Marketing Innovation and R&D Capabilities More Than One Way to Innovation Success?

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http://www.jrc.ec.europa.eu/

Knowledge for Growth – Industrial Research & Innovation (IRI)

Marketing Innovation and R&D Capabilities –

More Than One Way to Innovation Success?

Mukesh Bhargavaa, Rabikar Chatterjeeb, Christoph Grimpec and Wolfgang Sofkad

a

Oakland University, Rochester, USA

b

Katz School of Business, University of Pittsburgh, USA

c

Copenhagen Business School, Denmark

d

University of Tilburg, Netherlands

Contributed paper

to be presented at the 3rd European Conference on Corporate R&D and Innovation CONCORD-2011, October 6th 2011, Seville (Spain)

Conference title

The dynamics of Europe's industrial structure and the

growth of innovative firms

<CONFERENCE STRAND>

R&D and innovation: Sources and constraints at company level

Industrial dynamics & the role of R&D and innovation for Europe's competitiveness

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Abstract

Existing literature has put considerable emphasis on the role of marketing and R&D capabilities as major drivers of firm performance. Little is known on how the marketing function itself may generate new products and services and how such marketing innovation interacts with technology-based innovation through R&D. While general marketing and R&D capabilities are typically assumed to complement each other, this is less clear when it comes to marketing innovation which may in fact substitute R&D. One reason is resource constraints that are typically faced by young firms. We analyze the role of marketing innovation and R&D capabilities for innovation performance of the firm. Based on a dataset of more than 700 firms from Germany we find that marketing innovation is in fact more important that technology-based innovation and that both substitute each other. However, this relationship only holds true for young firms which underpins the role of resource constraints in these firms.

Key words: Marketing innovation, technological innovation, firm capabilities, entrepreneurial firms

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The link between different types of firm capabilities and performance has received considerable attention in the literature (Krasnikov and Jayachandran, 2008). In this respect, particular emphasis has been put on the marketing and R&D capabilities as major drivers of firm performance (e.g., Song et al., 2005; Krasnikov and Jayachandran, 2008). Capabilities bundle knowledge and resources that are embedded in organizational routines (Helfat and Peteraf, 2003). The dynamic capabilities perspective suggests that a firm can leverage the performance impact of existing resources through resource configuration, complementarity, and integration (Song et al., 2005). In fact, marketing and R&D capabilities have frequently been shown to complement each other in achieving successful commercialization of new products or services (e.g., Song et al., 2005; King et al., 2008). They enable the pursuit of innovation and can therefore be characterized as critical for achieving competitive advantage (King et al., 2003).

Existing research, however, has confined innovation almost exclusively to technology innovation through R&D as it is assumed to be central to many industries (Berry and Taggart, 1994; Helfat, 1997). At the same time, the role of marketing in the innovation process has been described as increasing a firm’s ability to recognize customer needs, improving the firm’s position relative to competitors, and targeting valuable customer segments (Moorman and Slotegraaf, 1999; Teece, 1988). In fact, marketing capability has even been measured using advertising expenditure as a proxy for how well a firm may appropriate the value from its technology resources (Mizik and Jacobson, 2003; King et al., 2008). Little is known how marketing may generate new products or services itself. Hence, in this paper we explicitly focus on marketing innovation capabilities, as opposed to general marketing capabilities, and their interaction with R&D capabilities in the innovation process of the firm. We follow the Oslo Manual of the OECD, comprising guidelines on how to collect and interpret innovation data, and define marketing innovation as the implementation of a new marketing method which the firm has not used before. It involves significant changes in product design or packaging, product placement, product promotion or pricing and must be part of a new marketing concept or strategy that represents a significant departure from the firm’s existing marketing methods. Seasonal, regular and other routine changes in marketing instruments are not marketing innovations (OECD, 2005). The “100 Calorie Packs” introduced by Kraft Foods in 2004, offering a well-known product like Oreo cookies in a new packaging with a fixed amount of calories, may serve as an example for a marketing innovation. Kraft’s new product line was an immediate success, getting the firm high sales from these innovative products without any technology innovation.

Krasnikov and Jayachandran (2008) have demonstrated that marketing tends to be more important for firm performance than R&D. While this holds for a firm’s general marketing capability, it is unclear whether marketing innovation capabilities will have the same impact when it comes to innovation performance. Moreover, research has pointed to the complementary resource interaction between marketing and R&D (Song et al., 2005; King et al., 2008), creating interconnected resources that positively reinforce each other. In fact, most research on resource interconnectedness centers on complementarity (Milgrom and Roberts,

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1995; Athey and Stern, 1998), i.e. the marginal effect of one resource on performance increases with the level of another resource and vice-versa (Sigglekow, 2002; Tanriverdi and Venkatraman, 2005). However, both marketing innovation and technology innovation involve new products which may lead to trade-offs in terms of resources that can be devoted to either marketing innovation or R&D. Both the investments into R&D and into marketing innovation should eventually result in isolating mechanisms against competitors, e.g. patents, trademarks, or brands (Suarez and Lanzolla, 2007). Investing into both at the same time might lead to firms getting bogged down. Moreover, management attention is limited (Ocasio, 1997), and firms may hence need to focus, i.e. substitute marketing innovation for technology innovation and vice-versa. Competences between marketing (residing in the sales personnel) and R&D (residing in the scientists) do not necessarily match, leading to overstretched resources. In this respect, we suggest that firm age may have a decisive role to play. Young firms are typically more resource-constraint than established firms (Brush, 1993; Brush et al., 2001; Teng, 2007). Trade-offs between marketing innovation and R&D resources should therefore be higher, leading to a substitute relationship of these resources in young firms.

By focusing on marketing innovation, our research responds to the call that the role of marketing in a new product development context has not sufficiently been researched (Krasnikov and Jayachandran, 2008). We use resource-based theory (RBT) to explore heterogeneous effects from firm capabilities on innovation performance instead of general firm performance. In that sense, our research aims at contributing to the literature in several ways. To the best of our knowledge, we draw for the first time a conceptual distinction between general marketing capabilities and marketing innovation capabilities and explore their interaction with R&D capabilities. In doing so, we bring together research streams on firm resources and entrepreneurship, which has long recognized the importance of resource-constraints that young firms typically face. Moreover, we test our hypotheses empirically using a novel dataset of more than 700 firms in Germany. Our research provides insights for management on how to effectively use marketing innovation in connection with technology innovation to increase innovation performance.

