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79
Journal of Marketing
Vol. 71 (April 2007), 79–93
© 2007, American Marketing Association
ISSN: 0022-2429 (print), 1547-7185 (electronic)
Don O’Sullivan & Andrew V. Abela
Marketing Performance
Measurement Ability and Firm
Performance
Marketing practitioners are under increasing pressure to demonstrate their contribution to firm performance. It has
been widely argued that an inability to account for marketing’s contribution has undermined its standing within the
firm. To respond to this pressure, marketers are investing in the development of performance measurement abilities,
but to date, there have been no empirical studies of whether the ability to measure marketing performance has any
actual effect on either firm performance or marketing’s stature. In this study of senior marketing managers in high-
technology firms, the authors examine the effect of ability to measure marketing performance on firm performance,
using both primary data collected from senior marketers and secondary data on firm profitability and stock returns.
They also explore the effect of ability to measure marketing on marketing’s stature within the firm, which is
operationalized as chief executive officer satisfaction with marketing. The empirical results indicate that the ability
to measure marketing performance has a significant impact on firm performance, profitability, stock returns, and
marketing’s stature within the firm.
Don O’Sullivan is College Lecturer, Department of Management and Mar-
keting, University College Cork, and Visiting Associate Professor of Mar-
keting, Melbourne Business School, University of Melbourne (e-mail:
d.osullivan@ucc.ie). Andrew V. Abela is Assistant Professor of Marketing,
Catholic University of America (e-mail: abela@cua.edu). The authors
gratefully acknowledge the three anonymous
JM
reviewers for their feed-
back and suggestions and Bruce Clark for his comments on previous ver-
sions of the article. The authors thank the CMO Council and, in particular,
the council’s Marketing Performance Measurement Steering Committee
and Marketing Performance Measurement Task Force for their support in
undertaking this study.
To read and contribute to reader and author dialogue on
JM
, visit
http://www.marketingpower.com/jmblog.
“The effective dissemination of new methods of
assessing marketing productivity to the business
community will be a major step toward raising
marketing’s vitality in the firm and, more important, toward
raising the performance of the firm itself” (Rust et al. 2004,
p. 76). In response to the pressure on marketers to demon-
strate their value to the firm, there have been several high-
profile calls for more research in the area of marketing per-
formance measurement (MPM) and several conceptual and
empirical research papers (e.g., Donthu, Hershberger, and
Osomonbekov 2005; Lukas, Whitwell, and Doyle 2005;
Rust et al. 2004). Furthermore, there have been regular calls
for marketing practitioners to develop and enhance their
ability to account for marketing’s contribution to firm per-
formance (Ambler 2003; Bolton 2004). An assumption
underlying these related academic and practitioner concerns
is that developing and applying MPM ability leads to both
greater status for marketing at the board level (see, e.g.,
Webster, Malter, and Ganesan 2005) and improved firm
performance (Morgan, Clark, and Gooner 2002). However,
to date, the relationship between MPM ability and either
firm performance or marketing’s stature within the firm has
not been demonstrated empirically.
The primary purpose of this article is to test whether
MPM ability contributes to actual firm performance or to
marketing’s stature within the firm, which we operational-
ize as chief executive officer (CEO) satisfaction with mar-
keting. A secondary purpose is to explore two potentially
distinct aspects of MPM ability: the ability to measure per-
formance across a range of marketing activities (e.g., adver-
tising, trade promotion, direct mail) and the ability to assess
marketing performance using a comprehensive set of met-
rics (e.g., financial, nonfinancial).
We focus on firms in the high-technology sector. We
chose high-tech firms because of the recognition that within
this sector, marketing has been under intense pressure to
demonstrate its contribution to firm performance. There are
two primary reasons for this pressure. First, high-tech com-
panies tend to have more of an engineering orientation than
a marketing orientation, and thus top management tends to
be more skeptical about the value of marketing (Davies and
Brush 1997). Second, during the period we studied (early
2000s), the sector experienced the collapse of the “technol-
ogy boom,” which led to sharply increased scrutiny of mar-
keting activities (Mohr and Shooshtari 2003). We begin by
reviewing the MPM literature and generating several
testable hypotheses.
Measurement and Performance
A long-standing caricature of marketing practitioners is that
they love to spend money and hate to assess the results of
that spending (e.g., Adler 1967). Marketers’ inability to
account for the function’s contribution to firm performance
is recognized as a key factor that has led to marketing’s loss
of stature within organizations (Kumar 2004; Lehmann
2004; Webster, Malter, and Ganesan 2005). This is reflected
80 / Journal of Marketing, April 2007
in increased demand for greater accountability (Doyle
2000; Morgan, Clark, and Gooner 2002; Rust et al. 2004).
In addition, there have been several high-profile calls for
more research in the area of MPM. Most notably, MPM
topics have been consistently listed among the Marketing
Science Institute’s (1998, 2000, 2002, 2004, 2006) top
priorities.
Marketing performance measurement is the assessment
of “the relationship between marketing activities and busi-
ness performance” (Clark and Ambler 2001, p. 231).
Because the problem in question is the inability to account
for marketing activities, our specific interest is in market-
ing’s ability to assess this relationship. Given that the goal
of MPM research is to demonstrate the value of the market-
ing activities, in line with the work of Rust and colleagues
(2004), our focus is on marketing not as the “underlying
products, pricing, or customer relationships” (Rust et al.
2004, p. 76) but rather as the “marketing activities” them-
selves, which we define as marketing communication, pro-
motion, and other activities that represent the bulk of the
typical marketing budget.
Marketing performance measurement research can be
divided into three research streams: measurement of mar-
keting productivity (e.g., Morgan, Clark, and Gooner 2002;
Rust, Lemon, and Zeithaml 2004), identification of metrics
in use (e.g., Barwise and Farley 2003; Winer 2000), and
measurement of brand equity (e.g., Aaker and Jacobson
2001; Ailawadi, Lehmann, and Neslin 2002). Rust and col-
leagues (2004) build on the work of Srivastava, Shervani,
and Fahey (1998) to describe a “chain of marketing produc-
tivity” that extends from marketing activities to shareholder
value. Marketing activities influence intermediate outcomes
(customer thoughts, feelings, knowledge, and, ultimately,
behavior), which in turn influence financial performance of
the firm. The MPM research we cited examines how mar-
keters can measure the relationships along the chain of mar-
keting productivity; which metrics firms use or could use
along this chain, particularly financial, nonfinancial, and
market-based assets; and contextual factors, particularly the
firm’s market orientation (e.g., Clark and Ambler 2001).
Underlying all this work is the assumption that such
measurement effort is beneficial to the firm and is not just a
post hoc justification of marketers’ efforts—that improve-
ments in marketing’s ability to account for its activities will
actually raise the performance of the firm. In the face of
senior management demands that marketers demonstrate
their value, the desire for justification is understandable.
However, overcoming the inability to account for the func-
tion’s contribution to firm performance requires that
resources and management attention be expended on mea-
surement efforts (Bonoma and Clark 1988). Incurring such
cost assumes that the firm will benefit, and testing this
assumption is the primary purpose of this article.
Hypotheses
We develop hypotheses based on a theoretical framework
that links MPM ability to firm performance and CEO satis-
faction with marketing. We begin by hypothesizing that
MPM ability has an effect on actual firm performance.
