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Journal of Service Research
DOI: 10.1177/1094670509336743
2009; 12; 3 originally published online May 18, 2009; Journal of Service Research
Benjamin Schneider, William H. Macey, Wayne C. Lee and Scott A. Young
Financial and Market Performance
Organizational Service Climate Drivers of the American Customer Satisfaction Index (ACSI) and
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Journal of Service
Research
Volume 12 Number 1
August 2009 3-14
© 2009 The Author(s)
10.1177/1094670509336743
http://jsr.sagepub.com
3
Organizational Service Climate Drivers
of the American Customer Satisfaction
Index (ACSI) and Financial
and Market Performance
Benjamin Schneider
William H. Macey
Wayne C. Lee
Scott A. Young
Valtera Corporation
The climate for service is conceptualized and studied as a correlate of customer satisfaction and corporate financial and
market performance, with customer satisfaction as a mediator of the climate-performance relationship. Brief reviews of
relevant literatures yield three hypotheses: (1) customer satisfaction will be a significant correlate of organizational finan-
cial and market performance, (2) organizational service climate will be a significant correlate of organizational customer
satisfaction, and (3) customer satisfaction will mediate the relationship between service climate and financial and market
performance. The hypothesized relationships are supported in a 3-year longitudinal study of Fortune 200 service companies
(not units within companies) from diverse service sectors with path analyses supporting full mediation for customer satis-
faction in the link between service climate and corporate financial and market performance. Management implications for
corporate competitive advantage through a focus on service climate are discussed.
Keywords: service climate; linkage research; employee attitudes; customer satisfaction; financial performance; mediation
T
he goal of the present article is to further examine, at
the company level of analysis, relationships among
service climate, customer satisfaction, and the financial
and market performance of companies. While the indi-
vidual bivariate relationships among these three vari-
ables have been documented, we are aware of no study
at the company level of analysis where all three mea-
sures have been simultaneously studied using indicators
of known reliability and validity. In addition, the role of
customer satisfaction as a mediator of the relationship
between service climate and financial outcomes has been
neither formally proposed nor tested, as it is here.
In what follows, we first briefly review the research
on the financial consequences of customer satisfaction.
Then we turn to the literature on the linkage of service
climate to customer satisfaction. Finally we explore
the role of customer satisfaction as a mediator of the rela-
tionship between service climate and market and financial
performance of companies. Based on these reviews, three
hypotheses linking service climate, customer satisfaction,
and financial and market performance of the firm are
derived.
Consequences and Antecedents
of Customer Satisfaction
The pursuit of customer satisfaction has long been a
focus of interest (see Oliver 1997 for a review), and the
financial and market performance consequences of cus-
tomer satisfaction have been documented (e.g., Anderson,
Fornell, and Mazvancheryl 2004). Significant relation-
ships have been found between customer satisfaction
Authors’ Note: We appreciate the comments and data analysis help
from Holly Lam, the suggestions of Larry James with regard to data
analysis, and the feedback from Mark Ehrhart and Del Nebeker on an
earlier version. The three anonymous JSR reviewers and the editor
were very helpful and patient in working with us; any remaining
errors are ours.
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4 Journal of Service Research
and return on investment (ROI; Zeithaml 2000) and oper-
ating profits (Zahorik and Rust 1992). In a recent longi-
tudinal study, Anderson, Fornell, and Mazvancheryl
(2004) also showed that customer satisfaction, assessed at
the company level of analysis via the American Customer
Satisfaction Index (ACSI), is significantly related to
shareholder value. Gruca and Rego (2005) extended the
findings of Anderson et al. by revealing in an archival
longitudinal study how the relationship between the
ACSI and shareholder value occurs as a function of
increased future cash flow and a reduction in the vari-
ability of cash flow over time. Based on this research
literature we hypothesize that a sample of service compa-
nies should also reveal this relationship between customer
satisfaction and financial and market performance.
Hypothesis 1: Organizations’ customer satisfaction
will be significantly and positively related to
those same organizations’ financial and market
performance.
The antecedents or drivers of customer satisfaction
are mostly conceptualized and studied as experiences
customers have that precede their evaluations of their
experiences in terms of satisfaction (Heskett, Sasser, and
Schlesinger 1997). For example, customer perceptions
of both service and product quality have been shown to
drive customer satisfaction (see Kasper, van Helsdingen,
and Vries 1999 for a review) as has the effectiveness of
recovery tactics (Tax and Brown 2000).
In addition to these advances there has been signifi-
cant commentary (Deshpande, Farley, and Webster 1993;
Shah et al. 2006) and research (see Dean 2004 for a
review) on internal organizational issues, including the
experiences of service employees and their relationship
to customer satisfaction. In the cross-disciplinary area of
service management, for example, research from both
marketing perspectives (Bettencourt and Brown 1997;
Gwinner et al. 2005; Wall and Berry 2007) and organiza-
tional behavior (OB) perspectives (Liao and Chuang
2004; Payne and Webber 2006) support the idea that
organizational variables contribute in important ways to
customer satisfaction.
Identifying the organizational correlates of customer
satisfaction is both conceptually and practically useful.
