
that a firm continuously changes: price (Rahinel and
Ahluwalia 2015). Thus, we expect that behaviorally loyal cus-
tomers who have spent more in the past will place a greater
importance on price than those who have spent less.
Price is likely more salient to behaviorally loyal customers
than are rewards and convenience because payment for the ser-
vice is imminent and hence they have to make a tangible sacri-
fice, whereas the benefits from receiving rewards points or hav-
ing a more convenient service time is relatively delayed.
Furthermore, behaviorally loyal customers have spent a greater
portion of their budget with the firm and hence are likely to be
more conscious of price. This is because an individualspast
behavior serves as a basis for making inferences that guide his
or her future actions. The salience of inference (in this case,
price) is determined by its consistency with an individuals
existing self-schema (in this case, past spending or behavioral
loyalty) (Tybout and Yalch 1980). Alternatively, the benefits of
rewards and convenience are less directly tied to customers
schema associated with spending. Thus, we contend that re-
wards and convenience will diminish in importance for behav-
iorally loyal customers. Therefore, we hypothesize:
H2: As customersbehavioral loyalty increases, the impor-
tance of (a) price will increase, whereas the importance
of (b) rewards and (c) convenience will decrease.
The mitigating effect of attitudinal loyalty
Attitudinal loyalty can shift customersexpectations of the ser-
vice relationship. Ringberg et al. (2007) note that customers
with a vested interest in seeing their relationship with the service
provider continue tend to adopt a relational cultural frame of
mind. Since attitudinally loyal customers are favorably
predisposed to the service provider, they lower their service
expectations (Hess Jr et al. 2003) and adopt a more accommo-
dating stance when responding to actions from the firm (Ashley
and Varki 2009). For example, customersstrong emotional ties
to the cosmetic company Mary Kay helped it weather numerous
controversies. Further, customersallegiance to Hyundai has
persisted despite the fact that it raised the price on various car
models to become a premium brand (Bhasin 2011).
Customerspositive predisposition resulting from their atti-
tudinal loyalty will likely reduce the salience of what they have
given up for the relationship on account of their behavioral
loyalty. Behavioral loyalty implies having spent more but does
not necessarily imply positive experiences with the firm. Hence,
customers who are highly behaviorally loyal are likely to focus
on what they have spent with the firm and how the firm should
respond in return. However, this emphasis on price should shift
as attitudinal loyalty increases. Attitudinal loyalty resulting from
better service experiences can help balance the value equation
(Umashankar et al. 2016). In particular, the more that customers
feel that the firm is successfully creating value for them
through superior service experiencethe less emphasis they
should place on being Bpaid back^for their behavioral loyalty
through lower prices. Attitudinal loyalty is a value generating
Bpersonal journey^on its own, which results in positive emo-
tions that are then stored as a memory (Cole 2015). Positive
emotions should result in the customer being less calculative
in terms of expecting more value from lower prices and should
him or her to behave more as a relational partner (Ringberg et al.
2007).
Attitudinal loyalty should not only weaken behaviorally
loyal customersfocus on price, but also shift their attention
toward the benefits of the service relationship. Service re-
wards and convenience are benefits that customers deem as
valuable (Bolton et al. 2000). For example, rewards are per-
ceived as Bbonus^perks that enrich the customer experience
(Bolton et al. 2000). Similarly, service convenience offers
customers the benefit of consuming the service more easily.
The ability of behaviorally loyal customers to focus more on
such benefits and less on the cost component of the value
equation is more plausible after a better service experience,
because the experience itself provides value and results in
more positive emotions toward the brand (Cole 2015).
Overall, we expect that better service experiences will result
in attitudinal loyalty, which will shift behaviorally loyal cus-
tomersfocus toward value creation through benefits (e.g.,
rewards and convenience) and away from value creation
through cost savings (e.g., price). More formally:
H3: Attitudinal loyalty will (a) reduce behaviorally loyal cus-
tomersfocus on price and will (b) increase their focus on
rewards and (c) increase their focus on convenience.
In total, we conjecture as the importance of price increases
for customers, firm revenue will decrease (H1a), whereas as
the importance of rewards (H1b) and convenience (H1c) in-
crease, revenue will increase. This has implications for cus-
tomersrelative price importance. If the relative importance of
price decreases, revenue will increase not only because the
importance of a revenue-detractor (price) decreases but also
because the importance of revenue-enhancers (rewards and
convenience) increases.
J. of the Acad. Mark. Sci.
We argue that in the context of a highly competitive service
industry in which firms encourage customers to become loyal
primarily through lower prices, behaviorally loyal customers
will place more emphasis on price (H2a) and less emphasis on
service rewards (H2b) and convenience (H2c). This suggests
to managers that rewards and convenience are unable to lure
such customersfocus away from price. However, after a bet-
ter service experience, the resulting attitudinal loyalty should
cause behaviorally loyal customers to decrease their emphasis
on price (H3a) and instead increase their focus on rewards
(H3a) and convenience (H3b). Thus, we hypothesize that
firms will be afforded the opportunity to shift customersat-
tribute importance by improving service experience in such a
way that revenue-enhancing attributes (e.g., rewards and con-
venience) will rise in importance and revenue-detracting attri-
butes (e.g., price) will decrease in importance. We depict the
conceptual framework in Fig. 1.
Method
Data
Our dataset contains customer-level transaction and survey
data from April 2008 to March 2011 from a Fortune 1000
international airline headquartered in the United States. The
survey data were supplied by the airline. The airline sends a
survey to its customers every 6 months to assess their satis-
faction with the firm and willingness to recommend, how
important various attributes were in making their purchase
decisions, perceptions of various aspects of the service en-
counter, and individual characteristics.
1
We restricted our sam-
ple to customers who had taken the survey at least three times
to ensure that we had adequate observations per customer to
observe variation over time.
2
The airline also provided us with
transaction data for the surveyed customers. This rich dataset
included information about the customerspurchase behaviors
over time and the firms marketing efforts. In total, we had
purchase and survey data for 700 customers with a total of
6423 purchases. We also collected economic data from the
Gallup-Healthways Poll to assess the influence of macro-
economic conditions on customersspending and attribute
importance.
Variable operationalization
The definitions, operationalization, related literature, and
data sources associated with the measures are presented
in Table 2.
Dependent variables The first outcome measure of interest is
Revenue, measured as the ($) amount spent by the customer
for a particular airline ticket.
The second outcome of interest is customersstated impor-
tance of price. Awidely used method of assessing the impor-
tance of an attribute to a customer to ask him/her directly. The
two most common of the direct approaches are direct rating
and point allocation methods (Bottomley et al. 2000). Direct
ratings are favored because respondents respond better to this
method, they provide more stable weights, and they are more
commonly used in practice (Bottomley et al. 2000;Gustafsson
and Johnson 2004). This method involves respondents rating
the importance of individual attributes, such as price (e.g.,
Homburg et al. 2014), on a scale ranging from not at all im-
portant to very important (Jaccard et al. 1986). In our study,
the airline asked its customers to rate how important price was
to their decision to purchase their most recent flight on a scale
of 1 to 5 (1 = not important, 5 = very important). We used this
single-item rating as our measure of price importance.
The survey also asked customers to rate how important the
perks associated with the loyalty program were in choosing their
most recent flight, which became our single-item measure of
rewards importance. Further, customers were asked to rate
how important the availability and convenience of flight timings
was in choosing their most recent flight, which became our
single-item measure of convenience importance.Insum,the
four dependent variables are revenue,price importance,rewards
importance,andconvenience importance.
3
Independent variables Behavioral loyalty has been mea-
sured several ways (Watson et al. 2015), including pro-
portion of purchase (Cunningham 1966), probability of
1
Because the sample firm has a policy of not sending a survey to the
same customer within 6 months of the last survey, we assume that the
completed survey responses reflect customersaverage attribute impor-
tance during the six-month interval. Accordingly, and consistent with past
research (e.g., Kumar et al. 2014), we assume that the importance ratings
and attitudinal loyalty perceptions persist and apply to each flight within
the 6 months interval until the customer updates them in a new survey.