Sevin (1965) argues that the implementation of robust per-
formance measures should result in greater marketing and
firm performance. Several arguments that link MPM to
improvements in marketing and firm performance have
been advanced (see, e.g., Rust et al. 2004). First, anticipa-
tion of the scrutiny of marketing efforts will encourage
greater attention to the activities that will be measured. The
idea that “what gets measured gets done” is well founded in
the management literature (see, e.g., Ouchi 1979) and is
assumed within the MPM literature. Second, Webster, Mal-
ter, and Ganesan (2005) contend that marketing’s contribu-
tion to the achievement of strategic goals is underrepre-
sented in firms that do not measure marketing performance
and that the performance of such firms may suffer as a
result. Third, it has been argued that MPM should lead to
learning, which should enable improved marketing deci-
sions and, consequently, performance (Morgan, Clark, and
Gooner 2002). Fourth, MPM offers performance feedback,
and performance feedback has consistently been found to
influence both managerial attitudes and behavior (Audia,
Locke, and Smith 2000; Curren, Folkes, and Steckel 1992;
Greve 1998; Miller 1994). Finally, feedback relative to
goals has been demonstrated to produce strong effects (e.g.,
Locke and Latham 1990). Thus:
H1: MPM ability positively influences firm performance.
It has long been recognized that the marketing function
typically plays a limited role in the process of strategy for-
mulation (Anderson 1982; Day 1992; Webster 1992). Sri-
vastava, Shervani, and Fahey (1998) argue that an important
reason for this is that marketers struggle to measure and
communicate to top management the impact of marketing
activities on firm performance. Lehmann (2004) and Web-
ster, Malter, and Ganesan (2005) observe that marketing has
the greatest influence and stature in firms in which there are
clear measures of marketing’s contribution. Accordingly,
H2: MPM ability is positively associated with CEO satisfac-
tion with marketing.
As we noted previously, although the primary purpose
of this article is to test empirically whether MPM ability
contributes to firm performance or to marketing’s stature
within the firm, a secondary purpose is to explore the ability
to measure performance across a range of marketing activi-
ties and the ability to assess marketing performance using a
comprehensive set of metrics. Although the two aspects are
clearly related in that they both contribute to a firm’s MPM
ability, we hypothesize that they are distinct. For example,
one firm may be able to measure the performance of its
advertising or public relations (activities) only in terms of
changes in awareness (nonfinancial metric), whereas
another firm may be able to measure them in terms of reve-
nue change (financial metric) and against specific goals and
competitor performance (benchmark metric) (Ambler 2003).
The focus of the broader marketing accountability lit-
erature has been on the importance of the ability to measure
disparate marketing activities (e.g., Rust, Lemon, and Zeit-
haml 2004; Webster, Malter, and Ganesan 2005). In addi-
tion, the discussion of MPM among practitioners has also
tended to focus on the activities dimension (e.g., McMaster
Marketing Performance Measurement Ability / 81
2002). However, within the existing academic literature on
MPM, the focus has tended to be on the metrics in use (e.g.,
Ambler, Kokkinaki, and Puntoni 2004; Barwise and Farley
2003, 2004; Lages, Lages, and Lages 2005; Lukas,
Whitwell, and Doyle 2005).
We hypothesize that both the activities and the metrics
aspects have separate but related effects on performance
and CEO satisfaction. Because the activities aspect pre-
cedes the metrics aspect both theoretically and logically, we
test the activities aspect first:
H3: The ability to measure performance across the range of
marketing activities a firm employs positively influences
firm performance.
H4: The ability to measure performance across the range of
marketing activities a firm employs is positively associ-
ated with CEO satisfaction with marketing.
As we noted previously, the identification of metrics in
use is one of the main streams of MPM research (e.g.,
Ambler, Kokkinaki, and Puntoni 2004; Barwise and Farley
2003, 2004; Lages, Lages, and Lages 2005; Lukas,
Whitwell, and Doyle 2005). An assumption underlying this
research stream is that choice of metrics matters.
Researchers in this area have concluded that in their choice
of metrics, firms should employ both financial and non-
financial metrics (Clark 1999; Rust et al. 2004) and that
they should compare these against goals and competitors
(Ambler 2003). Thus, we expect that firms that follow this
guidance and are able to assess marketing performance
using a broad set of metrics (financial and nonfinancial, in
relation to goals, and in relation to competitors) should out-
perform those that lack this ability. It has previously been
noted that the academic community’s focus on metrics in
use has had little impact on practicing marketers (Clark
1999). Reflecting this, we are interested in isolating the
impact of metrics ability on performance and CEO satisfac-
tion beyond that which is accounted for by activities ability.
Thus:
H5: The ability to provide a comprehensive set of metrics
positively influences firm performance.
H6: The ability to provide a comprehensive set of metrics is
positively associated with CEO satisfaction with
marketing.
Dashboards are a variation of a balanced scorecard
(Kaplan and Norton 1992) and are used as a means to report
key metrics to senior management from the array of infor-
mation generated by corporate information systems (Paine
2004; Wind 2005). Ambler (2003) describes a dashboard as
a refined set of marketing performance data, usually pre-
sented together, which communicate an overview of strate-
gic performance. Two important elements of dashboards are
that they provide automated or (close to) real-time reporting
(Iyer, Lee, and Venkatraman 2006; Wind 2005) and that
they enable users to “drill down” to program-level details
(Miller and Cioffi 2004). It has been noted that dashboards,
which are increasingly popular among marketing practition-
ers, have received only limited attention in marketing
research (Rogers 2003). Recently, Srivastava and Reibstein
(2005) have called for more research on the role of dash-
boards in managing marketing productivity.
Dashboards are viewed as a means by which informa-
tion can be summarized and readily communicated to
senior decision makers (Srivastava and Reibstein 2005). It
is argued that this distilling of data increases the perceived
value and managerial use of information (Peyrot et al.
2002), which in turn creates a closer link between market-
ing activities and firm goals (McGovern et al. 2004; Miller
and Cioffi 2004). Therefore, the use of a marketing dash-
board is hypothesized to act as a moderator in the relation-
ships between ability to measure and performance and
between ability to measure and CEO satisfaction.
H7: The greater the use of a marketing dashboard, the more
positively MPM will influence firm performance and
CEO satisfaction.
The study controls for firm size and firm age because
both variables have previously been shown to affect perfor-
mance (e.g., Ahuja and Lampert 2001; Miles, Covin, and
Heeley 2000). In controlling for firm age, the study follows
previous research on high-tech firms (e.g., Hill and Naroff
1984). Firm age is accepted as influencing performance
through the ability to learn in the customer relationship and
on competitive advantage outcomes (Zahra, Ireland, and
Hiltt 2000). We summarize the relationships outlined in this
section in Figure 1.
Method
Sample and Procedure
A survey of senior marketers in high-tech firms about MPM
ability, CEO satisfaction with marketing, and aspects of
firm performance produced the primary data for our
research. We used the membership list of the CMO Council
as the sample frame for our study. The CMO Council is a
U.S.-based, not-for-profit organization for senior marketers
in high-tech firms. The council’s membership is global,
though at the time of study, it was heavily skewed toward
North American firms. The membership list contains names
and background information (title, firm, and contact details)
for all members. We collected survey responses through an
online, structured survey. We supplemented the primary
data captured through the survey with secondary data on
aspects of firm performance.
The study sought responses from key informants.
Because the CMO Council’s membership is limited to
senior marketers, we included in the sample all members
other than those who worked in marketing services
providers, such as advertising agencies. We subsequently
analyzed the responses to ensure that the respondents had
senior marketing responsibilities (job title) before we
included them in further analysis. The views of key infor-
mants are widely used within the marketing literature (see,
e.g., Day and Nedungadi 1994; Moorman and Rust 1999;
Narver and Slater 1990).
Before constructing the questionnaire, we conducted
preliminary in-depth interviews with 17 chief marketing
officers (CMOs). These discussions focused on the intervie-
wees’ understanding of and motivations for measuring mar-
keting performance. A strong functional orientation was
apparent; interviewees were most interested in measuring
82 / Journal of Marketing, April 2007
FIGURE 1
Conceptual Model
the performance of the marketing function as opposed to
the broader marketing performance of the firm. In addition,
respondents were interested in assessing performance
impact at the firm level. In short, MPM was viewed as an
assessment of the marketing function’s contribution to firm
performance. The interviews provided a basis for the devel-
opment of our survey questionnaire.