The remainder of this paper is organized as follows. The following section will outline our theoretical background and establish at set of hypotheses. Data, measures and the empirical model are described in section 3 while section 4 will present the results. They are discussed in section 5, leading to conclusions, limitations, and implications for further research in section 6.

2 - Theory development

2.1 R&D, marketing, and firm performance

We use resource-based theory (RBT) to explore heterogeneous effects from firm resources. The traditional resource based theory and its dynamic capabilities theory extension posits that firm performance is correlated with the value, inimitability, substitutability, and rarity of the firm’s resources (Wernerfelt, 1984; Barney, 1991). Firms can achieve these attributes by introducing products with a high degree of novelty that provide superior value to customers

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(Penrose, 1959). The high degree of novelty can translate into an at least temporary monopoly-like market position in which competitors can neither imitate nor substitute the innovative product. This isolation from competition may stem from the high degree of novelty itself and the associated imitation costs or lead time advantages (Mansfield et al., 1981). Often times firms are able to extend the duration of this advantage through proactive strategies such as patenting or secrecy (Liebeskind, 1997; Suarez and Lanzolla, 2007). This perspective on firm incentives for innovation can be traced back to Schumpeter (1942).

However, within the literature the way to achieve highly novel products with superior value to customers is rather narrowly defined. Most studies follow more or less explicitly the idea of a knowledge production function with R&D expenditures as the crucial input (for a comprehensive review see Ahuja et al., 2008). Hence, there is a strong assumption that investments in scientists, engineers and laboratories will eventually generate highly profitable product innovations. This is strangely at odds with the high failure rates of technologically new products once they are introduced to the market (Gourville, 2006). This has resulted in a literature stream calling for a new market-orientation in firms with customer value as the ultimate goal for all firm functions (Day, 1994; Slater and Narver, 1998). We go one step further by arguing that creative changes in a firm’s marketing can be the source of successful product innovations. The degree of novelty of these marketing innovations is therefore not defined by technological uniqueness but through the unique value they provide to the customer as a source for competitive advantage. Hence, we consider the marketing department of a firm and its capabilities as the primary source of a marketing innovation irreverent whether the underlying technology is old or new.

This differs sharply from the perspective of marketing capabilities as support functions for the introduction and commercialization of new products stemming from R&D departments and their capabilities (Danneels, 2002; Nerkar and Roberts, 2004; Morgan et al., 2009). Song et al. (2005) define marketing capabilities for joint ventures (JV) based on Day (1994) as “those that provide links with customers: they enable JVs to compete by predicting changes in customer preferences as well as creating and managing durable relationships with customers and channel members.” In fact, there is ample theoretical and empirical evidence linking the marketing capability of a firm to performance, and more specifically to innovation performance. The literature on marketing orientation provides a link between this management philosophy and the consequences of innovation (such as innovativeness and new product performance) (e.g., Kirca, Jayachandran and Bearden, 2005; Han et al., 1998). Since the extant literature defines marketing orientation as the extent to which a firm engages in generation, dissemination and response to market intelligence pertaining to customer needs, competitor actions and channel requirements, there is evidence why this link is important. Further, Krasnikov and Jayachandran (2008) conclude that marketing capabilities have a stronger impact on firm performance than R&D capabilities. However, both can be assumed to be additive and/or complementary which supports the notion that their interaction provides super-additive effects on firm performance. We will delineate the mechanisms underlying success with marketing as well as R&D innovations and develop hypotheses on their relationships.

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2.2 Technological versus marketing innovation

We deviate from the perspective of marketing adding only value to the innovation process through the commercialization of technological inventions. Instead, we argue that deliberate changes in a firm’s marketing capabilities can be the source of innovative products or services. For this purpose, we narrow the definition of marketing innovation down to the implementation of a new marketing method involving significant changes to a firm’s marketing mix (“4Ps”) in product design or packaging, product placement, product promotion or pricing. The processes underlying technological innovation based on R&D and marketing innovation are fundamentally different. We adopt the simple innovation process model of Bessant and Tidd (2007) to structure the comparison of both. The model distinguishes between three stages in the innovation process. At the initial stage, innovation opportunities have to be generated through searching and scanning. Subsequently, the most promising options have to be selected based on expected outcomes, strategic goals and resource availability. Finally, the implementation stage requires the management of adequate funds, skills and knowledge.

Innovation opportunities for technological innovation stem typically from scientific discovery and R&D. This can occur within a firm’s own laboratory but is often times the development or application research of university research (e.g. Colyvas et al., 2002; Siegel et al., 2004). At other instances, leading suppliers provide opportunities through new machinery or novel materials (Pavitt, 1984). The feasibility of new technologies is often times ex ante difficult if not impossible to predict because no probability estimates exist. Hence, the selection stage of R&D projects has often times been linked to a real options scenario. Others have called it a “fuzzy front end” (Boeddrich, 2004). Project management literature has therefore concluded that a clearly structured learning process (“stage gate”) is required (Cooper et al., 2001; Cooper et al., 2002). Company scientists learn about the potentials of their research while actively engaging in it (Hall, 2005). This experience sharpens also their experience to judge and absorb external technological options (Cohen and Levinthal, 1989, 1990). Firms typically build broad portfolios of technological options because only a limited number will eventually succeed. At the implementation stage, resources for R&D are difficult to provide and manage. They typically require highly educated scientists and engineers operating in specialized laboratories. The very nature of knowledge as intangible innovation output and the underlying risks make it almost impossible to finance it through bank loans (Hall, 2005). This changes if firms can achieve legal protection through patents for their innovations. Patents provide them with a tangible signal of the degree of novelty of their innovation and shield them from direct imitation through competitors (Harabi, 1995; Levitas and McFadyen, 2009). However, the latter mechanism is only reliable in certain industries, such as pharmaceuticals (e.g. Arundel and Kabla, 1998). Innovative firms often times do not have the time to exploit the economic returns from their R&D investments because competitors introduce substitutes based on knowledge acquired through personnel turnover, codified in the patent application or embodied in the novel product itself (Arrow, 1962; Gallini, 2002; Ndofor and Levitas, 2004). In conclusion, technological innovation entails two dimensions of uncertainty. On the one hand, processes and procedures may turn out to be not technological feasible. On the other hand, competition may prevent firms from recouping their R&D investments.

