ORIGINAL EMPIRICAL RESEARCH
Do managers know what their customers think and why?
G. Tomas M. Hult
&Forrest V. Morgeson III
&Neil A. Morgan
Received: 31 August 2015 /Accepted: 16 May 2016
#Academy of Marketing Science 2016
Abstract The ability of a firm’s managers to understand how
its customers view the firm’s offerings and the drivers of those
customer perceptions is fundamental in determining the suc-
cess of marketing efforts. We investigate the extent to which
managers’perceptions of the levels and drivers of their cus-
tomers’satisfaction and loyalty align with that of their actual
customers (along with customers’expectations, quality, value,
and complaints). From 70,000 American Customer
Satisfaction Index (ACSI) customer surveys and 1068 firm
(manager) responses from the ACSI-measured companies,
our analyses suggest that managers generally fail to under-
stand their firms’customers in two important ways. First,
managers systematically overestimate the levels of customer
satisfaction and attitudinal loyalty, as well as the levels of key
antecedent constructs such as expectations and perceived val-
ue. Second, managers’understanding of the drivers of their
customers’satisfaction and loyalty are disconnected from
those of their actual customers. Among the most significant
Bdisconnects,^managers underestimate the importance of
customer perceptions of quality in driving their satisfaction
and of satisfaction in driving customers’loyalty and complaint
behavior. Our results indicate that firms must do more to en-
sure that managers understand how their customers perceive
the firm’s products and services and why.
Keywords Organizational learning .Customer satisfaction .
Customer orientation .American Customer Satisfaction Index
BMarketing is so basic that it cannot be considered a
separate function. It is the whole business seen from
the point of view of its final result, that is, from the
customers’point of view.^Peter Drucker (1954)
The recent literature in strategic marketing has centered on
marketing’s influence in the firm (e.g., Clark et al. 2014; Feng
et al. 2015; Germann et al. 2015; Homburg et al. 2015). The
core of this discussion views strategic marketing as a field of
study encompassing a focus on organizational, inter-organiza-
tional, and environmental phenomena and marketing strategy
as Ban organization’s integrated pattern of decisions that spec-
ify its crucial choices concerning products, markets, market-
ing activities and marketing resources in the creation, commu-
nication and/or delivery of products that offer value to cus-
tomers in exchanges with the organization and thereby en-
ables the organization to achieve specific objectives^
(Varadarajan 2010, p. 119). While this suggests that both man-
agers’views and customers’perceptions are important for
marketing strategy making, do managers consistently know
what their customers think and why?
This is an important question since customer satisfaction,
for example, has been shown to very significantly drive the
bottom-line performance of the firm (e.g., Fornell et al. 2016).
Constantine Katsikeas served as Area Editor for this article.
*G. Tomas M. Hult
Eli Broad College of Business, Michigan State University, East
Lansing, MI, USA
American Customer Satisfaction Index, LLC, Ann Arbor, MI, USA
Kelley School of Business, Indiana University, Bloomington, IN,
Robert H. Smith School of Business, University of Maryland,
College Park, MD, USA
CFI Group, Ann Arbor, MI, USA
J. of the Acad. Mark. Sci.
Strategically, a boundary-spanning alignment between man-
agers and customers is critically important to marketing strat-
egy making and deployment (Hult 2011), and to reaping the
benefits of customer satisfaction initiatives (and other market-
ing initiatives). Alternatively, understanding potential mis-
alignment between managers and customers is also an impor-
tant Bstrategic benefit^(Vargo and Lusch 2016,p.7)thatcan
be leveraged for enhanced customer satisfaction implementa-
tion (cf. Sleep et al. 2015) and, ultimately, achieving customer
loyalty (e.g., Watson et al. 2015).
Interestingly, tracing back more than 50years, marketing
analysts have encouraged managers to focus on deeply under-
standing their customers’product and service needs and re-
quirements (e.g., Hult et al. 2005). Essentially, answers to the
Bwhat^and the Bwhy^questions are widely viewed as a nec-
essary pre-condition, or knowledge, to being able to configure
afirm’s resources and capabilities to design, deliver, and com-
municate product and service offerings that satisfy customers
better than its competitors’offerings do (e.g., Hult and
Ketchen 2001; Narver and Slater 1990). Additionally, a large
and growing literature supports the significant firm perfor-
mance benefits of successfully delivering such superior cus-
tomer satisfaction (e.g., Aksoy et al. 2008; Fornell et al. 2006;
Fornell et al. 2016).
In their efforts to achieve these benefits, most large firms
monitor the satisfaction of their customers with the firm’s
product and/or service offerings (e.g., Morgan et al. 2005)
and use consumer survey (and other) data, combined with
increasingly sophisticated analytical techniques to help un-
cover the drivers of customers’satisfaction and loyalty.
However, there is only limited insight into whether these
and other efforts that firms may employ result in managers
successfully Bgetting inside their customers’heads^to under-
stand how they view the firm’s products and services and the
drivers of these perceptions. This is an important gap in mar-
keting knowledge for (at least) three reasons.
First, efforts to link firms’expenditures on satisfaction
monitoring and improvement efforts with customer satisfac-
tion outcomes largely treat intervening steps as a Bblack box^
(e.g., Dotson and Allenby 2010;Morganetal.2005). We posit
that a fundamental stage in this Bblack box^part of the process
is the extent to which managers correctly understand the levels
and drivers of customers’satisfaction with their firm’sp
and service offerings. Unless managers have such customer
understanding, any resource deployments designed to im-
prove customer satisfaction and loyalty are likely to be
misplaced. Thus, absent some calibration of the extent to
which managers within a firm accurately understand cus-
tomers’product and service needs and perceptions, it is im-
possible to say whether a firm needs to invest in getting man-
agers to better understand customers or in using their current
understanding more effectively to design, deliver, and com-
municate superior need-satisfying customer offerings. We
describe and illustrate one way in which firms can make cal-
ibrations of the extent to which their managers accurately
understand the firm’scustomers.
Second, using the above-mentioned approach to examine a
large sample of U.S. firms operating in consumer markets, we
provide compelling evidence that, on average, managers do
not accurately understand how their customers view their
firm’s products and services. We find that managers in most
firms systematically overestimate the extent of their cus-
tomers’satisfaction and loyalty, and also the levels of related
antecedents such as product and service expectations and per-
ceptions of value. Perhaps even more worrisome, our analyses
indicate that managers also fundamentally misunderstand key
drivers of their customers’satisfaction and loyalty. Thus,
while most large firms invest in customer satisfaction moni-
toring systems, analyze customer feedback data, and commu-
nicate this within the firm, we show that such efforts appear to
be insufficient to Bclose the gap^between what the firm’s
customers actually think of the firm’s products and service
offerings and why, and managerial understanding of these
key aspects of customers’product and service needs and
Third, we provide evidence that the fundamental discon-
nects between what customers actually think about a firm’s
products and services, and what the firm’s managers think
customers think, really matter. Specifically, we show that
firms in which the manager–customer understanding gap is
relatively larger have significantly lower levels of customer
satisfaction than firms in which this gap is relatively narrower.
Given the large and growing body of evidence linking cus-
tomer satisfaction with firms’accounting and stock market
performance (e.g., Fornell et al. 2016), our research suggests
that closing the gap between what customers actually think
and what managers think customers think is a key strategic
issue for most firms.
The results of our study reveal several important gaps be-
tween managers’beliefs about their customers and the actual
perceptions and intentions of those customers. Among the
most significant disconnects that we observe is that managers
overestimate their customers’satisfaction, their ratings of
some of its key drivers (expectations and perceptions of val-
ue), and the future loyalty intention expressed by their cus-
tomers, while also underestimating their customers’propensi-
ty to complain. Taken together, this pattern of overestimation
of their own firms’customer performance could lead man-
agers to fail to take needed steps to improve drivers of satis-
faction, linkage between satisfaction and loyalty, thereby po-
tentially damaging future financial performance and market
share. What is more, our results show that managers also mis-
understand the attributes that most strongly influence their
customers’perceptions, underestimating (for instance) the im-
portance of quality in driving satisfaction, and of satisfaction
in driving both loyalty and complaint behavior. Jointly, these
J. of the Acad. Mark. Sci.
perceptual gaps (along with others considered below) provide
strong evidence against both the depth and breadth of mana-
gerial knowledge of their own firms’customers.
The rest of the paper is organized as follows. First, we
develop the conceptual framework for our study. We then
describe the research method adopted and data collection pro-
cedures employed. Next,we present the results of our analyses
and discuss the nature and implications of our results. Then we
more fully discuss the significance of these gaps for the firm
attempting to manage and leverage customer satisfaction and
loyalty, and provide some strategies for how firms might be-
gin to close these gaps. Finally, we describe the limitations of
our study and identify interesting new avenues for future re-
search illuminated by our findings.