The questionnaire was divided into three sections that
contained questions related to MPM ability, firm perfor-
mance, and respondent profile. The questionnaire included
a 15-item scale to quantify the ability to measure perfor-
mance across a range of marketing activities and a 4-item
scale to measure the ability to assess performance using a
comprehensive set of metrics. These scales reflected the
views captured from our interviews with CMOs and a
review of the literature. To test for comprehension, rele-
vance, and completeness, we pilot-tested the questionnaire
with ten senior marketers from the CMO Council. Partici-
pants in the pilot phase were asked to identify any problems
they encountered with the e-mail invitation, the content of
the questionnaire, or the process of completing it online.
Participants were also asked to evaluate the clarity of the
questions and the response formats. No major difficulties
were identified, though we clarified some of the response
options and revised the questionnaire accordingly.
The survey was administered online between February
and March 2004. A total of 810 marketers received e-mail
notification of the survey. This was followed 14 days later
by a reminder e-mail to nonrespondents. Each e-mail con-
tained an embedded link to the survey. We received 214
usable response, for a response rate of 26.4%. This response
rate was highly satisfactory given that rates ranging from
12% to 20% are considered acceptable for cross-sectional
samples (Churchill 1991). We tested for nonresponse bias
using time-trend analysis (Armstrong and Overton 1977).
We selected two subsamples from early and late respon-
dents. Because these did not differ in terms of respondent
profile or the variables of interest, we concluded that non-
response bias was not a significant concern.
We collected survey responses over a four-week period.
After that time, we made the survey available through sev-
eral additional channels, most notably a BusinessWeek
research panel. This produced an additional 98 qualified
respondents, for a total of 312 responses. Subsequent analy-
sis of these additional 98 respondents indicated that they
were not materially different from the first 214 respondents
in terms of job title and sector. Furthermore, their responses
to the key issues under consideration in the study were
similar to those of the original 214 respondents. Conse-
quently, we included them for further analysis. In total, we
included 312 responses in subsequent analysis.
The job titles of respondents represented the range of
possible senior marketer titles: 17% were CMOs, 40% were
vice presidents of marketing, and 15% were marketing
directors. Of the 27% who answered “other,” most were
senior managers with titles such as president or vice presi-
dent of sales and marketing. Respondent firms were drawn
from a cross-section of information technology–related sec-
tors: 36% were software providers, 35% provided Internet-
related services, 3% provided components, 3% provided
computer systems, and 3% provided networking products
and services. Peripherals and integration accounted for 2%
and 1%, respectively. Of the 17% that responded “other,”
most were application service providers, information tech-
nology consulting services, or telecommunications-related
products and services. Firm age varied greatly among
Marketing Performance Measurement Ability / 83
1Scale items for each of the MPM measures appear in the
Appendix.
respondents, and most (>90%) were headquartered in North
America.
Measurement1
We calculated MPM ability as the simple average of a
firm’s scores on the activities and metrics scales. We
assessed MPM ability using a 15-item scale based on our
in-depth exploratory interviews with CMOs. We recorded
responses on a seven-point scale anchored by “poor” and
“excellent.” These activities included above- and below-the-
line promotional activities as well as marketing planning
and customer relationship management. As we noted previ-
ously, because the issue being addressed is marketers’
inability to account for marketing activities, our specific
interest here is in marketing’s ability to assess this relation-
ship. Having an ability does not necessarily mean using it,
but given the demands being placed on marketers in high-
tech firms at the time of this study to account for their con-
tribution more effectively, it seems highly unlikely that any
MPM ability would have remained latent. Therefore, we
assume that any firm in our sample that had an MPM ability
was indeed using it. Discussions with the CMO Council’s
Steering Committee and interviews with 17 CMOs during
the exploratory phase of our research indicated that this
assumption was reasonable.
In our study, metrics is a construct that consists of the
summed responses to four questions. Again, we captured
responses on a seven-point scale anchored by “poor” and
“excellent.” Over the past 40 years, ranges of metrics have
been proposed for MPM (for a review, see Clark 2001).
These include both financial and nonfinancial measures.
The inclusion of nonfinancial measures is considered an
important progression because it helps provide a more com-
plete description of marketing’s contribution. Financial and
nonfinancial measurement are two of the four aspects of
metrics ability we considered. The other two aspects of
metrics we assessed are related to benchmarking. Bonoma
(1989) was one of the first researchers to argue for greater
benchmarking of marketing performance. More recently,
Vorhies and Morgan (2005) have demonstrated the impact
of the benchmarking of marketing capabilities on firm per-
formance. Consequently, we included the ability to bench-
mark against plan and against competitors in our under-
standing of metrics. The resultant scale was reliable (α=
.83).
Dependent measures. Our dependent measures were
firm performance and CEO satisfaction with marketing. We
assessed firm performance using both primary and sec-
ondary data. Primary data were provided through our sur-
vey of senior marketers. In the past, the most common mea-
sures of output in firm-level marketing performance studies
have been profit, sales, market share, and cash flow
(Bonoma and Clark 1988). Financial measures, such as
sales and profit, continue to be the most important MPMs
(Clark 2000; Kokkinaki and Ambler 1999). Several studies
have suggested that managers balance profitability and sales
growth (McKee, Varadarajan, and Pride 1989; Slater and
Narver 1996), and others have considered market share a
measure of firm performance (Jaworski and Kohli 1993).
Accordingly, and in line with previous studies, we mea-
sured performance as the mean of a respondent’s rating for
his or her firm’s sales growth, market share, and profitabil-
ity performance relative to all other competitors. We cap-
tured responses on a five-point scale anchored by “very
poor” and “outstanding.” We measured CEO satisfaction
with marketing as the response to a single question. We cap-
tured responses on a five-point scale anchored by “excel-
lent” and “poor.”
Following the work of Rust, Moorman, and Dickson
(2002), we captured secondary data on firm performance
through two measures: return on assets (ROA) and stock
returns. We calculated ROA as the firms’ overall ROA for
the 12 months subsequent to our original study, as reported
in COMPUSTAT. This time lag enabled us to determine the
direction of causality between MPM and firm performance.
Using data provided by the University of Chicago’s
Center for Research in Security Prices (CRSP), we mea-
sured stock returns as the firms’ size-adjusted stock returns
for the 12 months subsequent to the original study. The
CRSP provides data on stocks traded on each of the major
U.S. stock exchanges: NYSE, AMEX, and NASDAQ. We
calculated returns as the difference between an individual
firm’s stock returns and the value-weighted average return
for all firms in the same size decile of the sample firm in
CRSP’s size decile portfolio for each month. Return data
were adjusted for both stock dividends and splits for each
firm by CRSP (Rust, Lemon, and Zeithaml 2004). We cal-
culated each firm’s return for the period, referred to as the
holding period return, as follows:
Ri= [(P1– P0)+ (D
1)/P0)],
where Riis the return on stock i, P1is share price in month
1, P0is share price in the previous month, and D1is the
dividend associated with Month 1.
We determined the value-weighted portfolio return for
each month from the CRSP portfolio assignment number.
We calculated returns by compounding both the return for
the firm and the value-weighted returns for the portfolio for
the 12 months. This enabled us to determine the return as
the difference between the compounded return for the firm
and the compounded portfolio return.
The cumulative size-adjusted excess return then
becomes the return on the stock less the return on the rele-
vant CRSP market capitalization decile portfolio:
CARi= [Σ(Rit – Rsizet)],
where Rit is the cumulative return on stock i over the 12
months and Rsizet is the matching return on the appropriate
CRSP market capitalization decile portfolio.
Potential moderator. Responding to recent calls for a
consideration of the role and impact of marketing dash-
boards, we included dashboards as a potential moderator.