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Marketing innovation processes are fundamentally different. Innovation opportunities arise largely from customers and competitors. Market research plays an important role in collecting and analyzing market data. This can imply the identification of emerging needs, customer segments or competitor moves. However, the identification of customer impulses with anticipatory demand has been found to be difficult (e.g. Frosch, 1996). This has led to an intensive discussion in strategic management literature on the performance pitfalls for the market-orientation of a firm from following each customer wish or competitor move without critically evaluating its strategic potential (Slater and Narver, 1998, 1999). The quantitative models underlying market research make it comparatively easier to make decisions on the most promising marketing innovation. Implementing marketing innovations requires personnel with backgrounds in areas such as advertising, customer management, market research or sales. Innovative pricing, advertising, distribution or packaging strategies can typically be implemented with a short to medium timeframe. This closeness to market introduction makes innovations more likely to receive funding from external investors (Levitas and McFadyen, 2009). Hence, marketing innovation stems from a targeted refocusing of marketing resources and capabilities. Opportunities for protecting marketing innovation are two-fold. On the one hand, innovative brands and designs can be legally protected through copyrights or design pattern protection. On the other hand, marketing innovations are designed to directly impact customer perception, awareness and preferences, for example through brands, which is why the underlying knowledge has a more tacit and embedded character. These elements make them difficult for a competitor to imitate.

While existing literature finds that general marketing capabilities are more important for firm performance than R&D capabilities (Krasnikov and Jayachandran, 2008), it is less clear whether a similar effect can be expected from marketing innovation capabilities. R&D capabilities and resulting technological innovation may yield products with a high degree of technological novelty. At the same time, technological innovation involves disclosure of knowledge to the extent that it is protected through patents. Competitors can therefore “invent around” the products (Arrow, 1962). However, complexity in design, lead time, or secrecy can still be effective means of protecting technological innovation. Moreover, R&D investments have been described as having a real options character because major parameters for success are ex-ante impossible to know or to estimate reliably. This refers mainly to technological uncertainties regarding the feasibility of the innovation project as well as market-related uncertainty regarding the adoption of the technological innovation by customers. As a result, innovation performance is not only dependent on the opportunities for appropriation but also on the uncertainty inherent to the innovation project.

In contrast to this, marketing innovation is largely based on marketing knowledge such as customer information which is embedded in the marketing and sales personnel and much more tacit. It is therefore not easily copied. At the same time, marketing innovation might suffer from the fact that existing knowledge from R&D activities is reused, leading to a much lower degree of novelty and an increased threat from competitors’ imitation. Regarding uncertainties, marketing innovation does not involve technological uncertainties but only market-related uncertainties. Consequently, based on this reasoning it seems sensible to assume that technological and marketing innovation may equally influence the innovation

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performance of the firm. Hence, our first hypothesis posits an additive relationship of technological and marketing innovation with innovation performance.

Hypothesis 1 (H1): Technological innovation based on R&D and marketing innovation have an additive relationship with innovation performance.

2.3 Reinforcing vs. mitigating interactions between R&D and

marketing innovation

Prior research has frequently dealt with the issue of firms developing several capabilities simultaneously. In this respect, firms may deliberately build capabilities by concentrating on resources that are interconnected (Dierickx and Cool, 1989). These resource interactions can turn out to act either as a substitute or complement in the relationship between capabilities and firm performance (King et al., 2008). Apart from this, capabilities may be additive in nature, without any resource interaction. In fact, most research has stressed the complementary nature of firm R&D and marketing resources (e.g., Song et al., 2005; King et al., 2008). Resources are complementary if they reinforce each other or, more technically, when the marginal effect of one resource on performance increases with the level of another resource and vice-versa (Sigglekow, 2002; Tanriverdi and Venkatraman, 2005). In our context, there are several arguments why marketing innovation resources increase the value of R&D resources and vice-versa.

Marketing in general has been found to enable the successful commercialization of new products stemming from R&D efforts. In this respect, general marketing capabilities reduce an innovation’s the risk of failure in that customer needs can be more accurately anticipated, products can be better positioned relative to competitors, and valuable customer segments can be targeted through a strong sales force (Moorman and Slotegraaf, 1999; King et al., 2008). We have argued that marketing innovation goes beyond that and creates new products itself. New products resulting from marketing innovation should typically be based upon existing technology resources. Firms may therefore effectively slow down the pace of technology evolution (Suarez and Lanzolla, 2007) and continue to appropriate the value from technology resources (Mizik and Jacobson, 2003) that may have otherwise already become obsolete. In that sense, marketing innovation serves as an instrument to secure pre-existing technology-based first-mover advantages (Lieberman and Montgomery, 1988, 1998). Marketing- and technology-based innovation could, however, also coincide in which case the resulting product will presumably exhibit a very high degree of novelty from the customer’s viewpoint. Joining marketing innovation and R&D resources will create a highly unique resource combination that will be difficult to imitate and thus highly valuable (Teece, 1986; Kogut and Zander, 1992). Moreover, when introducing a marketing innovation, firms may benefit from positive reputation or brand recognition effects of existing firm products based on innovative technology. Marketing innovations will therefore benefit from reputation spillovers (Teece, 1986), leading to higher legitimacy than without an established innovative technology base (King et al., 2008).

