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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: wenzel.drechsler@ 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).

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.

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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

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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

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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

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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,

IMPROVING MARKETING’S CONTRIBUTION TO NPD J PROD INNOV MANAG 303 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.

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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 1

5 1

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 1

5 1

(2)

BP IP GS MGk k k

BP= + + + ++ =

∑γ γ γ γ ε0 1 1 1

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

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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.

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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.

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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

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.

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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

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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)

Decision influence (MI1) H2 (+)

Initiated NPD projects (MI2)

Decision influence (MI1) 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)

Figure 2. Overview of Main Results NPD, new product development; n.s., not significant

310 J PROD INNOV MANAG W. DRECHSLER ET AL. 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)

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.

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.

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)

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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)

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)

Number of new product introductions

How many products and services has your firm marketed in the past five years? (1 = none, 7 = very many)

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) (1 = fully disagree, 7 = fully agree)

.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.

(1 = fully disagree, 7 = fully agree)

.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).

(1 = fully disagree, 7 = fully agree)

.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%)

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

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)

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.

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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.

IMPROVING MARKETING’S CONTRIBUTION TO NPD J PROD INNOV MANAG 315 2013;30(2):298–315

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