We assessed the use of a marketing dashboard as the mean
of responses to three questions related to the existence of a
dashboard and its functionality, which, as we noted previ-
84 / Journal of Marketing, April 2007
2When analyzing the impact of MPM on the ROA and stock
returns of public companies, we extracted size from COMPU-
STAT and age from company reports.
ously, include automated updating and program-level drill-
down capability (α= .88).
Control variables. We included two control variables
that are commonly recognized in the marketing and strategy
literature as influencing firm performance: firm size and
firm age. We operationalized firm size as the firm’s annual
revenue. Previous research has suggested that number of
employees, sales, and assets are all equally appropriate indi-
cators of a firm’s size (e.g., Harrison and Torres 1988). We
measured firm age as the number of years the firm has been
in business.2
Measurement properties. After data collection and
before testing our hypotheses, we conducted several proce-
dures to examine the psychometric properties of and to
purify our measures. We judged the 15-item activities scale
to have face validity because it reflects the primary activi-
ties of the marketing function as commonly outlined in the
literature. To ensure content validity, senior marketers on
the CMO Council’s MPM Steering Committee provided
expert screening of scale items (Churchill 1979; Malhotra
1996). Exploratory factor analysis indicated that the scale
comprised four factors. The first factor accounted for 36%
of the total variation in the scale, the second factor
accounted for 11% of the variation, and the third and fourth
factors each accounted for 7% of the variation. Thus, the
third and fourth factors explained little more than any one
of the individual items. To isolate key factors further, we
subjected the data to Varimax rotation. We set a factor load-
ing of .4 as the cutoff to establish themes and labels for the
factors. This is consistent with previous studies (e.g.,
Mitchell and Walsh 2004; Washburn and Plank 2002). In
deciding which items to use to compute a “factor score,” we
also applied Bedford’s criterion of a primary loading being
at least .2 greater than any cross-loading in addition to the
.4 criterion. The latter criteria resulted in the loss of three
items. Consistent with Gerbing and Anderson’s (1988) rec-
ommendations, we used confirmatory factor analysis to
evaluate and refine the resultant scales further. We con-
ducted the factor analysis using the 12 items remaining at
the end of the exploratory factor analysis. As part of this
analysis, it was specified that four factors should be
extracted rather than allowing for an unforced selection of
factors. The resultant four factors explained 65% of the total
variance. Varimax rotation identified the same factor struc-
ture (see Table 1) as that which we determined on the basis
of the exploratory analysis.
The four factors are labeled as follows:
•Direct: the ability to measure below-the-line and online mar-
keting activities (three items).
•MGT: the ability to measure performance of management
and analysis activities (four items).
•PR: the ability to measure public relations, analyst, and other
stakeholder relations activities (three items).
•Brand: the ability to measure performance of branding and
advertising activities (two items).
Our next step was to examine the discriminant validity
of our measures. Table 2, Panel A, presents the descriptive
statistics and correlation matrix for the variable set. Alpha
coefficients for all measures were greater than .7, indicating
that reliability is acceptable (Nunnally and Bernstein 1994).
As we expected, activities and metrics, the two aspects of
MPM in our conceptual model, were highly correlated, as
were the dashboard and metrics measures. Notably, CEO
satisfaction with marketing was minimally correlated with
overall firm performance, with a correlation coefficient of
.13.
The results of the tests for discriminant validity appear
in Table 2, Panel B. For each construct, average variance
extracted (AVE) exceeds the .5 level that Hair and col-
leagues (1998) recommend. In addition, the AVE for each
construct is higher than the squared correlation between
that construct and any other construct, indicating that dis-
criminant validity is not a problem (Fornell and Larcker
1981).
TABLE 1
Factor Matrix of MPM Activities
Factor 1 Factor 2 Factor 3 Factor 4
(Direct) (MGT) (PR) (Brand)
Branding .790
Advertising .805
Direct mail/e-mail campaigns .810
Telemarketing and contact management .804
Web site and Internet presence .621
Trade shows and events .641
Public relations and internal communications .797
Analyst and stakeholder relations .670
Channel marketing .693
Customer relationship management systems .729
Market research .690
Budgeting .595
Notes: All but the highest loadings are suppressed.
Marketing Performance Measurement Ability / 85
TABLE 2
Summary Statistics, Correlation Matrix, and Discriminant Validity
A: Summary Statistics and Correlation Matrix
M SD Items 1 2 3 4 5 6 7 8 9
1. Activities 04.1000.97 4 0
.72
2. Metrics 03.64 01.44 4 0.71 0
.83
3. Dashboard 03.17 01.74 3 0.56 0.68 0
.88
0
4. Firm size 32.06 71.12 1 –.01 –.03 0.050—
5. Firm age 23.58 23.79 1 0.09 0.12 –.010 0.39 —
6. CEO satisfaction 03.48 00.93 1 0.53 0.44 0.3500.06 –.04 —
7. Performance 05.02 01.32 3 0.27 0.23 0.270–.05 0.00.34
.84
8. ROA 05.79 12.67 1 0.31 0.24 0.1800.06 0.07 .23 .44 —
9. Stock returns 0–.1000.25 1 0.17 0.200.000 –.02 0.05 .10.09 .12 —
B: Discriminant Validity
Squared Correlations
AVE 1 2 3 4
1. Activities .56
2. Metrics .67 .50
3. Dashboard .80 .31 .46
4. Performance .80 .07 .05 .07
Notes: Alphas for multi-item measures are in italics on the diagonal in the correlation matrix.
Analysis and Results
Firm Performance: Primary Data
In H1and H2, we predicted that there would be a positive
relationship between MPM ability and both firm perfor-
mance and CEO satisfaction with marketing. We tested
these hypotheses using hierarchical moderated regression
models (Schoonhoven 1981). Reflecting our conceptual
model and to test H7, we considered marketing dashboards
a potential moderator of the relationship between MPM and
each of the dependent variables. We specified two equa-
tions, one for each dependent variable. We entered data into
the equations in two steps. The first step contained the main
effects associated with MPM and the potential moderator.
In addition, in testing the relationship to firm performance,
we entered the two control variables at this time. The sec-
ond step contained the interactions defined by mean center-
ing the main effects and creating products of dashboard and
MPM. Mean centering enabled us to control for the effect
of multicoliniarity, as Aiken and West (1993) and Cohen
and colleagues (2002) recommend. The introduction of the
interaction term failed to produce a significant effect on
either firm performance or CEO satisfaction with market-
ing. For performance, change in F(1, 283) = 1.206, p=
.273. For CEO satisfaction with marketing, change in
F(2, 290) = .558, p= .298. Given these results, we reesti-
mated the model including MPM and, in the case of firm
performance, the two control variables. The results appear
in Table 3.
TABLE 3
The Impact of MPM on Firm Performance and CEO Satisfaction: Primary Data
Firm Performance CEO Satisfaction
Model Statistics
Adjusted R2.148 .220
F statistic 16.566 84.219
d.f. 3, 285 1, 294
p
value <.001 <.001
Final Predictors batbbatb
MPM 0.253 04.608* .472 9.177*
Firm sizec0.320 04.722*
Firm agec–.136 –2.015*
*
p
< .001.
aStandardized coefficients.
bt refers to the t-statistic for the estimated coefficients.
cWe do not include firm size and firm age as control variables when considering the impact of MPM ability on CEO satisfaction with marketing,
because there is no basis in theory that would lead us to expect that these two variables affect the dependent variable.
86 / Journal of Marketing, April 2007
The regression coefficients indicate that as we hypothe-
sized, MPM ability is significantly associated with both
firm performance and CEO satisfaction with marketing.
These are the two hypothesized outcomes of MPM, and
therefore the primary data support H1and H2.
