Product innovativeness from the firm’s perspective: its dimensions and their relation with project selection and performance

The Journal of Product Innovation Management 18 (2001) 357–373

Product innovativeness from the firm’s perspective: Its dimensions and their relation with project selection and performance Erwin Danneelsa,*, Elko J. Kleinschmidtb a

b

Assistant Professor, Department of Management, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA Professor, Department of Marketing, DeGroote School of Business, McMaster University, 1280 Main St. West, Hamilton, Ontario, Canada L8S 4M4 accepted 22 June 2001

Abstract There has recently been tremendous interest in product innovativeness. However, it seems that we need a better understanding of exactly what product innovativeness means. This article presents a conceptual framework to clarify its meaning. The framework first distinguishes customer and firm perspectives on product innovativeness. From the customer’s perspective, innovation attributes, adoption risks, and levels of change in established behavior patterns are regarded as forms of product newness. Within the firm’s perspective, environmental familiarity and project-firm fit, and technological and marketing aspects are proposed as dimensions of product innovativeness. Next, the article offers a tentative empirical test of the proposed dimensions of product innovativeness from the firm’s perspective. A well-known dataset of 262 industrial new product projects is used to: 1) clarify the product innovativeness construct and examine its underlying dimensions, 2) examine the relation of product innovativeness with the decision to pursue or kill the project, and 3) examine the relationship between product innovativeness and product performance. Five dimensions of product innovativeness are found which have distinct relations with the Go/No Go decision and product performance: market familiarity, technological familiarity, marketing fit, technological fit, and new marketing activities. Most strikingly, measures of fit are related to product performance, whereas measures of familiarity are not. The article concludes that researchers need to be careful about which definitions and measures of product innovativeness they employ, because depending on their choice they may arrive at different findings. New product practitioners are encouraged to evaluate new product opportunities primarily in terms of their fit with their firm’s resources and skills rather than the extent to which they are “close to home.” © 2001 Elsevier Science Inc. All rights reserved.

1. Research problem There has recently been tremendous interest in product innovativeness. The Journal of Product Innovation Management [28] had a special issue on developing really new (as opposed to incremental) products. The Marketing Science Institute designated developing really innovative products as one of its top research priorities. Montoya-Weiss and Calantone [37] and Song and Parry [50] called for research examining the potential moderating effects of product innovativeness. The innovativeness of a new product is important for several reasons. Innovative products present great opportunities for firms in terms of growth and expansion into new areas. Significant innovations allow firms to establish competitively dominant positions, and afford new-

* Corresponding author. Tel.: ⫹1-508-831-5181. E-mail address: [email protected] (E. Danneels).

comer firms an opportunity to gain a foothold in the market. However, they are also associated with high risks and management challenges. Prior research has suggested that more innovative products require more firm resources and a different development approach to be successful [12,31, 32,58]. In spite of the importance of innovative new products, it seems that we need a much better understanding of exactly what product innovativeness means. Forty years ago Wasson [59] published an article in the Journal of Marketing entitled “What is new about a new product?” We argue that scholars have not yet adequately answered this question. There have been all kinds of ways to classify new products on the basis of their relative newness. Scholars and practitioners alike have used such labels as: “innovative/noninnovative,” “discontinuous/continuous,” “evolutionary/revolutionary,” “incremental/radical,” “major/minor,” “really new,” and “breakthrough.” Little has been said however about what criteria are used for such classification, and who

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applies those criteria: the firms developing the products, or the customers buying and using the products. This article intends to put existing research regarding product newness in a new light and to provide guidance to future research regarding the conceptualization and measurement of product newness.1 First, this article will present a conceptual framework to clarify the meaning of product innovativeness. Many different notions of product innovativeness have been proposed in the literature. We will provide an integrative theoretical perspective on the nature and effects of product newness. Our framework first distinguishes customer and firm perspectives on product innovativeness. From the customer’s perspective, we will regard innovation attributes, adoption risks, and levels of change in established behavior patterns as forms of product newness. Within the firm’s perspective, environmental familiarity and project-firm fit, and technological and marketing aspects are proposed as dimensions of product innovativeness. We will then formulate hypotheses regarding the association of the dimensions of newness from the firm’s perspective with project selection and new product performance. Next, the empirical section of the article explores the empirical applicability of the proposed dimensions of product innovativeness from the firm’s perspective, and examines their relation with project selection and performance. The article concludes with implications for academic research on product innovativeness, and provides practitioners with suggestions for making new product decisions.

2. Literature and hypotheses The extant literature regarding product innovativeness will be reviewed in three sections. The first section will discuss conceptualizations and measurements of product innovativeness. The second section will discuss the possible relation between product innovativeness and the go/no go decision during the development process. The third section will discuss the relation of product innovativeness with product performance. 2.1. The dimensions of product innovativeness Table 1 presents a summary overview of extant empirical research that has used the notion of product innovativeness. This overview shows that product innovativeness has been a key concept and measure in many empirical studies. It has been specified as independent variable, dependent variable, or moderator. In spite of the progress made by past research, the conceptualization of innovativeness remains rather vague, and its measures are not as fine-tuned as they could be. Prior studies have used widely varying conceptualiza-