We propose that there are two primary elements in any assess-
ment of how accurately a firm’s managers understand its cus-
tomers’product and service needs and requirements. First,
managers should know what their customers think of their
firm’s current product and service offerings. This is a funda-
mental purpose of any company’s customer satisfaction mon-
itoring and feedback systems (e.g., Morgan et al. 2005). The
control system literature suggests that if customers’percep-
tions of the firm’s products and services are the performance
standard, then any difference between managers’beliefs re-
garding customer perceptions of these products and services
and customers’true perceptions will result in an inefficient
and ineffective control system (e.g., Anthony 2007;
Schmenner and Vollmann 1994). If managers underestimate
their customers’satisfaction with the firm’s products and ser-
vices, they may invest in unnecessary satisfaction improve-
ment efforts (a Bfalse alarm^). Conversely, if managers over-
estimate customer perceptions of the firm’s product and ser-
vice offerings they may fail to make needed changes or may
even take actions that are counter-productive (a Bgap^). For
example, if managers think that their customers have a higher
level of price tolerance than is in fact the case, they may raise
prices beyond levels that customers are prepared to pay and
lose market share as a result. A good of example of this mis-
take is the now-infamous 2011 price increase enacted by video
retailer Netflix that rattled its customers and sent its share
prices plummeting (down more than 70% by the end of that
Second, managers should know why their customers hold
the perceptions of the firm’s product and service offerings that
they do. Even if managers correctly understand what their
customers think of the firm's products and services, it is man-
agers’beliefs about the drivers of these customer perceptions
that guide their efforts to improve the firm’s value offerings
(or the costs of delivering them). Thus, even if managers know
with some precision the level of their customers’current sat-
isfaction with their products and services, without correctly
understanding what drives this satisfaction, managers will
not be able to effectively and efficiently take actions that
may improve satisfaction in the future. Alternatively, if man-
agers are looking for ways to reduce the firm’s costs in ways
that have a minimal negative impact on resulting customer
satisfaction and/or loyalty, they will not be able to do so if
they have an inaccurate understanding of what drives their
customers’satisfaction and loyalty.
A simple way to assess the extent to which a firm’sman-
agers truly understand what customers think of the firm’s
products and services—and the drivers of those customer per-
ceptions—is to use a common set of measures that captures
these phenomena and compares responses from the firm’s
customers (what they actually think) and its managers (what
managers think customers think). This comparison can be
made both in terms of the Blevels^of perceptions on the same
product and service-related phenomena (e.g., perceived qual-
ity, perceived value) and in terms of the relationships between
antecedent product and service perception Bdrivers^(e.g., ex-
pectations, perceived quality) and their perceptual outcomes
(e.g., customer satisfaction, loyalty). Two obvious potential
difficulties in adopting this approach are: (1) the fact that
customer perceptions of products and services and the drivers
of these perceptions may be idiosyncratic to each individual
customer (and may certainly differ widely between firms and
industries), and (2) the need to meaningfully frame the same
product and service perception and driver questions for cus-
tomers and managers to allow valid comparisons.
The first of these issues may be addressed by using aggre-
gate survey measures of customer satisfaction and loyalty, and
common and generic antecedents that are specifically de-
signed to be comparable across customers. For individual
firms with a customer satisfaction monitoring system, the sur-
veys used to collect customer feedback data regarding percep-
tions of the firm’s products and services are specifically de-
signed to enable such aggregation across the firm’scustomer
base (e.g., Vavra 2002). For our study, however, we also need
to be able to compare customer (and manager) responses
across companies and industries. The only measurement
framework to receive widespread examination and use in the
academic marketing literature that allows such comparison
across a firm’s customers, between companies in the same
industry, and across industries is the American Customer
Satisfaction Index (ACSI), a theoretical model described in
detail by Fornell et al. (1996).
Theoretically, the ACSI model links customer perceptions
regarding expectations, perceived quality, and perceived value
as three central and generalizable drivers of customer satisfac-
tion, and complaints and attitudinal loyalty as the two primary
outcomes of satisfaction (for a detailed review of the model
we describe briefly below, see Fornell et al. 1996). These six
J. of the Acad. Mark. Sci.
constructs are described based on the established ACSI
&The customer satisfaction (ACSI) index score is calculated
as a weighted average of three survey questions that mea-
sure different facets of satisfaction with a product or
&Customer expectations is a measure of the customer’san-
ticipation of the quality of a company’sproductsor
&Perceived quality is a measure of the customer’s evalua-
tion via recent consumption experience of the quality of a
company’s products or services.
&Perceived value is a measure of quality relative to price
&Customer complaints are the percentage of respondents
who indicate they have complained to a company directly
about a product or service within a specified time frame.
&Customer loyalty is a combination of the customer’spro-
fessed likelihood to repurchase from the same supplier in
the future, and the likelihood to purchase a company’s
products or services at various price points (price
Customer satisfaction is the central mediator in the model
and is measured as a latent variable with questions asking the
consumer’s overall cumulative satisfaction with their experi-
ence (Boverall satisfaction^), the confirmation or disconfirma-
tion (either positive or negative) of prior expectations pro-
duced by the experience (Bconfirmation of expectations^),
and a comparison of the experience to an imagined ideal
product/service offering (Bcomparison to ideal^)(Fornellet
In the structural model, satisfaction has three primary
antecedents (or drivers): perceived quality, perceived val-
ue, and customer expectations. All three latent variable
drivers are anticipated to have direct, positive effects on
satisfaction, as more positive consumer perceptions of all
three should lead to a more satisfying experience. Yet,
both empirically and theoretically, the relationship be-
tween quality and satisfaction is expected to be the stron-
gest, as consumer satisfaction has typically been found to
be predominantly a function of a consumer’s quality ex-
perience (alternatively, perceptions of performance) with a
product or service (Fornell et al. 1996;Oliver2010). As
defined in the ACSI survey, there are three survey items
constitutive of the quality experience included in the per-
ceived quality latent variable: perceptions of overall qual-
ity (Boverall quality^), the degree to which the product or
service fulfills subjective individual requirements
(Bcustomization quality^), and how consistently and reli-
ably the good or service performs (Breliability quality^)
(Fornell et al. 1996).
The second latent variable anticipated to have a direct
and positive effect on customer satisfaction is perceived
value, which is measured in the survey as the level of
perceived quality relative to the price paid (Bquality given
price^), and the price paid relative to the perceived quality
of the good or service (Bprice given quality^). Adding
perceived value to the model incorporates price informa-
tion, an important determinant of end-state consumer sat-
isfaction in virtually every industry, yet still allows for
comparison of results across disparate companies, indus-
tries, and sectors where pricing structures can vary sub-
stantially. This is because the variables do not ask directly
about happiness with price paid—where perceptions are
more likely to differ systematically across categories with
widely different pricing structures—but rather asks about
price relative to quality (and vice versa) (Johnson and
Fornell 1991; Fornell et al. 1996). Because the perceived
value variable is measured as the ratio of price paid rela-
tive to the quality received (and vice versa), perceived
quality is also predicted to have a positive and direct
effect on perceived value, as shown in the model.
The third determinant of customer satisfaction in the
ACSI model is the level of quality/performance the re-
spondent expects to receive with the good or service prior
to the experience. Because expectations serve as a prima-
ry reference point in a consumer’s cognitive evaluation
process (in other words, a satisfaction Bstarting point^),
expectations are predicted, like both quality and value,
to positively impact satisfaction. Expectations capture all
of a customer’s prior knowledge (through recommenda-
tion, prior experiences, advertising, other sources of news
and information, etc.) and consumption experiences with
afirm’s products or services (Fornell et al. 1996;Oliver
2010). Similar to quality, expectations in the ACSI model
are measured as the consumer’santicipated perceptions of
overall quality (Boverall expectations^), customization
quality (Bexpectations customization^), and reliability
quality (Bexpectations reliability^). Furthermore, customer
expectations are also hypothesized to be positively related
to both perceived quality and perceived value. These hy-
pothesized relationships recognize the consumer’s ability
to learn from experience and to anticipate, based on this
prior knowledge, both the quality and value of a product
or service they experience.
The two outcomes of customer satisfaction included in the
ACSI model are customer complaints and customer loyalty.
Founded in exit, voice, and loyalty theory (Hirschman 1970),
when dissatisfied, customers have two basic options: leaving
the company and defecting to an alternative supplier (should
one exist), or voicing their dissatisfaction to the supplier in an
attempt to receive some kind of recompense. Thus, an increase
in satisfaction is hypothesized to be negatively related to com-
plaint rate, while likewise predicted to improve the loyalty of
J. of the Acad. Mark. Sci.
customers (Fornell et al. 1996). Customer loyalty is the ulti-
mate dependent variable in the model—as well as being an
essential and universal business objective—and it is modeled
in this study by a single manifest variable (repurchase inten-
tion, for reasons mentioned above) asking the consumer how
likely they are to remain a customer of the company. The
importance of expressed customer loyalty lies in its relation-
ship to outcomes like actual customer retention rate, as well as
in forecasting market share, revenue growth, and profitability.
The final relationship in the model is the effect of customer
complaint behavior on customer loyalty. The direction and
size of this relationship reveals, by and large, the efficiency
and quality of a company’s complaint recovery and complaint
handling system (Fornell et al. 1996). When the relationship is
positive, this shows that a company is successfully converting
complaining customers into loyal customers; when the rela-
tionship is negative, complaining customers are more likely to
defect, and anincrease in complaints will cost the firm a larger
number of customers.
Overall, our study is rooted in the above robust and rigor-
ously tested theoretical model at the consumer level.