Next, we tested H3–H6, which predict that the two
aspects of MPM, activities and metrics, each affect firm
performance and CEO satisfaction with marketing. Again,
we examined the interaction effects of activities with dash-
board and of metrics with dashboard to determine whether
either explained a significant level of variance when
included in the linear regression model. Table 4, Panel A,
provides the results of this analysis. Because entry of the
interaction effects did not explain a significant level of vari-
ance (for firm performance, change in F(2, 281) = .629, p=
.534; for CEO satisfaction with marketing, change in
F(2, 290) = .558, p= .573), we report a model that contains
the predictor variables only.
Again, we used hierarchical regression to test the rela-
tionship between the predictor variables and the dependent
variables. Because our aim was to isolate the impact of met-
rics beyond the impact of activities, we entered the data in
three steps. First, we entered the control variables. Second,
we entered activities and considered the extent to which this
explained a significant amount of variance. Third, we
entered metrics to examine the degree to which it explained
variance beyond that accounted for by activities.
Activities have a positive impact on firm performance
and CEO satisfaction with marketing, and therefore H3and
H4are supported in our analysis of the primary data. How-
ever, because the entry of metrics into the equation has a
significant impact on CEO satisfaction with marketing but
not on performance, the primary data reject H5but support
H6.
In our previous analysis of measurement properties, we
explored the multidimensional nature of activities. To
TABLE 4
The Impact of Activities and Metrics on Firm Performance and CEO Satisfaction
A: Activities and Metrics
Firm Performance CEO Satisfaction
Model Statistics
Adjusted R2.134 .235
F statistic 15.846 46.331
d.f. 3, 285 2, 293
p
value <.001 <.001
Final Predictors batbbatb
Activities 0.243 04.389*** .380 5.296***
Metrics .142 1.977***
Firm sizec0.315 04.630***
Firm agec–.135 –2.003***
B: Activities Factors
Firm Performance CEO Satisfaction
Model Statistics
R200.145 00.219
F statistic 13.150 28.560
d.f. 4, 283 3, 291
p
value <.001 <.001
Final Predictors batbbatb
Direct
MGT .235 3.568***
PR 0.147 02.389***
Brand .164 2.679***
Metrics 0.167 02.672*** .178 2.516***
Firm size 0.320 04.720***
Firm age –.123 –1.820***
*
p
< .05.
**
p
< .01.
***
p
< .001.
aStandardized coefficients.
bt refers to the t-statistic for the estimated coefficients.
cWe do not include firm size and firm age as control variables when considering the impact of MPM ability on CEO satisfaction with marketing,
because there is no firm basis in theory that would lead us to expect that these two variables affect the dependent variable.
Marketing Performance Measurement Ability / 87
examine the impact of the activities factors further, we cal-
culated the regression coefficients for each factor on each
dependent variable. The results of this analysis appear in
Table 4, Panel B. Because interaction effects were not sig-
nificant, the table reports the main effects of the four activi-
ties factors, metrics, and the control variables. Again, we
conducted a three-step hierarchical regression. For firm per-
formance, the entry of the PR factor into the model with
firm size and firm age explained a significant level of addi-
tional variance in firm performance (change in F(1, 284) =
15.832, p< .001). The subsequent entry of metrics into this
model in the third step also explained a significant level of
additional variance in firm performance (change in F(1,
283) = 7.139, p= .008).
For CEO satisfaction, the entry of the MGT and Brand
factors into the model with firm size and firm age explained
significant levels of additional variance (with MGT: change
in F(1, 293) = 59.783, p< .001; with Brand: change in
F(1, 292) = 15.233, p< .001). The subsequent entry of met-
rics into this model in the third step again explained a sig-
nificant level of additional variance (change in F(1, 291) =
6.329, p= .012). The limited impact of individual factors
suggests that a focus on individual dimensions is unwar-
ranted and that a consideration of the full spectrum of
activities provides a greater impact. The emergence of met-
rics as a significant predicator may be explained by the
reduction of the explanatory power of the activities variable
through disaggregation into its four factors.
Firm Performance: Secondary Data
In addition to collecting subjective measures of firm perfor-
mance from key informants, we also collected objective
performance data. We collected data on firm profitability
and stock returns for the 12 months subsequent to the origi-
nal study. This enabled us to counterbalance the problems
that arise in interpreting causality solely on the basis of evi-
dence from cross-sectional correlational studies. Our objec-
tive measures were ROA and size-adjusted stock returns.
We collected these from the COMPUSTAT and CRSP data-
bases, respectively. Because both databases are confined to
publicly quoted firms, our sample size was necessarily
reduced for this phase of the analysis (94 for ROA and 82
for stock returns compared with 312 for the primary
analysis).
This phase of the analysis followed the same process as
that for the primary data. First, we considered the potential
moderating impact of dashboards on the relationship
between MPM ability and performance through a two-step
hierarchical moderated regression model. For both mea-
sures of performance, the entry of the interaction effects
failed to generate a significant level of variance. For ROA,
change in F(1, 80) = .068, p= .794. For stock returns,
change in F(1, 74) = .292, p= .591. Reflecting this, the
results in Table 5 present a model that contains MPM abil-
ity and the control variables. Because MPM ability has a
significant impact on both ROA and stock returns, H1is
supported. This is consistent with findings from our analy-
sis of the primary data.
Second, we examined the relationship between both
ROA and stock returns and the two components of MPM
ability: activities and metrics. We followed the same proce-
dure as that in the examination of primary performance
data. We considered the interaction effects of activities with
dashboard and of metrics with dashboard. The entry of the
interaction effects did not explain a significant level of addi-
tional variance for either ROA (change in F(2, 80) = .031,
p= .969) or stock returns (change in F(2, 71) = .377, p=
.688). Because neither interaction effect explained signifi-
cant additional variance, we report only the main effects in
Table 6, Panel A.
As we discussed previously, activities and metrics are
conceptually related, and to reflect this, we carried out a
three-step hierarchical regression analysis. We entered firm
size and firm age into the model in the first step. In the sec-
ond step, we entered activities. Finally, in the third step, we
entered metrics to examine whether further variance was
explained. Activities have a positive impact on ROA, but the
results are not significant for stock returns. This finding
provides partial support for H3. Consistent with our analysis
TABLE 5
The Impact of MPM on Firm Performance: Secondary Data
ROA Stock Returns
Model Statistics
Adjusted R2.700.024
F statistic 3.119 1.651
d.f. 3,82 3,76
p
value .030 .185
Final Predictors batbbatb
MPM .289 2.704** 0.187 1.658*
Firm size .073 0.692** –.019 –.171*
Firm age .084 0.788** 0.134 1.189*
*
p
< .05.
**
p
< .01.
aStandardized coefficients.
bt refers to the t-statistic for the estimated coefficients.
88 / Journal of Marketing, April 2007
TABLE 6
The Impact of Activities and Metrics on Firm Performance: Secondary Data
A: Activities and Metrics
ROA Stock Returns
Model Statistics
Adjusted R2.173 .004
F statistic 7.078 1.117
d.f. 3,84 3,75
p
value <.001 .348
Final Predictors batbbatb
Activities 0.230 02.264**.115 .959
Metrics
Firm size 0.409 03.241** .023 .162
Firm age –.167 –1.347** .132 .940
B: Activities Factors
ROA Stock Returns
Model Statistics
R20.184 0.047
F statistic 7.528 2.295
d.f. 3,84 3,75
p
value <.001 0.085
Final Predictors batbbatb
Direct
MGT 0.243 02.504***
PR
Brand
Metrics .236 2.084*
Firm size 0.459 03.734*** .003 0.019*
Firm age –.151 –1.233*** .141 1.027*
*
p
< .05.
**
p
< .01.
***
p
< .001.
aStandardized coefficients.
bt refers to the t-statistic for the estimated coefficients.
of the primary data, dashboards do not have a significant
moderating effect. Consequently, we find no support for H7.