1 Throughout the article we will use the terms “product newness” and “product innovativeness” interchangeably.

tions and operationalizations of this construct. Conceptual weaknesses include a rather unrefined and unidimensional conceptualization, a failure to distinguish the perspective taken (customers’ or firm’s), and lack of distinction between newness as familiarity (close to the firm’s prior customers and technology) and as synergy (fit with the firm’s resources, skills, and capabilities). Green, Gavin, and AimanSmith [25] argued that innovativeness should be seen as a continuum with multiple dimensions. They (p.205) argued that: “a classification of projects as simply radical or incremental may be oversimplifying the construct.” Weaknesses in conceptualizations are carried through to construct measurement, which is often unidimensional and based on single items. In their extensive review of the new product development literature, Montoya-Weiss and Calantone [37] found that this stream of research has paid little attention to construct validity. We argue that the lack of consistency and comparability of the conceptualization and measurement of product innovativeness across studies is a liability for research, and may cause contradictory and confusing implications for new product management. There thus seems to be a need in the area of new product development for consistent, explicit, and precise definition and measurement of product innovativeness. To provide a foundation to this end, we will examine current conceptualizations of product newness, and anchor these in existing theoretical streams of literature. The three theoretical streams we will draw on are: adoption/diffusion research, environment-organization research, and resource-based theory of the firm. 2.1.1. Product innovativeness from the customer’s perspective The Booz, Allen, and Hamilton typology, the most often used typology of new products, distinguished customer and firm perspectives on product newness [9]. It categorizes new products along two dimensions of newness: newness to the developing firm and newness to the market (see Fig. 1). New-to-world products are new to both the firm and the market and are the most innovative type. Cost reduction products provide similar performance as existing products, but at lower cost, and are the least innovative type of innovation along both dimensions. Between these two extremes are Repositionings (existing products targeted to new markets), Additions to Existing Product Lines (which are somewhat new to the market and to the firm), Improvements/Revisions to Existing Products (which are somewhat new to the firm but not to the market), and New Product Lines Products (which are new to the firm but not to the market). The original Booz, Allen, and Hamilton matrix included nine cells and the above mentioned six types of new products [9, p.9]. Some researchers using the typology (e.g., [38]) reduced the matrix to four cells with four types, leaving out the cost reductions and repositionings, which arguably are not really new product types. Researchers have

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Table 1 Comparative overview of prior empirical product innovativeness research1 Study

Measure of product innovativeness used

Technological and marketing dimensions explicitly distinguished?

Innovativeness from whose perspective

Dependent variables

Ali [1]

Modified Booz, Allen, and Hamilton typology Newness to customers

No

Market performance As moderator

N/A

Not explicitly distinguished Customers

No

Firm and Customers

No Yes

Not explicitly distinguished Firm and Customers

No

Firm and Customers

N/A

Firm

No

Not distinguished in typology measure and firm perspective in items Firm

Market performance and Project performance New product performance Product success rate Product success rate Go/No Go decision Product success rate

Gatignon and Xuereb [23]

Newness to customers (radical vs. incremental dichotomy) Newness to firm (radical vs. incremental dichotomy) Modified Booz, Allen, and Hamilton typology (dichotomy) Seven dimensions that reflect innovativeness Synergy, newness to the firm, and newness to customers Individual items (dimensionality not assessed) Modified Booz, Allen, and Hamilton typology and individual items relating to familiarity and synergy (dimensionality not assessed) Two-dimensional typology based on customer and technological firm competences Newness of technology incorporated in product

No

Green, Gavin, and Aiman-Smith [25]

Four dimensions based on technology and business uncertainty

Kleinschmidt and Cooper [29] Meyer and Utterback [35] More [38]

Ali, Krapfel, and LaBahn [2] Atuahene-Gima [4]

Atuahene-Gima and Evangelista [5] Cooper[14] Cooper and de Brentani [15] Cooper and Kleinschmidt [18] Cooper and Kleinschmidt [19]

Danneels [20]

As independent variable As moderator

As independent variable As independent variable As independent variable As independent variable As independent variable

N/A (interpretive study)

N/A (interpretive study)

Not explicitly distinguished

New product performance

Yes

Not explicitly distinguished

One-dimensional classification (low, moderate, high) Individual items (dimensionality not assessed) Individual items (dimensionality not assessed) Dichotomy (really new vs. incremental) Dichotomy (two experimental conditions) Dichotomy (incremental vs. radical)

No Yes

Not explicitly distinguished Firm and Customers

Go/No Go decision Project lifespan Product financial performance Cycle time

N/A

Firm

No

Dichotomy (really new vs. incremental) Marketing and technological synergy

No Yes

Not explicitly distinguished Not explicitly distinguished Not explicitly distinguished Not explicitly distinguished Firm

As dependent and independent variable As dependent and independent variable As independent variable As independent variable As independent variable As moderator

Market familiarity

Yes

Firm

Market familiarity

Yes

Firm

Swink [55]

Technological innovativeness

Yes

Firm

Yoon and Lilien [62]

Dichotomy (original vs. reformulated) One-dimensional synergy with firm competences

No

Not explicitly distinguished Firm

Olson, Walker, and Ruekert [42] Schmidt and Calantone [46] Sivadas and Dwyer [48] Song and MontoyaWeiss [49] Song and Parry [50, 51] Souder and Jenssen [52] Souder and Song [53]

Zirger and Maidique [63]

Yes

Cycle time

Causal role of product innovativeness

No No

No

Go/No Go decision Project and and process outcomes Go/No Go decision New product success Product financial performance Product financial performance Commercial success Commercial success Development time, design quality, financial performance Market share Product success rate

As independent variable As independent variable As moderator As independent variable As moderator As moderator As moderator

As moderator As independent variable

1 This overview only includes empirical studies that have treated innovativeness at the product level. It does not include research conducted at the aggregate new product program (i.e, SBU or firm) level. This overview of past research is extensive, but inevitably not exhaustive.

