Improving Marketing’s Contribution toNew Product Development.docx

Improving Marketing’s Contribution to
New Product Development
Wenzel Drechsler, Martin Natter, and Peter S. H. Leeflang
In many firms, the marketing department plays a minor role in
new product development (NPD). However, recent
research demonstrates that marketing capabilities more strongly
influence firm performance than other areas such as
research and development. This finding underscores the
importance of identifying relevant capabilities that can
improve the position of marketing within the NPD process as
part of the quest to improve innovation performance.
However, thus far, it has remained unclear precisely how the
marketing department can increase its influence on NPD
to enhance a firm’s innovation performance. The results of this
study demonstrate that the relationship between
marketing capabilities and innovation performance is generally
mediated by the decision influence of marketing on
NPD. In particular, both marketing research quality and the
ability to translate customer needs into product charac-
teristics serve to increase marketing’s influence on NPD. This
increased influence, in turn, positively contributes to
overall firm innovation performance. Hence, these results show
that in addition to having the appropriate marketing
capabilities, the marketing department must achieve a status in
which these capabilities can translate into performance
implications.
Introduction
T
he creation of new products is an important strat-

egy for any firm trying to survive in highly com-
petitive and fragmented markets. Each new
product development (NPD) project usually involves
identifying customer needs and conducting a preliminary
market assessment (Calantone and di Benedetto, 1988).
However, managers often believe that marketing research
fails to provide timely, accurate, and relevant information
about consumers and markets relevant to decision making
(Johnson and Ambrose, 2009). In particular, they are
convinced that new product failure rates are still high1
due to a flawed understanding of consumer preferences
and market needs (Cooper and Kleinschmidt, 1995;
Johnson and Ambrose, 2009).
Identifying and assessing opportunities for the devel-
opment of new products usually falls under marketing’s
domain, because this process represents the customer–
product connection (Fine, 2009; Moorman and Rust
1999). Kandybin and Kihn (2004) propose that top inno-
vators heavily involve the marketing department in the
NPD process. However, the prevailing view in most com-
panies is that marketing offers no distinct function; in
other words, there is a sense that everyone can do market-
ing (McKenna, 1991; Sheth and Sisodia, 2005). This is
one of the reasons why marketing is in heavy decline and
further implies that little attention is paid to the marketing
department’s knowledge and opinion (Webster, Malter,
and Ganesan, 2005). In addition, prior research indicates
that especially in NPD, the research and development
(R&D) department often dominates the marketing depart-
ment (e.g., Rein, 2004; Workman, 1993).
The question arises regarding which specific capabili-

ties marketing departments require to contribute to the
success of NPD. From the R&D perspective, marketing
could increase its influence by improving the credibility
of its knowledge with respect to NPD (Workman, 1993).
Considering this concept, Atuahene-Gima and Evange-
lista (2000) show that increased “expert power,” defined
as “the degree to which an individual is regarded as
having expert knowledge about the relevant issues in the
NPD process,” leads to increased marketing department
influence on the NPD process.
Because the role of marketing has been either mini-
mized or eliminated in many firms, recent research has
considered how marketing departments can reestablish
their integrity and their influence on corporate decisions
(e.g., Verhoef and Leeflang, 2009; Webster, 2005).
According to Verhoef and Leeflang (2009) and Verhoef
et al. (2011), the marketing department’s ability to link its
Address correspondence to: Wenzel Drechsler, Goethe
University
Frankfurt, Department of Marketing, Strothoff Chair of
Retailing, Grueneb-
urgplatz 1, 60054 Frankfurt, Germany. E-mail: [email protected]
wiwi.uni-frankfurt.de. Tel: +49 (0)69 798 34637.
1 Estimates of failure rates vary from more than 50% to
approximately
80–90% (Johnson and Ambrose, 2009; Sachdev, 2001; Sudhir
and Rao,
2006).
J PROD INNOV MANAG 2013;30(2):298–315
© 2012 Product Development & Management Association
DOI: 10.1111/j.1540-5885.2012.01010.x

strategies and actions to financial performance measures
(accountability) is a major factor driving its overall influ-
ence within the firm. They also show that being innova-
tive with regard to NPD is another major driver of respect
and influence for marketing departments. However,
knowledge about specific capabilities that improve the
marketing department’s contribution to NPD is scarce
(Atuahene-Gima and Evangelista, 2000; Griffin and
Hauser, 1996). This lack of knowledge calls for the devel-
opment of a more specific framework that identifies these
relevant marketing capabilities in order to improve mar-
keting’s position in NPD and, subsequently, a firm’s
innovation performance.
The present study aims to empirically investigate the
relationship between marketing capabilities and innova-
tion performance, as mediated by the influence of mar-
keting on NPD. Therefore, this study first identifies
marketing capabilities that are relevant for NPD. Then the
study investigates whether these capabilities help the
marketing department strengthen its position and influ-
ence within the NPD process. Finally, the study links the
marketing department’s influence and capabilities to firm
innovation performance.
The study’s results provide evidence that firms with
strong marketing departments are more successful with
their new products. This competitive advantage is attrib-
uted to how marketing-related capabilities contribute to
the marketing department’s influence on NPD issues.
Hence, these findings reveal that firms need not only key
marketing capabilities and skills but also a marketing
department that itself functions as an expert to execute

the relevant functions with respect to NPD.
The remainder of the paper is organized as follows.
The first section discusses general characteristics of mar-
keting capabilities; it is followed by the introduction of
the conceptual model. The next section specifies the
hypotheses and expected effects. This section is followed
by a discussion of the data and variables and the presen-
tation of the model. The final sections present the empiri-
cal results and discuss the main conclusions.
Conceptual Model
The literature suggests that distinct capabilities more than
resources help some firms outperform others (Grant, 1996;
Teece, Pisano, and Shuen, 1997). In particular, these capa-
bilities are manifested in specific business activities such
as developing new products (Day, 1994) and are therefore
important to identify. Adopting a knowledge-based view,
the present study views capabilities as embodied in the
knowledge and skills of employees and thus located within
a firm’s different departments.
A closer look at the NPD process helps to identify
specific marketing capabilities that are relevant for the
success of new products. The new NPD process is defined
as the process of generating and transforming new
product ideas into commercial outputs as an integrated
flow (Calantone and di Benedetto, 1988). Throughout
this process, the marketing department’s main tasks are to
identify and understand customer needs and to assess the
market potential of new product ideas (Calantone and di
Benedetto, 1988; Ernst, Hoyer, and Rübsaamen, 2010).
Thus, the marketing department’s capabilities are based
on knowledge about customer needs, past experience,
forecasting, and responding to needs (Day, 1994). Prior

research proposes that by increasing such distinct knowl-
edge and by demonstrating relevant skills, the marketing
department should be able to achieve higher levels of
status within specific business activities such as NPD
(Leonard-Barton, 1992).
In the context of NPD, the marketing department
usually conducts marketing research in order to gain rel-
evant information about customer needs and new product
ideas. Formal research is undertaken because managers
expect the resulting information to minimize uncertainty
when making important decisions in areas such as NPD
(Deshpandé and Zaltman, 1982). Hence, the ability to
acquire knowledge based upon high-quality marketing
research is a crucial capability that should relate to the
influence of marketing on NPD. According to Moorman
and Rust (1999), marketing’s emphasis is also on provid-
BIOGRAPHICAL SKETCHES
Dr. Wenzel Drechsler received his Ph.D. from the Marketing
Department,
Goethe University Frankfurt, Germany. His research interests
include
pricing, marketing strategy, and innovation management. His
research
papers have been published in journals such as Journal of
Product
Innovation Management, Journal of Interactive Marketing,
Technology
Analysis and Strategic Management, and Journal of Business
Research.
Prof. Martin Natter is the Hans Strothoff Chair of Retail
Marketing at
the Goethe University Frankfurt, Germany. He received his