2
To account for the possible sample selection bias arising from our re-
stricted sample, we randomly selected 700 customers who made more
than one purchase in the same period but did not complete a survey and
compared this to our sample. We used a multivariate analysis of variance
(MANOVA) to determine whether there are significant differences be-
tween the two samples across a vector of five variables: yearly average
service purchase frequency, average service revenue per purchase, age,
relationship duration, and frequency of marketing emails. The MANOVA
result indicates that there is no significant difference between the two
groups (Wilks λ=0.99,F(5,1589) = 1.82, p>.10).
3
The revenue model was estimated merely to establish the importance of
the three service attributes. The emphasis of this paper is on modeling the
effects of behavioral and attitudinal loyalty on the importance of price,
rewards, and convenience.
J. of the Acad. Mark. Sci.
purchase (Farley 1964), probability of product repurchase
(Lipstein 1959), purchase frequency (Brody and
Cunningham 1968), repeat purchase behavior (Brown
1952), and purchase sequence (Jaccard et al. 1986).
Many studies have relied on asking customers to recall
their behavioral loyalty or anticipate their future spend-
ing (e.g., De Wulf et al. 2001; Sirohi et al. 1998). While
backward-looking metrics are generally favored (Watson
et al. 2015), behavioral loyalty becomes even more
meaningful when it is characterized by actual purchase
behavior as opposed to having customers recall how of-
tentheypurchasedsomethinginthepastusingascale
(Kumar and Shah 2004;ReinartzandKumar2000;Shah
et al. 2014).In particular, customerspast spending is a
key indicator of how much they have invested in the
service relationship, and as such, is an appropriate indi-
cation of behavioral loyalty (Reinartz et al. 2008). Thus,
we chose to measure behavioral loyalty as the cumula-
tive amount paid in dollars by the customer for past
flights and other services (e.g., baggage fees, cancelation
fees, upgrades) from the time he or she joined the firm.
Although we believe our specific metric is a key compo-
nent of an overall behavioral loyalty construct, we ac-
knowledge there is more than one valid metric and test
the robustness of this measure subsequently.
Consistent with past research (e.g., Liu-Thompkins and
Tam 2013;YiandJeon2003), we measured attitudinal
loyalty as a composite of satisfaction and willingness to
recommend. These measures were captured in the surveys
sent out by the airline to its customers using Likert scales
(15). The correlation between these two measures is high
(r= .71) so we averaged the two survey items to create a
two-item measure of attitudinal loyalty.
Control variables We included demographics such as the
gender (binary scale), age (raw data), and income (inter-
val scale) of the customer, all of which were collected in
the surveys. We also used self-reports of share of wallet
in which the customers were asked about the number of
flights taken in the last 12 months with the sample air-
line and with competing airlines (similar to the measure
used by Eisenbeiss et al. 2014). Share of wallet, defined
as share of flights in this context, was calculated as the
number of flights taken on the sample airline divided by
the total flights taken on all airlines. We accounted for
firm-initiated marketing emails, which influence cus-
tomersspending (Kumar et al. 2014), using the weekly
average number of emails sent to the customer in be-
tween flights. We accounted for whether the customer
was traveling for business vs. leisure using a self-
reported binary variable collected in the surveys. We also
controlled for whether the ticket purchased was a round
trip ticket and the distance (inmiles)oftheflight,both
of which directly relate to the price of the ticket, the
number of rewards that can be gained, and timings of
the service offerings. This data was provided by the firm
in the transaction dataset.
Further, we gathered data on control variables that
relate uniquely to the price importance, rewards impor-
tance, and convenience importance models. For the price
importance model, we captured economic well-being
using data from the Gallup-Healthways poll, which inter-
views 500 people daily about six domains: life evalua-
tion, emotional health, physical health, healthy behaviors,
and access to resources. Gallup-Healthways publishes
this data at the state-level on a monthly basis. Using
residence information provided in the transaction dataset,
Fig. 1 Conceptual framework
J. of the Acad. Mark. Sci.
Tab l e 2 Measures, definitions, operationalization, and data sources
Variable names Definitions and relevant citations Operationalization and relevant citations Data source
Dependent variables
Revenue How much the customer spends on a
service (Venkatesan and Kumar 2004)
The ($) price customer i paid for flight
j(Kumar et al. 2014)
Transaction history customer database
Price importance The importance that customers place on price
when making a purchase decision (Homburg
et al. 2014; Kujala and Johnson 1993)
Rating (Likert: 15) of the importance
of price to customer iin purchasing
flight j(Gustafsson and Johnson 2004;
Homburg et al. 2014; Jaccard et al. 1986)
Survey sent by airline
Rewards importance The importance that customers place on
rewards when making a purchase decision
(Yi and Jeon 2003)
Rating (Likert: 15) of the importance
of rewards to customer iin purchasing
flight j
Survey sent by airline
Convenience importance The importance that customers place on
convenience when making a purchase
decision (Seiders et al. 2007)
Rating (Likert: 15) of the importance
of convenience to customer i in purchasing
flight j
Survey sent by airline
Independent variables
Behavioral loyalty Repeat patronage of a business (Kumar and
Shah 2004;ReinartzandKumar2000;
Shah et al. 2014)
Cumulative amount spent ($) by the
customer on his/her flights and other
services (e.g., baggage fees, cancelation
fees, upgrades) from the time he or she
joined the firm (adapted from Liu-Thompkins
and Tam 2013;Reinartzetal.2008)
Transaction history customer database
Attitudinal loyalty The psychological component of a consumers
commitment to a brand that encompasses
beliefs of service superiority and positive
and accessible reactions toward the brand
(Oliver 1999; Kumar and Shah 2004)
An average of customersratings (Likert: 15)
of satisfaction and willingness to recommend
(adapted from Liu-Thompkins and Tam (2013)
and Yi and Jeon (2003))
Survey sent by airline
Control Variables
Gender The gender identification of the customer Whether customer i is a male (1) or female (0) Survey sent by airline
Age The age of the customer Age in years of customer iat flight jSurvey sent by airline
Income How much income the customer earns Interval scale (1 = lowest, 6 = highest)
of income ($) of customer iat flight j
Survey sent by airline
Share of wallet Share of category expenditures spent
on purchases at a certain company
(Eisenbeiss et al. 2014)
Number of flights taken on the sample
airline divided by the total flights
taken on all airlines (adapted
from Eisenbeiss et al. 2014)
Survey sent by airline
Marketing emails Email communication with customers
(Kumar et al. 2014)
Number of emails sent to customer
ibetween flights j-1 and j
Transaction history customer database
Business vs. leisure Whether the purpose of the flight is for
business or leisure (Kumar et al. 2014)
Whether flight j for customer i was
for leisure (0) or for business (1)
Survey sent by airline
Round trip Whether the trip is a round-trip or one-way
(Kumar et al. 2014)
Whether flight jforcustomeriwas
a roundtrip (0) or for business (1)
Transaction history customer database
Distance The distance traveled during the flight
occasion (Kumar et al. 2014)
Number of flying miles associated
with flight jfor customer i
Transaction history customer database
Economic well-being Captures the important aspects of how
people feel about and experience their
daily lives (Gallup-Healthways Website
2012; Kumar et al. 2014)
An average score of six domains
(range: 1100) of perceived
economic well-being: life evaluation,
emotional health, physical health,
healthy behaviors, work environment,
and basic access to resources
Gallup-healthways poll and
transaction history customer database
Ease of booking How easy it was to book the flight A Likert rating (15) for how easy it
was to book flight jfor customer i
Survey sent by airline
Redemption Whether the flight was redeemed
using loyalty points
Whether flight jwas redeemed by
customer iusing loyalty points (1) or not (0)
Transaction history customer database
J. of the Acad. Mark. Sci.
we matched customers to the monthly measure of eco-
nomic well-being in their respective states. In the re-
wards importance model, we controlled for redemption,
or whether the flight was redeemed using loyalty points,
which was provided to us in the transaction database.
Finally, in the convenience importance model, we con-
trolled for the ease of booking, which indicates the how
convenient purchasing the ticket was on a Likert scale.