In analyzing the impact of the activities factors, we
began by considering the potential moderating influence of
dashboards. Because the interaction effect was not signifi-
cant, we did not include it in further analysis. Table 6, Panel
B, summarizes the main effects of activities. The entry of
MGT into the model with firm size and firm age explained
a significant level of additional variance in ROA (change in
F(1, 84) = 6.268, p= .014). None of the four factors or met-
rics had a significant impact on stock returns, but the entry
of metrics into the model with firm size and firm age
explained a significant level of additional variance (change
in F(1, 75) = 4.344, p= .041). Again, the limited impact of
individual factors indicates that consideration of the full set
of activities may offer the greatest benefit as a driver of firm
performance.
Discussion
Summary of Findings
Taken together, our analysis of the primary and secondary
data indicates that MPM ability has a positive impact on
firm performance in the high-tech sector. We found that
firms with a strong MPM ability tend to outperform their
competitors, as reported by senior marketers. In addition,
the results suggest that MPM ability has a positive influence
on ROA and on stock returns. These findings are important
given the centrality of performance outcomes to current
academic and managerial interest in the MPM. We summa-
rize the findings in Table 7.
In addition, we found that MPM ability has a signifi-
cant, positive impact on CEO satisfaction with marketing.
Because increasing marketing’s stature at the board level is
Marketing Performance Measurement Ability / 89
TABLE 7
Summary of Hypothesized Results
Performance CEO Satisfaction ROA Stock Returns
MPM Activities Hypothesis Supported Hypothesis Supported Hypothesis Supported Hypothesis Supported
MPM + Yes + Yes + Yes + Yes
Activities + Yes + Yes + Yes + No
Metrics +No+Yes+No+No
MPM ×dashboard + No + No + No + No
Activities ×dashboard + No + No + No + No
Metrics ×dashboard + No + No + No + No
Notes: + = a positive hypothesized relationship. Yes = the hypothesis was supported. No = the hypothesis was not supported.
90 / Journal of Marketing, April 2007
a focus of attention for both academic and practitioner com-
munities, this is an important finding. Although the argu-
ment linking marketing accountability and marketing’s
influence is regularly posited, this is the first study to
demonstrate this relationship successfully. Our demonstra-
tion of the link between MPM ability and CEO satisfaction
with marketing lends support to researchers, such as Web-
ster, Malter, and Ganesan (2005), who have called for
greater accountability in marketing. Our findings also sup-
port the Marketing Science Institute’s (2004) ongoing prior-
itization of performance measurement as a means of raising
the profile of the discipline.
Marketing performance dashboards have captured the
attention of practitioners and, more recently, marketing aca-
demics. However, we did not find that the existence of
dashboards influenced the key relationships under consider-
ation in our study.
We find that activities ability has four dimensions
related to direct marketing, management, public relations,
and brand. The impact of these factors varies across the
dependent variables we considered. Notably, the ability to
measure brand and management activities influences CEO
satisfaction with marketing.
After we account for activities, the second component
of MPM ability, metrics, does not have a significant addi-
tional impact on firm performance. However, it signifi-
cantly affects CEO satisfaction with marketing.
Implications for Managers
The results of this study have several important managerial
implications. Development of MPM ability requires that
marketers divert part of their budget and attention away
from actual marketing programs and toward measurement
efforts; this would be counterproductive if it did not
improve performance. Our study provides support for just
such a diversion of resources, indicating that it can posi-
tively affect both firm performance and marketing’s stature
within the organization, at least for firms that operate in the
high-tech sector.
Given that MPM ability offers demonstrable benefits,
the question arises as to what should be measured and how.
Although this study clearly identifies four factors that make
up the activities aspect of performance measurement ability
(direct marketing, public relations, brand, and manage-
ment), it is noteworthy that each factor alone has relatively
weak relationships to firm performance and CEO satisfac-
tion with marketing. This implies that efforts to drive
improvement in ability to measure a single marketing activ-
ity, no matter how important the activity is to the firm, are
less valuable than a comprehensive effort to develop the
ability to measure performance across the entire range of
marketing activities employed.
In addition, our findings suggest that the ability to use a
comprehensive set of metrics is associated with higher CEO
satisfaction with marketing. In this respect, developing the
ability to measure performance with a combination of
financial and nonfinancial indicators that allow for a com-
parison of performance against plans and against competi-
tors would seem to be beneficial for marketers. However,
although a large part of the MPM literature has focused on
which metrics are being or should be used, this study finds
no separate impact on firm performance attributable to the
ability to use a range of metrics.
Finally, our study questions the merit of the current high
level of practitioner enthusiasm for marketing performance
dashboards. Such dashboards have been presented as a
means to present and communicate performance data more
effectively. However, we do not observe any moderating
effect of dashboards on the relationship between MPM and
firm performance or CEO satisfaction with marketing.
Because this is one of the first studies to explore the impact
of performance dashboards in marketing and given that
their adoption and functionality continue to evolve, our
findings are not definitive; researchers and managers alike
need to continue to explore this emerging area.
Limitations and Further Research
The membership list of the CMO Council, which we used
as the sample frame for our study, is not a complete list of
senior marketers in high-tech firms globally or even in
North America, and therefore the results of this study can-
not be generalized to the whole population of senior tech-
nology marketers. However, although we recognize the
sample frame as being incomplete, we deemed access to the
membership of the organization and the expected response
rate to be sufficient to justify its use. It would be useful to
replicate the research using a more complete sample frame.
Furthermore, it would be useful to extend the research to
other sectors because though pressure to demonstrate mar-
keting’s contribution is strongly evident in high-tech firms,
this pressure is also evident in many other industries. Given
the global nature of MPM research (e.g., Barwise and Far-
ley 2004), it would also be useful to replicate this study
with samples from outside the United States.
The research relies on the views of a single key infor-
mant (the senior marketer) in each firm. Although the key
informant approach is common, the use of multiple infor-
mants from a single firm may allow for a more rounded per-
spective. In addition, as we noted previously, the absence of
any evidence of the effect of dashboards might be due to the
way we conceptualized them in this study; therefore, alter-
native conceptualizations are worth exploring. Similarly,
although we found strong evidence of the impact of MPM
ability on CEO satisfaction with marketing, we assessed the
latter through a single-item measure. Future studies could
pursue a more comprehensive consideration of marketing’s
stature within the firm.
An assumption underpinning this research is that firms
with the ability to measure performance do so. We have
already outlined the rationale for this assumption. However,
notwithstanding the intense pressure on marketers to
account for the performance impact of their activities, it is
possible that a nontrivial portion of this ability remains
latent in some firms. Accordingly, assessing actual MPM in
future research would be useful.
Finally, although the focus of this study was on the rela-
tionship between MPM ability and firm performance and
CEO satisfaction with marketing, it is possible that there are
important mediating variables that could also be considered.
Marketing Performance Measurement Ability / 91
As we noted previously, it has been posited that improved
learning may be a route through which MPM ability is posi-
tively related to improved performance, and as such, further
research that incorporates this potentially mediating
variable would be worthwhile. In addition, although we
expect that measurement ability leads to improved market-
ing, we do not control for actual marketing performance in
this study.
Appendix
Measures
MPM Ability
MPM ability. For each of the following marketing
activities, please rate your company’s ability to measure
performance (1 = “poor,” and 7 = “excellent”).
•Marketing planning
•Branding
•Advertising
•Direct mail/e-mail campaigns
•Telemarketing and contact management
•Web site and Internet presence
•Tradeshows and events
•Promotions
•Sales and marketing collateral
•Public relations and internal communications
•Analyst and stakeholder relationships
•Channel marketing
•Customer relationship management systems
•Market research
•Budgeting
Ability to generate a comprehensive set of marketing
metrics. Please rate your company’s current ability to pro-
vide the following MPM information (1 = “poor,” and 7 =
“excellent”).