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Fig. 1. Booz, Allen, and Hamilton New Product Typology

also often relabeled the different types (Fig. 1 was reproduced from the original publication). The newness to the market dimension in the Booz, Allen, and Hamilton typology can be interpreted as assessing the innovativeness of the product to its potential customers. Booz, Allen, and Hamilton provided no additional detail concerning what makes a new product new to customers, that is, what criteria to use for classifying a new product as more or less new from the customer’s perspective. In order to understand how customers perceive newness we can draw on the rich and extensive body of work on innovation adoption and diffusion, which has studied how innovation characteristics impact innovation decisions and timing. This literature has studied the adoption and diffusion of various kinds of innovations, including new products but also process and administrative innovations. Rogers [44] proposed five innovation attributes to be related to whether and when an innovation is adopted: relative advantage, compatibility, complexity, trialability, and observability. In the extensive empirical adoption/diffusion literature these characteristics have been consistently related to innovation adoption [44]. Among new product researchers especially the first attribute has received attention: relative advantage. In the new product literature the notion of product newness to the customers buying and using the product has often been defined as product uniqueness or superior product advantage [for example, 2, 12]. Colarelli-O’Connor [12] defines “really new” products in terms of their ability to offer greater functionality, distinguished from incremental products by the leap in performance they provide. In their recent book about managing radical innovations, Leifer et al. [30, p.5] define a radical innovation project as one “with the potential to produce one or more of the following: an entirely new set of performance features, improvements in known performance features of five times or greater, a significant (30% or greater) reduction in cost.” They embrace a customer perspective to identify what constitutes radical innovation, especially when they emphasize that their definition is “driven by new value added to the marketplace rather than by technical novelty or newness to the firm [30, p.6]. In the meta-analysis by Montoya-Weiss and Calantone [37] the product’s ability to provide benefits or features not offered by alternative products emerged as one of the strongest correlates of new product success.

Other researchers in the adoption/diffusion literature have focused on the effect of the adoption risk on the decision to adopt and the timing of adoption. Gatignon and Robertson ( [22], see also [46]) distinguished uncertainty risk (created by a lack of standards to evaluate the innovation), performance risk (whether the innovation will perform as expected), social risk (associated with loss of social status by making an adoption mistake), and physical risk (risk of physical harm to user). Still other adoption researchers have focused on the change in established behavior patterns that the adoption of an innovation requires. For instance, Gatignon and Robertson [22] distinguish continuous innovations (which have minimal effect on behavior patterns), dynamically continuous innovations (which have a moderate effect on behavior patterns), and discontinuous innovations (which create new behavior patterns). Greater change in behavior requires the adopter to learn how to use or maintain the innovation. These innovation characteristics have been formulated to apply to various kinds of innovations, including product innovations. New product scholars studying the nature and effects of product newness from the customer’s perspective have not yet fully utilized the insights from the literature on innovation characteristics, adoption risks, and behavior changes in their conceptualizations and measures of product newness. 2.1.2. Product innovativeness from the firm’s perspective The second dimension of the Booz, Allen, and Hamilton typology [9] is newness to the firm, that is, how innovative the product is to the firm that develops it. Again, their framework provides no criteria for evaluating this form of product innovativeness. It also suggests that newness to the firm is unidimensional. Some researchers have made some more fine-grained distinctions in defining product newness from the firm’s perspective (cf. Table 1). Wheelwright and Clark [61] distinguished the following types of development projects (which includes both product or process projects): derivative, platform/next generation, and breakthrough/radical development projects. They take a firm perspective on newness when they state that these projects involve increasing amounts of resources, development time, and technology changes. In order to understand in what ways products can be new from the perspective of the firm, we can draw on two bodies of literature: the literature examining organization-environment relations [41,54,56], and the resource-based theory of the firm [33,43,60]. These theoretical perspectives allow us to distinguish two alternative conceptualizations of product newness to the firm: newness as familiarity versus newness as fit. The newness as familiarity conceptualization draws on organizational theory regarding the relationship between the organization and its environment [54]. In his classic work, Thompson [56, p.27] argued that all organizations establish a “domain,” which “identifies the points at which the orga-

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nization is dependent on inputs from the environment.” Applying this notion to the context of product innovation, Normann [41] argued that new products may enlarge the domain of the organization, and to the extent that they do so they make the organization face an unfamiliar domain. Normann [41, p.206] distinguished “between the domain, which is that part of the environment-the technological environment, the market environment, and so on–with which the organization is in more or less constant interaction, and the secondary environment.” Normann argued that people in the organization perceive and interpret events and signals from the domain more easily. Stimuli from wellknown parts of the environment, that is, the domain, benefit from established channels of communication and fit into existing cognitive structures. Some new product researchers have explicitly referred to the familiarity notion when examining the effects of product newness. Souder and Song [53] used market familiarity as a moderator variable in the relationship between management practices and new product success, using data from the U.S. and Japan. They found that a firm’s level of familiarity with its target market moderates the impact of new product development practices in the U.S., but less so in Japan. Souder and Jenssen [52] used market familiarity in a crosscultural comparison of U.S. and Scandinavian antecedents of new product success. They found that market familiarity moderates the impact of certain antecedents. They measured market familiarity with a dichotomy of familiar versus unfamiliar, defined as the extent of the innovating firm’s knowledge of the market and customers targeted by the firm’s new product. They (p. 198) conclude that: “the results in both the U.S. and Scandinavia reinforced the contingency notion that more exacting NPD practices are required to achieve success in unfamiliar market environments.” The newness as fit conceptualization is another way to look at the notion of product newness to the firm. For this conceptualization we draw on recent strategy theory referred to as the “resource-based view of the firm” [33,43, 60]. This perspective has been very popular in the strategic management literature for the last decade. The resourcebased perspective focuses on the resources that firms control and the productive uses they put those resources to. The firm is seen as a collection of resources, rather than products, and the resources can have many applications [60]. One of the productive uses of firm resources is in product innovation. Resources that enable the firm to develop new products include R&D expertise, knowledge of customer needs and competitive situations, sales force, market research skills, production facilities, and so forth Any new product needs these tangible and intangible resources to be successfully developed and commercialized. Resources are fungible, that is, they can be used for more than one product. For instance, a firm that has great knowledge of and access to a certain market, may further leverage that knowledge and distribution access by developing more products for that market.