Ph.D. from
Vienna University of Economics and Business Administration,
Austria.
His research interests include retail pricing, new product
decisions, and
competitive analysis. His research papers have been published
in jour-
nals such as Management Science, Journal of Marketing, and
Marketing
Science.
Prof. Peter S. H. Leeflang is the Distinguished Frank M. Bass
Professor
of Marketing, Department of Marketing, Faculty of Economics
and
Business, University of Groningen, the Netherlands. He also
holds the
BAT-chair of Marketing at LUISS Guido Carli at Rome, Italy,
and a
Research Chair at the Aston Business School of Aston
University,
United Kingdom. He is member of the Royal Academy of Arts
and
Sciences in the Netherlands.
IMPROVING MARKETING’S CONTRIBUTION TO NPD J
PROD INNOV MANAG 299
2013;30(2):298–315
ing knowledge and skills that connect the customer to
product design, functionalities, and quality issues. Hence,
marketing’s ability to translate customer needs into tech-
nical specifications, i.e., its technical skills (TS), should
on the one hand help reduce the number of products that

fail to satisfy market needs. On the other hand, good TS
should also be valued by other areas including the R&D
department as they increase the marketing department’s
credibility (Moorman and Rust, 1999; Workman, 1993).
The latter point also applies to the marketing depart-
ment’s general knowledge of marketing and business
issues (e.g., Enright, 2005). In particular, this capability
should allow the marketing department to support man-
agement in detailed business analyses concerning deci-
sions such as the selection of the most promising new
product ideas (Calantone and di Benedetto, 1988).
The discussion above shows that with respect to NPD,
the marketing department’s capabilities are mainly
knowledge based and rely on the acquisition, manage-
ment, and use of information. Usually, this knowledge is
tacitly held and difficult for rivals to copy due to its
imperfect imitability (Day, 1994; Krasnikov and Jay-
achandran, 2008).
The conceptual model in Figure 1 is based on the
assumption that specific knowledge (capabilities) of the
marketing department can only contribute to the success
of new products when the marketing department has the
power to influence important decisions. This assumption
is based on literature indicating that good capabilities and
skills within marketing departments are regarded as
crucial for fostering innovation success and strengthening
the marketing department’s position within the overall
innovation process (Atuahene-Gima and Evangelista,
2000). Hence, this discussion suggests that the relation-
ship between marketing capabilities and innovation per-
formance is mediated by the marketing department’s
influence on NPD. Therefore, the marketing department’s
knowledge-based capabilities (marketing research

quality [MR], TS, knowledge about marketing and
general management [MK]) are linked to the marketing
department’s influence on NPD. Following Homburg,
Workman, and Krohmer (1999), marketing’s influence is
defined as the exercised power of the marketing depart-
ment. Accordingly, this study uses two variables to
measure this influence: (1) the decision influence (DI) on
NPD (MI1) and (2) the fraction of new products or ser-
vices (NPD projects) initiated by the marketing depart-
ment (MI2). To assess their mediating role, these two
influence measures are directly linked to firm innovation
performance.
A firm’s innovation performance is linked to its
overall business performance to establish the convergent
Business
Performance (BP)
Controls:
•General innovativeness
•Number of new products
•Firm size
•Short-term emphasis
Marketing's
Influence on NPD
• Decision influence (MI1)

• Initiated NPD projects (MI2)
Controls
• Past market development
• Generic strategy
Controls:
Marketing Department Characteristics
• Accountability
• Integration/Cooperation with R&D
Firm Characteristics
• CEO – Technical background
• CEO – Marketing background
• B2C versus B2B
• Goods versus services
Environmental Characteristics
• Market-related turbulence
Innovation
Performance (IP)
Knowledge-Based

Marketing Capabilities
• Marketing research quality (MR)
• Technical skills (TS)
• Knowledge about
marketing & general management (MK)
Main paths of interest
Control paths
Figure 1. Conceptual Model
NPD, new product development
300 J PROD INNOV MANAG W. DRECHSLER ET AL.
2013;30(2):298–315
validity of the proposed conceptual model (Srivastava,
Shervani, and Fahey, 1999). In addition to considering
elements of financial performance such as profitability,
sales, and cost level, the overall business performance
measure used in this study also captures nonfinancial
elements of performance such as customer satisfaction
and customer loyalty (cf. Appendix A). To account for
both dimensions of business performance is consistent
with previous research indicating that nonfinancial per-
formance is closely related to improved financial perfor-
mance (Rust, Zahorik, and Kleiningham, 1995).
The conceptual model also controls for marketing
department characteristics, firm characteristics, and an

environmental characteristic (market-related turbulence).
Following Verhoef and Leeflang (2009), this study
uses accountability and integration/cooperation between
departments (in this case, marketing and R&D) as
potential antecedents that explain the influence of
marketing on NPD. Following Homburg et al. (1999),
firm characteristics such as the marketing background
of the chief executive officer (CEO) and the type of
industry (business-to-consumer [B2C] versus business-
to-business [B2B]) are also included as potential anteced-
ents in the model. Furthermore, the study controls for the
influence of a number of other variables that may affect
innovation and business performance, including firm
strategy, firm size, firm innovativeness, and past market
development (Song and Thieme, 2006).
Hypotheses Development and
Expected Effects
Marketing’s Knowledge-Based Capabilities
MR. The task of the marketing function is to develop a
thorough understanding of the market to ensure that the
firm produces goods and services that the consumer
requires and desires. Many firms perceive marketing
research to be an important support for their NPD
projects (Webster, 1992). In reality, however, marketing
researchers work under tight deadlines and even tighter
budgets, and they increasingly have trouble finding a
sufficient number of qualified respondents (Arnold,
2005). As a result, many marketing departments know
little or nothing about their customers (Schultz, 2003),
and MR is perceived to be low, which causes a decline in
the status of these departments within firms (Schultz,
2003).

The academic marketing literature includes numerous
studies on the use of market research (e.g., Deshpandé
and Zaltman, 1982). These studies suggest that the per-
ceived quality, actionability, and surprise factor of a given
piece of marketing research are positively linked with its
use. The increased use of high-quality marketing research
could improve the influence of the marketing department
because this department is usually in charge of collecting
market data. This assumption leads to the following
hypothesis:
H1: MR is positively related to the influence of marketing
on NPD.
TS. Studies of source credibility and the theory of
interpersonal trust (Giffin, 1967; Hovland, Janis, and
Kelley, 1953; Moorman, Desphandé, and Zaltman, 1993)
demonstrate that the perceived knowledge, expertise, and
technical competence of marketers induce a more posi-
tive attitude toward their ideas. For instance, the market-
ing department’s TS are relevant for effectively working
with important tools in NPD such as the House of Quality
(Hauser and Clausing, 1988; Natter, Mild, Feurstein,
Dorffner, and Taudes, 2001), in which customer require-
ments are translated into technical product specifications.
The greater the marketing department’s ability to link
customer requirements to technical product specifica-
tions, the more likely it is that a new product will be
successfully marketed (Hauser, 1993). TS of the market-
ing department may also contribute to the NPD success of
firms, because technical innovations can be better trans-
lated into customer benefits (e.g., via the positioning
strategy). Consequently, this study also assumes that a
marketing department’s TS, defined as its ability to trans-
late customer needs into product characteristics, will

increase its influence on NPD. In this vein, Leonard-
Barton (1992) underlines that some of the most necessary
elements of a core capability are excellent technical and
professional skills and a knowledge base relevant to
major products. On this basis, hypothesis H2 reads as
follows:
H2: The TS of the marketing department are positively
related to the influence of marketing on NPD.
MK. Marketing jobs have become more complex,
forcing chief marketing officers (CMOs) to become
general business managers (SpencerStuart, 2008). The
most promising CMOs are therefore those who happen to
be gifted marketers themselves and who are focused on
continually developing their team, both internally and
externally. This fact implies that apart from marketing
information (which is captured in marketing research),
general knowledge of marketing and management is also
a crucial asset (Glazer, 1991; Srivastava, Shervani, and
2013;30(2):298–315
Fahey, 1998). Within the academic marketing literature,
although knowledge is now considered to be an important
resource, there is a lack of attention to the influence of
marketing knowledge within organizations. In addition to
having marketing knowledge, a marketer should also
have some knowledge of general management and busi-
ness (e.g., Enright, 2005). If marketing departments are
able to achieve growth via their marketing knowledge,
then the marketing department may be perceived as a

growth champion within the firm (Landry, Tipping, and
Kumar, 2006). This assumption leads to the following
hypothesis:
H3: MK is positively related to the influence of marketing
on NPD.
Control Variables—Marketing Influence
Marketing accountability. The importance of market-
ing department accountability has been widely acknowl-
edged (Lehmann, 2004; Rust, Ambler, Carpenter, Kumar,
and Srivastava, 2004). Both Moorman and Rust (1999)
and Verhoef and Leeflang (2009) show a positive rela-
tionship between accountability and the overall influence
of the marketing department within the firm, and
O’Sullivan and Abela (2007) report that top management
tends to be satisfied with the marketing department when
the latter is accountable. Hence, this study expects that
accountability also drives the influence of marketing on
NPD.
Integration with R&D. Generally, cross-functional
cooperation is considered to be beneficial for firms
(Srivastava et al., 1998). Because managing the interface
between different departments such as marketing, R&D,
finance, and operations is a critical element of successful
NPD, this study accounts for possible interdepartmental
conflicts related to the coordination of their activities.
Interdepartmental conflicts create barriers to innovation
due to departmental differences in time horizons, com-
munication depth, and contact frequency (Roussel, Saad,
and Erickson, 1991). Thus, this study assumes that any
problems that arise between the marketing and R&D
departments potentially reduce marketing’s influence on
NPD.