However, a major gap in the literature is the capturing of these
phenomena and comparing assessments from a firm’s cus-
tomers (what they actually think) and the company’s man-
agers (what managers think customers think). As such, a sec-
ond issue regarding how to use the same survey instrument to
allow meaningful comparisons between a firm’s customers
and managers may be addressed by re-framing the ACSI sur-
vey questions to prompt managers to answer them as they
believe their customers would. This is consistent with the
management and psychology literature approach to studying
perspective-taking by managers and employees (e.g., Gilin et
al. 2013; Parker and Axtell 2001). Thus, rather than asking
managers for their own perceptions of the products or services
offered by their firms, managers can be asked what they be-
lieve their customers’perceptions of the firm’sproductsand
services to be. For example, the overall expectations question
in the ACSI survey asks consumers to consider their expecta-
tions of the overall quality of one of the firms’top brand
products or services prior to their most recent purchase and
consumption experience. To compare this with managers’be-
liefs regarding their customers’perceptions, the same question
could be framed as follows
:BThinking about your customers’
expectations of the quality they would receive, how would
you rate your customers’expectations of the overall quality
of your top brands?^Similarly, when consumers are asked
about their overall satisfaction with their experiences with a
company’s top brand’s products and services in the ACSI
survey, the firm’s managers can be asked: BPlease consider
allofyourcustomers’experiences with your top brands.
How satisfied do you think your customers are with your
Having developed a conceptual framework that allows us
to calibrate the extent towhich managers understand thelevels
and drivers of customers’perceptions of their products and
services, we now turn to an empirical illustration of our
Research design and data
To assess the extent to which managers understand their
customers, we analyze two distinct samples, one com-
prised of consumers of the products and services of firms
across a range of industries regarding their product and
service consumption experiences, and the other comprised
of senior managers employed in customer-facing roles
within these same companies. Our sample of consumers
was drawn from data collected by the American Customer
Satisfaction Index (ACSI). The ACSI interviews cus-
tomers of more than 250 of the largest consumer-
oriented firms in the United States each year. Data are
collected on a quarterly basis for different industries, with
approximately 25% of the total annual sample of respon-
dents interviewed each fiscal quarter, and each company
measured once annually. Only the largest, most economi-
cally significant companies within any measured industry
are included in the ACSI, resulting in a sample that pri-
marily includes customers of Fortune 500 companies. For
each measured company within an industry, approximate-
ly 250 interviews of customers that have recently pur-
chased and consumed the products/services offered by
the company are completed. Approximately 60,000 inter-
views are conducted during each annual cycle of ACSI
data collection. For the purposes of this study, 2009
ACSI data, including only interviews completed during
the 2009 calendar-year cycle of annual interviewing, were
The ACSI survey instrument used to collect this data is
standardized and generalized for applicability across the full
range of companies and industries measured, allowing for the
estimation of a common statistical model and facilitating
Consumers surveyed by the ACSI are asked questions with regard to a
specific product/service brand rather than the company marketing the
brand (where these are different). These named brands are the largest that
a company will sell in that specific marketplace. In many cases, compa-
nies have only one brand in that marketplace, or one major brand that
most consumers will have experienced. However, as a robustness check
we compared our results for the whole sample with those for the subset of
companies in our sample marketing only one brand in the same ACSI
industry and did not find any significant differences.
As a robustness check we also examined the impact of using 2010 ACSI
consumer data, and the conclusions of the analyses remain largely un-
changed. This is not surprising, as company-level ACSI satisfaction re-
sults tend to exhibit a significant amount of autocorrelation.
J. of the Acad. Mark. Sci.
comparison of the analyzed data between both similar and
dissimilar consumer experiences (Fornell et al. 1996;
Johnson and Fornell 1991; Johnson et al. 2002). The question-
naire seeks the customer’s perceptions regarding a general set
of issues that apply across different product and service cate-
gories, thereby allowing comparison across industries. While
the customer sample includes consumers who may user
Bsmaller^brands from the company, the very nature of our
sample—randomly drawing from a company’scustomers—
means that this group will be a very small group within the
overall sample. Specifically, the ACSI is designed to collect
data from customers of the largest brands in each of the 40
industries in which it collects data (seeking to collect data
from brands representing the majority of the sales in an indus-
try). The customer data were collected by ACSI and the man-
ager data were collected in strategic partnership with the ACSI
to stay as consistent as practically possible in achieving
aligned and matched responses at the disaggregate level of
the constructs (i.e., at the item level). The questions included
in the survey, along with abbreviated question wording and
question/item scale, are provided in Appendix 1.
The measured variables for each company are included in
the standard ACSI structural equation model for analysis (see
Fig. 1). Because ACSI estimates a type of latent variable-
partial least squares structural model (LV-PLS) for each com-
pany included in the study, multiple survey items are mea-
sured for each latent construct included in the model (i.e.,
three questions on expectations, three questions on quality,
two questions on value). This multiple-item approach ac-
counts for the 13 survey items included in Table 1, corre-
sponding to the six estimated latent variables in Fig. 1.
of the observed variables are asked on a 1–10 scale during
interviewing (with the exception of the Bno^-Byes^,0–1com-
plaint question shown in Appendix 1).
For the analysis conducted, the samples examined within
the structural model differ somewhat from what is normally
used in the ACSI. Instead of estimating company-level models
using respondent-level data, we utilize company-level mean
scores (i.e., the sum of the responses for each observed vari-
able for each company’s customers divided by the Nrespon-
dents for that company) for the manifest data used in our
analysis. This is because we are investigating the alignment
between the perceptions of a company’s customers in the
aggregate and marketing managers’perceptions of these
perceptions (also in the aggregate and at the company level,
although obviously with far fewer observations/cases, as we
The second sample we analyze in this study includes mar-
keting and sales managers employed by firms included in the
ACSI database. This sampling frame was designed to include
professionals who are knowledgeable of their customers’per-
ceptions of the firm’s products and services (Fornell et al.
2006) and influential in the company’scustomervalue-
creating processes (Srivastava et al. 1999) and marketing ac-
tivities (Vorhies and Morgan 2005). To collect this sample, we
first identified managers from each firm included in the ACSI
using the 2010 Dun and Bradstreet Information Services
(D&B) directory as the universe of potential managers. The
identified executives had managerial positions with titles such
as chief marketing officer, vice president of marketing, mar-
keting director, vice president of product development, vice
president of brand marketing, vice president of sales, and di-
rector of sales. Through a qualifying email invitation sent to
these professionals, a total of 1439 executives were identified
as willing to respond to the survey.
The surveys were made available and completed online via
a customized online interface in three waves. Each wave of
surveys was sent out on a different weekday (with 4 to 7 days
in between each mailing) and at different times of day to
maximize the likelihood of obtaining responses, as well as
responses that were not skewed by design measures. A total
of 1068 managers completed the survey, and these individuals
represent 122 different ACSI-measured firms. All data collec-
tion for this managerial sample was completed in early 2010,
providing the best possible match to the 2009 annual wave of
ACSI customer data. The managers that chose to participate in
the survey were asked the manager-framed ACSI consumer
questions as outlined in Appendix 1.
Finally, where multiple responses from managers within
any single firm were collected (e.g., five managers from
Company X completed the survey), data were aggregated to
the firm level as a simple average of these responses, similar to
the aggregation approach used with the customer-level ACSI
data described earlier. Following this procedure, and after fur-
ther trimming the sample to include only those manager-
respondents with strong knowledge of their customers (as de-
scribed in footnote 4), 97 matched company-level customer–
manager cases of data were available for analysis. The ACSI
customer data file, originally containing the 226 companies
The standard ACSI structural model typically includes a 14th survey
item, a question regarding price tolerance/reservation price included in
the customer loyalty latent variable. This question asks the respondent to
indicate how much the company could raise the price of the product/
service/brand experienced before he or she would definitely defect to a
competitor. During questionnaire design and pre-testing with academics
and managers, it was determined that this question would be too difficult
to meaningfully adapt to the marketing manager questionnaire, and it was
therefore excluded from both samples.
As part of the qualification/eligibility validation process, the responding
managers were asked to respond to the statement, BI have great knowl-
edge of our company’scustomers^using a 10-point Likert-type scale
ranging from Bstrongly disagree^to Bstrongly agree.^Respondents re-
ported an average score of 7.89 (standard deviation=1.82). In all of the
analysis that follows, we limited our sample of manager-respondents to
only those who answered above average on the Bknowledge of their
company’scustomers^question, i.e., scoring 8 or higher.
J. of the Acad. Mark. Sci.
measured in 2009 by the ACSI, was trimmed to match the
original managerial sample, leaving a sample of 97 complete
cases available for analysis. A list of the companies inthe final
sample is included in Appendix 2.
Analyses and results
To analyze the two samples outlined above to determine the
degree of alignment between the customers’perceptions and
the managers’perceptions of those perceptions, a multi-stage
modeling approach was utilized. First, as shown in Table 1,
we computed descriptive statistics for the ACSI company-
level manifest variable mean scores (hereafter the Bcustomer
sample^), along with those for the sample of manager re-
sponses (hereafter the Bmanager sample^).