This data came from the survey.
Revenue model
To establish that managers should indeed pay attention to
customersattribute importance, we regressed revenue
(the price paid by an individual customer for a given
flight ticket) on the main effects of price importance, re-
wards importance, and convenience importance. One
modeling challenge that arose is the issue of reverse cau-
sality: how much a customer generally spends (i.e., reve-
nue per ticket) can affect the emphasis he or she places on
price. We used the control function approach (Heckman
and Robb 1985) to address this potential source of
endogeneity. We chose economic well-being as an instru-
mental variable because it measures general perceptions
of access to resources and feelings of stability at the state
level, which should impact how sensitive customers gen-
erally feel about spending money, but may not impact
how much they actually spend (their flight purchases
may be needs-based). We confirmed that economic well-
being indeed affects price importance but not revenue. We
regressed price importance on economic well-being and
obtained the residuals. The residuals were then added to
the revenue model to control for endogeneity.
We accounted for individual heterogeneity by in-
cluding customer and transaction-specific characteris-
tics such as age, income, share of wallet, business vs.
leisure, round trip, and distance. We also accounted for
unobserved heterogeneity using random effects. A
Hausman test confirmed that a random effects specifi-
cation is preferred to fixed effects (i.e., we failed to
reject the null hypothesis). Further, we tested for auto-
correlation using the XTSERIAL procedure in STATA
and rejected the null hypothesis that autocorrelation
does not exist (p< .01). Hence we estimated a time
series model to account for the serial correlation across
observations for a given customer. The revenue time
series model for customer ifor flight jwas specified
as follows:
Revenueij ¼ρ0iþρ1Price Importanceij1:jþρ2Rewards Importanceij1:j
þρ3Convenience Importanceij1:jþρ4Genderiþρ5Ageij
þρ6Incomeij þρ7Share of Walletij þρ8Marketing Emailsij1:j
þρ9Business vs:Leisureij þρ10Round Tripij þρ11Distanceij þϖij þϕij
ð1Þ
Where:
Price Importance
ij-1:j
= importance of price to customer i
leading up to the decision to purchase flight j.
Rewards Importance
ij-1:j
= importance of rewards to cus-
tomer ileading up to the decision to purchase flight j.
Convenience Importance
ij-1:j
= importance of convenience
to customer ileading up to the decision to purchase flight j.
Gender
i
= whether customer iis a male (1) or a female (0).
Age
ij
= age in years of customer iat flight j.
Income
i
= income of customer iat flight j.
Share of Wallet
i
= share of wallet of customer iat flight j.
Marketing Emails
j-1:j
=numberofemailssenttocustomeri
between j-1 and jflights.
Business vs. Leisure
ij
= whether flight jfor customer iis a
leisure (0) or business trip (1).
Round Trip
ij
= whether flight jfor customer iis a roundtrip
(1) or one way (0) flight.
Distance
ij
= number of flying miles associated with flight j
for customer i.
ϖ
ij
= residuals from control function estimation with
economic well-being as the instrument.
ϕ
ij
=randomerror.
Attribute importance models
Our primary interest is in assessing the effects of behavioral
and attitudinal loyalty on customersattribute importance.
Hausman and autocorrelation tests confirmed the need to es-
timate random effects time series models of price importance,
rewards importance, and convenience importance. We esti-
mated the three models jointly because the errors might be
correlated due to the fact that customersvarious ratings of
importance are related to one another, as argued by our theory
and confirmed by interviews with managers. Since the surveys
were not equally spaced for each customer, we had an unbal-
anced panel. We used the XTSUR procedure in STATA,
which accommodates the unbalanced nature of the panel.
J. of the Acad. Mark. Sci.
The three models had primarily the same setup with
the same controls as those used in the revenue model,
except that a few control variables differed to adhere to
the identification demands of joint estimation. All three
models specified customersattribute importance as a
function of customers(lagged) behavioral and attitudi-
nal loyalty and their interaction. The models were
specified as follows:
Price Importanceij ¼β0iþβ1Behavioral Loyaltyij1þβ2Attitudinal Loyaltyij1
þβ3Behavioral Loyaltyij1*Attitudinal Loyaltyij1

þβ4Genderi
þβ5Ageij þβ6Incomeij þβ7Share of Walletij þβ8Marketing Emailsij1
þβ9Business vs Leisureij þβ10Economic WellBeingij þϑiþεij
ð2Þ
RewardsImportanceij ¼δ0iþδ1BehavioralLoyaltyij1
þδ2AttitudinalLoyaltyij1
þδ3BehavioralLoyaltyij1*AttitudinalLoyaltyij1

þδ4Genderiþδ5Ageij þδ6Incomeij þδ7ShareofWalletij
þδ8MarketingEmailsij1þδ9Business vs Leisureij
þδ10Round Tripij þδ11Distance
þδ12Redemptionij þϰiþπij ð3Þ
Where the variables that differed from the revenue model are:
Behavioral Loyalty
ij-1
= cumulative amount spent for
flights by customer iat flight j-1.
Attitudinal Loyalty
ij-1
= average of satisfaction and likeli-
hood to recommend for customer iat flight j-1.
Economic Well-Being
ij
= Gallup index for the state of res-
idence for customer iat flight j.
Redemption
ij
= whether flight jfor customer iwas pur-
chased using redemption points.
Ease of Booking
ij
= how easy it was to book flight jfor
customer i.
ϑ
i
,ϰ
i
,Ϯ
i
= time invariant error components.
ε
ij
,π
ij
,κ
ij
=randomerrors.
Results
Descriptive statistics
We present the descriptive statistics and correlation matrix for
the data in Table 3. Pertaining to the revenue model, we find
that customers who rated price as being extremely important
(5 on the scale) paid an average of $166.74 for a ticket, while
customers who rated price as being moderately important (3
on the scale) paid an average of $264.80 for a ticket. This
provides preliminary, model-free, evidence that price impor-
tance might be associated with changes in revenue. Further,
customers who rated rewards as extremely important paid an
average of $172.81 for a ticket, while customers who rated it
as moderately important paid an average of $166.02. This
suggests that rewards importance may not be strongly con-
nected to revenue. Finally, customers who rated convenience
as extremely important paid, on average, $175.76 for a ticket,
while those who rated it as moderately important paid an av-
erage of $118.94. This suggests that customers spend more
when they view convenience as important.
The average values for price importance, rewards impor-
tance, and convenience importance were 4.66, 4.03, and 4.46,
respectively. While the average importance of all three attri-
butes appear relatively high on a scale of 15, it is noteworthy
that considerable variance exists. If price were truly ubiqui-
tously important to each customer, then customers would eas-
ily put a value of 5 each time since there is no cost or addi-
tional effort required to do so. Yet, we dont find this to be the
case. For example, 30% of surveys reported a price impor-
tance value of 4 or less, with 17% rating price importance at
a value of 4 and 13% rating it at a value of 3 or lower. The
variables of rewards importance and convenience importance
also demonstrate variability with 58% and 45% of customers
assigning a value of 4 or less, respectively. Thus, while we
acknowledge that the data are positively skewed, we are still
able to capture variance across service experiences.