Firm characteristics. In their seminal work, Saxberg
and Slocum (1968) find inherent personality differences
between managers with marketing and R&D back-
grounds due to differences in their education. Marketing
professionals are trained in general problem solving and
decision making to ensure profitable company perfor-
mance. In contrast, R&D professionals, who often have a
technical background, are more interested in testing and
solving technical problems (Dougherty, 1992; Griffin and
Hauser, 1996; Saxberg and Slocum, 1968). Accordingly,
this study includes the CEO’s background as another
antecedent in the model. Marketing is expected to play a
more important role within firms that have a CEO with a
marketing background (Homburg et al., 1999). For this
purpose, this study includes two variables that reflect a
CEO’s marketing or technical background. Following
Verhoef and Leeflang (2009), this study also includes two
industry characteristics (B2C versus B2B and services
versus goods) as potential determinants of a marketing
department’s influence.
Market-related turbulence. Many different industries
now face increasing market turbulence. In particular,
shorter product life cycles, rising development costs, and
more intense competition force companies to think about
how to become more efficient and successful in their
innovation activities (Carrilo and Franza, 2006; Ches-
brough, 2007). Correspondingly, a firm needs a strong
marketing department that can face these challenges and
introduce high-quality market knowledge to help the
company successfully innovate. However, in turbulent
times, marketing budgets are often reduced or allocated
to other departments (Deleersnyder, Dekimpe, Steen-
kamp, and Leeflang, 2009; Nielsen 2009), thus reducing

the influence of the marketing department on NPD.
The Mediating Role of Marketing Influence on
Innovation Performance
Many firms, except for those that emphasize strongly the
role of the marketing department in NPD, suffer from
high new product failure rates (Kandybin and Kihn,
2004). This observation suggests that the marketing
department is likely to have an impact on the outcomes of
the NPD process not just by merely participating but also
by its status within the firm, which determines the degree
of influence it exercises on NPD (Atuahene-Gima and
Evangelista, 2000). The marketing department’s influ-
ence is reflected in its exercised power with respect to
specific activities important to the success of the firm
(Emerson, 1962; Homburg et al., 1999). This definition
implies that the NPD-specific knowledge of the market-
ing department can only contribute to the success of new
products when the marketing department has the power to
influence important decisions. Marketing has the oppor-
tunity to achieve this status within the NPD process by
2013;30(2):298–315
demonstrating that its underlying capabilities and skills
are of high quality (Leonard-Barton, 1992). This
improved status should in turn allow marketing to influ-
ence performance outcomes (Wall, Stark, and Standifer,
2001). As such, the marketing department’s specific NPD
capabilities are expected to influence a firm’s innovation
performance indirectly through its influence on NPD.
Thus, this discussion leads to the following hypothesis:

H4: The relationship between marketing capabilities and
innovation performance is mediated by marketing’s
influence on NPD.
Control Variables—Performance Outcomes
To assess the impact of marketing departments’ DI on
innovation performance, this study controls for several
important constructs, including firm size (Laursen and
Salter, 2006) and firm innovation orientation: i.e., general
innovativeness (Han, Kim, and Srivastava, 1998). The
study also includes the firm’s time frame for its strategic
decisions because a short-term emphasis potentially
impedes product/service innovations (Verhoef and
Leeflang, 2009). Because only a small fraction of newly
introduced products do not fail, the firm’s number of new
products introduced during the past five years is also
accounted for.
Concerning firm strategy, Gemuenden and Heyde-
breck (1995) demonstrate that cost leaders tend to be less
innovative and hence less successful. However, other
researchers demonstrate that both cost leaders and differ-
entiators can achieve a high level of customer satisfaction
and business performance (Olson, Slater, and Hult, 2005;
Vorhies and Morgan, 2003; Yamin, Gunasekaran, and
Mavondo, 1999). These findings suggest that it is impor-
tant to account for a firm’s generic strategy (cost leader-
ship versus differentiation) in explaining business
performance. Finally, business performance is clearly
related to developments in the firm’s core market over the
last five years.
Research Methodology

Data Collection
A total of 1850 questionnaires were sent out to finance,
marketing, and R&D executives of German for-profit
firms with more than 200 employees. Several methods
were used to encourage responses. Respondents were
offered either customized reports or two popular articles
related to marketing and R&D. Nonrespondents were
called after four weeks, and their cooperation was
requested. In total, 279 questionnaires were returned,
yielding a response rate of 15.1%. Sixteen respondents
were excluded from the sample because they did not
complete the entire survey, and 24 respondents were
excluded because they stated that they were public rela-
tions managers or executives who were not involved in the
innovation activities of their firms. Hence, the analyses are
based on 239 respondents. The key informants all
belonged to top management teams: CEOs (9.6%), CMOs
(63.8%), chief technology officers/R&D executives
(8.3%), and chief financial officers (18.3%). In only two
cases were there two respondents per firm. Following
previous research, the present study did not average these
replies but instead considered each respondent as a sepa-
rate case (Van Bruggen, Lilien, and Kacker, 2002; Verhoef
and Leeflang, 2009). Because the key informants worked
intensively in the area under study, single informant bias
should not affect the results (Kumar, Stern, and Anderson,
1993). This view is also supported by the high degree of
perceptual agreement between the multiple informants at
the above-mentioned two firms (correlation = .98).
Sample Description
Table 1 indicates that the average number of employees
serving as full-time equivalents per firm is 3520. Further-

more, the firms in the sample are active in a variety of
industries, although most firms are active in the industrial
sector (60%).
The descriptive statistics in Appendix B show that the
firms in the sample mainly operate in B2B markets with
a 2.97 average score on a 10-point scale (1 = “turnover
totally from B2B,” and 10 = “turnover totally from
B2C”). The firms primarily focus on goods with an
average score of 4.02 on a 10-point scale (1 = “turnover
totally from goods,” and 10 = “turnover totally from ser-
vices”). In 22% of cases, the CEO’s background is mar-
keting. Most of the firms (72%) pursue a differentiation
strategy. Compared with their competitors and relative to
their own stated objectives, firms show above-average
innovation (mean = 4.68) and business performance
(mean = 4.93). These figures correspond to a moderate
market increase over the last three years
(mean = 6.24 ª 5%).
Key Measures
To measure the key variables, the study uses a range of
established scales available in the extant literature on
marketing strategy and market orientation. For example,
2013;30(2):298–315
to operationalize the marketing department’s influence on
NPD decisions, a scale is used that was originally devel-
oped by Homburg et al. (1999). Each respondent was
asked to distribute 100 points to reflect the degree of

influence of four departments (marketing, sales, finance,
and R&D) on NPD decision making. The points assigned
to the marketing department are used to determine its
influence on NPD. The second marketing influence
measure (initiated NPD projects) is based on answers to
the following question: “What is the percentage of new
products or services introduced in the last five years that
were initiated by the following departments? Please
divide 100 points across the four departments: R&D,
Marketing, Sales, Other.” The points assigned to the mar-
keting department are used to indicate its influence.
To assess the relative influence of the marketing and
R&D departments, Table 2 reports the average percent-
age scores for both influence measures. The DI score of
the marketing department is 24%, whereas the influence
score of the R&D department is 43%. This result is
similar to the number of new products and services initi-
ated by each department. Over the last five years, 28% of
new products were initiated by the marketing department,
whereas the R&D department initiated 37% of all new
products.
To measure innovation and business performance,
Moorman and Rust’s (1999) subjective performance
scale is used both for the firm itself and for the firm
relative to its competitors. The present study uses this
type of performance data because previous studies find a
strong correlation between objective performance data
and subjective assessments of performance by key infor-
mants (Morgan, Kaleka, and Katsikeas, 2004; Olson
et al., 2005). More detailed information about the mea-
surement of each of the constructs, the items, the descrip-
tive statistics, and the literature source (as well as the
coefficient alphas and composite reliability) is provided
in Appendices A and B.

Validity and Reliability of Measures
The coefficient alphas of most of the multi-item scales are
greater than .80. The reliability and validity of the scales
are further assessed by exploratory and confirmatory
factor analysis (EFA and CFA). The EFA reveals suffi-
ciently high loadings per item per construct, and the items
belonging to each construct are classified as separate
factors. CFA for the reflective multi-item scales in the
model (see Appendix A) includes the marketing depart-
ment capabilities (including accountability). This model
demonstrates a good fit (goodness-of-fit index = .96; con-
firmatory fit index = .99; root mean square error of
approximation = .05), and all standardized factor load-
ings are greater than .5 (p < .05). The composite reliabili-
ties are all above .60 (Bagozzi and Yi, 1988; Steenkamp
and van Trijp, 1991).
It is also tested whether early respondents and late
respondents significantly differ in their response behav-
ior. The results show no nonresponse bias when using
Armstrong and Overton’s (1977) recommended test for
comparing early respondents and late respondents.
Finally, the existence of possible common method bias
was tested in two ways. First, potential common method
bias is assessed by using Harman’s single-factor test
(Malhotra, Kim, and Patil, 2006; Podsakoff, MacKenzie,
Lee, and Podskoff, 2003). In this single-factor test, all of
Table 1. Sample Descriptives
Industry
In Which Sector Is Your Firm Mostly Active? Fraction in %