Conceptually, such a focus introduces a systems perspec-
tive on customer satisfaction, one in which customers
and the organizations that serve them are seen as part of
an interactional and relational system with mutual
dependencies and psychological and physical closeness
(Bowen and Schneider 1988, Mills 1986). From a practical
vantage point, management can more easily change the
organizational issues customers encounter than they can
directly influence customers.
In the present article we focus on OB correlates of
customer satisfaction, especially organizational service
climate (Lytle, Hom, and Mokwa 1998; Schneider,
White, and Paul 1998). Organizational service climate
concerns employees’ perceptions of the degree to which
their companies are organized and managed to deliver
service quality. The research literature that has focused
on the relationship between organizational service cli-
mate and customer satisfaction has been called “linkage
research” (Wiley 1996).
Service Climate
and Customer Satisfaction
A characteristic of service marketing and manage-
ment as a field is its attempt to integrate operations man-
agement (OM), marketing, and human resources (HR)
perspectives. In that literature “HR” has come to encom-
pass human resources management, organizational
behavior (OB), organization theory (OT), and industrial
and organizational psychology (I/O) perspectives. For
example, although their book is called Services Marketing,
Lovelock and Wirtz (2004) devote several chapters to
process issues associated with OM and additional chap-
ters to HR issues like leadership and managing people
and also to ways of achieving synergies across these plus
marketing in pursuit of service quality.
One notable framework incorporating these diverse
perspectives is the service-profit chain proposed by
Heskett, Sasser, and Schlesinger (1997). Their frame-
work views the operational procedures of firms and the
treatment of human resources as key elements in driving
customer satisfaction and customer loyalty with subse-
quent increments in revenues and profits. While specific
empirical tests of the model have not always revealed
significant relationships where hypothesized (e.g.,
Loveman 1998; Pritchard and Silvestro 2005), the gen-
eral framework indicates that both OM and OB internal
processes impact customers since customers encounter
both technology (OM) and humans (HR) when interact-
ing with service firms.
The most robust and consistent organizational corre-
lates of customer satisfaction have come from the thread
of work on service climate originated by Schneider
(1980) and his colleagues (Schneider, Parkington, and
Buxton 1980) and recently reviewed by Schneider and
White (2004) and Schneider, Macey, and Young (2006).
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Schneider et al. / Organizational Service Climate Drivers 5
Schneider’s view of service climate emerged from work he
had done on conceptualizing the more generic construct of
organizational climate (Schneider 1975). He defined
climate as the meaning employees attach to the policies,
practices, and procedures and the behavior that gets
rewarded, supported, and expected in an organization.
He proposed that climate would prove most useful in
understanding organizational outcomes when it was stra-
tegically focused; he suggested that climate researchers
study a climate for something—for service, for innova-
tion, for safety. Schneider’s own research has empha-
sized the climate for service in organizations, but there is
also research on the climate for safety (e.g., Katz-Navon,
Naveh, and Stern 2005), the climate for justice (Colquitt,
Noe, and Jackson 2002), and so forth (for a review, see
Schneider, Ehrhart, and Macey in press).
In Schneider’s earliest and continuing studies, the
climate for service focused on the policies, practices, and
procedures and the behavior that gets rewarded, sup-
ported, and expected with regard to the delivery of ser-
vice quality to customers. So, for example, of interest
was not just leadership but also service quality leader-
ship, or the extent to which the leaders of work groups
emphasized by their policies and behavior that service
quality was the raison d’être of the work group. The
basic hypothesis in all of the work that followed is that
because employees and customers in service delivery
organizations are both physically and psychologically
close, they come to share impressions of their common
experiences. The meaning employees attach to the ser-
vice climate in which they work serves as implicit input
to employees about how important service quality is as
something requiring their attention. Customers observe
and experience the resultant employee behavior and
reach conclusions about the quality of service delivery
they have experienced and how satisfied they are with it.
The two should be linked because the parties to the ser-
vice delivery relationship are linked (Wiley 1996).
Indeed, in various studies of bank branches (Schneider
1980; Schneider and Bowen 1985; Schneider, White,
and Paul 1998), supermarket departments (Schneider
et al. 2005), and regions of an insurance company
(Schneider et al. 1996), Schneider and his colleagues
have supported the hypothesized relationship between
employee perceptions of service climate and customer
service quality experiences and customer satisfaction.
But it is not only Schneider and his colleagues who
have supported this linkage across the boundaries of
organizations. Recent supporting studies have been
reported in restaurants (Liao and Chuang 2004;
Salanova, Agut, and Peiro 2005; Wall and Berry 2007),
with hairstylists (Payne and Webber 2006), and in auto-
motive service stores (Sowinski, Fortmann, and Lezotte
2008). Indeed Dean (2004), in her very complete review
of this linkage research, concluded that there is “compel-
ling evidence” (p. 245) for the notion that internal orga-
nizational attributes, whether studied from marketing,
operations, human resources, or psychology perspec-
tives, link to customer experiences and financial out-
comes. Crotts, Dickson, and Ford (2005) reached very
similar conclusions in their review of a similar literature
focused on linkages of organizational variables, includ-
ing service climate, to customer satisfaction as did
Bowen and Ford (2002). Even with the robustness of
these findings from these reviews, Dean (2004) noted
that it is not time to be sanguine but rather that additional
conceptual and data-gathering work is required.