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Conversely, technology resources resulting from R&D also enhance the value of marketing innovations. New technologies may be applied to these innovations, enabling a frequent product update and a better recognition of customer needs (Moorman and Slotegraaf, 1999). These arguments suggest that marketing innovation and R&D positively reinforce one another or that marketing innovation and R&D constitute complementary capabilities. As such, we expect innovation performance will be higher when a firm possesses strong marketing innovation and technology resources. Therefore, we hypothesize the following:

Hypothesis 2a (H2a): Investments into marketing innovation and R&D capabilities

are complements, i.e. the interaction term is positive.

However, marketing innovation and R&D capabilities could also substitute each other – a position best explained by considering the nature of the innovation process. First of all, resources of the firm dedicated to innovation activities are limited and hence investments into these capabilities can be devoted either to marketing innovation or R&D. Both a marketing- and a technology-based innovation promise to involve isolating mechanisms that shield firms from competitors and allow for the appropriation of the innovation’s value (Suarez and Lanzolla, 2007). These isolating mechanisms could be for example underlying patents, trademarks, or brands, for which continuous and long-term investments are required. The limited resources available to the firm could force a specialization on just one type of innovation activity. Investing into both at the same time might lead to an overstretch of resources, increasing the overall risk of failure of the innovation.

Apart from this, the attention-based view has suggested that management attention is scarce and one of the most precious resources of the firm (Ocasio, 1997). Consequently, the decision on how to allocate attention to certain activities has been characterized as a central explanation why some firms succeed in both adapting to challenges from the environment and introducing new products to the market. In order to achieve sustainable strategic performance, managers need to focus their efforts on a limited number of issues and activities (Ocasio, 1997). As a result, attempting to allocate management attention to both marketing- and technology-based innovation may lead dysfunctional effects, compromising the overall innovation performance of the firm. We therefore expect that investments into both marketing innovation and R&D capabilities mitigate innovation performance and that firms hence need to focus, i.e. substitute marketing innovation for technology innovation and vice-versa. The resulting competing hypothesis is given as:

Hypothesis 2b (H2b): Investments into marketing innovation and R&D capabilities are substitutes, i.e. the interaction term is negative.

2.4 Firm

age

The previously outlined interactions between investments in R&D and/or marketing innovation capabilities appear to be especially relevant for young firms. Young firms combine advantages from flexibility and disadvantages from resource constraints. This dichotomous

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nature of their resource development has made them a primary topic of recent academic research (e.g. Lichtenstein and Brush, 2001; Teng, 2007).

On the one hand, combining the development of R&D and marketing innovation capabilities requires a versatile management. R&D and marketing innovation draw from the same resource pool of the firm. A combination ob both requires flexible interaction and communication between employees with very different professional backgrounds. Innovation in R&D is largely driven by skilled employees and scientists operating often times in close collaboration with universities. Marketing departments, though, rely on competences in market research, advertising or sales. Valuable expertise arises from collaboration with leading customers. The frictional losses from combining R&D with marketing appear to be especially pronounced as firms age. They become more and more resistant to novel combinations and change (Hannan, 1989). This lack of flexibility in the decision making of experienced firms has been captured through several constructs in the management literature. It includes the increasing importance of dominant logics (Prahalad and Bettis, 1986), shared beliefs and dominant coalitions (Daft and Weick, 1984). In young firms, though, decision making on both R&D and marketing is often times concentrated with the entrepreneurs or a small team around them. The exchange of knowledge, coordination and assessment of success is quick and direct. This fits with the characterization of entrepreneurship as opportunity seeking where skilled entrepreneurs both exploit and create opportunities for their new ventures (Brown et al., 2001; Stevenson and Jarillo, 1990). As a result, young firms can be expected to benefit more from an interaction of R&D and marketing innovation capabilities than experienced firms.

Hypothesis 3a (H3a): For young firms, the interaction between investments into marketing innovation and R&D is positive.

On the other hand, young firms suffer from a lack of resources. This includes both financial resources because of a lack of cash-flows during the entrepreneurial stage as well as underdeveloped capabilities which need time for refinement. Often times these limitation of young firms can be traced back to a lack of legitimacy. Legitimacy can be defined as “a generalized perception or assumption that the actions of an entity are desirable, proper, or desirable within some socially constructed systems of norms, values, beliefs, and definitions (Suchman, 1995: p. 574)”. The environment of a firm (e.g. investors, customers, suppliers, policymakers) can be expected to be more willing to provide access to resources for firms with higher degrees of legitimacy (for a recent review see Rao Singh et al., 2008). The lack of legitimacy of young firms can be characterized as a liability of newness. Important external stakeholders can treat the long existence of a firm as a sign for cultivated processes and products, reliability and predictability (Henderson, 1999). Established firms with existing R&D or marketing innovation capabilities may therefore find it easier to enrich one with the other. In contrast, young firms may lack this legitimacy both in terms of R&D and marketing. Hence, combining both investments in R&D and marketing innovation capabilities may overstretch the resources of young firms.

Hypothesis 3b (H3b): For young firms, the interaction between investments into marketing innovation and R&D is negative.

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3 - Empirical study

3.1 Data

We test out theoretical hypotheses empirically using data from the “Mannheim Innovation Panel” (MIP). MIP is the German contribution to the Community Innovation Survey (CIS) of the European Union and surveys the innovation activities of German firms. The methodology and questionnaire used fully comply with CIS standards and follow the OECD Oslo manual. CIS surveys target the decision makers within firm’s innovation activities. Typical respondents are CEOs, heads of innovation management units or R&D departments. Herein lies the central advantage of CIS survey as decision makers provide direct, importance-weighted measures for a comprehensive set of questions on innovation inputs, processes and outputs (Criscuolo et al., 2005). Several contributions to recent management, strategy or innovation literature have relied on the self-reported information provided by CIS surveys (e.g., Laursen and Salter, 2006a; Sofka, 2008). They can provide complementary insights to studies relying on more traditional measures of innovation such as patents.