As seen in Table 1, the mean scores on the thirteen mea-
sured ACSI survey items for the customer and manager sam-
ples exhibit both some similarities and some noteworthy dif-
ferences. While for some variables only small differences in
mean scores exist (e.g., expectations of reliability, overall per-
ceived quality, and perceived customization quality)—sug-
gesting that customers and managers are relatively well-
aligned in these areas—for others the differences are more
noteworthy (e.g., overall expectations, comparison to ide-
al)—suggesting a larger gap between the two sets of percep-
tions. Furthermore, even where the differences are only slight,
for all but two items (and one of these is the number of com-
plaints voiced) manager perceptions are more positive than
customer perceptions, suggesting something of an ingrained
over-optimism among managers. Finally, the standard devia-
tions are significantly larger for the managerial sample, sug-
gesting greater variation between companies’managers and
their perceptions than corresponding consumer perceptions,
although some of this variance is undoubtedly due to the
smaller underlying managerial sample (respondents per
Next, following the data aggregation procedures discussed
earlier, with the customer and managerial survey responses
aggregated to company-level means and the cases matched
across the two samples, we estimated two structural equation
models: a customer model, including the company-level ag-
gregated mean scores derived from the ACSI surveys of con-
sumers, and a managerial sample model, including the cases
for managers asked the same questions. Following the analyt-
ical techniques originally adopted for estimation of this model
(Fornell et al. 1996), in this study we utilize partial least
squares-based structural equation modeling (PLS-SEM)
methods to estimate both the latent variable scores and the
paths between the constructs shown in the ACSI model (see
Fig. 1above). PLS-SEM is a very popular and widely used
method in marketing research, especially in consumer
Fig. 1 Theoretical ACSI research
Tabl e 1 Observed variable descriptive statistics
Customer sample Manager sample
Mean SD Mean SD
Overall expectations 8.147 0.426 8.600 0.984
Expectations customization 8.444 0.417 8.726 1.045
Expectations reliability 7.745 0.506 7.783 1.698
Overall quality 8.382 0.533 8.455 0.921
Customization quality 8.375 0.575 8.436 0.835
Reliability quality 8.223 0.588 7.920 1.536
Quality given price 8.040 0.614 8.301 1.030
Price given quality 7.665 0.739 7.905 1.256
Overall satisfaction 8.350 0.601 8.509 0.888
Confirmation of expectations 7.534 0.554 7.736 1.286
Comparison to ideal 7.462 0.625 7.960 1.101
Customer complaints 0.136 0.124 0.097 0.076
Repurchase intentions 8.090 0.684 8.458 1.203
J. of the Acad. Mark. Sci.
satisfaction studies (Kristensen and Eskildsen 2010). Previous
studies estimating the ACSI model have predominantly used
this technique as well (e.g., Rigdon et al. 2011), and therefore
employing the same methods will provide replicable results
(e.g., weights, scores, path estimates) comparable to a major-
ity of earlier studies examining the model (e.g., Fornell and
Bookstein 1981; Henseler et al. 2009; Hulland et al. 2010;
Morgeson et al. 2015; Vilares et al. 2010).
Beyond replicating the methods used in earlier re-
search on the ACSI model, for the purposes of our
study there are additional benefits of PLS-SEM that
recommend this technique over alternative approaches.
PLS enables researchers to assess both latent variables
at the observation level (measurement model), a feature
important to the between-model mean-comparisons inte-
gral to our study, and the relationships between latent
variables on a theoretical level (structural model) (Hair
et al. 2012;Hairetal.2014,2017). Moreover, while
PLS-SEM is similar to traditional covariance-based,
maximum likelihood structural equation modeling (CB-
SEM), in the sense that the measurement and structural
models are analyzed simultaneously, PLS relies on ordi-
nary least squares estimation (implemented iteratively
via the PLS-SEM algorithm) to solve the models, there-
by relaxing the assumption of multivariate normality
underlying CB-SEM. Given some of the features of
the data we examine here (and particularly vis-à-vis
the manager data sample, wherethesampleissmall
and the manifest variables exhibit larger variance),
relaxing this assumption during analysis is optimal
(Compeau and Higgins 1995).
PLS-SEM is also preferable to alternative (CB-SEM)
methods when the researcher is focused on optimized predic-
tion of dependent variables, as we are in this study. While CB-
SEM focuses on maximizing overall model fit and inter-item
covariance among a matrix of observed variables, PLS-SEM
is a Bbiased^method that maximizes the relationship between
specified latent variable predictor and response variables
(Chin 1998). The scores thus capture the variance most useful
for predicting the endogenous latent variables (Hair et al.
2014). Finally, simulations have shown PLS-SEM to be ro-
bust against inadequacies often experienced in modeling this
type of data (i.e., consumer satisfaction data), such as
multicollinearity, skewness, and omission of regressors (i.e.,
omitted variable bias) (Cassel et al. 1999). Because of all of
the aforementioned advantages, PLS-SEM has routinely been
suggested as the preferred estimation method for customer
satisfaction studies (Fornell 1992).
For this and most studies, PLS-SEM analysis is con-
ducted in two stages. In the first stage, the researcher
ensures that the measures used as operationalizations of
the underlying constructs are both reliable and valid (the
measurement model). After the adequacy of the mea-
surement model has been established, the researcher
proceeds to the second stage and interprets the resulting
model coefficients (the structural model). The subse-
quent sections report the results and key statistics for
Tabl e 2 PLS measurement
model statistics Measurement variables Customer model Manager model
(Latent variable) Unstd. Std. Unstd. Std.
weight loading weight loading
Overall expectations (LVexpectations) 0.345 0.956 0.516 0.907
Expectations customization 0.358 0.971 0.416 0.846
Expectations reliability 0.342 0.942 0.272 0.664
AV E /Cronbach’sα0.915 0.953 0.660 0.743
Overall quality (LV quality) 0.349 0.985 0.472 0.932
Customization quality 0.345 0.980 0.406 0.894
Reliability quality 0.330 0.963 0.291 0.678
AV E /Cronbach’sα0.952 0.975 0.709 0.789
Quality given price (LV value) 0.549 0.989 0.620 0.964
Price given quality 0.464 0.985 0.435 0.926
AV E /Cronbach’sα0.975 0.974 0.893 0.884
Overall satisfaction (LV satisfaction) 0.350 0.988 0.468 0.908
Confirmation of expectations 0.341 0.982 0.296 0.742
Comparison to ideal 0.333 0.959 0.406 0.875
AV E /Cronbach’sα0.953 0.975 0.713 0.799
-All weight and loadings significant at the p<0.05 level for both models
J. of the Acad. Mark. Sci.
Results for the two measurement models (customer sample
and manager sample), including factor weights and loadings,
and evidence of convergent and discriminant validity, are pre-
sented in Table 2. The measurement model results for the two
samples indicate some divergence in the manifest-latent vari-
able relationships between the two samples, but none that
diminish the applicability of the specified model to either
sample. In the customer sample measurement model, all of
the manifest variables load strongly and significantly on their
respective latent variables, and generally the model appears
stable and well-specified. Consistent with prior testing of the
ACSI model using customer data, each of the standardized
loadings score at the 0.940 level or higher, indicating very
strong manifest-latent variable relationships. The Cronbach’s
αstatistics for each of the four multi-item latent constructs are
above α=0.950, and the average variance extracted (AVE)
statistics for each latent variable is above 0.910 (from 0.915
to 0.975), also suggesting strong convergent validity (Fornell
and Larcker 1981; Voorhees et al. 2016).
In the manager sample measurement model, a somewhat
less Btight^data-to-latent-variables fit in this case is apparent.
respective latent variables, with most falling below the >0.900
levels observed in the customer sample model. However, all
of the estimated latent variables meet the standard thresholds
for acceptability, with Cronbach’sαstatistics greater than 0.7,
and AVE’s ranging from 0.660 to 0.893 for each of the latent
variables as well. Table 3provides the item loadings and
cross-loadings for the two samples. These results show that
all of the items load most strongly on their own constructs for
Having examined the results from the measurement models
for the two samples, we turn now to the results for the struc-
tural models. Table 4provides descriptive statistics for the
latent variables for the two samples, as well as inter-
construct correlations. These results confirm and extend upon
the conclusions drawn from the measurement model statistics.