For example, she noted that the size of the unit of
analysis studied (e.g., banks vs. bank branches) was
inconsistent across studies, with almost all studies being
done of units within larger organizations; that different
studies failed to use the same measure of customer satis-
faction or of the organizational behavior variables; and
that the industries studied tended to be dominated by
banks and insurance companies, creating potential gen-
eralizability issues. These deficiencies in the research to
date are what we address in the present effort. That is,
here we use a widely accepted measure of customer sat-
isfaction (the American Customer Satisfaction Index;
ACSI; Anderson and Fornell 2000) and a standard mea-
sure of service climate (Schneider, White, and Paul
1998) and conduct the research at the organizational
(company) level of analysis on a sample of service com-
panies from diverse service industries (e.g., airlines,
information technology, and financial). These thoughts
lead to the following hypothesis:
Hypothesis 2: Organizational service climate percep-
tions will correlate positively and significantly with
organizational customer satisfaction.
The relationship between service climate percep-
tions and customer satisfaction in which we have inter-
est will be at the organizational or company level of
analysis, not just the unit (e.g., branch) level of analy-
sis, where most prior research on this link has been
studied (Dean 2004). In unit-within-company studies
(e.g., bank branches within a bank), the focus is on
within-company differences. That is, variability across
units within a company with regard, for example, to ser-
vice climate, is correlated with variability across those
units in customer satisfaction.
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6 Journal of Service Research
Switching to the company level of analysis moves one
from a within-company vantage point to a between-
company situation. That is, it is one thing to know that
Branch A is better than Branch B on service climate and it
is another to know that Company A is better than Company
B on service climate—especially if we can show that
company-level differences in service climate are related
to differences in customer satisfaction and the financial
and market performance that we hypothesize follow. This
is an important consideration because within-company
differences in both service climate and customer satisfac-
tion may exist in part because of the tendency for man-
agement to allocate more resources to the units that have
been successful in the past and that have the greatest
chance to be successful in the future. Furthermore, a com-
pany level of analysis provides for a more robust context
in which to test these relationships because the variance
in service climate will be higher across different compa-
nies than across different organizational units within the
same company. In fact, lack of sufficient variation in
organizational variables when a sample consists of units
within a company (rather than different companies alto-
gether) has been cited as a reason for the failure to find
empirical support for theoretically and conceptually
sound hypotheses pertaining to links among organiza-
tional constructs (Parasuraman, Berry, and Zeithaml
1991). At the company level of analysis the research con-
cerns corporate competitive advantage, a level of analysis
not often connected with service climate research much
less connected to the relationship between service climate
and corporate financial and market performance. It is this
latter relationship to which we turn next.
Mediating Role of Customer Satisfaction
As positioned above, Hypothesis 2 extends the well-
replicated relationship between service climate and cus-
tomer satisfaction by evaluating that relationship at the
company level of analysis. We further expand the evi-
dence regarding the relationship between service climate
and organizational performance by examining the medi-
ating role of customer satisfaction in the service climate-
financial performance linkage across a broad sample of
companies form many industries.
Previous research on service climate has emphasized
the linkage to customer satisfaction and has not focused
on the relationship of service climate to financial out-
comes. In an exception to this, Sowinski, Fortmann, and
Lezotte (2008) failed to find such a relationship across
the stores of an automotive supply chain. Perhaps one
reason for this failure is that in that study, units within a
company (stores within a supply chain) were studied, but
with a study across companies such as the one proposed
here the variability potentially introduced would provide
a more robust test of the direct effects hypothesis.
Theory and evidence support both a direct effect of
climate on financial and market performance and a
mediated effect (Schneider et al. 2005). For example,
Denison (1990) showed that employee perceptions (of
corporate culture broadly defined) were useful direct and
significant predictors of company financial performance.
On the other hand, Siehl and Martin (1990) suggest that
such findings should not typically be expected because
so many variables intervene between employee perceptions/
experiences and the long-term financial performance of
companies. For example, perceptions are not behavior,
and it is the behavior of employees that might produce
financial performance, as shown in Schneider et al.
(2005). Another intermediate possibility, and the one we
adopt here, was the one proposed in the service-profit
chain (Heskett, Sasser, and Schlesinger 1997) that the
chain goes from employees (in their case employee satis-
faction) through customer satisfaction to financial perfor-
mance. Based on the evidence shown for this mediated
model, we propose that customer satisfaction will medi-
ate the relationship between service climate and financial
performance. Because the literature is not clear at all
about whether mediation will be partial mediation (the
Heskett, Sasser, and Schlesinger 1997 service-profit
chain logic) or full mediation (the Siehl and Martin 1990
logic and Schneider et al. 2005 finding), we state our
third hypothesis in its general form and then compare the
competing full and partial mediation alternatives:
Hypothesis 3: Customer satisfaction will mediate the
relationship between organizational service climate
and financial and market performance.