Industrial sector (including FMCG) 60.00
Public services 2.90
Construction 4.60
Trade and retail 6.30
Catering services 1.30
Financial services 3.80
Transport, storage, and communications 6.30
Other business services 15.00
Firm Size in FTEs
Number of Employees Fraction in %
�500 35.80
501–1000 25.80
1001–1500 12.90
1501–2000 4.20
2001–2500 5.00
2501–3000 2.50
3001–3500 2.50
>3500 11.30
Mean (SD) 3520 (11,099)
n = 239.
FMCG, fast-moving consumer goods; FTE, full-time
equivalents; SD, stan-
dard deviation.
Table 2. Influence on New Product Development (NPD):
Average Percentage Scores of the Marketing and
Research and Development (R&D) Department
Marketing R&D Othersa
Decision influence on NPD 24 43 34
Initiated NPD projects 28 37 35

a Including sales and finance.
2013;30(2):298–315
the items in the study are subject to EFA. Common
method bias is assumed to exist if (1) a single factor
emerges from unrotated factor solutions or (2) a first
factor explains the majority of the variance in the vari-
ables. The results of the EFA of all included items reveal
that the relevant factors explain 73.4% of the variance. If
one general factor were derived, then it would explain
only 17.2% of the variance. Next, Lindell and Whitney’s
(2001) marker variable technique is used. In particular,
the survey included a question that was not related to the
topic (degree of confidence in the economy) and corre-
lated this question with the derived constructs. The results
show no significant correlation between the answers to
this question and the important constructs and questions
in the model. Together, these two tests indicate no evi-
dence of common method bias.
Model Specification
Based on the conceptual model in Figure 1, the following
econometric model is used to test the hypotheses:
MI MC MD
FC MT
k k k m m
m

k d d
d
k f f
f
= + +
+ + +
=
+
=
+
=
∑ ∑
∑
α α α
α α
, , ,
,
0
1
3

3
1
2
5
1
4
10 εεk MIk k, ,=( )1 2
(1)
IP MI MC Zk k
k
m m
m
c c
c
IP= + + + +
=
+
=
+
=
∑ ∑ ∑β β β β ε0
1
2

2
1
3
5
1
4
(2)
BP IP GS MGk k
k
BP= + + + ++
=
∑γ γ γ γ ε0 1 1
1
2
4 (3)
Equation (1) is used to analyze the antecedents of the
marketing department’s influence on NPD. In this equa-
tion, MIk represents the two variables measuring the mar-
keting department’s influence on NPD. MCm is the three
knowledge-based marketing capabilities, MDd is the two
general marketing department characteristics (account-
ability and integration/cooperation), and FCf is the four
general firm characteristics. MT captures the degree of
market turbulence in a firm’s core market. The distur-
bance terms of equation (1) are represented by ek,MIk. In

equations (2) and (3), IP and BP represent a firm’s inno-
vation and business performance respectively. Zc repre-
sents the three control variables (e.g., firm size), GS
represents the two variables for a firm’s generic strategy
(cost leadership versus differentiation), and MG accounts
for past growth in the firm’s core market. eIP and eBP are
the disturbance terms of equations (2) and (3).
To account for contemporaneous correlations between
the error terms, the model is estimated using seemingly
unrelated regression (SUR). To justify the use of SUR,
the study conducted two tests. First, it employed the
Breusch–Pagan test to detect contemporaneous correla-
tions between the error terms. The resulting test statistic
for the system of equations was highly significant
(p = .000), indicating that contemporaneous correlation
exists and that it is appropriate to use SUR. Second, it
tested the performance of SUR as compared with ordi-
nary least squares (OLS). In particular, the model was
estimated as separate equations using OLS. The estima-
tion results based on OLS feature higher standard errors
for the coefficients, which emphasizes the usefulness of
estimating the system of equations via SUR.
It is also tested whether multicollinearity might possi-
bly affect the estimation results. The majority of the cor-
relation coefficients are less than .4 (see the correlation
matrix in Appendix B), and the variance inflation factor
scores are all below two, which indicates that no severe
multicollinearity problems exist (Hair, Anderson,
Tatham, and Black, 1998).
Model Results
To test whether the proposed marketing capabilities lead

to a higher influence of the marketing department on
NPD and whether their effects on innovation perfor-
mance are mediated by marketing’s influence, the study
applies a modeling procedure as follows. First, the study
assesses the relationship between the three marketing
capabilities and the two marketing influence measures
(MI1 and MI2). This relation is represented by equation
(1). The results are presented in the next subsection. Next,
the study assesses the mediating effects by computing the
indirect effects of the marketing capabilities on innova-
tion performance (Baron and Kenny, 1986; Zhao, Lynch,
and Chen, 2010).
The Impact of Marketing Capabilities on the
Marketing Department’s Influence on NPD
The estimation results in Table 3 clearly show that the
quality of marketing research and the capability to trans-
late customer needs into technical product specifications
positively contribute to both marketing influence mea-
sures (p � .05). These results provide strong support to
H1 and H2. The contribution measure of explained vari-
ance (EV) suggests that MR and the TS of the marketing
2013;30(2):298–315
department have a particularly great impact (sum of
EV = 32.5%) on marketing’s DI on NPD (MI1). Further-
more, these capabilities enable the marketing department
to influence NPD by initiating NPD projects (MI2) within
the firm (sum of EV = 36.8%). With an EV score ranging
from 17.0% to 19.9%, MR emerges as the main underly-

ing capability that helps marketing departments achieve
high status within NPD. MK does not seem to drive the
influence of marketing, and this may be because every
department within a firm must have a certain degree of
knowledge about general management and business.
Thus, such knowledge does not function as a distinguish-
ing capability.
Accountability does not have a significant impact on
the variables that measure the marketing department’s
influence on NPD. The underlying reason might be that
the ability to link marketing strategies and actions to
financial performance is more relevant for existing prod-
ucts (e.g., due to the availability of past data and experi-
ence). Finally, it is surprising that the degree of
cooperation between the marketing and R&D depart-
ments does not have a significant impact on the marketing
department’s influence on NPD. This finding might be
the result of the strong differences in the influence
(status), which could result in a lack of communication
between both departments. A low average value of 2.89 in
this variable (see Appendix B) confirms this view.
The results indicate that having CEOs with marketing
backgrounds (p < .10) positively affects the influence of
marketing on NPD decision making. The CEO’s back-
ground has no effect on the number of new products
initiated by marketing. Furthermore, the influence of the
marketing department is greater in B2C firms than in B2B
firms (p < .05). Together, these results confirm the find-
ings of previous studies. For instance, Homburg et al.
(1999) show that the marketing background of a CEO and
the type of industry (B2C) are indeed positively related to
the influence of marketing within the firm. As expected,
the results also show a marginal negative effect of market
turbulence on marketing’s degree of influence on deci-

sion making (p < .10). This negative effect seems to
confirm that in turbulent times, marketing budgets are
among the first to be cut, thus reducing marketing’s
influence.
The literature suggests that gathering market informa-
tion can be critical, depending on the industry, because of
frequent changes in customer expectations and shifts in
technology (Harmancioglu, Grinstein, and Goldman,
2010). Therefore, the robustness of the results is evalu-
ated by estimating additional moderated regression
models to test the interaction between marketing capa-
bilities (marketing research and TS), firm focus (B2C),
industry (services versus goods), and market-related tur-
bulence. The results show no significant interactions;
Table 3. Estimation Results: The Impact of Marketing
Capabilities on the Marketing Department’s Influence (MIk)
on New Product Development (NPD) (Equation [1])
MI1 MI2
Decision Influence on NPD Initiated NPD Projects
Coefficient Standard Error EV (%) Coefficient Standard Error
EV (%)
Constant -13.13 8.718 2.32 7.785
Knowledge-based marketing capabilities
Marketing research quality (MR) 3.10*** 1.096 17.0 2.25**
.979 19.9
Technical skills (TS) 2.61*** 1.008 15.5 1.75** .900 16.9
Knowledge (MK) (marketing and general management) 1.20
1.224 5.9 .18 1.093 1.4
Controls

Marketing department characteristics
Accountability 1.60 1.071 9.0 1.17 .957 10.6
Marketing–R&D cooperation 1.03 .866 7.1 .51 .774 5.7
Firm characteristics
CEO—technical background -2.90 2.729 6.4 -1.48 2.437 5.3
CEO—marketing background 5.08* 2.925 10.4 3.23 2.612 10.7
B2C .96** .426 13.5 .96** .380 21.9
Services versus goods .33 .406 4.9 -.07 .363 1.6
Market-related turbulence -2.37* 1.378 10.3 -.86 1.231 6.1
R2 (adjusted R2) .25 (.21) .16 (.12)
* p � .10; ** p � .05; *** p � .01.
B2C, business-to-consumer; CEO, chief executive officer, EV,
contribution to explained variance; R&D, research and
development.
2013;30(2):298–315
however, the main effects remain the same, which dem-
onstrates that MR and TS are the most important market-
ing capabilities in the context of NPD.
In summary, the estimation results demonstrate that it
is important for a marketing department to possess and
develop specific knowledge that is highly relevant to the
development of new products. In particular, the quality of
marketing research activities and the department’s ability
to connect customer needs to products help it to play an
important role in a firm’s NPD projects.