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Fig. 2. Dimensions of Product Innovativeness

Many researchers have measured the fit of projects with firm resources, although they did not label it as product newness. The fit (or often called “synergy”) of a project with a firm refers to how well the internally available resources fit the requirements for the new product project, that is, the extent to which the new product fits with the firm’s resources and capabilities. The extant literature has not couched the notion of “fit” within the resource-based theory. A further distinction that can be made regarding product newness from the firm’s perspective involves taking a technical versus marketing perspective. New product development consists of bringing together two main components: markets and technology [20,21]. Green, Gavin, and AimanSmith [25] distinguished technical and business aspects of the newness of a product, but focused on the technical dimensions. Field research by Danneels [20] suggested that the variety of resources used in product innovation can be classified into customer/market resources and technological resources. According to his framework, product innovation requires the firm to have competences relating to technology (enabling the firm to make the product) and relating to customers (enabling the firm to serve certain customers). His study conceptualized the newness of a product to a firm as the extent to which the product can draw on customer and/or technological competences existing within the firm. Fig. 2 presents an overview of the various dimensions of product innovativeness discussed above. The empirical part of the present article is limited to examining product innovativeness from the firm’s perspective, that is, what makes a product new to the firm. Even though the dataset we use also contains items assessing the innovativeness of the product to customers, we chose not to include those items in the present analysis because we were skeptical as to their validity. Firms may tend to overestimate the uniqueness of

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their product innovations to their customers, or otherwise have inaccurate perceptions of their customers’ views. We feel that customers themselves are the only proper informants regarding how new they perceive a new product to be and in what ways it is new to them (cf. our suggestions for future research). Based on the above theoretical discussion regarding product innovativeness from the firm’s perspective, it can be expected that: H1A: Product innovativeness, viewed from the firm’s perspective, has a familiarity component and a fit component. H1B: Familiarity and fit both consist of a marketing component and a technological component.

It is important to understand the dimensions of product newness from the firm’s perspective because they are likely to be related with two crucial outcomes: which projects the firm chooses to pursue (i.e., new product selection) and the performance of the product once it is brought to market. The rationale for the relation of these two outcomes to product newness will be presented in the next two sections. 2.2. Relation of product innovativeness with the Go/No Go decision Much new product research has compared successful and failed products (for an extensive review, see [37]). Fewer studies have investigated killed projects: development projects that were terminated, such that the product was not launched into the market. Cooper and Kleinschmidt [18] conducted one of the few studies comparing killed projects with successful and failed projects. They compared these three categories of projects across a large number of variables (67 in total) to find descriptors that were significantly different across the projects. Regarding the variables describing product newness they found an inconsistent and complex pattern. The present study uses the same database as Cooper and Kleinschmidt [18] to first explore the underlying dimensions of product newness developed from theory, and then to examine the association of these dimensions with the decision to pursue or kill a project. The findings from other empirical work have been similarly mixed. More [38] compared rejected and accepted industrial new product projects across 44 project characteristics and found that products targeting markets and using technologies new to the firm were more likely to be rejected. He argued that projects in new areas present greater uncertainties and thus risk to the developing firm. Like Cooper and Kleinschmidt [18], More did not examine the dimensionality of the 44 characteristics. Green, Gavin, and Aiman-Smith [25] distinguished among technical and business dimensions of product “radicalness,” and found that more innovative projects in technical or business terms were more likely to be terminated. They suggested that such projects are more risky and less clearly tied to market needs,

and therefore receive less internal organizational support. In contrast with these findings, Schmidt and Calantone [46] found that innovative products were less likely to be killed. In a decision-making experiment, they compared the tendency to terminate new product projects in the face of negative project feedback across low innovative and high innovative projects. They found that the managers participating in the experiment were more reluctant to kill highly innovative projects. Schmidt and Calantone [46] used a dichotomous, unidimensional operationalization of product newness. Innovative products often present great opportunities for firms wishing to extend their activities into new markets and technologies, involving greater emotional and strategic commitment, and may therefore be tougher to terminate [20,46]. The termination of a project is a tough decision that may lower company morale and create anxiety over job security [7,8]. Therefore, there is likely to be a tendency to continue projects, a tendency that can only be countered in the face of strong evidence that the product will likely fail. The termination decision is made under uncertainty regarding the likely technical and commercial feasibility of the new product [7]. The evaluation of products situated in familiar environments, that is, targeted at a familiar market or using a familiar technology, benefit from clearer signals regarding potential success. The familiarity of the firm’s domain facilitates the perception and interpretation of signals from the domain [41]. Signals from the secondary environment, on the other hand, cannot be so readily perceived and interpreted. Therefore, we expect that products targeted outside the organization’s domain, that is, involving unfamiliar environments, will likely persist in the absence of strong signals indicating likely failure. In other words, managers may find it harder to evaluate whether to kill a product if they are venturing into markets or technologies new to them. Development for such products may continue until launch because of the lack of strong evidence to make the tough kill decision. Based on this rationale and the empirical findings of Schmidt and Calantone [46] we hypothesize that: H2A: The higher the market familiarity of the new product, the more likely the product is to be killed. H2B: The higher the technological familiarity of the new product, the more likely the product is to be killed.

We hypothesize that companies will be most likely to continue development of the products that most closely draw on their existing resources, that is, they will favor those products that have the best fit with their resources. Researchers in corporate diversification have found that firms tend to expand in the direction of current resources in order to utilize productive resources that are surplus to current operations [10]. Leveraging firm resources by applying them to more products allows firms to extract more value out of their resource base. March [34] argued that organizations have a tendency to exploit their existing re-

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sources rather than to explore new ones. This implies that firms will tend to utilize their existing resources in new products, and will be unfavorable in evaluating projects that require resources that the firm does not have [20]. In other words, projects with a greater fit to the firm’s existing resources will receive greater support, and be less likely to be terminated. Therefore: H3A: The lower the marketing fit of the new product, the more likely the product is to be killed. H3B: The lower the technological fit of the new product, the more likely the product is to be killed.