Methodology
Sample and Measures
Company-level data were collected for relevant vari-
ables (service climate, customer satisfaction, and finan-
cial and market performance) in a diverse sample of
companies for the years 2003, 2004, and 2005; the
maximum sample was 44 companies for any 1 year. The
sample of companies was in diverse service sectors as
follows (this breakdown is for 2003, which is representa-
tive of the other 2 years): airlines (5), finance/insurance
(10), freight (3), health (2), hospitality (4), restaurants
(2), retail (14), and utilities (4). For the organizational
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Schneider et al. / Organizational Service Climate Drivers 7
service climate measure, respondents were members of a
panel maintained by a market research firm, the mem-
bers of which are remunerated for their time spent com-
pleting surveys of various types and interests.
Organizational service climate was assessed with an
8-item scale in which the items focused on the degree to
which the attributes of the work setting promoted service
quality. The items were responded to on a 5-point rating
scale from 1 = poor to 5 = excellent. Cronbach’s alpha
for this scale averaged .91 across the years with essen-
tially no variance from year to year.
The measure used was a commercial adaptation of the
one used by Schneider, White, and Paul (1998). A sample
item is “How would you rate the tools, technology, and
other resources provided to employees to support the
delivery of superior quality service?” Importantly, 7 of
the 8 items focus directly on service quality, making the
measure one of service climate, not generic organiza-
tional climate (Schneider, Ehrhart, and Macey in press).
As noted, the survey data for the service climate index
were gathered electronically by a market research firm,
and we analyze for the present study those companies for
which we had a minimum of 10 employees responding
with an average of approximately 30 per company (range
11-45). A unique characteristic of the climate survey data
is that even with the same companies being sampled over
time, the employees responding to the climate survey
were different each year. This might give some readers
pause with regard to how stable the results would be over
time with different employees responding. But the evi-
dence in the present case is that the service climate data
were reliable over time: Over the 3 years the company-
level correlations for service climate were (a) 2003 to
2004, r = .69, p < .01; (b) 2003 to 2005, r = .72, p < .01;
and (c) 2004 to 2005, r = .64, p < .01).
In addition, where data are aggregated across indi-
viduals to form a single unit or company score, it is
typically shown that employees substantially agree on
their perceptions, permitting the aggregation of such
data to the unit or company level of analysis (e.g., Bliese
2000; James et al. 2008). Thus, if people in a company
agree in their perceptions as implicitly hypothesized
here, then aggregating the data will produce a reliable
indicator for each company. As the literature on data
aggregation has evolved, three indicators of agreement
have been suggested for use (see Bliese 2000 and Klein
et al. 2000 for reviews): r
wg
, ICC(1), and ICC(2). The
first, r
wg
, is directly interpretable as a measure of within-
unit agreement. Unlike an index such as Cronbach’s
alpha, which provides an index of reliability based on the
similarity of patterns of responses, r
wg
examines absolute
agreement. ICC(1) is a variant of a one-way analysis of
variance (ANOVA) and indicates the ratio of the between-
organization variance in climate perceptions relative to
the total variance across respondents from all organiza-
tions. ICC(1), like ANOVA, requires mean differences to
be present as well as smaller within-organization vari-
ance than between-organization variance. ICC(1) values
are thought of as an index of the reliability of a single
respondent in a group, and researchers typically accept a
significant F test in ANOVA as indicating such reliability
(Klein et al. 2000). ICC(2), on the other hand, is an index
of the reliability of the aggregate mean. When the group
size is large then ICC(2) will also be large given a sig-
nificant ICC(1), because as the sample increases, whether
it be of items in a survey or people in a company, adding
more to the aggregate always increases internal consis-
tency reliability (Nunnally and Bernstein 1994). All
three indices are calculated because each yields a differ-
ent data-based perspective with regard to the appropri-
ateness of aggregating individual perceptions to produce
a unit (company) indicator (Bliese 2000).
Table 1 shows the results of the various data aggrega-
tion statistics by scale and by data set; they generally
indicate support for data aggregation across employees
within companies to form the service climate index.
Table 1 shows that the ANOVA F statistic for service
climate across data sets (across years as well as for the
roll-up sample) is statistically significant, indicating a
significant effect for company on the results. This means
that the within-company variance is smaller than the
between-company variance and that the means across
companies differ significantly as well. In addition, the
corresponding ICC(1), ICC(2), and r
wg
results generally
fall within usual suggested guidelines. That is, Klein
et al. (2000, pp. 517-518) suggest that a significant F or
significant ICC(1), an r
wg
of .70 or higher, and an
ICC(2) of .70 are reasonable standards to meet. The data
shown in Table 1, while not strong, generally support that
Table 1
Aggregation Statistics by Year and Roll-Up Sample
for Organizational Service Climate
Year F ICC(1) ICC(2) r
wg
Service climate 2003 3.44, p < .01 .05 .71 .84
Service climate 2004 3.96, p < .01 .09 .75 .68
Service climate 2005 3.20, p < .05 .05 .69 .80
Service climate roll-up 2.78, p < .01 .07 .72 .79
Note: The aggregation statistics shown for ICC(1) and ICC(2) are
analyses across organizations; for r
wg
they are the average of the
within-organization agreement.