Marketing Capabilities and
Innovation Performance
The results of this analysis are presented in Table 4. To
get a more differentiated picture of the effects, the study
first directly relates the three marketing capabilities to
innovation performance (Model 1). Second, it relates the
two marketing influence measures (MI1 and MI2) to inno-
vation performance in the absence of the three marketing
capabilities measures (Model 2). Third, it establishes the
relationships between the three marketing capabilities
and innovation performance (Model 3 = equation [2])
while accounting for the marketing influence measures.
In Model 1, the most important marketing capability is
MR (EV = 19.6%), which has a significant positive effect
on innovation performance (p < .05). The results in
Model 2 reveal that the DI of marketing (MI1) has a
strong positive significant effect (p < .01) on innovation
performance. In Model 3, both the level of DI that mar-
keting departments enjoy (p < .01) and MR (p < .05) have
a significant effect on innovation performance.
The results in Table 4, however, indicate that there is
no direct effect of marketing’s TS on innovation perfor-
mance before (Model 1) or after controlling for its influ-
ence (Model 3). Together, marketing capabilities and
influence explain a considerable amount of the variance
in Model 3 (sum of EVs = 42.6%), where marketing DI
(EV = 14.8%) and research quality (EV = 11.8%) are the
major driving factors. These figures highlight the impor-
tance of marketing DI (MI1) and capabilities in driving
innovation performance. However, no significant effects
result from the second marketing influence measure
(MI2) on innovation performance.

The Mediating Role of Marketing’s DI
Given the significant impact of marketing’s DI (MI1) on
innovation performance, this section tests whether this
influence also mediates the impact of marketing’s spe-
cific capabilities on innovation performance (H4).
Recent research demonstrates that one just has to
assess the significance of the indirect effect of an inde-
pendent variable on an outcome variable, i.e., the effect
that an independent variable exhibits via an intervening
(mediating) variable, in order to determine the kind of
Table 4. Estimation Results: Innovation Performance—The
Mediating Role of Marketing’s Influence (Equation [2])
Model 1 (with Marketing
Capabilities)
Model 2 (with
Marketing Influence)
Model 3 = Equation (2)
(Full Model)
Coefficient
Standard
Error
EV
(%) Coefficient
Standard
Error

EV
(%) Coefficient
Standard
Error
EV
(%)
Constant 3.29*** .500 3.14*** .481 2.98*** .539
Knowledge-based marketing capabilities
Marketing research quality (MR) .12** .053 19.6 — — — .13**
.059 11.8
Technical skills (TS) -.04 .049 8.0 — — — -.07 .054 7.4
Knowledge (MK) (marketing and
general management)
.01 .056 2.1 — — — .03 .060 2.4
Marketing influence on NPD
Decision influence on NPD (MI1) — — — .01*** .004 24.2
.01*** .004 14.8
Initiated NPD projects (MI2) — — — .00 .004 6.6 -.01 .004 6.3
Controls
Innovativeness of the firm .06* .034 14.9 .04 .037 6.6 .04 .037
5.5
Number of new product introductions .21*** .052 35.8 .33***
.056 38.3 .33*** .056 31.4
Firm size -.01 .044 1.5 -.03 .047 4.0 -.06 .048 6.4
Short-term emphasis -.06** .027 18.1 -.09*** .030 20.2 -.08***
.030 14.1
R2 (adjusted R2) .18(.15) .20(.18) .22(.19)

* p � .10; ** p � .05; *** p � .01.
EV, contribution to explained variance; NPD, new product
development; R&D, research and development.
2013;30(2):298–315
mediation (Zhao et al., 2010).2 To fully understand the
magnitude of marketing capabilities’ contribution to
innovation performance and the role of the marketing
department’s influence, the indirect effects are computed
to assess the type of mediation. Following the product-
of-coefficients method, the indirect effects (l) of MR and
TS on innovation performance are computed by multi-
plying their estimated coefficients in Table 3 with the
corresponding coefficient of marketing’s DI on NPD in
Table 4 (Model 3). Following Preacher and Hayes
(2008), the present study assesses the significance of the
indirect effects (l) by bootstrapped standard errors and
bias-corrected 95% confidence intervals.
The results show that MR (lMR = .031 [= 3.10*.01],
p < .05) and marketing departments’ TS (lTS = .026
[= 2.61*.01], p < .05) also exhibit positive significant
indirect effects on innovation performance through the DI
of marketing. These results confirm that the effect of TS
and MR on innovation performance is indeed mediated
by the DI of marketing (MacKinnon, Warsi, and Dwyer,
1995; Shrout and Bolger, 2002). Following the typology
proposed by Zhao et al. (2010), the mediating effect in
case of MR is complementary mediation, i.e., the medi-
ated effect (indirect effect) and direct effect both exist and
point in the same direction. In the case of TS, the result is

indirect-only mediation because TS do not show any sig-
nificant direct effect on innovation performance. Hence,
the performance impact of the marketing department’s
TS is fully carried through marketing’s DI on NPD (i.e.,
the indirect effect equals the total effect) (Shrout and
Bolger, 2002). In the case of MR, 19.3% of the total
effect (.031/[.031 + .13]) on innovation performance is
carried through marketing’s DI on NPD quality.3
In general, the above-described mediation analysis
highlights that marketing research and the marketing
department’s TS unfold performance contribution
through the department’s DI on NPD. As such, it is nec-
essary for a firm not only to develop these capabilities but
also to ensure that the marketing department has the
power to translate this knowledge into performance-
driving activities and decisions within the NPD process.
Innovation Performance and Business Performance
Models 2 and 3 in Table 5 confirm that innovation per-
formance significantly improves overall business perfor-
mance (p < .01). Past market development strongly
affects firm business performance (p < .01), whereas a
firm’s generic strategy shows no significant differentiat-
ing effect. The latter result supports findings from previ-
ous research, showing that the fit between the generic
strategy and its implementation is more important than
the chosen strategy (Olson et al., 2005).
Additional Analyses
Prior research indicates that marketing potentially
directly contributes to a firm’s business performance

2 Unlike Baron and Kenny’s (1986) proposition, there is no
need for a
significant zero-order direct effect in the case of mediation.
Further, it is not
necessary to compare the size of the coefficients across the
different equa-
tions in order to detect mediating effects (Zhao et al., 2010). 3
Baron and Kenny (1986) would call this result partial
mediation.
Table 5. Estimation Results: Business Performance (Equation
[3])
Model 1 (with Marketing
Influence)
Model 2 = Equation (3)
(with Innovation Performance) Model 3 (Full Model)
Coefficient
Standard
Error Coefficient
Standard
Error Coefficient
Standard
Error
Constant 4.52*** .146 2.61*** .217 2.59*** .227
Innovation performance — — .45*** .040 .44*** .040
Marketing influence on NPD
Decision influence on NPD (MI1) .01*** .003 — — .00 .003
Initiated NPD projects (MI2) .00 .003 — — .00 .003

Controls
Generic strategy
Cost leadership .05 .171 .11 .157 .10 .157
Differentiation .01 .123 .02 .112 .01 .113
Past market development .09*** .026 .07*** .024 .08*** .024
R2 (adjusted R2) .06(.04) 32(.31) .32(.31)
* p � .10; ** p � .05; *** p � .01.
NPD, new product development.
2013;30(2):298–315
through its distinct knowledge-based capabilities (Kras-
nikov and Jayachandran, 2008). Therefore, it is also
tested whether there are direct effects of the marketing
department’s influence on business performance and
whether these effects are mediated by innovation perfor-
mance. Model 3 shows that the effect of marketing DI on
business performance is mediated by innovation perfor-
mance with an indirect effect of lDI = .005 (p < .01).
Computing the indirect effects of MR (lMR = .014,
p < .05) and TS (lTS = .012, p < .05) reveals that these
two marketing capabilities also indirectly affect overall
business performance through marketing DI and innova-
tion performance.
Robustness Checks
One might argue that perceptions of influence may
depend on the background of the respondent (Atuahene-

Gima and Evangelista, 2000). This problem would imply
that the results potentially suffer from respondent back-
ground bias, which in turn would imply that pooling
responses from respondents with different functional
backgrounds are not allowed. To test whether this
problem affects the estimation results, several robustness
checks were conducted.
First, equations (1) and (2) were reestimated by
including interaction terms among the respondent back-
ground (marketing versus others), the marketing capabili-
ties, and the two influence measures. The results did not
show any significant interaction in these models, which
gives a first indication that the respondent background
bias does not affect the results. Second, a Chow test was
applied to test whether respondents’ background affects
overall model results. This means that the hypothesis of
parameter homogeneity between the two groups of
respondents was tested (Chow, 1960; Leeflang, Dick,
Wittink, Michel, and Philippe, 2000). The null hypothesis
of homogeneity was not rejected, which underlines that
pooling the observations from respondents with different
functional backgrounds is statistically justified. A third
test of respondent bias involves the comparison of two
random samples. In particular, Equations (1) to (3) were
reestimated based on two equally sized random samples.
The results remained stable, which again emphasizes the
robustness of the results.
Discussion and Managerial Implications
The prevailing view in most companies is that marketing
is not a distinct function, and therefore, everyone can do
marketing. As a result, the status of the marketing depart-
ment is in a steep decline, which is especially observable