2.3. Relation of product innovativeness with product performance Kleinschmidt and Cooper [29] suggested that product innovativeness is noticeably absent from the many studies on new product success factors. However, if the measures of project-firm fit are regarded as measures of how new the product is to the firm, then one can find quite a few relevant studies. Below we review studies that have examined the effect of product innovativeness on product performance. Some of this work used definitions and measures that can be explicitly identified as reflecting product newness in terms of either familiarity or fit, other work used the notions interchangeably. It should be noted that none of these authors conceptualize their measures of synergy/fit as dimensions of innovativeness. Cooper [14] conducted a discriminant analysis to relate success or failure to 18 factors he found to underlie new product characteristics. He found that several factors relating to product innovativeness differentiated between successes and failures: uniqueness/superiority of the product, market knowledge and marketing proficiency, technical and production synergy and proficiency, marketing and managerial synergy were associated with a higher success rate, and newness to the firm was weakly associated with a lower success rate. Zirger and Maidique [63] measured “synergy with existing competences” using three items which asked to what extent the new product benefited from its closeness to the company’s existing products/markets/technologies. They then used discriminant analysis to classify projects as successful or failed using several constructs, including synergy, and found that projects considered successful showed higher synergy. They did not differentiate marketing and technological dimensions of synergy. Kleinschmidt and Cooper [29] draw on the same database used in the current study to examine the effect of innovativeness on performance. Unfortunately, their unidimensional measure of product innovativeness (loosely based on the Booz, Allen, and Hamilton typology) combines customer and firm perspectives of the newness of the product, and does not distinguish between marketing and technological aspects of newness. They define three levels

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of innovativeness and find that the low and high levels of innovativeness are characterized by higher product performance. Using a similar classification approach, Song and Montoya-Weiss [49] compared new product success rates across two levels of product innovativeness: really new versus incremental products, and found that the more innovative products had a higher mean profitability. They did not distinguish technological and marketing dimensions of innovativeness. Cooper and de Brentani [15] studied the effect of 18 determinants on new business-to-business service success, and found synergy with the firm’s resources and capabilities to be the strongest predictor of success. Newness of the service to the firm nor to the market were found to be predictors of success. Unfortunately, Cooper and de Brentani did not conduct a simultaneous estimation of the effects of their dimensions of newness on new service success. In their study of product development in the chemical industry, Cooper and Kleinschmidt [19] again measured product innovativeness using a modified version of the Booz, Allen, and Hamilton typology. They also included measures of project fit and familiarity measured along several items (they did not label these measures as referring to product innovativeness). They compared success rates among the various Booz, Allen, and Hamilton product types, and found only modest differences. They also compared means for the fit and familiarity measures across successful and failed projects, and correlated scores on these measures with a continuous measure of overall success, and found only moderate associations between some of these measures and success. Cooper and Kleinschmidt [19, p.109] concluded that synergy and familiarity measures “may not be particularly effective tools in success/failure prediction and project selection for these large chemical companies.” Unfortunately, they did not examine the interrelations among the various measures of newness, or estimate the simultaneous and distinct effects of their measures of newness. Song and Parry [50,51] examined the separate effect of marketing and technological synergy on new product financial performance. Song and Parry [50] find that among Japanese firms marketing synergy positively impacts a product’s financial performance, an effect mediated by the level of competitive and marketing intelligence, and technical synergy positively impacts performance through its impact on technical proficiency. Song and Parry [51] find that both types of synergy increase product performance through their impact on the quality of implementation of marketing and technical tasks during the development process. Recently, Song and Montoya-Weiss [49, p.127] stated that “there is very little conclusive evidence concerning the impact of product innovativeness on new product success.” We suggest that this lack of consistent evidence may be due to the imprecise conception and measurement of product newness. Especially the existing empirical findings regard-

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ing the effect of familiarity are equivocal. However, since projects within the organization’s domain will benefit from easier perception and interpretation of environmental signals, as well as from the organization’s established communication channels with the domain, we expect: H4A: The higher the market familiarity of the new product, the higher product performance will be. H4B: The higher the technological familiarity of the new product, the higher product performance will be.

The new product literature has long studied the effect of resources on new product performance. However, most of the literature has used the term “product-firm synergy” to describe the fit of new product projects with firm resources. Overall, prior research has shown a positive impact of project-firm fit (the extent to which the project can draw on existing in-house resources) on new product performance. In other words, new products that fit with firm competences are more likely to be successful. We expect that when a firm can draw on its existing resources, that is, when it pursues a project with high fit, it is more likely to be successful. Therefore, we hypothesize: H5A: The higher the marketing fit of the new product, the higher product performance will be. H5B: The higher the technological fit of the new product, the higher product performance will be.

In the above theoretical section of the article, we have synthesized the various streams of literature regarding product newness. Next follows the empirical part, which examines to what extent the various dimensions of product newness from the firm’s perspective suggested above hold up when confronted with an existing dataset. The empirical section of this article has three goals: 1) to clarify the product innovativeness construct and explore its underlying dimensions, 2) to explore the relationship of product innovativeness with the decision to pursue or kill the project, and 3) to explore the relationship between product innovativeness and product performance. This article will provide three sections of data analysis to provide answers to these questions.