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8 Journal of Service Research
aggregation of the data is reasonable for tests of the hypoth-
eses concerning organizational service climate.
People have similar perceptions of their work situa-
tions because they do not work in isolation of each other—
they work in teams, they serve each other, they are
susceptible to the same processes (Bliese 2000) includ-
ing internal marketing (Gronroos 1981, 1990; Reynoso
and Moores 1997) with regard to what the organization
is and what it stands for, they interact informally at the
water cooler and via e-mail to discuss what is happening
to them and around them and why, and they come to
construct common meanings of the world in which they
work (Berger and Luckman 1960, Schneider and Reichers
1983). In short, employees form a collective impression
of what the focus or foci of their organization is and this
impression tends to transcend specific units within orga-
nizations (Bliese 2000), especially relative to what the
employees in other companies perceive and experience.
Customer satisfaction was operationalized via the
ACSI (Anderson and Fornell 2000). The ACSI was
developed for use in the United States in 1994 (it was first
developed in Sweden in 1989). On a quarterly basis a
random sample of 250 customers of 200 of the Fortune
500 companies with the largest sales volume across the
major industries in the United States are surveyed. There
is considerable reliability (Fornell et al. 1996) and valid-
ity evidence for the ACSI. For example, evidence indi-
cates that a 1-point increase in annual ACSI score for a
typical company is equal to an increase of 11.4% of ROI,
an increase of $654 million in market value of equity
above and beyond the accounting book values of assets
and liabilities, an increase of $55 million a year in net
operating cash flow; and a reduction of variance in future
cash flows of more than 4% (Anderson and Fornell 2000;
Anderson et al. 2004; Gruca and Rego 2005). The ACSI
is unique in linkage research because it provides for a
standardized measure of customer satisfaction across
firms, thus addressing one of Dean’s (2004) suggestions.
More about the ACSI can be found at theacsi.org; this
Web site provided the ACSI company data used in the
present study for the years 2003, 2004, and 2005.
Organizational financial and market performance was
operationalized by Tobin’s q (Tobin 1969), as was done by
Anderson et al. (2004) in their demonstration of the rela-
tionship between the ACSI and shareholder value. Tobin’s
q is an index that is forward looking, comparable across
firms, and used extensively as an index of firm market
and financial performance (Anderson et al. 2004).
Tobin’s q is forward looking because it is based on the
stock price of the firm, considered to be a market indicator
of future growth by companies. More specifically, it is the
ratio of the market value of the firm to the replacement
costs of its assets (the actual formula is shown later). The
formula thus addresses the extent to which the firm is
wisely using its assets, that is, the extent to which it is
succeeding in exceeding the replacement costs of the
firm. Tobin’s q is related to accounting-based measures
of past firm performance like Return on Assets (ROA),
but these other measures are not forward looking like
Tobin’s q is and are thus less desirable as an indicator of
total market and financial performance.
We accessed the COMPUSTAT data base for the years
2003, 2004, and 2005 and calculated Tobin’s q using the
formula and procedures shown in Rao, Agerwal, and
Dahloff (2004, p. 130), as follows:
Tobin’s q = (share price × number of common stock out-
standing + liquidating value of the firm’s preferred stock +
short-term liabilities - short-term assets + book value of
long-term debt)/book value of total assets.
Data Analysis Strategy
We did not have data for all three variables for all
companies and all years. For example, whereas we had
data on service climate for 44 companies in 2003, we
had customer satisfaction (ACSI) data for only 33 of
those companies for 2003, 20 of those companies for
2004, and 25 of those companies for 2005. A similar
pattern appears with regard to matching the data across
companies for the ACSI and Tobin’s q and for service
climate and Tobin’s q in that across years there are
fewer common companies for the analyses. Thus we did
not have an equal number of cases for all variables for
all companies across the 3 years. This lack of compara-
bility in sample size across years is unfortunate, but the
market research firm that collected the data was unable
to match sufficient samples of people within companies
across the 3 years so in years where the sample of
respondents for a company did not exceed 10 people, we
were forced to drop that company for analyses for that
year. In addition, the ACSI data were not always avail-
able for all companies across every year.
To deal with these differences in sample size across
the 3 years we first conducted analyses within years
(e.g., relating service climate to the ACSI and the ACSI
to Tobin’s q in 2003) and then, although the sample gets
quite small, across years (e.g., relating service climate in
2003 to the ACSI in 2004 and to Tobin’s q in 2005) using
simple zero-order correlation coefficients. In an attempt
to compensate for variable and small sample sizes, we
created a roll-up sample across companies and years,
thereby maximizing the number of unique companies for
which all three forms of data were available. That is, if
we had data on a company for all three data points but
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Schneider et al. / Organizational Service Climate Drivers 9
not for all 3 years for all data, it would still qualify for
the roll-up sample because it had all three data points.
Development of the roll-up sample is in keeping with
the dictum of using the largest sample available with
full data as the best estimate of the relationships of
interest. As a decision rule, we randomly chose 1 year of
data to represent a company in the roll-up sample from
the years for which data were available when there was
more than 1 year of data for a company. Within each
year for which data existed, the data were standardized
prior to creating the roll-up sample to control for any
variance in the variables attributable to time (e.g., dif-
ferences in the economy by year). The resultant roll-up
sample contained 816 unique survey respondents across
36 unique companies.