The quality of all surveys depends upon high standards for administration, non-response and response accuracy because the survey participants provide self-reported, subjective assessments (Criscuolo et al. (2005) provide a recent discussion). CIS surveys are unique compared to most other surveys because of their multinational application for more than a decade within the European Union member states. Experience and feedback cycles with regard to quality management and assurance are extensive. CIS surveys are subject to substantial pre-testing and piloting in various countries, industries and firms with regards to interpretability, reliability and validity (Laursen and Salter, 2006a). The questionnaire contains detailed definitions and examples to increase response accuracy. Besides, our CIS survey was administered via mail limiting potential negative effects and biases from telephone interviews (e.g. Bertrand and Mullainathan, 2001). What is more, a comprehensive non-response analysis provides no evidence for any systematic distortions between responding and non-responding firms.

The core of our dataset stems from the MIP survey conducted in 2007 covering the three preceding years as the observation period. The 2007 MIP questionnaire is the first one containing questions on firm’s marketing innovations. Firms were surveyed again in 2008. We draw the dependent variable on innovation success from the following observation year (t+1). This limits the coverage of our dataset to firms which participated both surveys (2007 and 2008) but provides clarity in interpretation eliminating potential simultaneity issues. We complement this dataset with the official Herfindahl-Hirschman-Index on concentration in competition for the year 2005 provided by the German Monopolies Commission. We add patent statistics derived from the European Patent Office (EPO).

Our final sample consists of 738 firm observations. We inspect the dataset for indications of issues arising from multicolinearity based on correlations, variance inflation factors and condition indices. Our dataset shows no such indications by any conventionally applied

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standard (e.g. Neter et al., 1990; Rabe-Hesketh and Everitt, 2007). Appendix A provides full details.

3.2 Variables

Dependent variable: Innovation performance

Researchers have used a variety of constructs for measuring innovation performance (for an overview see OECD, 2005). They range from innovation inputs such as R&D expenditures to a broad range of Output measures, e.g. patents, new processes and products. We adopt the latter approach. However, the existence of a technologically novel product is hardly a good predictor for the economic performance of an innovation (e.g. Gourville, 2006). Market acceptance turns a technological novelty into a successful product innovation. Therefore, we focus on the sales derived with new products and scale it by firm’s overall turnover (Laursen and Salter, 2006b). Our dependent variable is therefore the share of turnover achieved with new products. We derive this variable from the MIP survey of 2008 (reporting year 2007) to clarify causality links. All independent variables stem from the previous survey year.

Independent focus variables: Investments into marketing innovation and R&D capabilities

The focus variables of this analysis are the investments of firms into marketing innovation and R&D capabilities as a share of sales respectively. The measurement of the latter is rather straightforward. We use a firm’s expenditure for R&D. However, several studies have highlighted the importance of prolonged R&D engagements over time as opposed to one-time activities (e.g. Cohen and Levinthal, 1990). To account for differences in firm’s past R&D activities we calculate the patent stock for each firm through the depreciated sum of all patents it had filed in the European Patent Office database from 1978 until 2005. We follow existing literature (e.g. Aerts and Schmidt, 2008; Griliches and Mairesse, 1984) and use an annual depreciation rate of 15%.

Investments into marketing innovation capabilities are more difficult to capture as the construct is not as clearly defined or can be assumed to follow a widely shared understanding. Hence, our survey introduces respondents to a detailed definition of marketing innovation:

“A marketing innovation is the implementation of a new marketing method which your enterprise has not used before. It involves significant changes in product design or packaging, product placement, product promotion or pricing and must be part of a new marketing concept or strategy that represents a significant departure from the firm’s existing marketing methods. Please note that seasonal, regular and other routine changes in marketing instruments are not marketing innovations.”

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The questionnaire then asks respondents to indicate whether their firm has introduced marketing innovation in any of the following areas: Product design, advertising/brands, sales channels, pricing policy.1

Respondents are subsequently asked for their total marketing expenditures in 2006 based on the following definition: “Marketing expenditures include all internal and external expenditures for advertisement (incl. trade marketing), for the conceptual design of marketing strategies, market and costumer research, and the installation of new distribution channels. Pure selling costs do no count as marketing expenditures.”

Finally, respondents are requested to estimate the share of their marketing expenditures dedicated to marketing innovation. We use the latter information to calculate firm’s investments in marketing innovation capabilities (as a share of sales). We add the share of non-innovative marketing expenditures as a control variable to the empirical model.

Control variables

Several other factors have been identified in the literature as influencing firm’s innovation performance (for an extensive review see Ahuja et al., 2008). A major factor is the availability of resources. This has been identified as a major hurdle for innovation decisions in young and small firms (e.g. Freel, 2005). Hence, we introduce firm’s age (number of years since founding), its number of employees (in logs) and whether it is part of a company group (dummy) to the model. We control for different degrees of internationalization through the share of exports of sales and whether it engages in process innovation. The latter may captivate resources otherwise available for product innovation.

What is more, important differences may arise from the industry level. We therefore introduce several control variables on the industry level. First, differences in the level of competitive intensity influence investment decisions for innovation (e.g. Aghion et al., 2001). The German Monopolies Commission calculates a Herfindahl-Hirschman-Index on the degree of market concentration in Germany. We add its 2005 values at the three-digit-NACE industry level to the model. Secondly, marketing cannot be expected to equally important in all industries. It has been found to be especially pronounced in consumer product segments. Most publicly available information comparing marketing expenditures across industries covers advertising expenditures only. Hence, we calculate a measure for the industry intensity of marketing expenditures based on the MIP survey. The survey sample is drawn as a stratified random sample and can therefore be considered representative for Germany (for a

1_{Each of these areas is clarified with a brief description and examples: „Product design: Introduction of}

significant changes in product design (incl. packaging) as a result of new marketing concepts (e.g. introduction of new design or packaging concepts to target new customer groups); Advert./Brands Introduction of new media or techniques for product promotion (e.g. first time use of a new media for advertising products, first time use of brand names, introduction of a new branding system, introduction of loyalty cards); Sales channel Introduction of new methods for product placement (incl. new sales channels) (e.g. franchising, direct selling, exclusive retailing, new concepts for product presentation); Pricing policy Introduction of new methods for pricing of products (e.g. pricing by demand, first time use of discount systems)”.