3. Data Data for the following analysis derive from the wellknown NewProd II dataset collected in Canada in 1985– 1986 by Robert Cooper and Elko Kleinschmidt. Even though this dataset was collected some time ago, we feel it is appropriate to use for the present study. Numerous articles have been published based on this dataset (e.g., [16,17, 18,29]), and these articles have been highly cited. This dataset thus forms the basis for much of the “accepted wisdom” in the new product development area. Therefore, re-examining these data in fact implies re-examining the

basis of our accepted wisdom. The present manuscript will use more sophisticated statistical tools (factor analysis and multiple regression) than the original analyses did, and builds on stronger theoretical bases (organizational theory and resource-based view) than the prior articles based on these data. As a consequence of these advances, we provide a more refined conceptualization and measurement of the core construct. It is not uncommon to re-examine “classic” data once more sophisticated conceptual and statistical tools become available. For example, Van den Bulte and Lilien [57] recently re-examined the medical innovation diffusion data collected by Coleman, Katz, and Menzel [13] in the 1950s (see [57] for references to other researchers who also re-examined the same data). This dataset is also attractive for the purposes of this article because of its level of detail. NewProd II provides extensive new product development data at the project level, including a wide range of measures relating to product innovativeness. The dataset is unique in that it has so many measurement items relating to innovativeness (19 items), and that it also has data on cancelled projects. The original questionnaire and computer datafile were obtained from Cooper and Kleinschmidt. NewProd II contains data from 125 industrial firms on 123 projects that respondents considered successes, 79 failures, and 60 projects that were killed (i.e., discontinued during the development process and never launched), for a total sample of 262 projects. All projects involved industrial physical products. Data were collected through face-to-face interviews with the person most knowledgeable about new product development in each firm. The interviews were administered at the company site by a professional interviewer, who followed a detailed protocol for asking the questions and recording the responses. More information about the composition of the sample can be found in Cooper and Kleinschmidt [16 –18,29].

4. Measures Measures were developed based on variables measured in NewProd II. Items relating to product performance and product innovativeness were measured on zero-to-ten Likert scales with anchor phrases (see Tables 2 and 3). 4.1. Go/No Go 60 projects were killed and 202 were continued and eventually launched. Terminated projects were coded as 0, continued projects as 1. Respondents were instructed to select killed projects that had passed through at least half of the development process.

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Table 2 Measurement of product performance Item

Factor Loading

—How successful was the product from an overall profitability standpoint—the degree to which the product exceeded your firm’s minimum acceptable profitability level, however profitability is measured. [Barely met—Far exceeded] —Indicate the sales achieved by this product, relative to the sales of your firm’s other new products. [Far less—Far greater] —Indicate the profits achieved by this product, relative to the profits of your firm’s other new products. [Far less—Far greater] —To what extent did the new product meet your sales objectives? [Fell far short—Far exceeded] —To what extent did the new product meet your profit objectives? [Fell far short—Far exceeded]

.82

Number of Factors Extracted Percent Variance Explained Mean (Standard Deviation) of Scale Alpha of Scale

1 80.8 24.6 (14.6) .94

4.2. Product performance The financial performance of the product is measured with five items referring to the sales and profitability of the product (see Table 2). Product performance is only available for the successful and failed projects (n ⫽ 202), since no performance data exist for the killed projects (they were never introduced). Principal component analysis (see Table 2) reveals that the construct is unidimensional (only the first eigenvalue ⬎1), and reliability analysis indicates high scale reliability (alpha⫽0.94). The scale for this construct was formed by simple summation of the scores on the five items. 4.3. Innovativeness Ideally, we would have developed a set of items to tap the various aspects of the domain of product innovativeness that we identified in our theoretical discussion, as prescribed in the conventional scale development procedure outlined by Churchill [11]. However, because we used secondary data we were limited to the items already present in that dataset. All items in the database referring to the newness of the product from the firm’s perspective were incorporated in the present analysis. Cooper and Kleinschmidt generated a battery of 19 items based on several sources: extensive review of prior research, interviews with new product development practitioners in 12 companies, and extensive personal exposure to a great number and variety of new product projects through consulting. Table 3 shows the exact wording of the items. The dimensionality of the innovativeness items is analyzed in the next section.

5. The dimensions of product innovativeness We followed the “updated paradigm for scale development” as suggested by Gerbing and Anderson [24]. They recommend exploratory factor analysis to be followed by confirmatory factor analysis, and finally scale construction and reliability assessment. The next section will detail these three steps.

.88 .93 .91 .95

First, an exploratory factor analysis was performed on all the items in the NewProd II database that refer to newness of the product to the firm (19 items in total). Principal components was used for factor extraction. Given the limited prior theory regarding dimensions of product innovativeness, the exploratory factor analysis was unrestricted both in terms of number of factors and in terms of the intercorrelations among the factors, again following the recommendations by Gerbing and Anderson [24]. While the fifth factor did not meet the traditional eigenvalue greater than one criterion (its eigenvalue is 0.88), a scree plot of the eigenvalues suggests that five factors should be retained. Hair et al. [26] suggested that if the number of variables is less than 20, the eigenvalue greater than one criterion tends to extract too few factors. Since the purpose of our analysis is to refine the theoretical and operational definition of product innovativeness, we favored representativeness over parsimony in deciding on the number of factors to extract [compare 24, 26]. The total variance explained by the five factors was 70.5%. Subsequently, oblique rotation (oblimin) was used to rotate the resulting factors. Oblique rotation was appropriate since there was no a priori reason to assume that factors should be uncorrelated (orthogonal). Hair et al. [26] recommend the use of oblique rotation when the primary purpose of the factor analysis is to derive theoretically meaningful constructs, rather than to reduce the number of variables. However, we also conducted an orthogonal (varimax) rotation, which yielded the same factor structure, even though the factor loadings were not as sharply patterned as those of the oblique rotation. Table 3 shows the rotated factor loadings (factor loadings greater than 0.30 are highlighted). Interpretation and labeling of the factors was straightforward. The pattern of factor loadings shows that items split into factors according to marketing and technological dimensions (confirming Hypothesis 1B). Within marketing newness, three distinct dimensions occur: one reflecting the unfamiliarity of the market, one reflecting the fit of the product with existing marketing resources, and one reflecting whether the product required new types of marketing activities. Similarly, the