To test Hypotheses 1 (the relationship between cus-
tomer satisfaction and financial and market performance)
and 2 (the relationship between service climate and cus-
tomer satisfaction), simple zero-order correlation coeffi-
cients both within years and across years as well as in the
roll-up sample were calculated. To test Hypothesis 3 (cus-
tomer satisfaction as mediator), path analysis was used
to evaluate the relationships between organizational ser-
vice climate, customer satisfaction, and financial perfor-
mance. Path analyses were run for various combinations
of the data across years (e.g., service climate 2003/ACSI
2004/Tobin’s q 2005; service climate 2003/ACSI 2003/
Tobin’s q 2003; and so forth) to estimate the reliability of
the paths over time. A final pair of path analyses was run
with the maximum sample of companies available, the
roll-up sample, to assess whether the relationship between
service climate and Tobin’s q is fully or partially medi-
ated by customer satisfaction as indexed by the ACSI.
Schneider et al. (2005), drawing on James and Brett
(1984) and James, Mulaik, and Brett (2004), showed that
when a hypothesized mediated model is being tested,
path analysis is the appropriate methodology for testing it.
Note that we used path analysis rather than structural
equation modeling (SEM) because we had single-measure
indicators for both the ACSI and Tobin’s q, precluding
estimates for the measurement model underlying SEM.
In addition, the relatively small sample size involved
makes for difficulties in interpreting SEM effect sizes.
Results
Table 2 shows the zero-order correlation coefficients
at the company level of analysis among the three vari-
ables of interest for the 3 years and the roll-up sample.
Hypothesis 1, concerning the relationship between the
ACSI and Tobin’s q, receives support. That is, regardless
Table 2
Relationships Among Service Climate, the ACSI, and Tobin’s q
ACSI Tobin’s q
2003 2004 2005 Roll-Up 2003 2004 2005 Roll-Up
Service climate 2003 .42* .51* .38 .17 .18 .16
n 33 20 25 29 29 27
Service climate 2004 .39 .37* .44* .48* .51* .37
n 20 31 22 22 22 18
Service climate 2005 .44* .46* .45** .30 .27 .30
n 24 21 34 26 26 22
Service climate roll-up .48** .30
n 36 36
Tobin’s q 2003 .45* .35 .55**
n 25 20 24
Tobin’s q 2004 .45* .35 .59**
n 25 20 24
Tobin’s q 2005 .54** .48* .54**
n 23 17 21
Tobin’s q roll-up .37*
n 36
Note: ACSI = American Customer Satisfaction Index; see text for Tobin’s q. n = sample of companies for the correlation coefficients. The mean
service climate scores across years range between 3.32 and 3.47, the ACSI score range is between 70.97 and 72.79, and for Tobin’s q the range
is between 1.22 and 1.30. Tobin’s q is correlated on average .67 with Return on Assets (ROA) on this sample.
*p < .05.
**p < .01.
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10 Journal of Service Research
Table 3
Path Model Results
Model Paths Coefficient Significance
Model 1: roll-up sample
(full mediation)
Service climate—ACSI .48 .003
ACSI—Tobin’s q .37 .026
χ
2
(1, N = 36) = 15.82, p < .01
Tucker-Lewis index = 1.00
RMSEA = .000
SRMR = .049
CFI = 1.00
Model 2: roll-up sample
(partial mediation)
Service climate—ACSI .48 .003
ACSI—Tobin’s q .30 .113
Service climate—Tobin’s q .16 .394
Note: ACSI = American Customer Satisfaction Index; RMSEA =
Root Mean Square Error of Approximation; SRMR = Standardized
Root Mean Square Residual; CFI = Comparative Fit Index. All are
standard indicators of model fit to the data. The fit of Model 2 to the
data is perfect as it is fully identified. It is presented to show the
magnitude and significance of the paths in question.
of the lag involved (within 2003, between 2003 and
2004, and so forth), the ACSI is significantly related
(range of correlation coefficients is .35-.59, p < .05, two-
tailed) to Tobin’s q in 8 of the 10 such possible relation-
ships. Support for Hypothesis 1 also suggests that the
sample studied here is representative of the relationship
between the ACSI and Tobin’s q that already exists in the
literature (Anderson et al. 2004).
Hypothesis 2, concerning the relationship between
service climate and the ACSI, also receives support. As
shown in Table 2, again 8 of the 10 possible correlation
coefficients for these two variables reach significance
(range of correlation coefficients is .38-.51, p < .05, two
tailed). While not a direct test of Hypothesis 3, the data
in Table 2 also suggest preliminary support for Hypothesis
3 concerning the ACSI as a mediator of the relationship
between service climate and Tobin’s q. That is, service
climate is significantly related to the ACSI in 8 of 10
possibilities but is only significantly related to Tobin’s q
in 2 of 10 possibilities. The challenge in testing Hypothesis
3, concerning customer satisfaction as a mediator of the
relationship between organizational service climate and
market and financial performance, is having a sufficient
sample size on which to run the path analysis. Thus, in
Table 2 we show that the sample of companies available
for every pair-wise correlation shrinks when the lags get
longer. The largest sample available for testing the “ulti-
mate” model (service climate 2003/ACSI 2004/Tobin’s q
2005) is only 11 companies. However, Table 2 reveals
that the relationships among the three variables are rela-
tively invariant as a function of time lag, supporting our
decision to create a roll-up sample to maximize the
sample size for testing mediation.