within the NPD process. This development is surprising
because it seems that top innovators strongly involve the
marketing department in the NPD process. Hence,
strengthening the marketing department’s position with
respect to NPD should be a priority to improve innovation
performance.
This problem leads to the following questions:
(1) How can the marketing department strengthen its
position in the NPD process and drive innovation perfor-
mance? (2) What role does the marketing department’s
position play in influencing innovation outcomes and
firm performance? With this idea in mind, the aim of this
study was to identify relevant marketing capabilities that
positively contribute to firm innovation performance and
help strengthen the marketing department’s influence on
NPD. To ensure generalizable results, the analyses are
based on a substantial sample of 239 respondents in B2B
and B2C firms operating in a variety of industries (goods
and services).
The empirical results summarized in Figure 2 clearly
show that marketing as a distinct function is important for
driving innovation success. In particular, this study shows
that in contrast to the commonly held view, not everyone
in a firm can do marketing, and therefore, the marketing
department should have a higher status in NPD.
This recommendation is based on the following facts.
First, if the marketing department demonstrates that its
MR and its ability to translate consumer needs into tech-
nical specifications (TS) is high, it can achieve a higher
status within NPD. Hence, this result answers the ques-
tion regarding how the marketing department can achieve
a higher influence in the NPD process and drive innova-
tion performance. Second, the results of the mediation

analysis depicted in Figure 2 show that these distinct
marketing capabilities coupled with an influential status
of the marketing department in NPD together drive inno-
vation performance. In the case of MR, the results show
a positive direct and indirect effect through marketing’s
influence on innovation performance. In addition, mar-
keting’s TS exhibit an indirect influence on innovation
performance. In particular, nearly 20% of the total effect
of the MR on innovation performance is mediated by
(carried through) marketing’s influence and its relation to
innovation performance. In the case of TS, this effect is
much stronger. Because TS show only an indirect effect
on innovation performance, their performance contribu-
tion can be fully attributed to marketing’s influence. This
latter finding demonstrates that without having the status
to influence NPD decisions, marketing’s ability to trans-
late customer needs into technical product specification
2013;30(2):298–315
does not have any performance implications. Accord-
ingly, this result, together with the fact that the marketing
department still plays a minor role in many firms, seems
to confirm the widespread belief that reductions in the
customer–product connection have led to a rise of new
product failure rates.
The previously discussed results have the following
managerial implications. First, because marketing
research skills and the ability to translate customer needs
into technical product specifications are concentrated
within the marketing department, firms should value mar-

keting as a distinct function especially in the development
of new products. Second, to show that marketing has its
right to exist as a distinct function within the firm, mar-
keters involved in NPD projects should not only have a
strong knowledge of the market but also have a good
understanding of a firm’s product portfolio and those
products’ characteristics. Because NPD-specific market-
ing capabilities appear to have an indirect influence on
firm-level innovation performance, firms should enrich
their marketing teams with marketers who have special-
ized backgrounds including a deep knowledge of market-
ing research methods. Third, the marketing department
should use methods and tools that help to achieve a good
understanding of the markets and customer needs so that
these needs can be translated into technical product speci-
fications. In particular, the marketing department should
find ways to regularly interact with consumers in order to
get a better understanding of their needs. Particularly, the
Internet provides new forms of support for NPD. For
instance, an attractive tool for sourcing, filtering, and
evaluating new product ideas is the use of idea markets,
which utilize widely distributed knowledge, market
power, and the Internet to support the crucial initial tasks
of the NPD process (Soukhoroukova, Spann, and Skiera,
2012). Applying such tools encourages the innovation
Decision influence (MI1)
Marketing Capability Marketing Influence (MIk)
Hypothesis
(expected effect) Result
H1(+)
Initiated NPD projects (MI2)

H2 (+)
Initiated NPD projects (MI2)
H3 (+)
n.s.
n.s.Initiated NPD projects (MI2)
Marketing research quality (MR)
Technical skills (TS)
Knowledge (MK)
(marketing and general management)
Marketing Capabilities and Innovation Performance (IP):
The Mediating Role of Marketing’s Decision Influence (MI1)
The Impact of Marketing Capabilities on the Marketing
Department’s Influence on NPD
MI1
MR IP
(+) (+)
(+)

MI1
TS IP
(+) (+)
(n.s.)
Result:
Complementary mediation
(19% of the total effect)
Result:
Indirect-only mediation
(full mediation)
Marketing research quality (MR) Technical skills (TS)
H4
Figure 2. Overview of Main Results
NPD, new product development; n.s., not significant
2013;30(2):298–315
process to shift from being mainly internally driven to
integrating external know-how and ideas (Laursen and
Salter, 2006).
Notably, the overview of results in Figure 2 also indi-
cates that general knowledge about marketing and man-

agement does not help marketing departments to increase
their status in the context of NPD. This result may be
because currently, general knowledge in marketing and
management is seen as nothing special; for instance,
engineers must attend courses on the principles of mar-
keting and general management. From an educational
perspective, the results presented above indicate that uni-
versities should provide marketing students with more
than a broad base of knowledge in marketing from
diverse areas. More importantly, marketing students
should be given highly specialized tool sets such as con-
joint analysis and latent class segmentation for perform-
ing high-quality marketing research along with better
technical training (e.g., education regarding the use tools
such as the House of Quality) so that they will be able to
more effectively translate customer requirements into
new product specifications. Additional analyses show that
MR, TS, and the DI of the marketing department also
indirectly contribute to overall business performance.
This result demonstrates that top management officials
should strengthen the marketing department’s position
within the firm and especially within NPD.
Limitations and Further Research
The first limitation of this study is that it does not distin-
guish between different stages in the NPD process. The
impact of the marketing department is different during
each stage of the NPD process. For example, the influ-
ence of marketing may be greater during earlier stages
such as idea generation, idea screening, and concept
development, and perhaps also during later stages such as
testing and commercialization. A second limitation is that
this study only accounts for problems concerning the
coordination between marketing and R&D on an aggre-
gate level. However, the degree of conflict between the

marketing and R&D departments may also differ accord-
ing to the particular stage in the NPD process and, con-
sequently, across firms. The third limitation is that this
study uses self-reported performance outcomes to assess
the impact of the marketing department within a firm.
Even though prior studies have found a strong correlation
between objective performance data and subjective
assessments of performance by key informants (Morgan
et al., 2004; Olson et al., 2005), this limitation can poten-
tially lead to biased estimates of the relationship between
the marketing department’s influence and a firm’s perfor-
mance outcomes (Cano, Carrillat, and Jaramillo, 2004).
These limitations provide avenues for future research.
For instance, it would be interesting to investigate and
identify more specific capabilities that are relevant during
the different stages of the NPD process. Based on these
results, it would also be worthwhile to investigate what
type of information supplied by marketing research teams
is particularly important to managers involved in NPD.
Future research could identify “ideal” combinations of
marketing and R&D departments for different firms and
industries (cf. Homburg, Jensen, and Krohmer, 2008). In
exploring relevant R&D capabilities, one might begin to
identify combinations of marketing and R&D that maxi-
mize innovation performance. These combinations would
allow for a more detailed assessment of marketing’s
influence on NPD and its performance outcomes. From
the managerial point of view, such ideal combinations of
marketing and R&D would help managers to increase
their understanding of the specificities of marketing and
R&D configurations at the firm level and thus develop
alternative solutions.
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Appendix A. Used Scales
Construct/Variable (Inspired
or Based on) Items

Coefficient
Alpha/Composite
Reliability
CEO functional
background (Homburg
et al., 1999)
What is the primary background of the CEO within your firm?
– Marketing, engineering, science, finance law, other (1/0)
NA
Decision influence of the
marketing department on
NPD (Homburg et al.,
1999)
Distribute 100 points over the following departments:
Sales, Marketing, R&D, Finance: Departments with a high
influence on NPD receive more
points than departments with a low influence.
NA
Initiated NPD projects by
the marketing
department
What is the percentage of introduced new products or services
in the last five years that were
initiated by the following department? Please divide 100 points
across four departments:
1. R&D, 2. Marketing, 3. Sales, 4. Other.

NA
Lack of cooperation
(Maltz and Kohli, 1996)
(formative)
To what extent have the marketing department and the R&D
department experienced
problems concerning coordination of activities in the past three
years? (1 = no problems at
all, 7 = very many problems)
To what extent have the marketing department and the R&D
department hindered each
other’s performance in the past three years?
(1 = not hindered at all, 7 = hindered a lot)
.84/NA
B2B versus B2C (Verhoef
and Leeflang, 2009)
Please indicate the percentage of your turnover that arise from
B2B or B2C markets:
B2B (1) . . . B2C (10)
NA
2013;30(2):298–315
Appendix A. Continued

Construct/Variable (Inspired
or Based on) Items
Coefficient
Alpha/Composite
Reliability
Goods versus services
(Verhoef and Leeflang,
2009)
Please indicate the percentage of your turnover that arise from
goods or service markets:
Services (1) . . . Goods (10)
NA
Firm size (ln) What are approximately the total number
employees in your firm (in FTEs)? NA
Innovativeness of the firm
(Covin and Slevin, 1989)
In our firm, top managers have a strong emphasis on selling
goods/services known and
proven in the market (1) . . . a strong emphasis on R&D,
technological leadership and
innovations (10)
NA
Number of new product
introductions

How many products and services has your firm marketed in the
past five years?
(1 = none, 7 = very many)
NA
Market-related turbulence
(Menon, Bharadwaj,
Adidam, and Edison,
1999) (formative)
Can you indicate the level of change in the last three years in
the most important market
where your firm was active on the following elements
– production/process technology
– introduction of new products/services
– R&D activities
– competitive intensity
– customer preferences
(1 = no change, 7 = very frequent changes)
.78/NA
Marketing research quality
(Deshpandé and
Zaltman, 1982)
(reflective)
– The marketing department possesses high-quality market
information.
– The results of conducted market research can be used to solve
problems within our
organization.
– The used market information is of high quality.