Importantly, we also see some variance for a given customer
across surveys: the average variance across surveys for the
same customers is .41 for price importance, 1.19 for rewards
importance, and .53 for convenience importance. A random
sample of any two customers (we repeated this several times)
reveals differences in ratings across time and between cus-
tomers. For example, from our data we see that Customer A
changed his/her ratings of price importance from 5 to 3 to 4
whereas Customer B changed it from 4 to 3 to 5. Again, since
the general belief is that price is highly important to most
J. of the Acad. Mark. Sci.
Tab l e 3 Descriptive statistics and correlations
Variable MeanSDRange 1 234567891011121314151617
1. Revenue 177.21 117.39 $43.72$1154.42 1
2. Price importance 4.66 .64 15.11 1
3. Rewards importance 4.03 1.09 15 .0079
a
.21 1
4. Convenience importance 4.56 .73 15 .0036
a
.39 .21 1
5. behavioral loyalty 1628 2279 $175$22,774 .33 .071 .088 .015
a
1
6. Attitudinal loyalty 4.51 .71 15 .0047
a
.15 .22 .11 .078 1
7. Gender .55 .49 0: female 1: male .12 .14 .045 .12 .25 .016 1
8. Age 54.34 13.47 2292 years .026 .07 .11 .021
a
.077 .11 .014
a
1
9. Income 4.40 1.19 16.16.092 .047 .026 .27 .049 .27 .11 1
10. Share of wallet .50 .24 0100% .0037
a
.14 .10 .035 .11 .14 .11 .077 .11 1
11. Marketing 12.16 14.03 098 emails .033 .042 .10 .021 .28 .045 .19 .14 .16 .017
a
1
12. Business vs. Leisure .57 .49 0: leisure 1: business .17 .10 .042 .038 .31 .043 .34 .16 .22 .049 .22 1
13. Round trip .64 .48 0: one-way
1: round trip
.43 .023 .018
a
.015
a
.081 .026
a
.037 .03
a
.079 .006
a
.13 .061 1
14. Distance 847.2 238.5 3981941 miles .10 .038 .061 .007
a
.092 .039 .026 .12 .006
a
.049 .053 .092 .12 1
15. Economic well-being 66.3 .60 64.968.2 .01
a
.026 .003
a
.037 .10 .019
a
.023
a
.027
a
.050 .039 .14 .036 .016
a
.043 1
16. Redemption .032 .18 0: none 1: redeemed .11 .024 .037 .019
a
.045 .029 .021
a
.01
a
.008
a
.024 .039 .048 .14 .016
a
.008
a
1
17. Ease of booking 4.59 .71 15 .013
a
.13 .15 .12 .088 .29 .071 .044 .004
a
.10 .002
a
.029 .0001
a
.021
a
.043 .06
a
1
a
Signifies a non-significant correlation (p>.10)
J. of the Acad. Mark. Sci.
customers, that customersprice sensitivities are static, and
that there is no Bcost^to customers to assign price importance
a value of 5 (highly important), we were surprised to see any
variance at all.
Average revenue spent per flight is $177.21 with a standard
deviation of $117.39. The average attitudinal loyalty ratings
are 4.51 on a scale of 15 with a standard deviation of .71.
Average behavioral loyalty is $1628 with a standard deviation
of $2324. The correlations of the independent variables are all
within acceptable limits and thus were concurrently included
in the model.
Revenue model results
The results of the revenue model are presented in Table 4.As
expected, price importance has a negative effect on revenue
(ρ=5.10, p< .10), providing support for H1a. Rewards
importance does not directly affect revenue (ρ= .27,
p> .10), suggesting that although customers value rewards
(the average of rewards importance was significantly above
the neutral point), they are not willing to pay for them. Thus,
we do not find support for H1b. Customers are, however,
willing to pay for service convenience (ρ=5.47,p<.05),
providing support for H1c. Overall, these results suggest that
increases in the importance of price are costly to the firm
whereas increases in the importance of convenience are ben-
eficial. Further, the results suggest that offering rewards may
not stimulate spending the way that managers have previously
thought (e.g., Nagy 2010). This is consistent with more recent
work (e.g., Watson et al. 2015) that shows that spending is not
necessarily encouraged by incentive strategies. Next, we con-
sidered what drives the importance of these attributes, with a
particular focus on price.
Attribute importance model results
The results of the price importance (Model 1), rewards impor-
tance (Model 2), and convenience importance (Model 3) mod-
el estimations are presented in Table 5. We find support for
H2a (Model 1). Specifically, behavioral loyalty has a positive
effect on price importance (β=.000083,p< .01), indicating
that customers who have spent more in the past are more
concerned about price than are those who have spent less.
This supportsthe contention that behaviorally loyal customers
are indeed more price sensitive. Further, we find support for
H2b (Model 2) and H2c (Model 3). Specifically, as behavioral
loyalty increases, the importance of rewards decreases (δ
=.00023 p< .01). Similarly, the effect of behavioral loyalty
on convenience importance is negative (λ=.000078,
p< .01). Taken together, these results uncover customers
relative emphasis on price: as customersbehavioral loyalty
increases, they shift their focus toward price and away from
the benefits of rewards and convenience. We explore the no-
tion attribute tradeoffs more deeply in a subsequent analysis.
We shift our focus to the estimation results of the moderat-
ing effect of attitudinal loyalty.
We find support for H3a (Model 1). Specifically, greater
attitudinal loyalty decreases the positive effect of behavioral
loyalty on price importance (β=.000018, p< .01). This
finding points to the particular benefits of increasing attitudi-
nal loyalty for behaviorally loyal customers. The results also
support H3b (Model 2) and H3c (Model 3). Specifically, as
customersbehavioral loyalty increases, greater attitudinal
loyalty causes them increase their emphasis on rewards (δ=
.000046, p< .01) and on convenience (λ=.000012,p<.01).
Overall, the results of the moderating effect of attitudinal loy-
alty suggests that as customers experience better service and
become attitudinally loyal, they shift their focus away from
price and toward rewards and convenience. Again, we further
test the notion of attribute tradeoff subsequently.
Robustness check with an alternate measure of behavioral
loyalty
In the hypothesized model, we used the cumulative amount
paid in dollars by the customer to the firm as our measure of
behavioral loyalty. However, we recognize that other indica-
tors of behavioral loyalty exist, including the number of pur-
chases made by the customer. To test an alternative measure of
behavioral loyalty, we re-estimated our hypothesized models
using number of purchases as the measure of behavioral loy-
alty. The results show that as the number of purchases in-
crease, the importance of price increases (β=.014,p<.05)
whereas the importance of rewards (δ=.041, p<.01)and
convenience (λ=.016, p< .01) decrease. Further, attitudinal
loyalty weakens the positive effect of number of purchases on
price importance (β=.0025, p< .10) and weakens the
Tabl e 4 Results of the effect of attribute importance on revenue
Variable Name Revenue
Price importance 5.10(2.91)*
Rewards importance .27(1.74)
Convenience importance 5.47(2.47)**
Gender .047(.34)
Age .12(.21)
Income .31(.14)**
Share of wallet .32(7.32)
Marketing emails .14(.11)
Business vs. Leisure 1.29(.34)***
Roundtrip .024(.19)
Distance .00055(.000025)**
ϖ
ij
12.95(3.18)
Results are presented with parameter estimates and standard errors in
parentheses
*p<.10,**p<.05,***p<.01
J. of the Acad. Mark. Sci.
negative effect of number of purchases rewards importance
(δ=.0089,p< .01) and convenience importance (λ=.0027,
p< .10). In other words, as attitudinal loyalty increases, a
greater number of purchases by the customer makes price less
important but makes rewards and convenience more impor-
tant. These results replicate the results of the hypothesized
model with cumulative spend as the measure for behavioral
loyalty. Still, we chose to keep the original measure of cumu-
lative spend in our analysis because it is well aligned with our
theoretical unpinning of value-alignment and the amount that
customers have committed to the service relationship.
Tradeoff analysis
Thus far, we have inferred that customers make tradeoffs by
comparing the signs of the coefficients of behavioral and atti-
tudinal loyalty in the price importance model versus the signs
of their coefficients in rewards and convenience importance
models. For example, we found that behavioral loyalty in-
creases the importance of price whereas it decreases the im-
portance of rewards, and from these results, we made the
claim that behaviorally loyal customers are trading rewards
to get a better price. To further substantiate such claims and
to provide additional hypothesis support, we calculated ratios
of predicted attribute importance at high and low levels of
behavioral and attitudinal loyalty.