The path analyses conducted on the roll-up sample data
to assess Hypothesis 3 are shown in Table 3. Model 1, the
full mediation model, suggests strong and significant
relationships between organizational service climate and
the ACSI and between the ACSI and Tobin’s q. Model 2,
the partial mediation model, does not fit the data as well
as Model 1, so Model 1 is the preferred model and indi-
cates the data fit a full mediation configuration.
Disagreement has existed among researchers as to
whether support for full mediation requires that the path
between the mediator and the outcome remains signifi-
cant in the partial mediation model (cf. Baron and Kenny
1986; James and Brett 1984). More recent writings sug-
gest that the requirement for the path between the medi-
ator and the outcome to remain significant is not
necessary, with Kenny himself (Kenny, Kashy, and
Bolger 1998) now taking this position along with James,
Mulaik, and Brett (2004). Regardless, the relatively
small decrease in the magnitude of the path between the
ACSI and Tobin’s q (from .37 to .30), compared to the
much smaller coefficient (.16) for the direct path between
organizational service climate and Tobin’s q, tends to
support full rather than partial mediation. This analysis
formally documents the picture of the data shown in raw
correlation form in Table 2. That is, Table 2 shows that
the relationships between service climate and customer
satisfaction are consistently stronger than the relation-
ships between service climate and Tobin’s q.
Because of the stability of the relationships among the
three variables of interest shown in Table 2, it matters
little which pair-wise correlations are chosen as input
into the path analysis as the coefficients are generally
reliable for the different models, providing further sup-
port for full mediation in Hypothesis 3. As the coeffi-
cients are essentially the same for each of these analyses
(as well as for an analysis across the averaged correla-
tions across years), we do not present them here although
they are available from the authors.
Discussion
Support for Hypotheses 1, 2, and 3 was shown in
that the ACSI correlates significantly with Tobin’s q
(Hypothesis 1), service climate correlates significantly
with the ACSI (Hypothesis 2), and the ACSI serves as a
mediator of the relationship between service climate and
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Schneider et al. / Organizational Service Climate Drivers 11
Tobin’s q (Hypothesis 3). Because we had data for 3
years, the fact that the relationships of interest (see
Table 2) were reliably replicated concurrently (within
years) for each of the 3 years suggests these patterns of
concurrent relationships are quite stable. An inspection
of the relationships longitudinally also suggests stability
over time in that (1) service climate reliably predicts the
ACSI, (2) the ACSI reliably predicts Tobin’s q, and
(3) service climate is not a reliable predictor of Tobin’s
q (although for 2004 it was). Finally, in the roll-up sam-
ple the same pattern of results was revealed.
Because we collected data for the three variables over
the 3 years, we may infer from the pattern of the relation-
ships in Table 2 that it is service climate that yields cus-
tomer satisfaction over time and then Tobin’s q rather
than the reverse. That is, because Tobin’s q is signifi-
cantly related to service climate in only 2 of 10 such
relationships, it would be difficult to make the argument
that Tobin’s q is a primary direct cause of service climate.
Furthermore, it would not be logical to assume that
Tobin’s q causes customer satisfaction because customers
would not typically be aware of company financial per-
formance. In sum, given the evidence and conceptual
logic presented here, we may conclude that organiza-
tional service climate leads to customer satisfaction, cus-
tomer satisfaction leads to corporate financial performance,
and customer satisfaction fully mediates the relationship
between service climate and financial performance.
The major contributions of the present effort are
(a) studying in one sample of companies the mediating
role of customer satisfaction in the relationship between
service climate and market and financial performance;
(b) replicating over 3 years the linkages among employee
perceptions of service climate, customer satisfaction,
and market and financial performance; and (c) identify-
ing service climate as an antecedent over time of market
and financial performance through the effects it has on
customer satisfaction. The latter contribution has impor-
tant managerial implications that we will address after
noting some limitations of the present effort.
Limitations
The sample of companies was relatively small and
select, both of which qualities have the potential to limit
generalizability of the results beyond the present sam-
ple. On the other hand, our findings in support of previ-
ously existing data on the ACSI-Tobin’s q relationship
and the service climate-customer satisfaction relation-
ship suggest that the findings here are not unique to this
sample. The fact that we had a quite diverse sample of
companies, companies from numerous diverse service
sector industries, also suggests that the results reported
here might well generalize. In addition, we had a rela-
tively small sample of employees in each of the compa-
nies studied, averaging about 30 employees per company.
Nevertheless, indicators of within-company agreement
and between-company differences were all statistically
significant, suggesting this is not actually a limitation in
the present case. In fact, it is perhaps noteworthy that
even relatively small samples of employees within com-
panies can yield a statistically reliable indicator of orga-
nizational service climate.