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detailed description see Peters, 2008). On this basis marketing expenditures can be projected at the industry level. We project these values on the two-digit-NACE industry level and introduce them to the empirical model as a share of industry sales2. Thirdly, we add six industry dummy variables at the grouped two-digit NACE level to capture remaining industry effects: Low-tech manufacturing, medium high-tech manufacturing, high-tech manufacturing, distributive services, knowledge-intensive services and technological services. A detailed industry breakdown to these groups can be found in Appendix B. Finally, we control for regional differences within Germany. Innovation activities in Eastern Germany have been found to differ significantly from West Germany following the economic stress introduced by re-unification (e.g. Czarnitzki, 2005). We add a dummy variable controlling for this effect.

3.3 Method

Our dependent variable – the share of sales with new products in t+1 – is censored between 0 and 1. Therefore, we choose tobit model regressions to account for this effect. We estimate separate tobit models to test our theoretical hypotheses. As an initial step we will compare the effects of investments in R&D and total marketing expenditures. Subsequently, we split the latter up into expenditures for innovative and non-innovative marketing. We add multiplicative interaction terms of R&D and marketing innovation expenditures to a separate model and finally split the sample along the median age to investigate the effect of firm age.

4 - Results

4.1 Descriptive

statistics

Table 1 provides descriptive statistics for the full sample as well as firms with and without investments in marketing innovation. We test for mean differences between the two groups as an initial empirical step.

Firms in our sample derive an average of 23% of their sales from new products. This innovation performance measure is significantly higher for firms with marketing innovation investments. The average firm spends 2.1% of its sales on marketing overall but only 0.4% on marketing innovation. The rest goes into non-innovative marketing. The tests of mean differences suggest that marketing innovation is more prevalent in firms with overall higher marketing budgets as well as industries with higher marketing intensity. R&D expenditures of the average firm in our sample are 4.8% of sales and have to be considered as comparatively high. However, they do not differ significantly between firms with and without marketing innovation investment. Hence, there is no evidence from the descriptive statistics on potential interactions.

2_{Firms own marketing expenditures and sales are deducted from the ratio of its industry to avoid issues}

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The average firm in our sample is almost 20 years old and has 275 employees. Significant differences stem only from firm size with marketing innovation firms being significantly larger reinforcing the previous indication that the availability of resources may be an important factor. There are hardly any remaining significant industry differences with the exception of firms in high-tech manufacturing (e.g. pharmaceuticals) being more likely to invest in marketing innovation.

Table 1: Descriptive statistics and tests on mean differences

Variable Full sample Firms w/ inv. in

mkt. innov.

Firms w/o inv. in mkt. innov.

Mean Std.

Dev. Mean Std. Dev. Mean Std. Dev. T-test on mean diff. Share of sales with new products in t+1 0.225 0.236 0.244 0.237 0.201 0.234 ** Share marketing exp. of sales (ratio) 0.021 0.039 0.029 0.047 0.012 0.021 *** Share innov. mkt. exp. of sales (ratio) 0.004 0.011 0.008 0.014 0.000 0.000 *** Share non-innov. mkt. exp. of sales (ratio) 0.017 0.032 0.021 0.038 0.012 0.021 *** Share R&D exp. of sales (ratio) 0.048 0.113 0.046 0.088 0.051 0.137

Patent stock per 100 empl. (ratio) 1.361 10.818 1.163 5.705 1.599 14.779 Company age (years) 19.603 15.264 19.863 15.289 19.292 15.252

No of employees 275 902 347 1156 189 419 ** No of employees (log) 4.197 1.558 4.284 1.633 4.092 1.459 * Share exports of sales (ratio) 0.246 0.269 0.245 0.265 0.248 0.274 Location East Germany (d) 0.337 0.473 0.313 0.464 0.366 0.482 Firm is part of group (d) 0.389 0.488 0.410 0.493 0.363 0.482

Process innovation (d) 0.588 0.493 0.649 0.478 0.515 0.501 *** Herfindahl-Hirschman-Index 2005 (*1000) 4.942 9.060 5.171 9.246 4.667 8.838

Low-tech manuf. (d) 0.359 0.480 0.353 0.479 0.366 0.482 Medium high-tech manuf. (d) 0.210 0.408 0.194 0.396 0.229 0.421 High-tech manuf. (d) 0.148 0.355 0.169 0.375 0.122 0.328 * Distributive services (d) 0.080 0.271 0.077 0.267 0.083 0.277 Knowledge-intens. services (d) 0.047 0.213 0.047 0.212 0.048 0.213 Technological services (d) 0.156 0.363 0.159 0.366 0.152 0.359

Industry marketing intensity (ratio) 0.013 0.006 0.014 0.006 0.012 0.006 ***

No. of obs. 738 402 336

(d) dummy variable; * p<0.10, ** p<0.05, *** p<0.01

In conclusion, the descriptive analysis provides preliminary evidence on the performance effects of investments in marketing innovation. However, these differences may also stem from the availability of firm resources. A multivariate analysis is therefore required to disentangle the effects.

4.2 Innovation performance models

Table 2 shows the results of the tobit regression models, calculated as marginal effects. We estimate five models in different specifications. All of them include our set of control variables which turn out to be largely consistent across the specifications. We will therefore describe the results for the control variables for all models at the end of this section. Model I is our baseline model for which we use the share of general marketing expenditure and R&D expenditure of sales to replicate prior analyses focusing on the importance of general

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marketing capabilities in comparison to R&D capabilities. Our results show that both variables contribute additively to innovation performance, confirming prior findings (e.g., Krasnikov and Jayachandran, 2008). However, we are not able to substantiate a higher impact of marketing capabilities on performance than R&D capabilities which is probably due to our choice for the dependent variable that is innovation performance instead of general firm performance.

Model II splits up the general marketing expenditures into expenditures for marketing innovations and non-innovative marketing. Interestingly, our results show that only the innovative component of marketing is important for innovation performance while non-innovative marketing expenditure turn out to have no effect at all. Again, R&D capabilities are important, too. However, the results indicate that marketing innovation capabilities are more important than R&D capabilities for achieving innovation success.3 This finding confirms our first hypothesis (H1) in that both R&D and marketing innovation contribute to innovation performance.