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Table 3 Measurements and factor loadings of innovativeness dimensions Item

Market Familiarity —To what extent was this product aimed at new customers to your firm—customers that you had not sold to before? [All existing customers—Totally new customers] —To what extent did this product take you up against new competitors— competitor firms that you had never faced before? [All familiar competitors—Totally new competitors] —To what extent did this product cater to new customer needs—customer needs that you had not served before? [Had served before—Totally new needs served] —To what extent was the market for this product new or different for your firm—new or different from the markets you normally sell into? [Our existing market—Totally new market to us] —To what extent did this product represent a new product category— a type of product that your firm had not made and/or sold before? [Existing product category for our firm—Totally new product category for us]—item was dropped

Market familiarity

Technological familiarity

Marketing fit

Technological fit

New marketing activities

.56

.28

.21

.18

.34

.81

.04

.05

.06

.01

.74

.13

.11

.03

.08

.69

.00

.14

.10

.19

.63

.36

.03

.01

.04

.10

.83

.01

.10

.04

.13

.82

.03

.04

.01

.08

.75

.12

.13

.25

.07

.03

.49

.27

.32

.03

.00

.86

.02

.07

.02

.05

.88

.06

.04

.03

.01

.47

.41

.16

.01

.00

.03

.89

.06

.05

.04

.01

.90

.08

.07

.12

.12

.72

.00

Technological Familiarity —To what extent did the technology involved in the development of this product represent new or different technology for your firm? [Our existing technology—Totally new technology for us] —To what extent did the engineering and design work involved in this new product project represent new or different work for your firm—a type of engineering or design work you had not done before? [Very familiar work for us—Totally new work for us] —To what extent did the production technology and production process represent a new and different one for your firm—a type of production you had not done before? [Existing production process to us—Totally new production process to us] Marketing Fit —To what extent was your existing company’s salesforce (or your distributors’ sales forces) more than adequate to handle the selling of this product? [Totally inadequate—Far more than adequate] —To what extent were your firm’s advertising and promotion people, skills, and resources more than adequate for the advertising and promotion of this product? [Totally inadequate—Far more than adequate] —To what extent were your firm’s marketing research people, skills, and resources more than adequate for the gathering of market information needed for this product? [Totally inadequate—Far more than adequate] —To what extent was your firm’s customer service group—people, skills, resources—more than adequate to handle the customer service needed for this product? [Totally inadequate—Far more than adequate] Technological Fit —To what extent were your firm’s R&D or product development resources, people, and skills more than adequate to handle the development or this product? [Totally inadequate—Far more than adequate] —To what extent were your firm’s Engineering resources, people, and skills more than adequate for the engineering and design work involved in this product? [Totally inadequate—Far more than adequate] —To what extent were your firm’s Production or Operation resources, facilities, and people more than adequate for the production of this product? [Totally inadequate—Far more than adequate]

(Continued)

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Table 3 (Continued) Item

New Marketing Activities —To what extent did this product require a new sales force or distributor salesforce system—different from your existing salesforce or distributor salesforce system? [Used our existing salesforce—Required totally new salesforce] —To what extent did the product require a new form of advertising and promotion—different from that used for your existing products? [Used existing advertising and promotion methods—Required totally new advertising and promotion methods] —To what extent was the marketing research or gathering of market information done for this product a departure from existing practices in your firm? [Used existing market research methods—Required totally new market research methods] —To what extent were the customer service and service facilities you provided for this product new or different for your firm? [Used existing customer service facilities—Required totally new customer service facilities] Eigenvalue Prior to Rotation1 Mean of Scale Standard Deviation of Scale Alpha of Scale

Market familiarity

Technological familiarity

Marketing fit

Technological fit

New marketing activities

.14

.04

.18

.03

.71

.06

.01

.10

.10

.80

.00

.04

.00

.10

.83

.03

.16

.10

.14

.79

1.03 21.0 10.2 .78

2.11 14.4 7.7 .82

.88 23.3 8.7 .85

2.72 20.0 6.1 .85

6.65 13.3 10.8 .85

1 Because the oblique rotation allows for correlation among the factors, it is not meaningful to report Percent Variance Explained for each factor separately. The total Percent Variance Explained by all the factors is 70.5%.

technological aspect consists of familiarity with the technology, and fit with existing technological resources. These findings largely support Hypothesis 1A, with the qualification that the additional dimension of new marketing activities was found. The questionnaire did not ask questions regarding whether the product required new technological activities (such as hiring engineers with different skills, setting up new manufacturing equipment). Had such questions been asked, a sixth factor reflecting new technological activities might have emerged. It is interesting to note that such widely different aspects as salesforce, advertising, and market research form the marketing factors, and R&D and production issues group in the technological factors. The item asking whether the product category was new to the firm loads on both the market and technological familiarity factors. This indicates that the degree of product newness is determined along both dimensions. The higher factor loading for market familiarity indicates that respondents tend to base their evaluation of the newness of a product category more on whether it is targeted at a new market than whether it uses a new technology. Because of the vagueness of the notion “new product category” this item was dropped. The item asking for the adequacy of existing customer service loads on both the marketing fit and technological fit factors. This makes sense because customer service consists mainly of providing technological assistance and expertise