While generally the latent variables exhibit significant
Tabl e 3 Latent variable loadings
and cross-loadings Indicators Latent variables
Customer model Expectations Quality Value Satisfaction Complaints Loyalty
Overall expectations 0.956 0.853 0.661 0.824 −0.422 0.607
Expectations customization 0.971 0.885 0.689 0.849 −0.456 0.595
Expectations reliability 0.942 0.875 0.642 0.798 −0.466 0.503
Overall quality 0.897 0.980 0.806 0.969 −0.685 0.804
Customization quality 0.893 0.985 0.788 0.958 −0.648 0.751
Reliability quality 0.875 0.963 0.749 0.904 −0.656 0.662
Quality given price 0.760 0.865 0.989 0.924 −0.688 0.699
Price given quality 0.598 0.703 0.985 0.803 −0.639 0.568
Overall satisfaction 0.849 0.962 0.901 0.988 −0.753 0.782
Confirmation of expectations 0.821 0.945 0.892 0.982 −0.722 0.760
Comparison to ideal 0.853 0.926 0.781 0.959 −0.669 0.807
Customer complaints −0.468 −0.680 −0.674 −0.732 1.000 −0.711
Repurchase intentions 0.595 0.758 0.647 0.801 −0.711 1.000
Manager model Expectations Quality Value Satisfaction Complaints Loyalty
Overall expectations 0.907 0.673 0.598 0.700 −0.219 0.391
Expectations customization 0.846 0.594 0.451 0.537 −0.142 0.502
Expectations reliability 0.664 0.458 0.223 0.335 −0.200 0.241
Overall quality 0.698 0.932 0.597 0.785 −0.319 0.500
Customization quality 0.573 0.894 0.480 0.721 −0.223 0.441
Reliability quality 0.537 0.678 0.295 0.436 −0.223 0.405
Quality given price 0.600 0.625 0.964 0.685 −0.147 0.468
Price Given quality 0.424 0.404 0.926 0.498 −0.053 0.286
Overall satisfaction 0.604 0.762 0.681 0.908 −0.259 0.547
Confirmation of expectations 0.446 0.476 0.395 0.742 −0.302 0.269
Comparison to ideal 0.641 0.721 0.506 0.875 −0.237 0.413
Customer complaints −0.226 −0.306 −0.114 −0.307 1.000 −0.259
Repurchase intentions 0.476 0.533 0.415 0.503 −0.259 1.000
J. of the Acad. Mark. Sci.
correlations and in the expected directions, the relationships
are weaker for the manager sample than for the customer
Tab le 5summarizes the mean scores of the latent con-
structs in the model for both managers and customers and
shows t-test statistic significance levels for the mean dif-
ferences in each of the latent constructs between the two
samples. Comparing the two samples, the mean scores for
the customer sample are lower for each latent variable,
with managers only less Bpositive^than customers (and
then only very slightly so) about their customers’percep-
tions of the quality of their product and service consump-
tion experiences. The mean differences are significant for
four of the six latent variables at the p< .05 level and for
one further latent variable at the p<.10 level. These re-
sults show that managers significantly overestimate the
levels of their customers’pre-purchase product and service
expectations, customers’perceptions of the value of the
products and services that the firms provide, the level of
customer satisfaction with the firms’products and services,
and their customers’attitudinal loyalty (repurchase inten-
tions). The only variable that managers significantly un-
derestimate is the level of complaining behavior about the
firm’s products and services reported by customers.
Figure 2provides standardized parameter estimates,
significance of the parameter estimates, and explained
) for each of the endogenous variables for
the two structural models. In the customer sample model,
Tabl e 4 Latent variable
descriptive statistics and
Customer Sample (n= 97) Mean SD 1 2 3 4 5
1 Customer expectations (LV) 8.09 0.42 1
2 Perceived quality (LV) 8.30 0.55 0.91** 1
3 Perceived value (LV) 7.86 0.65 0.69** 0.80** 1
4 Customer satisfaction (LV) 7.77 0.57 0.86** 0.97** 0.88** 1
5Complaints 0.14 0.12−0.47** −0.68** −0.67** −0.73** 1
6 Customer loyalty 8.11 0.71 0.59** 0.76** 0.65** 0.80** −0.71**
Manager Sample (n=97) 12345
1 Customer expectations (LV) 8.41 0.95 1
2 Perceived quality (LV) 8.28 0.87 0.72** 1
3 Perceived value (LV) 8.08 1.08 0.56** 0.56** 1
4 Customer satisfaction (LV) 8.08 0.90 0.68** 0.79** 0.64** 1
5Complaints 0.09 0.08−0.23* −0.31** −0.11 −0.31** 1
6 Customer loyalty 8.50 1.21 0.48** 0.53** 0.42** 0.50** −0.26**
Tabl e 5 Customer vs. manager
mean-level construct differences Construct Sample Mean Standard deviation Standard error Mean difference
Expectations Customers 8.093 .416 .044 -.317**
Managers 8.410 .947 .099
Quality Customers 8.305 .547 .057 .0250
Managers 8.280 .873 .092
Value Customers 7.857 .648 .068 -.228
Managers 8.085 1.075 .113
Satisfaction Customers 7.766 .566 .059 -.316**
Managers 8.082 .896 .094
Complaints Customers .1352 .119 .012 .041**
Managers .0941 .076 .008
Loyalty Customers 8.106 .707 .074 -.394**
Managers 8.500 1.212 .127
†Significant at p<0.10
**Significant at p<0.01
J. of the Acad. Mark. Sci.
the Customer Expectations latent variable is strongly and
positively related to Perceived Quality (β= 0.91; p< .001),
explaining 83% of the variance in Perceived Quality, but
insignificantly predictive of Perceived Value (β=−0.20;
p> .05) and Customer Satisfaction (β=−0.06; p>.05).
Perceived Quality is a strong and positive predictor of
both Perceived Value (β= 0.98; p< .001) and Customer
Satisfaction (β= 0.79; p< .001). Perceived Value is a sig-
nificant predictor of Customer Satisfaction (β= 0.29;
p< .001), although its effect is much smaller than the ef-
fect of Perceived Quality on Satisfaction. Finally, the
specified predictors explain a large proportion of the var-
iance in both Perceived Value (R
= 0.64) and Customer
= 0.97). Customer Satisfaction is a strong
negative predictor of Customer Complaints (β=−0.73;
p< .001), explaining 54% of the variance in this variable.
Customer Satisfaction is also a strong positive predictor
of Customer Loyalty (β= 0.61; p< .001), and together
Customer Satisfaction and Customer Complaints
(β=−0.27; p< .001) explain 68% of the variance in
Turning to the manager model, and the differences
between the two models across the samples become
clearer. Here, the Customer Expectations latent variable
is again strongly and positively related to Perceived
Quality (β=0.72; p< .001), although the effect is sub-
stantially smaller than in the customer sample model,
and Customer Expectations explains only 52% of the
variance in Perceived Quality. Interestingly, and unlike
the customer sample model, for the manager model
Customer Expectations is a relatively stronger and sig-
nificant predictor of Perceived Value (β=0.31; p<.05)
but is not a significant predictor of Customer
Satisfaction (β= 0.14; p> .05). On the other hand,
Perceived Quality is not nearly as strong a predictor
of either Perceived Value (β= 0.34; p< .01) or
Customer Satisfaction (β=0.54; p< .001) as in the cus-
tomer model. Perceived Value is a significant predictor
of Customer Satisfaction (β= 0.26; p< .001), with a
strength similar to that of the customer model. The
specified predictors explain a smaller proportion of the
variance in both Perceived Value (R
Customer Satisfaction (R
= 0.64) than is the case for
the customer sample model. In addition, Customer
Satisfaction is a significant but weaker predictor of
Customer Complaints (β=−0.31; p< .01), explaining on-
ly 10% of the variance in this variable. Likewise, while
Customer Satisfaction is a significant predictor of
Customer Loyalty (β=0.47; p> .001), Customer
Satisfaction and Customer Complaints (β=−0.12;
p> .05) both have much weaker relationships than ob-
served in the customer model and explain only 25% of
the variance in Customer Loyalty.
To confirm the comparisons of the results for the two
models offered above, we formally test whether or not
each of the pairs of parameter estimates in the two models
is equal (nine tests in all). While for covariance-based
structural equation modeling several established tech-
niques exist for comparing estimates between sub-group
models—most notably, the chi-square test of difference,
where each pair of model parameters is constrained to
equality and the changes in chi-square values are indica-
tive of significant parameter estimate differences—no sin-
gle similarly accepted method exists for LV-PLS.
However, options exist to draw this comparison.
Following the recommendations of Eberl (2010), Chin
(1998), and Wetzel et al. (2009), we used independent
samples t-tests that assume unequal variances (standard
Fig. 2 Structural model results
for the customer and manager
samples. Notes: 1. *** Significant
at p< 0.001; ** Significant at
p< 0.01; * Significant at p<0.05.
2. Standardized estimates are used
along each path, with customer
results on top and manager
sample beneath. 3. R
endogenous variable is included
in parenthesis (customer sample
followed by manager sample). 4.
Relative Goodness-of-Fit = 0.953
for customer sample and 0.901 for
J. of the Acad. Mark. Sci.
errors) between the samples, and a more conservative es-
timate of degrees of freedom, to compare the paths across
the two models. The results from these tests are
presentedin Table 6.
The results in Table 6provide a final confirmation of
the extent of the differences between the two samples and
models. Of the nine parameter estimates included in each
model, six significant differences in the estimates are
found, suggesting that overall the relationships are con-
siderably more dissimilar than similar for these two sam-
ples. Taken together, the observed differences in these
estimates and their statistical significance across the two
models provide a calibration of the extent to which mana-
gers understand the drivers of customers’views of the
firm’s product and service offerings.
To provide an initial indication of the potential im-
pact of such manager–customer perception differences
or misalignment, we examined the levels of satisfaction
reported by the customers of firms in which the manag-
er–customer perceptual differences are relatively larger
and smaller (satisfaction was emphasized given that
the original LV-PLS-tested ACSI model maximizes
explanatory power on Customer Satisfaction). To
accomplish this we first computed the firm-level mean
differences between each firm’s managers and customers
on each of the six ACSI constructs contained in
Tab le 5. We then aggregated these to a firm-level over-
all score representing the cumulative perceptual differ-
ences between the firm’s managers and customers across
all six ACSI constructs. Finally, we identified and
grouped the firms with the relatively largest and
smallest manager–customer perception differences in
our sample and examined the difference in mean
Customer Satisfaction scores across the two groups.
We tested the significance of the differences in observed
customer satisfaction across the two groups of firms
using t-tests. As shown in Table 7,theresultsofthis
analysis reveal that the average Customer Satisfaction
reported for the group of firms with the relatively larg-
est gaps between customer perceptions of the firm’s
products and services and managers’views of those
same customer perceptions is significantly lower than
that of the group of firms with the smallest customer–
manager perception gaps.