In particular, to test whether behavioral loyalty actually
causes customers to shift their focus toward price and away from
rewards and convenience, we used the parameter estimates of
behavioral loyalty from Models 13(seeTable5) and multiplied
them by high (maximum) and low (minimum) values of behav-
ioral loyalty. We multiplied the parameter estimates of the
control variables by their mean values, which are listed in
Tab le 3. We then divided predicted price importance by predict-
ed rewards importance when behavioral loyalty is high. This
gave us the ratio of price to rewards importance at a high level
of behavioral loyalty. We repeated the same procedure for low
behavioral loyalty. Table 6A shows the results of the ratio cal-
culations. From the results, we can see that as behavioral loyalty
increases, the ratio of price to rewards becomes larger (.65
.70; p< .05), implying that price increases in importance relative
to rewards. Similarly, when we calculated the ratio of the pre-
dicted values of price and convenience importance at high and
low levels of behavioral loyalty, we see that as behavioral loy-
alty increases, the ratio becomes larger (.64 .69; p< .05). This
demonstrates that behaviorally loyal customers are shifting their
focus toward price and away from convenience. Thus, we find
additional support for H2a, H2b, and H2c.
To examine how the moderating effect of attitudinal loyalty
affects attribute tradeoffs, we calculated the ratios of the predict-
ed values of price to rewards importance and price to conve-
nience importance at high and low levels of both behavioral and
attitudinal loyalty (see Fig. 2and Table 6B). This gave us four (2
[behavioral loyalty: high vs. low) × 2 [attitudinal loyalty: high
vs. low]) predicted values for both ratios. From Table 6B we can
see that as attitudinal loyalty goes from low to high, the ratios of
predicted price to rewards (3.10 .63; p< .001) and price to
convenience (2.97 .63; p< .001) decrease for behaviorally
loyal customers, signaling that their focus shifts away from price
and toward rewards and convenience. This provides additional
support for H3a, H3b, and H3c. However, when behavioral
loyalty is low, there is no significant difference in the ratios at
high and low levels of attitudinal loyalty. Overall, this suggests
that changes in the attitudinal loyalty are more consequential for
Tabl e 5 Effects of behavioral loyalty and attitudinal loyalty on price, rewards, and convenience importance
Variable name Price importance (Model 1) Rewards importance (Model 2) Convenience importance (Model 3)
Behavioral loyalty .000083(.000029)*** .00023(.000034)*** .000078(.000027)***
Attitudinal loyalty .14(.014)*** .21(.021)*** .085(.016)***
Behavioral loyalty x Attitudinal loyalty .000018(.0000049)*** .000046(.0000073)*** .000012(.0000058)***
Gender .17(.038)*** .093(.00071) .092(.013)
Age .0010(.0000279)*** .0014(.000031)*** .00019(.00010)*
Income .035(.016)** .0030(.00021)** .039(.00029)***
Share of wallet .059(.00034)*** .13(.055)** .0088(.042)***
Marketing emails .000062(.000016)*** .00076(.00079) .00013(.000043)***
Business vs. Leisure .071(.00071)*** .072(.00086)*** .039(.0027)***
Round trip .021(.00062)*** .050(.00067)*** .0022(.0022)
Distance .00011(.00000092)*** .00013(.00000099)*** .000061(.0000034)***
Economic well-being .0098(.00011)*** NA NA
Redemption NA .028(.00072)*** NA
Ease of booking NA NA .024(.00016)***
Results are presented with parameter estimates and standard errors in parentheses
*p<.10,**p<.05,***p<.01
J. of the Acad. Mark. Sci.
behavioral loyal customers. We discuss the managerial implica-
tions of this subsequently.
Discussion
Researchers and practitioners have become more interested in
understanding the impact that customer relationships have on
service marketing policies, certainly because of the dramatic
shift in many leading markets toward a service economy. A
critical element of creating sustainable customer relationships
in service contexts is stimulating behavioral loyalty with the
hope that behaviorally loyal customers will focus more on the
service relationship and less on price. However, the
association between behavioral loyalty and price sensitivity
is controversial. Further, little is known about whether other
service attributes, such as rewards and convenience, are able
to draw customersfocus away from price, especially after
positive service experiences in which attitudinal loyalty is
formed. In response to these gaps, this paper (1) helps recon-
cile the debate on whether behavioral loyalty indeed allows
service firms to command higher prices, (2) determines ways
in which managers can shift customersfocus from price to
service rewards and convenience, and (3) incorporates the
moderating role of attitudinal loyalty. We next describe how
our findingsaddresses key substantive concerns in the areas of
service experience, loyalty, and relative price importance.
Attention to customersattribute importance
Should firms pay attention to customersattribute importance?
The simple answer is yes. We find that as the importance of
price increases to customers, they spend less and revenue de-
creases. New to the literature is our finding that as the impor-
tance of service convenience increases, revenue also in-
creases. While many service firms currently articulate their
value proposition around price and rewards, they should con-
sider articulating the ways in which they offer convenience to
their customers. For example, hotels could emphasize the
proximity of its hotel to attractive destinations and the conve-
nient service hours of its top amenities. Airlines could empha-
size (via their advertisements and searchable features on their
websites) the multiple timings of their flights, their direct and
non-stop routes, and reduced baggage delivery time. Some
firms such as Delta airlines and W Hotels already practice this,
but we recommend that this should become more widespread.
The finding that that rewards have no effect on revenue de-
spite being rated as highly important by customers generates
interesting implications for managers. This gapbetween what
Tabl e 6 Tradeoff analysis results
Tradeoff between importance of price and rewards Tradeoff between importance of price and convenience
Behavioral loyalty Behavioral loyalty
Low High Low High
A: Effect of behavioral loyalty on price tradeoffs
.65 .70 .64 .69
B: Effect of interaction between behavioral and attitudinal loyalty on price tradeoffs
Attitudinal loyalty Low .65 3.10 .64 2.97
High .65 .63 .64 .63
To interpret the tradeoffs, compare any two values. The higher the relative number, the more that price matters compared to the other service attribute
(e.g., rewards and convenience) and the lower the relative number, the more that the other service attribute matters compared to price. For example, in
Tab le 5A, if you go from low to high behavioral loyalty, the values increase, demonstrating that price increases in importance compared to rewards or
convenience. In Table 5B, you can see that at low levels ofbehavioral loyalty, the effect of attitudinal loyal on price tradeoffs is nonexistent. However, at
high levels of behavioral loyalty, the values in the cells decrease dramatically as you go from low to high attitudinal loyalty. This suggests a shift away
from price and toward rewards and convenience
0
0.5
1
1.5
2
2.5
3
3.5
Low High
Behavioral Loyalty
sdraweRotecirPfooitaR
Effect of Behavioral and Attitudinal Loyalty
on Tradeoff between Price and Rewards
Low Attitudinal Loyalty
High Attitudinal Loyalty
0
0.5
1
1.5
2
2.5
3
3.5
Low High
Behavioral Loyalty
ecneinevnoCotecirPfooitaR
Effect of Behavioral and Attitudinal Loyalty on
Tradeoff between Price and Convenience
Low Attitudinal Loyalty
High Attitudinal Loyalty
Fig. 2 Tradeoff analysis results of interaction between behavioral and
attitudinal loyalty
J. of the Acad. Mark. Sci.
customers view as important and what they are willing to pay
forimplies that service firms may not be providing the Bright^
types of rewards to customers. Many rewards perks have be-
come ubiquitous and fail to generate the perceived benefits they
once commanded (Yi and Jeon 2003). Further, some rewards
programs have set the bar for obtaining rewards so high that
customers are deterred from purchasing anything at all (Liu
2007). For example, Starbucks received considerable backlash
after almost tripling that amount needed to be spent to earn a free
coffee. Capitalizing on this backlash, competitor Dunkin
Donuts tried to entice angry Starbucks customers to make the
switch (Adamczyk 2016). Service managers should better un-
derstand the types of loyalty perks that attract behaviorally loyal
customers and how to design the rewards program to provide
Battainable^value.
Behavioral loyalty and revenue
Does behavioral loyalty translate into more revenue? The sim-
ple answer is no. As customersbehavioral loyalty increases,
their focus on price increases, and as a result, revenue de-
creases. In reality, firms often assume that loyal customers
are the least price sensitive. With this notion in mind, firms
systematically attempt to charge their loyal customers more.