Model III focuses on the resource interactions between marketing innovation and R&D and therefore includes an interaction term between these variables. While the main effects from Model II can be confirmed, it turns out that the interaction effect is actually negative, lending support to H2b while H2a has to be rejected. Apparently, marketing innovation and R&D capabilities are substitutes; there are, as a result, two distinct ways to achieve innovation performance. At the same time, this finding suggests that the asserted complementary relationship between general marketing and R&D capabilities (e.g., King et al., 2003; King et al., 2008) does not hold when marketing innovation capabilities are concerned.

Models IV and V show the results from the median split along the firm’s age. Results for firms younger than 16 or 16 years old are displayed in Model IV while the results for firms older than 16 years are shown in Model V. Our results show that the negative interaction between marketing innovation and R&D capabilities from Model III only holds true in case of the young firms. There is no significant interaction for the older firms, and even the marketing innovation capabilities loose their significance in explaining innovation performance. Hence, H3b receives support while H3a has to be rejected. Marketing innovation capabilities are still more important for innovation performance than R&D capabilities in young firms. However, older firms seem to rely significantly more on technology-based innovation through R&D capabilities, a finding that requires interpretation.

3_{An F-test confirms that the coefficient of the marketing innovation expenditure variable is significantly}

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Table 2: Estimation results (marginal effects) after tobit regressions

Model I Model II Model III Model IV Model V Share of sales with new products (t+1)

Full sample Full sample Full sample Firms ≤ 16 y.

Firms > 16 y. Share marketing exp. of sales (ratio) 0.67**

(0.30)

Share innov. marketing exp. of sales (ratio) 2.70** 4.55*** 4.34*** 2.90

(1.18) (1.44) (1.65) (3.17)

Share non-innov. mkt. exp. of sales (ratio) 0.17 0.32 0.04 1.07

(0.41) (0.42) (0.53) (0.68)

Share R&D exp. of sales (ratio) 0.64*** 0.64*** 0.73*** 0.56*** 1.06*** (0.12) (0.12) (0.12) (0.13) (0.26) Interaction innov. marketing * R&D -11.29** -9.05* -7.39

(5.08) (5.42) (23.52) Patent stock per 100 empl. (ratio) 0.00* 0.00* 0.00* 0.00 0.00**

(0.00) (0.00) (0.00) (0.00) (0.00) Company age (years) -0.00*** -0.00*** -0.00*** 0.00 -0.00**

(0.00) (0.00) (0.00) (0.00) (0.00) No of employees (log) 0.02** 0.02** 0.03*** 0.01 0.05***

(0.01) (0.01) (0.01) (0.01) (0.02) Share exports of sales (ratio) 0.23*** 0.23*** 0.23*** 0.24*** 0.15*

(0.05) (0.05) (0.05) (0.06) (0.08) Location East Germany (d) 0.08*** 0.07*** 0.08*** 0.08** 0.04

(0.03) (0.03) (0.03) (0.03) (0.05) Firm is part of group (d) 0.04 0.04 0.04 0.06* 0.00

(0.03) (0.03) (0.03) (0.04) (0.04) Process innovation (d) 0.04 0.04 0.04 0.02 0.06

(0.02) (0.02) (0.02) (0.03) (0.04) Herfindahl-Hirschman-Index 2005 (*1000) 0.00** 0.00** 0.00** 0.00 0.01***

(0.00) (0.00) (0.00) (0.00) (0.00) Industry marketing intensity (ratio) 5.98*** 5.90*** 5.60*** 7.66*** 1.74

(2.14) (2.14) (2.14) (2.96) (3.07) Medium high-tech manuf. (d) 0.07** 0.07** 0.07** 0.08** 0.06

(0.03) (0.03) (0.03) (0.04) (0.04) High-tech manuf. (d) 0.10*** 0.10*** 0.10*** 0.07* 0.15*** (0.03) (0.03) (0.03) (0.04) (0.04) Distributive services (d) -0.11* -0.11* -0.11* -0.06 -0.20** (0.06) (0.06) (0.06) (0.07) (0.10) Knowledge-intens. services (d) -0.02 -0.03 -0.02 0.05 -0.17 (0.06) (0.06) (0.06) (0.06) (0.12) Technological services (d) 0.10*** 0.10*** 0.10*** 0.10** 0.10* (0.03) (0.03) (0.03) (0.04) (0.05) Constant -0.09* -0.09* -0.10** -0.14* -0.10* (0.05) (0.05) (0.05) (0.08) (0.06) Sigma 0.25*** 0.25*** 0.25*** 0.28*** 0.20*** (0.01) (0.01) (0.01) (0.01) (0.01) McKelvey & Zavoina's R2 0.257 0.260 0.266 0.244 0.301

N 738 738 738 404 334

LR Chi2 207.55 210.75 215.69 107.84 112.19

P-value 0.00 0.00 0.00 0.00 0.00

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Regarding the control variables it turns out that the accumulated technological knowledge measured through the patent stock per 100 employees matters considerably for explaining innovation performance. This measure also substantiates the high importance of absorptive capacities for innovation performance. Firm age is generally negatively associated with innovation performance while firm size in terms of employees has a positive effect. Moreover, a high international orientation of the firm measured as the share of exports propels innovation performance, as does a location in East Germany. The impacts of firms being part of a group and being a process innovator are largely neglectable. Competitive intensity is accounted for by way of a Herfindahl-Hirschman Index and the general industry marketing intensity. Both show positive and significant effects on innovation performance, suggesting that higher product market competition but also higher marketing orientation propel the innovation performance of firms.

5 - Discussion and conclusion

Our results conform to the overall patterns observed in past studies, even though we have attempted to isolate the budget spent on marketing innovation as a way to operationalize marketing innovation capability. Traditional measures of marketing capability and resources measure advertising intensity or expenditures, pricing and distribution capabilities or general ability to plan and execute a marketing plan (Vorhies and Morgan 2005). Given the scope of our research and the focus on the success of new products, which remain of considerable academic and managerial interest, there are many advantages in focusing on the innovation of the marketing function. Marketing budgets are under scrutiny as many people in the top management feel that these resources are not as productive as they should be. Additionally, the technological changes in search and buying habits of consumers and firms have opened up new ways to communicate and distribute goods and services. Innovation in marketing provides a measure of the dynamic marketing capability. It is through innovation that the companies can learn about the markets and document efficient marketing methods.