(1 = fully disagree, 7 = fully agree)
.82/.80
Technical skills (Moorman
and Rust, 1999)
(reflective)
The marketing department in our firm
– is effective at translating customer needs into new products or
services.
– has no sufficient knowledge and skills to translate customer
needs into technical
specifications. (reverse coded)
.61/.62
Knowledge (marketing and
general management)
(reflective)
– The marketing knowledge of the employees of the marketing
department is good.
– The knowledge on business economics of employees of the
marketing department is good.
.83/.83
Accountability of the
marketing department
(Moorman and Rust,
1999) (reflective)

The marketing department in our firm
– is effective at linking their activities to financial outcomes.
– shows the financial outcomes of their plans.
– has little attention for financial outcomes of their activities
(reverse coded).
.83/.79
Innovation performance
(Moorman and Rust,
1999) (formative)
Relative to your firm’s stated objectives, how is your firm
performing on innovation success?
(1 = much worse, 7 = much better)
Relative to your competitors, how is your organization
performing on innovation success?
(1 = much worse, 7 = much better)
.80/NA
Business performance
(Moorman and Rust,
1999) (formative)
Relative to your firm’s stated objectives, how is your firm
performing on: (1 = much worse,
7 = much better)
Relative to your competitors, how is your organization
performing on: (1 = much worse,
7 = much better)

– Customer satisfaction, customer loyalty, turnover,
profitability, market share, cost level
.87/NA
Past market development Over the last three years, what was the
average annual market growth or decline for the
largest market where your firm was active?
(1 = decrease by more than 20%, 9 = increase of more than
20%)
NA
Generic strategy (Porter,
1980; Verhoef and
Leeflang, 2009)
Please indicate which of the following generic business
strategies is most applicable for your
firm:
– Cost leadership: strategy to obtain the lowest costs in the
market
– Differentiation: focusing on being better in different features
of the product/service that
are important to customers
– Cost focus: targeting a relative small segment in the market
that is cost-consciousness
– Differentiation focus: targeting a relatively small segment in
the market that desires a
unique and good product and that is willing to pay a higher
price for this
NA

Short-term orientation
(Baker, Black, and Hart,
1988)
If you would describe the orientation of your firm is this:
a short-term orientation (1) . . . a long-term
orientation (10)
NA
B2B, business-to-business; B2C, business-to-consumer; CEO,
chief executive officer; FTE, full-time equivalents; NA, not
applicable; NPD, new product
development; R&D, research and development.
2013;30(2):298–315
Appendix B. Correlation Matrix of Constructs in Model (n =
239)
M SD (1) (2) (3) (4) (5) (6) (7) (8) (9)
(1) Decision influence on NPD 23.76 21.25 1
(2) Initiated NPD projects 27.59 17.96 .543** 1
(3) Innovation performance 4.68 1.11 .165** .041 1
(4) Business performance 4.83 .83 .116* .027 .543** 1
(5) Marketing research quality 4.48 1.30 .358** .293** .219**
.213** 1
(6) Technical product skills 4.97 1.36 .324** .253** .068 .105
.352** 1
(7) General marketing knowledge 4.99 1.24 .302** .207**
.119* .163 .422** .420** 1
(8) Accountability 4.28 1.36 .312** .245** .091 .061 .362**

.370** .507** 1
(9) Marketing-R&D cooperation 2.89 1.41 .040 .029 -.043 -.049
-.041 -.049 -.019 .016 1
(10) CEO—technical background .28 .45 -.144* -.102 .053 .039
-.073 -.070 -.102 -.169** .043
(11) CEO—marketing background .22 .41 .131* .102 .112 .073
.087 .065 -.030 .043 -.009
(12) Short-term emphasis 4.02 2.20 .129* -.017 -.199** -.162* -
.163* -.017 -.065 -.002 .082
(13) Cost leadership .12 .32 .018 .088 -.010 .021 -.008 .062
.018 -.041 .029
(14) Differentiation .71 .45 .078 -.025 -.039 -.046 .120 .144
.075 .092 -.031
(15) B2C 2.93 3.05 .265** .250** .161* .072 .290** .136*
.149* .218** .016
(16) Services versus goods 4.02 3.12 .099 .045 .056 -.008 .119 -
.003 .056 .056 -.152*
(17) Firm size 3234.34 11,119.66 -.014 .073 .050 -.041 .217
.087 .126 .017 .048
(18) Innovativeness of the firm 4.53 1.78 -.174** -.154* .128*
.010 -.069 -.049 -.127* -.159* .015
(19) Number of new product introductions 5.32 1.17 .042 .046
.375** .353** .112 .159* .095 .026 .023
(20) Past market development 6.24 1.87 -.083 -.107 .074 .208**
-.008 -.068 -.024 -.109 .035
(21) Market turbulence 4.33 .89 -.052 -.006 .143* -.004 .123
.025 -.058 .042 .053
(10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)
(1) Decision influence on NPD
(2) Initiated NPD projects
(3) Innovation performance
(4) Business performance
(5) Marketing research quality

(6) Technical product skills
(7) General marketing knowledge
(8) Accountability
(9) Marketing-R&D cooperation
(10) CEO—technical background 1
(11) CEO—marketing background -.081 1
(12) Short-term emphasis .066 -.097 1
(13) Cost leadership -.083 -.066 .085 1
(14) Differentiation -.014 .023 -.082 -.486 1
(15) B2C -.139* .012 .041 -.005 .077 1
(16) Services versus goods -.121 .031 -.018 -.027 -.011 .239**
1
(17) Firm size .000 -.090 -.061 -.041 .117 .095 .029 1
(18) Innovativeness of the firm .223** .094 -.133* -.006 .040 -
.147* -.183** .054 1
(19) Number of new product introductions .027 .158* -.134* -
.078 .033 -.003 -.165* .135* .185** 1
(20) Past market development .043 -.067 -.074 -.034 -.124 -
.134* -.029 .060 .121 .127* 1
(21) Market turbulence .010 .045 -.099 -.136* .158* .035 .140*
.065 .115 .182** .042 1
** Correlation is significant at the .01 level (two-tailed). *
Correlation is significant at the .05 level (two-tailed).
B2C, business-to-consumer; M, mean; NPD, new product
development; SD, standard deviation; R&D, research and
development.
2013;30(2):298–315
This document is a scanned copy of a printed document. No

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Users should refer to the original published version of the
material.
International Economic Journal
Vol. 22, No. 1, March 2008, 113–126
Does trade regionalism increase stock
market segmentation within
a trading bloc?
Chee Wooi Hooy*
Finance Section, School of Management, University Sains
Malaysia, Penang, Malaysia
(Received 29 June 2006; final version received 7 February 2007
)
Whether trade regionalism remains a stumbling block to
economic globalization remains
a debatable issue. This paper investigates the impact of trade
regionalism on stock market
segmentation for the case of AFTA, EU and NAFTA. We
exploit the pricing error of the
trading-bloc capital asset pricing model (TB-CAPM) as an
indicator for stock market seg-
mentation based on Akdogan (1992) and Adler and Qi (2003).
We use intra-trade ratio as the
indicator for trade regionalism. Controlling for other
determinants for market integration,
evidence is found that trade regionalism helps to explain stock

market segmentation in EU
and NAFTA, which are both dominated by developed countries.
For NAFTA, the higher the
intra-trade ratio the lower the degree of market segmentation
within the trading bloc. For
EU however, the intra-trade ratio contributes to higher stock
market segmentation.
Keywords: stock market segmentation; trade regionalism; TB-
CAPM; intra-trade ratio
Introduction
The global trend in trade regionalism has entered a new era. The
number of trading
blocs formally registered with WTO has been accelerating over
the last few years. Before
the 1990s, only 27 agreements were established. The number
jumped to a surprising
162 by 2005, and continues to surge to a total of 205 as of July
2007.1 A hot debate on
this phenomenon of increasing regionalism is whether it will
hinder or promote a mul-
tilateral trading system. Many scholars, including Bhagwati
(1993) and Frankel et al.
(1995) found that the recent trade regionalism is more likely to
be welfare reducing
and works against world economic globalization. WTO,
however, believes that such
development is a complementary progress as long as it can help
trade flow more freely
*Email: [email protected]
ISSN 1016-8737 print/ISSN 1743-517X online
© 2008 Korea International Economic Association
DOI: 10.1080/10168730801887083

http://www.informaworld.com
114 C. W. Hooy
within the bloc, without barriers being raised on trade outside
the bloc (see GATT
Article 24). Regionalism is seen as an instrument that allows
groups of countries to
negotiate rules and commitments that go beyond what are
possible multilaterally, in
the hope that they will develop into topics of discussion in
WTO later. This view is
attributed mainly to MacMillan (1993).
In principle, when real sectors get more integrated, capital
markets will become
less segmented. The intuitive sense is that international finance
serves to facilitate
international trade. When a country becomes more integrated
with their trading bloc
counterparts in terms of trade, it will take an initiative to lock-
in economic and political
reforms to accelerate the transition to a market economy
(Hoekman and Kostecki,
2001). This will further lead to policy coordination in both
monetary and fiscal aspects
among the member countries. With increasing real sector
integration, intra-bloc capital
flows are likely to be enhanced across borders. The
liberalization effort will also reduce
cross-market frictions, and the law of one price will play its
role to prevent investors
from taking advantage of any pricing discrepancies among the
bloc members. If the