For example, one of the authors of this paper, who belongs to
highest tier of an airlines loyalty program, recently tried to
purchase an international airline ticket after logging in and
identifying himself. The author was quoted an unusually high
price. With a sense that the firm might be over-charging him
because of his loyalty status, the author searched for the same
flight as a Bguest,^and the price quoted was substantially
lower. This example shows that airlines target their behavior-
ally loyal customers with higher prices. We caution against
this strategy since behaviorally loyal customers have been
shown to be more price sensitive, and they might be especially
displeased with the firms tactic of charging them more.
Customer tradeoffs among service attributes
Many service offerings are designed with differing levels of
price, rewards, and convenience, and firms often expect that
by offering more rewards and convenience, they can com-
mand higher prices (Frei 2008;Nagy2010). In contrast, we
find that behaviorally loyal customersfocus on price is not
offset by the benefits of service rewards or convenience. This
is likely because many service firms, especially those highly
competitive industries, inundate customers with promotions
and constantly change their prices, making this attribute more
likely to be noticed by customers (Rahinel and Ahluwalia
2015). For example, hotels such as Starwood and Marriott
email their customers price-related deals, especially to their
most loyal customers. Further, while airlines such as Delta
and American Airlines allow customers to sort by price,
arrival time, departure time, travel time, and schedule, price
is always listed as the first sorting option. They even tag their
cheapest flights with a banner that says Blowest fare.^These
tactics reinforce customersfocus on price and diminish the
perceived value of other service attributes.
We recommend that service firms try to loosen customers
focus on price by emphasizing other service attributes in new
ways. It would behoove them to emphasize the convenient tim-
ings of their service offerings (e.g., several flights a day, direct
flights) and the perks of belonging to a loyalty program. For
behaviorally loyal customers in particular, service firms might
consider positioning service rewards and convenience as a de-
crease in cost (e.g., through redemption of rewards to pay less
and the cost savings of time, distance, and effort) as opposed to
an increase in benefits. Such a strategy might prove fruitful in
enticing behaviorally loyal customers, who tend to be cost-fo-
cused, to shift their focus toward other service attributes.
Benefits of improving attitudinal loyalty
While past research has acknowledged that customers make
tradeoffs between attributes (e.g., Luce et al. 1997;Sunetal.
2012), no work has shown that the various aspects of loyalty
(behavioral and attitudinal) are key drivers of these tradeoffs.
We find that investments in attitudinal loyalty appear to do for
customers who exhibit little behavioral loyalty. However, we
identify attitudinal loyalty as an important moderator that helps
firms achieve the desired outcome of reducing the price sensi-
tivity of behaviorally loyal customers. While behaviorally loyal
customers tend to focus on what they have Bgiven^to the firm in
terms of monetary resources, and in exchange expect lower
prices, attitudinal loyalty softens their focus on price because
they are deriving value from better service experiences. Such
value allows theses customers to focus on the potential benefits
of the relationship, which include rewards and convenience.
Our finding that both behavioral and attitudinal loyalty
independently increase price importance, yet their interaction
effect decreases it, warrants additional discussion. This
finding provides evidence that customers who only exhibit
either behavioral or attitudinal loyalty toward the firm are
not necessarily the most profitable. Kumar and Shah (2004)
argue that a customers past spending is not a sufficient indi-
cation that she or he wants to continue a mutually beneficial
relationship with the firm. Rather, a customer must also ex-
hibit attitudinal loyalty and feel an emotional attachment to the
firm. Our findings are consistent with the line of reasoning that
both behavioral and attitudinal loyalty must be present to
positively influence firm outcomes.
How to increase attitudinal loyalty
We have established the importance of attitudinal loyalty in
easing behaviorally loyal customersfocus on price. A natural
J. of the Acad. Mark. Sci.
follow-up question is what service firms can do to increase
attitudinal loyalty. To answer this question, we used additional
measures from the survey data featured in this paper. We had
access to customersreported perceptions of eight service fea-
tures associated with their most recent flight: online check-in,
gate agent friendliness, check-in speed, baggage retrieval
speed, flight crew friendliness, boarding process orderliness,
aircraft cleanliness, and aircraft comfort. The respondents rat-
ed the quality of the features on a Likert scale (1 = low,
5 = high). We used the quality ratings of the eight features
as our independent variables (correlations among the ratings
were within acceptable limits; r< .50) and attitudinal loyalty
as the dependent variable. The results show that of the eight
features, baggage retrieval is the only feature that does not
directly affect attitudinal loyalty (β=.003,p> .10). Of the
remaining seven service features, flight crew friendliness
(β= .20, p< .01), boarding process orderliness (β=.14,
p< .01), and aircraft comfort (β=.13,p< .01) have the largest
positive effects on attitudinal loyalty.
It appears that features related to the core service, in this
case, the flight, have a larger impact on attitudinal loyalty than
do more peripheral features. This empirically validates the
argument that to enhance the attitudinal loyalty, value must
be embedded especially in core offerings (Meyer and
Schwager 2007). We recommend that under circumstances
of resource constraint, service firms focus more on improving
customerssatisfaction with core service features related to
their actual consumption of the service, and less on pre or
post-consumption features. This will result in larger improve-
ments in attitudinal loyalty, which will mitigate the positive
effect of behavioral loyalty on price sensitivity and instead
shift customersfocus toward rewards and convenience.
Limitations and directions for future research
Our research has limitations that pave the way for future re-
search. Time series survey data is notorious for having only a
few data points per customer. This paper is no exception. It
would be beneficial to have survey data, possibly from an
omnibus panel, with several more data points per customer
to assess trends over time. Further, we faced the limitation of
single-item measures of attribute importance since the firm
used single questions to survey their customers about the im-
portance of price, rewards, and convenience. We also assumed
that a given survey represents customersperceptions and
stated attribute importance pending any updates made in a
subsequent survey. Future research should consider utilizing
other (multi-item) measures of attribute importance that are
updated more frequently. Conjoint analysis might be a useful
technique to derive customersattribute importance and the
part-weights can be compared to customerssurvey responses
to assess the multi-dimensionality of attribute importance.
We made assertions about how the competitive nature of
the airlines industry causes customers to be cost-focused, and
as a result, it drives the positive link between behavioral loy-
alty and pricesensitivity. Further, wow convenienceis defined
might be idiosyncratic to this industry. It would be worthwhile
to examine whether and how behavioral loyalty influences
price importance in contractual settings in which switching
costs are high and price might be less important. Further, fu-
ture research could examine the effect of behavioral and atti-
tudinal loyalty on relative price sensitivity across industries.
While we used value-alignment and relationship theories to
motivate our hypotheses, we were unable to measure these un-
derlying processes. Rather, we relied on insights from the liter-
ature to inform our hypotheses. Future research would benefit
from capturing the underlying mechanisms driving the links
between behavioral and attitudinal loyalty and relative price
importance.
Acknowledgments The authors thank Ankit Anand, Andrew Petersen,
and the attendees at the 2015 Winter American Marketing Association
and North American Society for Marketing Education in India
(NASMEI) conferences for their valuable feedback on the paper. They
also thank the review team, the Associate Editor, and the Editor for their
detailed and constructive feedback. The authors thank Renu and the man-
aging editor for copyediting the manuscript.
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... Less than 10% of the studies discussing the satisfaction-loyalty-profitability chain actually examine the loyalty-profitability link (Watson et al., 2015) though. Recent research has started to revisit the proposed linear relationships in the loyalty-spending chain but came up with conflicting results (Thakur, 2019;Umashankar et al., 2017). For example, compared with less loyal customers, loyal customers may demand more price discounts (Wieseke et al., 2014) or assign more importance to price ( Umashankar et al., 2017), thus finding negative relationships between loyalty and profitability. ...
... Recent research has started to revisit the proposed linear relationships in the loyalty-spending chain but came up with conflicting results (Thakur, 2019;Umashankar et al., 2017). For example, compared with less loyal customers, loyal customers may demand more price discounts (Wieseke et al., 2014) or assign more importance to price ( Umashankar et al., 2017), thus finding negative relationships between loyalty and profitability. Such conflicting results imply the possibility of a nonlinear relationship between BLOY and firm profitability. ...