Marketing innovation capabilities are shown to have an impact on the new product success. Further, the relationship between marketing innovation capability and R&D is additive, supporting the notion that this innovation expenditure can lead to more successful products. A surprising finding is that the nature of the relationship changes in case of a small firm. It is quite likely that resource constraints and the need to trade-off may drive this relationship as these may lack established budgets and structures. It is also quite likely that in small companies, the investment in marketing innovation may be counterproductive as they might lack basic knowledge and network to pull of the basic tasks required.

This research points to the need for further work on firms that balance both marketing and R&D capabilities simultaneously. An emerging stream of research examines “ambidexterity” as a firm capability in terms of their ability to balance resource exploitation and exploration. There is emerging evidence that marketing capabilities (specifically marketing orientation) plays the crucial role in balancing learning (and building up for the future) and exploiting (taking care of the present) (Yalcinkaya et al., 2007; Kyriakopoulos and Moorman, 2004). It would be important to investigate not merely the fact that firms balance these capabilities, but how this is done so that we can get a better understanding of the success factors.

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Our limitations are that we have to rely on a relatively short time period. There are advantages to having panel data to help unravel some of these relationships that cannot be accomplished with the type of data we have. Further work also needs to be done to improve the measure of marketing innovation linking this to goals and specific decisions as a way to improve the overall measure. Further, data from other countries at different stages of economic growth and types of industries would add to the generalizability of the results.

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Appendix

Appendix A: Correlation matrix and variance inflation factors (VIF)

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17)

(1) Share innov. mkt. exp. of sales (ratio) 1.00

(2) Share non-innov. mkt. exp. of sales (ratio) 0.48 1.00

(3) Share R&D exp. of sales (ratio) 0.15 0.15 1.00

(4) Patent stock per 100 empl. (ratio) 0.00 -0.01 0.07 1.00

(5) Company age (years) -0.09 0.03 -0.06 0.03 1.00

(6) No of employees (log) -0.18 -0.08 -0.17 0.10 0.17 1.00

(7) Share exports of sales (ratio) -0.03 0.02 -0.03 0.04 0.09 0.29 1.00

(8) Location East Germany (d) 0.08 -0.04 0.24 -0.04 -0.24 -0.28 -0.19 1.00

(9) Firm is part of group (d) -0.08 -0.08 -0.11 0.10 0.08 0.51 0.19 -0.20 1.00

(10) Process innovation (d) -0.01 -0.09 -0.03 -0.01 0.02 0.11 -0.08 0.02 0.07 1.00

(11) Herf.-Hirsch.-Index 2005 (*1000) -0.02 0.00 -0.02 0.00 -0.05 0.02 0.01 0.01 0.04 0.01 1.00 (12) Medium high-tech manuf. (d) -0.04 -0.03 -0.04 0.07 0.02 0.12 0.29 -0.09 0.05 -0.02 0.01 1.00 (13) High-tech manuf. (d) 0.00 0.04 0.03 0.01 0.00 -0.03 0.17 0.00 0.08 -0.07 0.24 -0.22 1.00 (14) Distributive services (d) -0.05 -0.04 -0.11 -0.04 0.02 0.02 -0.18 -0.07 0.04 0.05 0.07 -0.15 -0.12 1.00 (15) Knowledge-intens. services (d) -0.03 -0.05 -0.08 -0.02 -0.02 0.03 -0.18 0.06 -0.02 0.08 -0.09 -0.11 -0.09 -0.07 1.00 (16) Technological services (d) 0.19 0.14 0.44 0.00 -0.09 -0.26 -0.24 0.15 -0.14 0.00 -0.13 -0.23 -0.18 -0.13 -0.10 1.00 (17) Industry marketing intensity (ratio) 0.13 0.14 0.16 0.02 0.01 -0.16 0.06 0.05 -0.08 -0.08 0.04 -0.09 0.32 -0.27 -0.18 0.15 1.00

VIF 1.38 1.36 1.37 1.03 1.09 1.62 1.38 1.23 1.39 1.04 1.10 1.35 1.46 1.25 1.16 1.67 1.30

Mean VIF 1.30

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Appendix B: Industry classification

Industry NACE Code Industry Group

Mining and quarrying 10 – 14 Low tech manufacturing Food and tobacco 15 – 16 Low tech manufacturing Textiles and leather 17 – 19 Low tech manufacturing Wood / paper / publishing 20 – 22 Low tech manufacturing

Petroleum 23 Medium-low tech manufacturing

Chemicals 24 High (244)/Medium-high tech

manufacturing

Plastic / rubber 25 Medium-low tech manufacturing Glass / ceramics 26 Medium-low tech manufacturing Metal 27 – 28 Medium-low tech manufacturing Manufacture of machinery and equipment 29 Medium-high tech manufacturing Manufacture of office machinery, computers, radio,

TV etc. 30, 32 High tech manufacturing

Manufacture of electrical machinery 31 Medium-high tech manufacturing Medical, precision and optical instruments 33 High-tech manufacturing

Manufacture of motor vehicles, other transport

equipment 34 – 35 High (353)/Medium-high tech manufacturing Manufacture of furniture, jewellery, sports

equipment and toys; recycling 36 – 37 Low-tech manufacturing Electricity, gas and water supply 40 – 41 Low-tech manufacturing

Construction 45 Low-tech manufacturing

Retail and motor trade 50, 52 Distributive services Wholesale trade 51 Distributive services Transportation 60 – 63 + 64.1 Distributive services

Financial intermediation 65 – 67 Knowledge-intensive services Real estate activities and renting 70 – 71 Distributive services

ICT services 72, 64.3 Technological services Technical services 73, 74.2, 74.3 Technological services

Consulting 74.1, 74.4 Knowledge-intensive services