WTO is correct that trade regionalism will promote
globalization, then the integration
process goes beyond the bloc basis. This means that the
reduction in capital market
segmentation will happen not only within the bloc, but at the
same time, with the rest
of the world.
Unfortunately, empirical works relating trade regionalism to
stock market
integration/segmentation remain scarce in the literature. This is
probably due to
the fact that trade flow information is often not incorporated in
the investor’s pric-
ing decision. The impact of trade flow can always prevail
through various economic
fundamentals because they are more easily accessed by the
investors. Therefore,
macroeconomics-oriented variables are often taken as quite
reasonable factors in
explaining stock market integration. Among the macroeconomic
variables that have
been considered in asset pricing models include trade openness,
business cycle,
exchange rates and interest rates. In addition, others also
explore the role of microstruc-
ture oriented variables such as dividend yield, liquidity, book-
to-market, or even market
volatility to explain stock market integration.2
The works of Heaney, Hooper and Jaugietis (2000) and Heaney
and Hooper (2001)
are the only studies we found to relate integration of the stock
market with trading
bloc grouping. Both papers utilized cluster analysis to examine
whether stock markets

form a grouping in a hierarchical dendogram and they recorded
clear evidence of world
stock market segmentation that is very much consistent with the
existence of regional
trading bloc and economic ties between the sample countries.
Many other market
integration studies have pointed out the role of trade
regionalism on market integration.
Nevertheless, it was not in their objective to test for the direct
relationship between
the two. These include Heaney and Hooper (1999) and Ng
(2002) on AFTA; Akdogan
(1992), Corhay, Rad and Urbain (1993), Johnson and Soenen
(1993), Johnson, Lindvall
and Soenen (1994), Monadjemi and Perry (1996), Choudhry
(1996), Kanas (1998)
and Fratzscher (2002) on EMU; Soydemir (2000), Seabra
(2001), Edwards and Susmel
(2001), Chen, Firth and Rui (2002), Heaney, Hooper and
Jaugietis (2002) and Johnson
and Soenen (2003) on MERCOSUR; and Adler (1995), Ewing,
Payne and Sowell (1999),
Adler and Qi (2003) on NAFTA. These works have provided
strong grounds for further
empirical investigation on this issue, but call for a more direct
approach.
The focus of this paper is to explore how trade regionalism
affects the segmentation
of stock markets within the trading bloc. We aim to find out if
trade regionalism
has helped to integrate stock markets among the trading
members, or whether it
has induced more segmentation among the stock markets within
the trading bloc.

International Economic Journal 115
The empirical findings of this paper allow the macroeconomic
policymakers who are
engaged in the trading bloc agreements to understand better the
likely impact of real
sector convergence on the financial sector integration. In
addition, the findings allow
the related central banks and security exchanges to take note of
the sources of their
asset pricing divergences with those of the trading-bloc binding
member countries.
This paper contributes in two ways. First, we use a mix of a
determinant model for
market integration, and a cross-section asset pricing approach to
investigate the impact
of trade regionalism on trading bloc stock market segmentation.
Our approach is
different from the cluster analysis of Heaney, Hooper and
Jaugietis (2000) and Heaney
and Hooper (2001). Our model has controlled other important
determinants in the
market integration process. This leads to the second
contribution of this paper. The
modeling of trade regionalism as a determinant for market
segmentation/integration
has extended the determinant model used in Carrieri, Errunza
and Hogan (2007)
in explaining stock market integration. In fact, we find that
trade regionalism plays
a relatively important and significant role in explaining stock
market segmentation
within a trading bloc, as compared to the other integration

determinants suggested in
Carrieri, Errunza and Hogan (2007).
This paper is organized as follows. The next section outlines the
framework of
analysis and the data employed. The third section presents the
statistical results and
discussions on the major findings. Concluding comments are in
the final section of the
paper.
Methodology and data
Empirical indicators are used to measure the degree of trade
regionalism and stock
market segmentation in a trading bloc. The level of trade
regionalism is represented by
the intra-trade ratio, which is referred to as the Trade
Regionalism Index (TRI). The
TRI for trading bloc T is defined as:
TRIT,t =
1
nT
nT∑
i=1
BTTit
WTTit
(1)
where nT is the number of member countries in trading bloc T,

BTTit is the total bilateral
import and export of country-i with the other member countries
of the same bloc, and
WTTit is the total trade (import plus export) of country-i with
the world market. This
base measure for goods movement serves to compare real
integration within a trading
bloc in comparison to total movement to the world market.3
7 According to Korajczyk (1996), pricing errors estimated
within the framework of
some form of asset pricing model can be used to measure how
segmented a subject
market is to the world market. Korajczyk (1996) developed an
index to capture the
dynamic integration states of market integration from the
pricing errors estimated
under the framework of International Arbitrage Pricing Theory
(IAPT). The idea was
applied and extended in Levine and Zervos (1998) using both
IAPT and World Capital
Asset Pricing Model (WCAPM) to study stock market
development and economic
growth.
In this paper, we are interested in measuring stock market
segmentation among
members that bind under a trade agreement. A variant of asset
pricing model applied
by Akdogan (1992) and Adler and Qi (2003) is appropriate for
this purpose. The model,
116 C. W. Hooy

which we shall term as Trading-Bloc Asset Pricing Model (TB-
CAPM henceforth)
asserts that the pricing of cost of capital can be determined in a
linear return-generating
process given as follows:
rit = αi + βirTB,t + εit ; ∀ i (2)
where rit represent the excess market returns of individual
country-i and rTB,t represent
the excess returns of a portfolio of equal weighted stock indices
from a trading bloc.
Given that investors are mean-variance optimizers, the beta βi
measures the systematic
risk of stock market-i with respect to the trading-bloc portfolio
movements. The error
term εit captures the idiosyncratic risk that is orthogonal to the
trading bloc portfolio.
The aggregate relative risk aversion of individual investors is
assumed to be constant
(Chan, Gup and Pan 1992). Akdogan (1995) and Adler and Qi
(2003) have explored the
use the above TB-CAPM setting in pricing the ‘regionalism
effect’ in EU and NAFTA,
respectively.
With the assumption of perfect trading bloc integration, the
intercept αi in model (2)
should be equal to zero. This is essentially another version of
Black’s (1972) zero-beta
CAPM. If αi is significantly different from zero, the hypothesis
of perfect integration
with the trading bloc is rejected. The pricing error captures the
deviation from an
integration state. It is therefore appropriate to interpret it as a
measure of the degree
of segmentation among the stock markets in the trading-bloc

portfolio.
The pricing error is used to construct an indicator for trading-
bloc stock market
segmentation. Following Levine and Zervos (1998), the pricing
error is adjusted using
the following transformation:
SMSIT = − |αT| (3)
where αT =
∑nT
i=1(αi/nT). We refer to this as the stock market segmentation
index
(SMSI). By making the adjustment, the SMSI is negatively
correlated with the degree
of segmentation. The value of the index decreases with higher
official barriers and
taxes to international asset trading, bigger transaction costs, and
larger impediments
to the flow of firm information (see Korajczyk, 1996).
Therefore, the measure indicates
that a higher value of SMSI corresponds to a lower degree of
market segmentation.
Perfect integration with the world market occurs when SMSI is
equal to zero.
There are various econometric techniques that can be used to
generate a stochastic
time series for SMSI. While Korajczyk (1996) used a simple
rolling approach, Levine and
Zervos (1998) only estimated a static model for cross-sectional
pricing errors. In this
paper, we follow a popular cross-sectional regression method
suggested by Fama and

MacBeth (1973). This is a two-step procedure based on rolling
time-series regression.
In the first step, we estimate model (2) for t = 1, 2, . . . , N,
where N is the number
of observations for the first 5 years, and i = 1, 2, . . . , nT. The
estimated beta, β̂TBi , is
obtained, and treated as the ex-ante beta for period N + 1,
denoted as β̂Wi,N+1. In the
second step, the ex-post excess return for period N + 1 is
regressed on the ex-ante beta.
The following cross-sectional regression is estimated for i = 1,
2, . . . , nT:
ri,N+1 = αN+1 + γN+1β̂TBi,N+1 + εi,N+1 (4)
From here, the pricing error is obtained and SMSIT,N+1 is
computed. The two steps are
repeated by moving one observation down the 5-year window,
i.e. for t = 2, 3, . . . , N +
1 to compute SMSIT,N+2. The time series SMSIT,t is generated
by repeating the whole
International Economic Journal 117
process until the last observation. After computing SMSIT,t and
TRIT,t for each trading
bloc, we estimate the following model:
SMSI = a + b1TRI + b′Z + ε (5)
In model 5, the SMSI is regressed to the TRI, controlling for a
set of information related
to the stock market integration process, as captured by the
vector of Z.4 Following
Carrieri, Errunza and Hogan (2007), we included three variables

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