•Financial indicators of marketing performance
•Nonfinancial indicators of marketing performance
•Benchmark indicators of marketing performance against
plans
•Benchmark indicators of marketing performance against
competitors
Outcomes
Primary firm performance outcomes. Please indicate
your firm’s performance over the last year relative to all
other competitors in the primary market that you serve (1 =
“very poor,” and 7 = “outstanding”).
•Sales growth
•Market share
•Profitability
Secondary firm performance outcomes.
•ROA (COMPUSTAT)
•Size-adjusted stock returns (CRSP)
CEO satisfaction with marketing. In your opinion, what
is your CEO’s evaluation of your company’s current mar-
keting performance?
•Excellent
•Above average
•Average
•Below average
•Poor
Potential Moderator
Use of a marketing dashboard. Please rate your com-
pany’s current ability to provide the following MPM infor-
mation (1 = “poor,” and 7 = “excellent”).
•High-level “dashboard” of key marketing performance
indicators
•Automated reporting of performance from the full range of
marketing activities
•Automated “drill-down” information for detailed analysis of
individual marketing programs
REFERENCES
Aaker, David A. and Robert Jacobson (2001), “The Value Rele-
vance of Brand Attitude in High-Technology Markets,” Journal
of Marketing Research, 38 (November), 485–93.
Adler, Lee (1967), “Systems Approach to Marketing,” Harvard
Business Review, 45 (May–June), 105–118.
Ahuja, Gautam and Curba M. Lampert (2001), “Entrepreneurship
in the Large Corporation: A Longitudinal Study of How Estab-
lished Firms Create Breakthrough Inventions,” Strategic Man-
agement Journal, 22 (June), 521–44.
Aiken, Leona S. and Stephen G. West (1993), Multiple Regres-
sion: Testing and Interpreting Interactions. Newbury Park, CA:
Sage Publications.
Ailawadi, Kusum L., Donald R. Lehmann, and Scott A. Neslin
(2002), “A Product-Market-Based Measure of Brand Equity,”
Working Paper No. 02-102, Marketing Science Institute.
Ambler, Tim (2003), Marketing and the Bottom Line. London: FT
Prentice Hall.
———, Flora Kokkinaki, and Stefano Puntoni (2004), “Assessing
Marketing Performance: Reasons for Metrics Selection,” Jour-
nal of Marketing Management, 20 (3–4), 475–99.
Anderson, Paul F. (1982), “Marketing, Strategic Planning, and the
Theory of the Firm,” Journal of Marketing, 46 (Spring), 15–26.
Armstrong, J. Scott and Terry S. Overton (1977), “Estimating
Nonresponse Bias in Mail Surveys,” Journal of Marketing
Research, 14 (August), 396–402.
Audia, Pino G., Edwin A. Locke, and Ken G. Smith (2000), “The
Paradox of Success: An Archival and a Laboratory Study of
Strategic Persistence Following Radical Environmental
Change,” Academy of Management Journal, 43 (October),
837–53.
Barwise, Patrick and John U. Farley (2003), “Which Marketing
Metrics Are Used and Where?” Working Paper No. 03-111,
Marketing Science Institute.
92 / Journal of Marketing, April 2007
——— and ——— (2004), “Marketing Metrics: Status of Six
Metrics in Five Countries,” European Management Journal, 22
(June), 257–62.
Bolton, Ruth N. (2004), “Linking Marketing to Financial Perfor-
mance and Firm Value,” Journal of Marketing, 68 (October),
73–75.
Bonoma, Thomas V. (1989), “Marketing Performance: What Do
You Expect?” Harvard Business Review, 67 (September),
44–48.
——— and Bruce H. Clark (1988), Marketing Performance
Assessment. Boston: Harvard Business School Press.
Churchill, Gilbert A. (1979), “A Paradigm for Developing Better
Measures of Marketing Constructs,” Journal of Marketing
Research, 16 (February), 64–73.
——— (1991), Marketing Research: Methodological Founda-
tions. London: Dryden Press.
Clark, Bruce H. (1999), “Marketing Performance Measures: His-
tory and Interrelationships,” Journal of Marketing Manage-
ment, 15 (November), 711–33.
——— (2000), “Managerial Perceptions of Marketing Perfor-
mance: Efficiency, Adaptability, Effectiveness and Satisfac-
tion,” Journal of Strategic Marketing, 8 (March), 3–24.
——— (2001), “A Summary of Thinking on Measuring the Value
of Marketing,” Journal of Targeting, Measurement and Analy-
sis for Marketing, 9 (June), 357–70.
——— and Tim Ambler (2001), “Marketing Performance Mea-
surement: Evolution of Research and Practice,” International
Journal of Business Performance Management, 3 (Winter),
231–44.
Cohen, Jacob, Patricia Cohen, Stephen West, and Leona Aiken
(2002), Applied Multiple Regression/Correlation Analysis for
the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum
Associates.
Curren, Mary T., Valerie S. Folkes, and Joel H. Steckel (1992),
“Explanations for Successful and Unsuccessful Marketing
Decisions: The Decision Maker’s Perspective,” Journal of
Marketing, 56 (April), 18–31.
Davies, Warnock and Kathleen E. Brush (1997), “High-Tech
Industry Marketing: The Elements of a Sophisticated Global
Strategy,” Industrial Marketing Management, 26 (January),
1–84.
Day, George S. (1992), “Marketing’s Contribution to the Strategy
Dialogue,” Journal of the Academy of Marketing Science, 20
(May), 323–30.
——— and Prakash Nedungadi (1994), “Managerial Representa-
tions of Competitive Advantage,” Journal of Marketing, 58
(April), 31–44.
Donthu, Naveen, Edmund Hershberger, and Talai Osomonbekov
(2005), “Benchmarking Marketing Productivity Using Data
Envelopment Analysis,” Journal of Business Research, 58
(November), 1474–82.
Doyle, Peter (2000), Value-Based Marketing. New York: John
Wiley & Sons.
Fornell, Claes and David F. Larcker (1981), “Evaluating Structural
Equation Models with Unobservable Variables and Measure-
ment Error,” Journal of Marketing Research, 18 (February),
39–50.
Gerbing, David W. and James C. Anderson (1988), “An Updated
Paradigm for Scale Development Incorporating Unidimension-
ality and Its Assessment,” Journal of Marketing Research, 25
(May), 187–92.
Greve, Henrich R. (1998), “Performance, Aspirations and Risky
Organizational Change,” Administrative Science Quarterly, 43
(March), 58–86.
Hair, Joseph F., Rolph E. Anderson, Ronald L. Tatham, and
William C. Black (1998), Multivariate Data Analysis. Upper
Saddle River, NJ: Prentice Hall.
Harrison, Richard J. and David L. Torres (1988), “The Changing
of the Guard: Turnover and Structural Change in the Top-
Management Positions,” Administrative Science Quarterly, 33
(June), 211–33.
Hill, Joanne and Joel L. Naroff (1984), “The Effect of Location on
the Performance of High Technology Firms,” Financial Man-
agement, 13 (January), 27–37.
Iyer, Bala, Chi-Hyon Lee, and N. Venkatraman (2006), “Managing
in a Small World Ecosystem: Lessons from the Software Sec-
tor,” California Management Review, 48 (Spring), 27–47.
Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orienta-
tion: Antecedents and Consequences,” Journal of Marketing,
57 (July), 53–70.
Kaplan, Robert S. and David P. Norton (1992), “The Balanced
Scorecard: Measures That Drive Performance,” Harvard Busi-
ness Review, 70 (January–February), 71–80.
Kokkinaki, Flora and Tim Ambler (1999), “Marketing Perfor-
mance Assessment: An Exploratory Investigation into Current
Practice and the Role of Firm Orientation,” Working Paper No.