The literature contains a large and growing body of
evidence linking firm-level ACSI customer satisfaction
Tabl e 7 Customer satisfaction in most vs. least manager–customer aligned firms
Firm alignment group N Mean customer
Standard deviation Std. error Mean difference Std. error t df Sig. (2-tailed)
15 7.89 .554 .143 .462 .232 1.996 28 .056
15 7.43 .704 .182
Tabl e 6 Path coefficient
differences Path Customer model Manager model Difference
Expectations →Quality 1.170 0.91 0.049 0.664 0.72 0.065 0.506*
Expectations →Val u e −0.326 −0.20 0.212 0.314 0.31 0.138 −0.640*
Quality →Value 1.184 0.98 0.165 0.402 0.34 0.149 0.781*
Expectations →Satisfaction −0.079 -.06 0.057 0.135 0.14 0.087 −0.215*
Quality →Satisfaction 0.840 0.79 0.053 0.518 0.54 0.096 0.321*
Valu e →Satisfaction 0.244 0.29 0.025 0.233 0.26 0.064 0.011
Satisfaction →Complaints −0.157 −0.73 0.014 −0.026 −0.31 0.008 −0.131*
Satisfaction →Loyalty 0.717 0.61 0.092 0.604 0.47 0.126 0.113
Complaints →Loyalty −1.471 −0.27 0.430 −1.870 −0.12 1.494 0.399
*Significant at p<0.05
J. of the Acad. Mark. Sci.
scores with firms’accounting and stock market perfor-
mance (e.g., Anderson et al. 2004; Aksoy et al. 2008;
Morgan and Rego 2006;TuliandBharadwaj2009). The
results contained in Table 7therefore suggest that the
size of the gap between what customers actually think
and what managers think customers think of their firm’s
products and services has a significant negative effect
on firms’performance outcomes. For example, Gruca
and Rego (2005) show that for the average firm tracked
in the ACSI, one point of customer satisfaction is worth
$55 million in next year cash-flows. This indicates that
gap we observe between the two groups is of clear
economic as well as statistical significance.
Discussion and implications
the perceptions of senior managers (employed in
customer-facing roles) about their customers’views of
their firms’products and services align with customers’
actual perceptions. Based on a comparison of data and
models from a survey of managers in predominantly
Fortune 500 firms and their actual customers, we find
important disconnects between what customers perceive
and what managers think their customers perceive in
relation to the firm’s product and service offerings.
These differences cannot simply be explained by the
managers in our sample having little knowledge about
the firm’s customers since (1) these managers are in
roles within the firm where they should have a good
understanding of customers, and (2) we excluded sur-
veys from managers who rated their own knowledge
of the firm’s customers as being less than eight on a
ten-point scale. Thus, the differences that we observe
are between customers of a firm and managers within
that firm who are confident that they understand their
customers’perceptions and their drivers, and who are
not only in a position to use this knowledge to make
marketing decisions but also have the authority to allo-
cate resources to address marketplace issues.
We find a number of important customer–manager
Bdisconnects^in our analyses. First, our results show
that managers overestimate the positivity of customer
perceptions of the firm’s products and services.
Importantly, this suggests that managerial beliefs regard-
ing customer perceptions will likely present a Btoo-rosy^
picture if relied upon in isolation to guide the firm’s
marketing decisions and resource allocations with res-
pect to the firm’s product and service offerings. Our
results show that managers’beliefs regarding customer
perceptions of the firm’s products and services were
more positive than customers self-reported perceptions
for 11 out of 13 variables reported in Table 2.
indicates the prevalence of an ingrained optimism re-
garding customer perceptions of firms’product and ser-
vice offerings among managers, and these differences
are also statistically significant for five of the six latent
Since the large consumer-focused firms in our sample
typically have customer satisfaction monitoring and feed-
back systems in place, this finding has a number of im-
portant implications. Assuming that satisfaction and loy-
alty as captured in the ACSI survey questions does not
produce results that are systematically different from
those produced by these firms’own customer feedback
questions (the similarity across most market research ven-
dor satisfaction surveys and firm-specific surveys indi-
cates that this is a reasonable assumption), there could
be a number of reasons for the customer–manager discon-
nect in Blevels^of perceptions of the firm’s products and
services. Logically, either managers are not being exposed
(at least not completely) to their firms’customer feedback
data, or they are not interpreting (and/or remembering) it
accurately. In either case, while the managerial Bfixes^
required may be different, the clear implication is that
firms’existing customer satisfaction monitoring efforts
generally do not currently constitute good control
In particular, the significant Brosy view^bias we ob-
serve among managers regarding their overestimation of
the positivity of customers’views of the firm’s products
and services is likely to result in managers failing to act
when they should. The combination of managers
overestimating customers’perceived value of the firm’s
product and services, customers’satisfaction with the
firm’s products and services, and customers’likelihood
to re-purchase these same products and services from
the firm in the future is clearly problematic from this
perspective; these overly optimistic managers are likely
to miss trouble signs when they appear. This is
compounded by managers significantly underestimating
the proportion of their customers who have complained
about the firm’s products/services in the recent past. In
practice, it likely means that, all else being equal, man-
One of the two variables for which this is not the case is the percentage
of customers who have complained about their experiences with the
firm’s products/services within the past 6 months. While the manager
sample number is lower than that self-reported by customers, this is also
a further indicator of a Brosy view^bias among managers.
J. of the Acad. Mark. Sci.
agers are less likely to see a need to improve the firm’s
product and service offerings and their value to the
firm’s customers than may actually be required by cus-
tomers to remain loyal to that firm.
Second, our results also clearly show that managers
generally do not accurately understand the drivers of
customers’perceptions of the firm’sproductsandser-
vices. While the relatively lower incidence of Bdriver
analysis^as a component of firms’customer satisfaction
monitoring systems noted in prior research (e.g.,
Morgan et al. 2005) makes this result less surprising
than the Blevels^results discussed earlier, the implica-
tions of this finding may be even greater. Specifically,
this suggests that even when managers do recognize a
need to take actions to improve customers’perceptions
of the firm’s product and service offerings, they are
unlikely to do so in ways that have the strongest direct
effects on the desired customer perception outcomes.
For example, our results indicate that managers are like-
ly to underinvest in raising customer quality perceptions
as a route to enhancing customer satisfaction (cf. Habel
and Klarmann 2015). In this respect, our findings may
also provide an explanation for overemphasis on cost-
tive to that on quality improvements or achieving dif-
ferentiation (Mithas and Rust 2015;Rustetal.2002).
Where managers overestimate their own customers’per-
ception of the firm’s performance, cutbacks that under-
mine the delivery of service, for example, may seem
Perhaps even more damaging, managers are also likely
to underinvest in efforts to raise customer satisfaction
since they believe it has a much weaker relationship with
customers’complaining behavior than is in fact the case.
The literature shows that customer complaints have a sig-
nificant negative effect on stock returns (e.g., Luo 2007;
Luo and Homburg 2008) and future sales growth and
margins (e.g., Morgan and Rego 2006). Thus, any such
underinvestment in a key driver of complaint behavior has
significant negative implications for firm performance. In
addition, there are also likely to be important cost and
efficiency downsides that result from failing to accurately
understand the drivers of customers’perceptions of the
firm’s products and services. Managers with such inaccu-
rate understanding of the drivers of customer perceptions
are likely to inefficiently allocate available resources
among available satisfaction and loyalty driver improve-
ment options. To the extent that they are held accountable
for demonstrable perceptual outcomes (as they increasing-
ly are through performance incentives tied to satisfaction
results), managers may also spend more on relatively
weaker drivers to achieve the required perceptual out-
comes (and thus cost the firm money).
For managers, the results of our study should serve as
a wake-up call that all is not well with most firms’cus-
tomer satisfaction and complaint monitoring systems. For
firms with such monitoring systems already in place
(such as those in our sample), the first priority should
be to establish the extent and nature of the manager–
customer perception Blevel^and Bdriver^disconnects
within the firm. The approach adopted in our study
may provide a useful starting point in doing so.
own firm’s customer feedback survey measures and
translating these into managerial versions of the same
questions and items in much the same way our study
converted the ACSI survey measures. Managers can then
compare the results of their internal managerial samples
with those of their existing customer data to establish the
extent and nature of the manager–customer (mis-) align-
ment in their own firm.
In the interim, senior managers may be well advised to
ensure that actual customer feedback data and driver
analysis is appended to all action recommendations and
resource requests related to efforts to enhance customer
satisfaction and/or loyalty within the firm. This will not
solve the control system Bgap^problem of failing to iden-
tify when actions to enhance customer satisfaction and/or
loyalty required are created by the managerial Brosy
view^bias that we identify. However, it will at least
ensure that managers are forced to examine and consider
the firm’s actual customer feedback data concerning what
drives their customers’product- and service-related per-
ceptions and behaviors. This should allow resources to be
more efficiently deployed in any customer satisfaction and
loyalty improvement efforts.
For firms without formal customer feedback systems,
our results indicate that in any efforts to introduce such
systems, managers should give great consideration to
how they can communicate and establish the credibility
of the customer feedback produced among managers
within the firm. Enhancing managers’perceptions of
the credibility of customer feedback data should enhance
the likelihood that they will pay attention to it (e.g.,
Morgan et al. 2005) and reduce the likelihood that mana-
gers will substitute their own views of what they think
customers think. Significant attention should also be
given to how the results of the firm’s customer feedback
system can be effectively communicated to managers
within the firm. The importance of these considerations
suggested by our results may require new or revised
customer feedback system designs and will likely also
J. of the Acad. Mark. Sci.
have significant resource cost and allocation implications
in implementing such systems.
Limitations and future research
While our study provides new and important insights
regarding the extent to which managers understand their
customers’product and service perceptions and the
drivers of these perceptions, it has some limitations that
are inherent in the research design and data availability.