... This study addresses this gap in the literature. In particular, whereas existing studies predominantly focus on the firm level (Petersen et al., 2018) and customer reward programs from large organizations (Homburg et al., 2009), the present research responds to calls for more research at the customer level and therefore proposes a nonlinear link between BLOY (as defined by repeat purchase behavior; Dick and Basu, 1994;Umashankar et al., 2017) and customer spending (as defined by amount spent per purchase). This study contributes to the extant literature in several ways. ...
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... The second stream focused on dual recovery strategies that partially employ customers in firm-initiated SFR strategies (Giebelhausen et al., 2014;Ringberg et al., 2007;Umashankar et al., 2017;Yim et al., 2012). Drawing on the idea of rapport between customers and service employees, DeWitt and Brady (2003) argued that existing rapport increased postfailure customer satisfaction, decreased negative word of mouth, and did not increase customers' motivation to complain about poor service. ...
... Consumers' negative responses to service failures have become significantly higher as a consequence of these factors. With fashion providers already pressured by cancellations of orders from global suppliers (Statista, 2020b) and customers, there is an increased desire among providers to maintain interpersonal relationships with customers to reduce the negative effects of customers' dissatisfaction shared online (Chen et al., 2018;Christodoulides et al., 2021;Esmark Jones et al., 2018;Sun et al., 2017;Umashankar et al., 2017). ...
... Customers with less experience of a specific brand are less determined to seek a resolution from the provider or consider how their actions may impact the provider, compared to customers who have a connection with and experience of a provider.Additionally, a crisis, such as the Covid-19 global pandemic, may influence customers' sentiments towards life and consumption behavior until the situation improves. Just as positive brand relationships and psychological loyalty characteristics can influence customers to remain with a brand despite the marketing efforts of other brands(Thomson, 2006;Umashankar et al., 2017), customers' attachment to brands can overcome the influence of crisis situations on their perceptions of a brand's recovery procedures. Yet, loyal customers' evaluation of service recoveries will differ depending on how they incorporate the circumstances of a crisis situation.Customers with no attachment to a brand may be indecisive regarding their perception of service recovery and loyalty to brands, as they are motivated by habitual purchasing or monetary benefits(Gorlier & Michel, 2020). ...
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While the debate on online service failure and recovery strategies has been given considerable attention in the marketing and information systems literature, the evolving Covid‐19 pandemic has brought about new challenges both theoretically and empirically in the consumption landscape. To fully understand customers' responses to service failure during a crisis we asked 70 millennials from three European Countries—Italy, France, and the UK—to describe their responses to service failure during the Covid‐19 pandemic (30 completed a 4‐week diary and 40 completed a 4‐week qualitative survey). Drawing on phenomenological, constructivist, and hermeneutical approaches, and utilizing an actor–network theory perspective, the current study proposes a new framework for understanding customers' responses to online service failure and recovery strategies during the Covid‐19 pandemic. Conclusions highlight implications for theory, policy, and management practice through extending comprehensions of service failure recovery processes by examining how marketing policies generate different social impacts during a crisis situation which facilitate the achievement of customer satisfaction and positive outcomes.
... Price is typically perceived by consumers as a cost, and for the price they pay, consumers expect benefits which deliver value (Lee & Cunningham, 2001). Price and value are particularly influential in services marketing because transaction and switching costs are often high and/or noticed by consumers (Tanford et al., 2011); consumers expect price discounts as a reward for their loyalty (Wieseke et al., 2014); and loyalty schemes employed by service providers, actually cause consumers to place more importance on price and less importance on rewards and convenience (Umashankar et al., 2017). Most consumers consider price in relation to perceived quality (Chiang & Jang, 2007), with the relationship between price and quality determining perceived value. ...
... At such times, avoiding switching costs may be considered a form of convenience, as such costs are commonly perceived to comprise of the monetary cost, and the time and effort associated with switching to a new service provider (Tanford et al., 2011). Evidence from previous research suggests that convenience may affect consumer loyalty to service firms (e.g., Martínez & del Bosque, 2013), with convenience influencing both consumers' evaluations of services and consumers' purchasing behavior (Umashankar et al., 2017). This leads us to hypothesize that: ...
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Rollover contracts are becoming increasingly commonplace in a range of service markets. Such contracts automatically renew for a further term when a contractual period comes to an end. Rollover contracts represent a unique form of loyalty, because a consumer who signs a rollover contract at the time of initial purchase, signals a commitment to stay loyal to the firm before they have even experienced the service delivery. Prior studies of automatic renewal have predominantly been undertaken in the domain of consumer economics, and the psychological dimension of buyer decision making has yet to be explored. The desire for convenience was found to have the strongest influence on consumers’ propensity for rollover service contracts. Additionally, trust as a mediator of reputation, and perceived value also influence consumers’ attitudes and behavioral intentions for selecting these products. It is concluded that service providers should ensure that consumers recognize that there is an element of reciprocity which is mutually beneficial to both parties in the provision of such contracts.
... Moreover, TDC also helps developing innovation capability (Wang and Dass, 2017;Francis and Bessant, 2005) that, in turn, helps drive business growth and further consolidation of firms' competitive position in the market (Guan et al., 2006). The sighting and seizing of opportunities for TDC become more structured if a firm has a strong customer focus (CF) (Kiseleva et al., 2016) and pick up idea early-on from changing customer preferences (Umashankar et al., 2016;Bharadwaj et al., 2012). Management sponsorship (MS) (Borzillo, 2009) plays a crucial role in developing a CF and guiding TDC development toward attaining TL in the industry (Thamhain, 2004). ...
... This study also suggests that MS has a significant moderating effect on CF and TDC relationship (H3). However, MS does not have a significant moderating effect on TDC and TL relationship (H4) because the role of the CTO becomes more crucial, achieving TL than the support required by the firm's management as proposed by van der Hoven et al. (2012). ...
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Purpose This study aims to develop and empirically examine a model that investigates the mediating role of technology development capability (TDC) on the relationship between customer focus (CF) and technology leadership (TL). Design/methodology/approach The analysis and interpretation of a partial least square model include the following two stages: the first stage focuses on assessing the reliability and validity of the measurement model, while the second pertains to evaluating the structural model. Findings The general understanding prevailing in practice and literature is that management sponsorship (MS) has a positive impact on the relationship between CF and TDC. It is also generally understood that MS has a positive impact on TL initiatives. However, the findings of this study contradict these assumptions. Specifically, the study indicates that MS has a negative moderating effect on CF and technology development capabilities, and has no impact on CF and TL relationships. Originality/value CF, TDC and MS, and their interplay and impact in enhancing firms’ TL position, have not been explored thus far in an integrated manner in the context of the automobile industry in developing countries. The research relating to CF, technology development capabilities, technology innovation capabilities, MS and TL reported in the literature has drawn inferences from case studies, anecdotal evidence and experiences of industry leaders, experts and consultants. This study attempts to fill the gaps mentioned above by presenting and testing a new moderated mediation model that shows the relationship between CF, TDC, MS and TL.
... Moreover, TDC also helps developing innovation capability (Wang and Dass, 2017;Francis and Bessant, 2005) that, in turn, helps drive business growth and further consolidation of firms' competitive position in the market (Guan et al., 2006). The sighting and seizing of opportunities for TDC become more structured if a firm has a strong customer focus (CF) (Kiseleva et al., 2016) and pick up idea early-on from changing customer preferences (Umashankar et al., 2016;Bharadwaj et al., 2012). Management sponsorship (MS) (Borzillo, 2009) plays a crucial role in developing a CF and guiding TDC development toward attaining TL in the industry (Thamhain, 2004). ...
... This study also suggests that MS has a significant moderating effect on CF and TDC relationship (H3). However, MS does not have a significant moderating effect on TDC and TL relationship (H4) because the role of the CTO becomes more crucial, achieving TL than the support required by the firm's management as proposed by van der Hoven et al. (2012). ...