99-114, Marketing Science Institute.
Kumar, Nirmayla (2004), Marketing as Strategy: Understanding
the CEO’s Agenda for Driving Growth and Innovation. Boston:
Harvard Business School Press.
Lages, Luis F., Carmen Lages, and Cristiana R. Lages (2005),
“Bringing Export Performance Metrics into Annual Reports:
The APEV Scale and the PERFEX Scorecard,” Journal of
International Marketing, 13 (3), 79–104.
Lehmann, Donald (2004), “Metrics for Making Marketing Mat-
ter,” Journal of Marketing, 68 (October), 73–75.
Locke, Edwin and Garry P. Latham (1990), A Theory of Goal Set-
ting and Performance. Englewood Cliffs, NJ: Prentice Hall.
Lukas, Bryan A., Gregory J. Whitwell, and Peter Doyle (2005),
“How Can a Shareholder Value Approach Improve Marketing’s
Strategic Influence?” Journal Business Research, 58 (April),
414–22.
Malhotra, Naresh K. (1996), Marketing Research: An Applied Ori-
entation. London: Prentice Hall.
Marketing Science Institute (1998), 1998-2000 Research Priori-
ties: A Guide to MSI Research Programs and Procedures.
Cambridge, MA: Marketing Science Institute.
——— (2000), 2000-2002 Research Priorities: A Guide to MSI
Research Programs and Procedures. Cambridge, MA: Market-
ing Science Institute.
——— (2002), 2002-2004 Research Priorities: A Guide to MSI
Research Programs and Procedures. Cambridge, MA: Market-
ing Science Institute.
——— (2004), 2004-2006 Research Priorities: A Guide to MSI
Research Programs and Procedures. Cambridge, MA: Market-
ing Science Institute.
——— (2006), 2006-2008 Research Priorities: A Guide to MSI
Research Programs and Procedures. Cambridge, MA: Market-
ing Science Institute.
McGovern, Gail J., David Court, John A. Quelch, and Blair Craw-
ford (2004), “Bringing Customers into the Boardroom,” Har-
vard Business Review, 82 (November), 70–80.
McKee, Daryl O., Rajan P. Varadarajan, and William M. Pride
(1989), “Strategic Adaptability and Firm Performance: A
Market-Contingent Perspective,” Journal of Marketing, 53
(July), 21–35.
McMaster, Mark (2002), “ROI: More Vital Than Ever,” Sales &
Marketing Management, 154 (1), 51–52.
Miles, Morgan P., Jeffery G. Covin, and Michael B. Heeley
(2000), “The Relationship Between Environmental Dynamism
and Small Firm Structure, Strategy, and Performance,” Journal
of Marketing Theory and Practice, 8 (Spring), 63–75.
Miller, Amy and Jennifer Cioffi (2004), “Measuring Marketing
Effectiveness and Value: The Unisys Marketing Dashboard,”
Marketing Performance Measurement Ability / 93
Journal of Advertising Research, 44 (September–October),
237–243.
Miller, Danny (1994), “What Happens After Success: The Perils of
Excellence,” Journal of Management Studies, 31 (May),
318–35.
Mitchell, Vincent-Wayne and Gianfranco Walsh (2004), “Gender
Differences in German Consumer Decision-Making Styles,”
Journal of Consumer Behavior, 3 (June), 331–46.
Mohr, Jakki J. and Nader H. Shooshtari (2003), “Introduction to
the Special Issue: Marketing of High-Technology Products and
Innovations,” Journal of Marketing Theory and Practice, 11
(Summer), 1–12.
Moorman, Christine and Ronald T. Rust (1999), “The Role of
Marketing,” Journal of Marketing, 63 (Special Issue), 180–97.
Morgan, Neil A., Bruce H. Clark, and Rich Gooner (2002), “Mar-
keting Productivity, Marketing Audits, and Systems for Mar-
keting Performance Assessment: Integrating Multiple Perspec-
tives,” Journal of Business Research, 55 (May), 363–75.
Narver, John C. and Stanley F. Slater (1990), “The Effect of a
Market Orientation on Business Profitability,” Journal of Mar-
keting, 54 (October), 20–35.
Nunnally, Jum C. and Ira Bernstein (1994), Psychometric Theory.
New York: McGraw-Hill.
Ouchi, William G. (1979), “A Conceptual Framework for the
Design of Organizational Control,” Management Science, 25
(September), 833–48.
Paine, Katie D. (2004), “Using Dashboard Metrics to Track Com-
munication,” Strategic Communication Management, 8
(August–September), 30–34.
Peyrot, Mark, Nancy Childs, Doris Van Doren, and Kathleen Allen
(2002), “An Empirically Based Model of Competitor Intelli-
gence Use,” Journal of Business Research, 55 (September),
747–58.
Rogers, Beth (2003), “What Gets Measured Gets Better,” Journal
of Targeting, Measurement and Analysis for Marketing, 12
(January), 20–26.
Rust, Ronald T., Tim Ambler, Gregory S. Carpenter, V. Kumar,
and Rajendra K. Srivastava (2004), “Measuring Marketing Pro-
ductivity: Current Knowledge and Future Directions,” Journal
of Marketing, 68 (October), 76–90.
———, Katherine N. Lemon, and Valarie A. Zeithaml (2004),
“Return on Marketing: Using Customer Equity to Focus Mar-
keting Strategy,” Journal of Marketing, 68 (January), 109–127.
———, Christine Moorman, and Peter R. Dickson (2002), “Get-
ting Return on Quality: Revenue Expansion, Cost Reduction,
or Both?” Journal of Marketing, 66 (October), 7–24.
Schoonhoven, Claudia B. (1981), “Problems with Contingency
Theory: Testing Assumptions Hidden Within the Language of
Contingency ‘Theory,’” Administrative Science Quarterly, 26
(September), 349–77.
Sevin, Charles H. (1965), Marketing Productivity Analysis. New
York: McGraw-Hill.
Slater, Stanley F. and John C. Narver (1996), “Competitive Strat-
egy in the Market-Focused Business,” Journal of Market-
Focused Management, 1 (Spring), 159–74.
Srivastava, Rajendra K. and David J. Reibstein (2005), “Metrics
for Linking Marketing to Financial Performance,” Report No.
05–200, Marketing Science Institute.
———, Tasaduq A. Shervani, and Liam Fahey (1998), “Market-
Based Assets and Shareholder Value: A Framework for Analy-
sis,” Journal of Marketing, 62 (January), 2–18.
Vorhies, Douglas W. and Neil A. Morgan (2005), “Benchmarking
Marketing Capabilities for Sustainable Competitive Advan-
tage,” Journal of Marketing, 69 (January), 80–94.
Washburn, Judith H. and Richard E. Plank (2002), “Measuring
Brand Equity: An Evaluation of a Consumer-Based Brand
Equity Scale,” Journal of Marketing Theory and Practice,10
(Winter), 46–62.
Webster, Frederick E. (1992), “The Changing Role of Marketing
in the Corporation,” Journal of Marketing, 56 (October), 1–17.
———, Alan J. Malter, and Shankar Ganesan (2005), “The
Decline and Dispersion of Marketing Competence,” MIT Sloan
Management Review, 46 (Summer), 35–43.
Wind, Yoram (2005), “Marketing as an Engine of Business
Growth: A Cross-Functional Perspective,” Journal of Business
Research, 58 (July), 863–73.
Winer, Russell S. (2000), “What Marketing Metrics Are Used by
MSI Members?” Report No. 00-119, Marketing Science
Institute.
Zahra, Shaker A., Duane R. Ireland, and Michael A. Hiltt (2000),
“International Expansion by New Venture Firms: International
Diversity, Mode of Market Entry, Technological Learning, and
Performance,” Academy of Management Journal, 43 (October),
925–50.