Perhaps most obvious, our study uses data only on large
Fortune 500-type firms. Such larger firms generally have
customer feedback systems in place, but managers within
such large organizations may also be further removed
from their firm’s customers than is often the case with
smaller firms. There is therefore a need to conduct sim-
ilar studies for mid-size and small firms to establish the
generalizability of our findings. In addition, while many
of the firms in our sample have global operations, in our
study we only collect customer and manager data for the
firms in our sample operating in the United States. Data
collection and analysis of this issue across different
countries is required to establish the degree to which
our findings are generalizable across countries.
Beyond the need for additional research to overcome
these limitations, our study also has numerous implica-
tions for future research. Here, we focus on three issues
that we believe may provide particularly fruitful avenues
for theoretically important and managerially relevant in-
quiry. First, why are managers overly positive in their
views of what customers perceive of their firm’s product
and service offerings? Cognitive limitations and biases
arising from the use of judgmental heuristics such as rep-
resentativeness, availability, and adjustment and anchor-
ing (e.g., Chinader and Schweitzer 2003;Tetlock2000;
Kahneman and Tversky 1979) may help explain differ-
ences between what customers think and what managers
think customers think. But what explains the systematic
positivity bias we observe? Is it that the within-firm ob-
jective data on product and service quality and costs ob-
served by managers is systematically greater than the per-
ceptions of customers?
Second, many large firms systematically track the sat-
isfaction of their customers using actual consumer survey
data and use sophisticated analysis techniques to uncover
the drivers of satisfaction among their customers. Yet, as
our results show, this is clearly insufficient if the goal is to
allow managers to understand customers’perceptions of
the firm’s product and service offerings and the drivers of
those perceptions. There may be two basic reasons why
such disconnect is apparent. First, it is possible that the
data and analysis results of the firm’s customer feedback
systems are not being communicated effectively within
the firm. This may be a sender issue (e.g., using insuffi-
cient or ineffective media or messages) and/or a receiver
issue (e.g., insufficient time or cognitive resources).
Moreover, managers may be skeptical of the results of
their firms’customer feedback systems and instead trust
their own perceptions as a substitute for findings from this
data and base their marketing decisions on such percep-
tions. Which is it? Or is it a combination of the two?
Third, given the indications of the negative impact of
the manager–customer perception gaps we uncover for
customer satisfaction outcomes, what works and doesn’t
work in closing the gap between what managers think
customers think and what customers actually think?
Most firms currently spend the overwhelming majority
of their customer feedback monitoring budgets on data
collection and analysis (e.g., Morgan et al. 2005).
Should they focus greater attention on establishing the
credibility of the customer feedback data collected and
analyses performed on this data among managers and
employees within the firm? If so, what are the predic-
tors of customer feedback data and data analysis output
credibility among managers? These issues are becoming
increasingly important to tackle in the new era of big
data. Alternatively, is the problem that results are sim-
ply under- or ineffectively communicated to managers?
If so, what communication approaches work best to
ensure that customer feedback data and insights are suc-
cessfully received by managers and employees? For
example, can data visualization approaches help bridge
the sender–receiver communication gap?
American Customer Satisfaction Index (ACSI) and a
sample of surveys of managers employed within
ACSI-measured companies, this study provides evidence
that managers generally fail to accurately understand
both what customers think of their firm’sproductsand
services and why customers hold the perceptions that
they do. These findings suggest that despite often being
the single biggest line-item of most firms’market re-
search expenditures, existing customer feedback systems
are not performing an effective management control
role. In addition, firms need to do much more to com-
municate and establish the credibility of the insights
produced by their customer feedback systems.
J. of the Acad. Mark. Sci.
Tabl e 8 Survey items, item wording, and scale
ACSI Item (Scale) Original customer respondent question wording Manager respondent revised question wording
(1 = Bnot very high^and 10 = Bvery
Thinking about your overall expectations of the quality
you would receive from (Company/Brand), how
would you rate your expectations?
Thinking about your customers’expectations of the
quality they would receive, how would you rate your
customers’expectations of the overall quality of your
Expectations of Customization
(1 = Bnot very well^and 10 = Bvery
At the same time, you probably thought about things
you personally require from (Company/Brand), how
would you rate the degreeto which you expected that
these personal requirements would be met?
Think about the things your customers personally
require from the products or service that your
company sells. To what degree do you think your
customers expected your top brands to meet their
Expectations of Reliability
(1 = Bvery often^and 10 = Bnot very
Thinking about your expectations before your recent
experiences with (Company/Brand), how often did
you expect that things could go wrong (Company/
Thinking about your customers’expectations before
their most recent experiences with your top brands,
how often doyour customers expect that things could
go wrong with your top brands?
(1 = Bnot very high^and 10 = Bvery
First, please consider all your experiences with
(Company/Brand). How would you rate the overall
quality of (Company/Brand)?
Now, please consider all of your customers’experiences
with your top brands. How would your customers
would rate the overall quality of your top brands?
Quality as Customization
(1 = Bnot very well^and 10 = Bvery
Now, thinking about your personal requirements from
(Company/Brand), please tell me how well
(Company/Brand) has actually met your
Now, thinking about your customers’personal
requirements from yourtop brands, how well do your
customers believe that your top brands actually met
their personal requirements?
Quality as Reliability
(1 = Bvery often^and 10 = Bnot very
How often do things go wrong with (Company/Brand)? How often do your customers believe that things
actually go wrong with your top brands?
Price given Quality
(1 = Bvery poor price given the
quality^and 10 = Bvery good
price given the quality^)
Given the quality of (Company/Brand), how would you
rate the prices that you pay for (Company/Brand)?
Given the quality of your top brands, how would your
customers rate the price that they paid?
Quality given Price
(1 = Bvery poor quality given the
price^and 10 = Bvery good
quality given the price^)
Given the prices you pay at (Company/Brand), how
would you rate the quality of (Company/Brand)?
Given the price that the customers paid for your top
brands, how would they rate the quality?
(1 = Bvery dissatisfied^and
First, please consider all your experiences to date with
(Company/Brand). How satisfied are you with
Please consider all of your customers’experiences with
your top brands. How satisfied do you believe your
customers are with your top brands?
Confirmation of Expectations
(1 = Bfalls short of the customers’
expectations^and 10 = Bexceeds
To what extent has (Company/Brand) fallen short of
your expectations or exceeded your expectations?
To what extent has your top brands fallen short of the
customers’expectations or exceeded their
Comparison to Ideal
(1 = Bnot very close to the ideal^
and B10^=Bvery close to the
Forget (Company/Brand) for a moment. Now, I want
you to imagine an (Company/Brand). How well do
you think (Company/Brand) compares with that ideal
(product or service)?
Forget your top brands for a moment. Now, we want
you to imagine an ideal (product or service in your
category) from the customers’standpoint. How well
do you think your customers believe that your top
brands compares with that ideal product/service?
(1 = BNo^and 2 = BYe s^for
customers and Percentage for
Have you complained to (Company/Brand) within the
past 6 months?
What percentage of your customers do you think
complained about any of your top brands within the
past 6 months?
(1 = Bvery unlikely^and 10 = Bvery
The next time you are going to choose a (product or
service) for your needs, how likely is it that it will be
The next time your customers are going to purchase the
same product/service which you sell, how likely is it
that they will purchase from your company?
J. of the Acad. Mark. Sci.
Aksoy, L., Cooil, B., Groening, C., Keiningham, T. L., & Yalcin, A.
(2008). The long term stock market valuation of customer satisfac-
tion. Journal of Marketing, 72(July), 105–122.
Anderson, E. W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer
satisfaction and shareholder value. Journal of Marketing, 68(4),
Anthony, R. N. (2007). Management control systems (12th ed.). New
Cassel, C., Hackl, P., & Westlund, A. (1999). Robustness of partial least
squares method for estimating latent variable quality structures.
Journal of Applied Statistics, 26(4), 435–446.
Chin, W. W. (1998). The partial least squares approach to structural equa-
tion modeling. In G. A. Marcoulides (Ed.), Modern methods for
business research (pp. 295–336). Mahway: Lawrence Erlbaum
Chinader, K. R., & Schweitzer, M. E. (2003). The input bias: the misuse
of input information in judgment of outcomes. Organizational
Behavior and Human Decision Processes, 91(2), 243–254.
Clark, T., Key, T. M., Hodis, M., & Rajaratnam, D. (2014). The intellec-
tual ecology of mainstream marketing research: an inquiry into the
place of marketing in the family of business disciplines. Journal of
the Academy of Marketing Science, 42(3), 223–241.
Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive
theory to training for computer skills. Information Systems
Research, 6(2), 118–143.
Dotson, J., & Allenby, G. (2010). Investigating the strategic influence of
customer and employee satisfaction on firm financial performance.
Marketing Science, 29(5), 895–908.
Drucker, P. F. (1954). The practice of management.NewYork:
Harper & Brothers.
Eberl, M. (2010). An application of PLS in multi-group analysis:
The need for differentiated corporate-level marketing in the
mobile communications industry. In V. E. Vinzi, W. W. Chin,
J. Henseler, & H. Wang (Eds.), Handbook of partial least
squares: Concepts, methods and applications in marketing
and related fields.NewYork:Springer.
Feng, H., Morgan, N. A., & Rego, L. L. (2015). Marketing depart-
ment power and firm performance. Journal of Marketing,
Fornell, C. (1992). A national customer satisfaction barometer: the
Swedish experience. Journal of Marketing, 56(1), 6–21.