Article
Purpose This study aims to develop and empirically examine a model that investigates the mediating role of technology development capability (TDC) on the relationship between customer focus (CF) and technology leadership (TL). Design/methodology/approach The analysis and interpretation of a partial least square model include the following two stages: the first stage focuses on assessing the reliability and validity of the measurement model, while the second pertains to evaluating the structural model. Findings The general understanding prevailing in practice and literature is that management sponsorship (MS) has a positive impact on the relationship between CF and TDC. It is also generally understood that MS has a positive impact on TL initiatives. However, the findings of this study contradict these assumptions. Specifically, the study indicates that MS has a negative moderating effect on CF and technology development capabilities, and has no impact on CF and TL relationships. Originality/value CF, TDC and MS, and their interplay and impact in enhancing firms’ TL position, have not been explored thus far in an integrated manner in the context of the automobile industry in developing countries. The research relating to CF, technology development capabilities, technology innovation capabilities, MS and TL reported in the literature has drawn inferences from case studies, anecdotal evidence and experiences of industry leaders, experts and consultants. This study attempts to fill the gaps mentioned above by presenting and testing a new moderated mediation model that shows the relationship between CF, TDC, MS and TL.
Chapter
The previous chapter discussed customer participation and involvement in online brand communities (OBCs). In this chapter, we continue to explore contemporary viewpoints on OBCs and how customers’ participation and their level of involvement lead to different types of loyalty. The chapter offers a conceptual schema based on the work of Ozuem, Willis, Howell, Helal, et al. (2021a), which will enable us to discuss how different layers of customer participation will lead to different types of loyalty intentions.
Chapter
Service failure recoveryeWOMTechnology as resilienceBEC model Service failure recovery eWOM Technology as resilience BEC model
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Pay-what-you-want (PWYW) pricing is a participative pricing strategy that has emerged in many industries. A prominent example is the voluntary gift-sending in live streaming. Prior research has examined factors driving viewers’ gifting decisions in a live streaming session. In this paper, we focus on the dynamics of payment behavior over time under PWYW. Based on individual-level observations of more than 60,000 viewers over a period of 165 days since their registration on a live streaming platform, we find a declining pattern in PWYW amount with the increase in individual tenure. We then propose and test several potential explanations, including variety seeking, alternative ways to interact, and substitution effect. The empirical results are consistent with the substitution effect that the cumulative spending in the past could crowd out the current PWYW amount, because viewers may believe that they have already contributed a significant amount. We further investigate several moderating factors for the overall negative effect of past spending. Besides contributing to the PWYW literature, our research would help firms understand the dynamics of individual PWYW behavior and design mechanisms to incentivize long-life customers to pay.
Article
Full-text available
Customer loyalty programs are frequently used by companies to establish and improve relationships with customers by providing them with rewards. Loyalty programs investigated in the literature focus mainly on tangible rewards and economic benefits offered to the customers. However, some research done on intangible rewards of loyalty programs suggest that they can be superior to tangible benefits in affecting customer loyalty. Previous research drew conclusions in industry-specific settings. The aim of the paper is to assess the impact of tangible and intangible benefits on customer loyalty using an on-line customer panel representing different industries. The data collected from over 300 customers is subjected to CFA/SEM analysis in R environment. The main contribution of the present study is that it represents the first attempt (to the best of authors" knowledge) to capture loyalty programs" tangible and intangible value in an Arab cultural context, given the fact the focus was on the participants from the United Arab Emirates. Several important dimensions of LP programs in an Arab country are revealed. Firstly, the study confirmed that social value of a loyalty program significantly impacts customer loyalty. In addition, it was confirmed that the flexibility of a loyalty program increased customer loyalty. Ultimately, it was established that customers value intangible benefits more than the tangible ones.
Article
Store loyalty is confirmed to be important for retailers as it can help them gain and retain a competitive advantage over their counterparts. Researchers suggest that to achieve store loyalty the retailer needs to cope with competition from both the task environment and the institutional environment. The task environment may require the retailer to provide merchandise of good quality and price and with a broad range of choice. In the institutional environment, consumers may develop diversified requirements towards a specific store where they conduct their shopping because they have diversified roles in society, e.g., as an employee, a member of the community, and/or as a religious believer. To meet challenges from the institutional environment, a store may donate to the community or ensure a good working environment for employees. According to institutional theory, once these rules are followed consumers perceive the store to be legitimate in the current institutional environment. This is referred as organizational legitimacy. Therefore, it is suggested that the consumers will support the store. This argument builds a solid foundation for the current research. It signifies the importance of institutional environment for establishing store loyalty. The current research further explores the influence of the institutional environment on store loyalty. According to institutional theory, the development of institutions is history-based and context-specific. Stakeholders based in different institutional environments may develop different institutional requirements. Therefore, it is necessary to investigate specific institutional requirements within their specific institutional environments. Furthermore, the formulation of organizational legitimacy can be categorized at a collective level or an individual level. Past research has explored the formulation of organizational legitimacy on the collective level. The individual level needs to be explored because it can influence the establishment of organizational legitimacy on the collective level. Finally, institutional theory also suggests that international companies will face many more challenges from the institutional environment when they expand into overseas markets. These three facets construct the three angles for the current research. Past research suggests that performative actions (measured by store image), and symbolic actions (measured by corporate social responsibility), can be employed to satisfy both the requirements of task environment and institutional environment, then to formulate organizational legitimacy, and then store loyalty. In the current research the influence of performative actions and symbolic actions on organizational legitimacy are explored from the abovementioned three facets. Firstly, the specific requirements of performative actions and symbolic actions in the specific context are investigated. The Chinese retail market is chosen as the investigation context due to its attractiveness in the international retail market. Secondly, the performative and symbolic actions influencing the formulation of organizational legitimacy on the individual level are explored. Thirdly, whether consumers in the host country develop higher requirements of performative and symbolic actions towards international retailers compared with local retailers is investigated. From the qualitative research, the consumers’ performative and institutional requirements towards the store operated by foreign or domestic retailers in the specific context are investigated, i.e., the Chinese retail market. Face-to-face in-depth interviews were conducted with 20 Chinese consumers from Ningbo (a city located in Eastern part of China). The results show that the respondents have clear economic and institutional requirements regarding stores operated by foreign or domestic retailers. From the economic perspective, store location, merchandise (assortment, price, and quality), store environment, and service are the focus. Interviewees also show their interest concerning the institutional actions taken by the store. They expect a store to show concern about the provision of a good working environment and equal payment for employees, donations they make to charity, and assurance of fair trade with local suppliers. For the second facet, questionnaires delivered to respondents from Ningbo resulted in the collection of 607 useful questionnaires. The statistical software Smart PLS is employed to analyse the data. The results show that individual consumers develop instrument evaluation towards the performative and symbolic actions of a store. Also, the consumer evaluates the symbolic actions from relational-moral perspectives. Both the instrumental evaluation and the relational-moral evaluation help the store achieve the perceived organizational legitimacy on the individual level and then the store loyalty. This signifies that the consumer may evaluate whether the store can bring economic benefits, efficiency, and so on. It is also important that the strategies of a store make consumers feel respected and reach their moral standards. It is suggested that the formulation of perceived organizational legitimacy on the individual level will influence the formulation of perceived organizational legitimacy on the collective level. As a result, the store needs to pay attention to the how its strategies, such as performative and symbolic actions, are evaluated by the individual consumer because it may influence the support of the group of consumers’ judgement towards the store. To achieve the third research goal, the data collected from the questionnaire are also investigated by multigroup analysis. The data are split into two groups, those consumers who show loyalty towards the store operated by domestic retailers or those who demonstrate loyalty to foreign retailers. The significant differentiations between the two groups show that consumers have higher instrumental evaluation towards the performative actions of a store operated by international retailers. For the store run by the international retailers, instrumental evaluation plays a more important role in formulating the perceived organizational legitimacy on the individual consumer. Other relationships within the conceptual framework reveal no significant differentiations between the two groups. This implies that the liability of foreignness from the institutional environment does not have significant influence on the international expansion of international retailers in the current research context. International retailers have not faced more institutional challenges from consumers in their host countries. Part of the reason for this is because the research is based on the grocery industry in which consumers show more concern about efficiency. Furthermore, it is possible that the institutional actions taken by international retailers are good enough to conquer the problem of the liability of foreignness. On the contrary, stores run by international retailers win consumers’ support by better performative actions compared with those of stores operated by domestic retailers.