Indeed, even with the relatively small sample of employ-
ees for each company and with a different sample of
employees in each company from year to year, (a) the data
for service climate were reliable over time and (b) the pat-
tern of relationships between the service climate measure
and the ACSI and Tobin’s q were reliable over time. Had
we a larger sample per company, the ICC(2) values
would have been higher—ICC(2) is a function of sample
size just like the Spearman-Brown prophecy formula
works on test reliability (Bliese 2000)—and perhaps the
relationships revealed here would have been stronger.
Management Implications
and Conclusion
The finding that employee perceptions of the way their
companies function with regard to service quality are
related to the ACSI is important because it suggests that
(a) employees are a valid source of data about how the
organization functions vis-à-vis the service quality that
will produce superior customer satisfaction and market
and financial performance and (b) interventions by orga-
nizations to increase the alignment of their internal pro-
cesses with the world of the customer may prove a unique
strategy for competitive advantage. We deal with each of
these in turn.
Employees Are a Valid Source of Data
A major outcome of this research effort and others in
the “linkage” tradition is that employee observations of
the ways their organizations function are valid. For
many years employee attitude surveys were thought to
be a way to gauge the state of employee morale and
perhaps understand employee absenteeism and turn-
over. But in the past 20 years we increasingly find that
data from employees vis-à-vis the strategic function-
ing of organizations have validity—like the results
shown here. The implication is that one place to begin
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12 Journal of Service Research
a change effort to improve service quality and increase
customer satisfaction is to discover from employees in
the organization what the present level of “customer cen-
tricity” is (Shah et al. 2006). This discovery process can
occur via employee surveys as in the present case,
through focus groups, through quality circles, and so
forth, but the important point is to focus on the business
practices (Reger 2005) that promote or fail to promote
service quality.
Internal Alignment With Customers
and Competitive Advantage
As the world of service increasingly becomes domi-
nated by technology, especially vis-à-vis customer rela-
tionship management (Freeland 2003), differentiating
oneself from competitors will become increasingly prob-
lematic unless one uses changes in organizational behav-
ior targeted to improve service quality as a vehicle for
such differentiation. The goal of such changes would be
the creation and maintenance of a climate that promotes
service excellence and service-focused behaviors on the
part of employees. The creation of such climates is not
easy but it is doable, and the potential payoffs in cus-
tomer satisfaction, cash flow, and market value are not a
myth; they exist in fact (Cable 2007).
Finally, management is encouraged to understand
that given the results here with regard to full media-
tion, change to a service-centric organization will not
immediately yield improvements in market and finan-
cial performance. On one hand, it takes time to pro-
duce a service climate; on the other hand, the new
service climate must be in existence for a period of time
sufficient to change customer satisfaction. It is only
after these two steps are completed that the likelihood
of market and financial performance improvements can
become a reality.
Conclusion
What happens inside a service organization does not
remain there. Service organizations have no boundaries,
or at best have permeable boundaries, so the internal
focus of the organization spreads to the customers it
serves through the employees who serve them. We show
that how the service quality focus of organizations is
experienced by employees links to the satisfaction cus-
tomers report with those organizations and that this, in
turn, is reflected in market and financial performance.
Any organization interested in taking action based on
evidence could find such findings important and of
potential competitive advantage.
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Benjamin Schneider is Senior Research Fellow at Valtera and
Professor Emeritus of Psychology at the University of Maryland.
Ben’s research interests are in service quality, organizational climate,
employee engagement, linkage research and the role of personality in
organizational life. He has published ten books and more than 150
articles and chapters and received career awards for science contribu-
tions from both the Services Special Interest Group (SERVSIG) of
the American Marketing Association and the Society for Industrial
and Organizational Psychology (SIOP) of the American Psychological
Association.
William H. Macey is CEO of Valtera Corporation, an international
Human Resources Management Consulting firm he founded more
than 30 years ago. He received his Ph.D. from Loyola University
Chicago and now specializes in helping organizations to create com-
petitive advantage through people, especially though employee
engagement. Bill has published widely in both academic and practi-
tioner outlets and his most recent book is (with Benjamin
Schneider,Karen Barbera and Scott Young), Employee Engagement:
Tools for Analysis, Practice, and Competitive Advantage (Wiley/
Blackwell, 2009).
Wayne C. Lee is a Consultant with Valtera’s Survey Practice Group.
Aside from client-based work, Wayne’s research and publications
focus on the application of non-traditional statistical techniques to
challenges in personnel selection and interpretation of employee sur-
vey data (e.g., classification trees - a non-linear/configural modeling
technique - applied to predicting employee turnover from pre-em-
ployment tests, evaluation of measurement equivalence of survey
instruments across unique populations through mean and covariance
analysis).
Scott A. Young is a Managing Consultant at Valtera Corporation
where he has worked for the last 11 years. He consults with the firm’s
organizational survey clients on content development and measure-
ment, reporting and interpretation of results, research, and action
planning. His research interests and past publications have focused on
employee engagement, service quality, and organizational climate.
He received his Ph.D. in industrial and organizational psychology
from Northern Illinois University.
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