Fornell, C., & Bookstein, F. L. (1981). Two structural equation models:
LISREL and PLS applied to consumer exit-voice theory. Journal of
Marketing Research, 19(4), 440–452.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation
models with unobservable variables and measurement error.
Journal of Marketing Research, 28(1), 39–50.
Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E.
(1996). The American customer satisfaction index: nature, purpose,
and findings. Journal of Marketing, 60(4), 7–18.
Fornell, C., Mithas, S., Morgeson, F. V., & Krishnan, M. S. (2006).
Customer satisfaction and stock prices: high returns, low risk.
Fornell, C., Morgeson, F.V., & Hult, G.T.M. (2016). Stock returns on
customer satisfaction do beat the market: gauging the effect of a
marketing intangible. Journal of Marketing,80(5), In Press.
Germann, F., Ebbes, P., & Grewal, R. (2015). The chief marketing officer
matters. Journal of Marketing, 79(May), 1–22.
Gilin, D., Maddux, W. W., Carpenter, J., & Galinsky, A. D. (2013). When
to use your head and when to use your heart: the differential value of
perspective-taking versus empathy in competitive interactions.
Personality and Social Psychology Bulletin, 39(1), 3–16.
Gruca, T. S., & Rego, L. L. (2005). Customer satisfaction, cash flow and
shareholder value. Journal of Marketing, 69(3), 115–130.
Habel, J., & Klarmann, M. (2015). Customer reactions to downsizing:
when and how is satisfaction affected? Journal of the Academy of
Marketing Science, 43(6), 768–789.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assess-
ment of the use of partial least squares structural equation modeling
in marketing research. Journal of the Academy of Marketing
Science, 40(3), 414–433.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). Aprimer
on partial least squares structural equation modeling (1st ed.).
Newbury Park: Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). Aprimer
on partial least squares structural equation modeling (2nd ed.).
Newbury Park: Sage Publications.
Henseler, J., Ringle, C. M., & Sinkowics, R. (2009). The use of partial
least squares path modeling in international marketing. Advances in
International Marketing, 20(1), 277–319.
Hirschman, A. O. (1970). Exit, voice, and loyalty: Responses to
decline in firms, organizations, and states.Cambridge:
Harvard University Press.
Homburg, C., Vomberg, A., Enke, M., & Grimm, P. H. (2015). The
loss of the marketing department’s influence: is it really hap-
pening? and why worry? Journal of the Academy of Marketing
Science, 43(1), 1–13.
Tabl e 9 Companies included in sample
1-800-Flowers Costco Kraft Southwest
ABCNews.com CVS Caremark Kroger Sprint
Adidas Darden Lowe’sStarbucks
Aetna Dell Macy’sStaples
Allstate Delta Marriott Starwood
Amazon DIRECTV McDonald’sSupervalu
Ameren DISH Network Mercedes-Benz Target
Apple eBay Microsoft UPS
AT & T E d i so n
Molson Coors USATODAY.com
Bank of America Exelon Motorola Verizon
Best Buy Expedia Nike V.F. Corp.
Blue Cross Blue
Farmers Nokia Volkswagen
Campbell Soup FedEx Office Depot Walgreens
CenterPoint Energy FirstEnergy OfficeMax Wal-Mart
Ford PepsiCo Wells Fargo
Choice Hotels Gap Philip Morris Winn-Dixie
Clorox General Mills Procter &
CMS Energy Google Rite Aid Whirlpool
Safeway Xcel Energy
Colgate-Palmolive Honda Sara Lee Yahoo!
Comcast J.C. Penney Sears
Continental Kellogg Southern
J. of the Acad. Mark. Sci.
Hulland, J., Ryan, M. J., & Rayner, R. K. (2010). Modeling customer
satisfaction: A comparative performance evaluation of covariance
structure analysis versus partial least squares. In V. E. Vinzi, W. W.
Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least
squares: Concepts, methods and applications in marketing and re-
Hult, G. T. M. (2011). Toward a theory of the boundary-spanning mar-
keting organization and insights from 31 organization theories.
Journal of the Academy of Marketing Science, 39(4), 509–536.
Hult, G. T. M., & Ketchen, D. J. (2001). Does market orientation matter?:
a test of the relationship between positional advantage and perfor-
mance. Strategic Management Journal, 22(9), 899–906.
Hult, G. T. M., Ketchen, D. J., & Slater, S. F. (2005). Market orientation
and performance: an integration of disparate approaches. Strategic
Management Journal, 26(12), 1173–1181.
Johnson, M. D., & Fornell, C. (1991). A framework for comparing cus-
tomer satisfaction across individuals and product categories. Journal
of Economic Psychology, 12(2), 267–286.
Johnson, M. D., Herrmann, A., & Gustafsson, A. (2002). Comparing
customer satisfaction across industries and countries. Journal of
Economic Psychology, 23(3), 749–769.
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of
decision under risk. Econometrica, 47(2), 263–291.
Kristensen, K., & Eskildsen, J. (2010). Design of PLS-based satisfaction
studies. In W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of
partial least squares. New York: Springer.
Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock
returns. JournalofMarketing,71(3), 75–88.
Luo, X., & Homburg, C. (2008). Satisfaction, complaint, and the stock
value gap. Journal of Marketing, 72(4), 29–43.
Mithas, S., & Rust, R. T. (2015). How information technology strategy
and investments influence firm performance: conjectures and empir-
ical evidence. MIS Quarterly, 40(1), 223–245.
Morgan, N. A., & Rego, L. L. (2006). The value of different customer
satisfaction and loyalty metrics in predicting business performance.
Marketing Science, 25(5), 426–439.
Morgan, N. A., Anderson, E. A., & Mittal, V. (2005). Understanding
firms’customer satisfaction information usage. Journal of
Marketing, 69(3), 131–151.
national differences in consumer satisfaction: mobile services
in emerging and developed markets. Journal of International
Marketing, 23(2), 1–24.
Narver, J. C., & Slater, S. F. (1990). The effect of a market orientation on
business profitability. Journal of Marketing, 54(4), 20–35.
Oliver, R. L. (2010). Satisfaction: A behavioral perspective on the
customer. London: ME Sharpe Incorporated.
Parker, S. K., & Axtell, C. M. (2001). Seeing another viewpoint: ante-
cedents and consequences of employee perspective taking. Academy
of Management Journal, 44(6), 1085–1100.
Rigdon, E. E., Ringle, C., Sarstedt, M., & Gudergan, S. P. (2011).
Assessing heterogeneity in customer satisfaction studies: across
industry similarities and within industry differences. Advances in
International Marketing, 22(1), 169–194.
Rust, R. T., Moorman, C., & Dickson, P. R. (2002). Getting return on
quality: revenue expansion, cost reduction, or both? Journal of
Marketing, 66(4), 7–24.
Schmenner, R. W., & Vollmann, T. E. (1994). Performance measures:
gaps, false alarms, and the ‘usual suspects’.International Journal
of Operations & Production Management, 14(12), 58–69.
Sleep, S., Bharadwaj, S., & Lam, S. K. (2015). Walking a tightrope: the
joint impact of customer and within-firm boundary spanning activ-
ities and perceived customer satisfaction and team performance.
Journal of the Academy of Marketing Science, 43(4), 472–489.
Srivastava, R. K., Shervani, T. A., & Fahey, L. (1999). Marketing, busi-
ness processes, and shareholder value: an organizationally embed-
ded view of marketing activities and the discipline of marketing.
JournalofMarketing,63(Special Issue), 168–179.
Tetlock, P. E. (2000). Cognitive biases and organizational correctives: do
both disease and cure depend on the ideological beholder?
Administrative Science Quarterly, 45(2), 293–326.
Tuli, K., & Bharadwaj, S. G. (2009). Customer satisfaction and stock
returns risk. Journal of Marketing, 73(6), 184–197.
Varadarajan, R. (2010). Strategic marketing and marketing strategy: do-
main, definition, fundamental issues and foundational premises.
Journal of the Academy of Marketing Science, 38(2), 119–140.
Vargo, S.L., & Lusch, R. F. (2016). Institutions and axioms: an extension
and update of service-dominant logic. Journal of the Academy of
Marketing Science, 44(1), 5–23.
Vavra, T. G. (2002). Customer satisfaction measurement simplified: a
step-by-step guide for ISO 9001:2000 certification. Milwaukee:
Vilares, M. J., Almeida, M. H., & Coelho, P. S. (2010). Comparison of
likelihood and PLS estimators for structural equation modeling: A
simulation with customer satisfaction data. In V. E. Vinzi, W. W.
Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least
squares: Concepts, methods and applications in marketing and re-
Voorhees, C. M., Brady, M. K., Calantone, R. J., & Ramirez, E. (2016).
Discriminant validity testing in marketing: an analysis, causes for
concern, and proposed remedies. Journal of the Academy of
Marketing Science, 44(1), 119–134.
Vorhies, D. W., & Morgan, N. A. (2005). Benchmarking marketing ca-
pabilities for sustainable competitive advantage. Journal of
Marketing, 69(1), 80–94.
Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015).
Building, measuring, and profiting from customer loyalty. Journal
of the Academy of Marketing Science, 43(6), 790–825.
Wetzel, M., Odekerken-Schroder, G., & Van Oppen, C. (2009).
Using PLS path modeling for assessing hierarchical constructs
models: guidelines and empirical illustration. MIS Quarterly,
J. of the Acad. Mark. Sci.