to customers. Because customer service is traditionally considered part of the marketing mix and because its loading on marketing fit is slightly higher, this item was included in the marketing fit scale. Second, after these refinements, the innovativeness measures were subjected to a confirmatory factor analysis. The five-factor model constructed above was assessed through structural equation modeling, using AMOS. The NFI, the TLI, and the CFI fit indices were 0.97, 0.97, and 0.98 respectively, indicating good fit of the confirmatory measurement model (␹2⫽296, df.⫽125). All the items loadings on their respective constructs were large (smallest ⫽ 0.60) and significant (smallest t-value ⫽ 8.23), evidencing convergent validity. In contrast, a one-factor model was assessed, in which all items load on a unitary product innovativeness construct. The NFI, the TLI, and the CFI fit indices were 0.86, 0.83, and 0.87 respectively, indicating poor fit of the one-factor confirmatory measurement model (␹2⫽1313, df.⫽135). The ␹2-difference test between the five-factor and the one-factor models indicates that the fivefactor model has significantly better fit (⌬␹2⫽1017, ⌬df.⫽10, p ⬍ .001). These findings again support the multidimensionality of product innovativeness suggested by Hypotheses 1A and 1B. Subsequently, we randomly split the sample in half, and performed both the exploratory factor analysis and the CFA on the subsamples. The results are basically the same. Both exploratory analyses indicate five factors, with the same

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Table 4 Bivariate correlations among innovativeness dimensions1

Market Familiarity Technological Familiarity Marketing Fit Technological Fit New Marketing Activities

Market Familiarity

Technological familiarity

Marketing fit

Technological fit

New marketing activities

1.0 .26* .37* .18* ⫺.64*

.26* 1.0 .19* .39* ⫺.28*

.37* .19* 1.0 .58* ⫺.46*

.18* .39* .58* 1.0 ⫺.19*

⫺.64* ⫺.28* ⫺.46* ⫺.19* 1.0

* p ⬍ .01 that 兩r兩 ⬎ 0 and p ⬍ .01 that 兩r兩 ⫽ 1. The coding of the familiarity items was reversed. In the original coding (cf. Table 3) a higher score indicated greater newness, i.e. greater unfamiliarity.

1

item assignments as in the entire sample. Again, in both subsamples the “new product category” item crossloads. The CFAs also have excellent fit: in sample 1 NFI⫽0.94, TLI⫽0.95, CFI⫽0.96; in sample 2: NFI⫽0.96, TLI⫽0.98, CFI⫽0.99. Third, scales for the dimensions of innovativeness were constructed by a simple sum of the items that loaded on each respective dimension. Their reliabilities range from 0.78 to 0.85. Reliability analysis shows that deletion of any item would decrease the alpha of its respective scale. Table 4 shows the correlations among these scales. The correlations range from low to moderately high, indicating that any given product may be innovative along some dimensions and noninnovative along others. In constructing the scales, the simple sum of items (rather than weighting items by factor weights) was used for two reasons. First, equal weighing of the items increases the replicability of the findings [26]. A sample yields idiosyncratic factor weightings. Second, there is no theoretical rationale for giving items different weights; all are assumed to be equally important and relevant to the measure of the construct [26]. Since some of the correlations among the constructs are high (the highest is ⫺0.64), there was a concern for discriminant validity. Discriminant validity was assessed in Table 5 Effect of innovativeness dimensions on Go/No Go decision1 Variables in the equation

Coefficient (standard error)

Significance (two-tailed)

Market Familiarity Technological Familiarity Marketing Fit Technological Fit New Marketing Activities Nagelkerke R2

ⴚ.051 (.022) .035 (.024) ⫺.047 (.027) .061 (.036) ⫺.029 (.022) 8.0%

.02 .15 .083 .089 .179 .022

The coding of the familiarity items was reversed. In the original coding (cf. Table 3) a higher score indicated greater newness, i.e. greater unfamiliarity. Continued projects were coded as 1, killed projects as 0. A negative coefficient indicates an increasing kill-likelihood as the value of the independent variable increases; a positive coefficient indicates an increasing likelihood of product continuation as the value of the independent variable increases.

two ways. First, the 99% confidence intervals around the correlation parameter estimates between any two scales were calculated [3]. None of the intervals included 1, that is, all correlations were significantly different from 1, evidencing discriminant validity. Second, pairs of innovativeness measures were examined in a series of two-factor confirmatory factor models [6]. We ran the model for each pair twice, once freeing the correlation between the constructs, and once setting the parameter to 1. The results indicate that the difference in ␹2 between the two models for each pair was significant at p ⬍ .01. These results of the discriminant validity tests further support that product innovativeness is multidimensional. We also checked for respondent bias. We compared three groups of respondents according to their functional background (technical, marketing, and other) for their means on the five innovativeness dimensions. We found only very small differences. An Anova showed these were not significant differences (smallest probability was 0.126). 6. The relation of product innovativeness with the Go/ No Go decision A logistic regression was conducted using the go/kill decision as the dependent variable and the newness dimensions as the independent variables. The sixty killed projects were coded as 0, the 202 launched products were coded as 1. The Nagelkerke [39] R2 was used to assess model fit. The results of this analysis are shown in Table 5. Inspection of Variance-Inflation-Factors among the innovativeness measures indicates the highest VIF was 1.943, far below the value of 10 which Neter, Wasserman, and Kutner [40] suggested indicates problematic multicollinearity. The regression coefficients indicate that only market familiarity is related with the go/no go decision. The more unfamiliar the market for the product, the more likely it was to be continued, supporting Hypothesis 2A. Conversely, the more familiar the market for the product, the more likely it was to be killed. None of the other coefficients obtain significance at the 5% level of confidence, contradicting Hypotheses 2B, 3A and 3B. Regarding the lack of association with technological

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Table 6 Effect of innovativeness dimensions on product performance1 Variables in the equation

Bivariate correlation

Significance of bivariate correlation

Beta

Significance of beta (two-tailed)

Market familiarity Technological familiarity Marketing fit Technological fit New marketing activities

⫺.091 .008 .326 .334 ⫺.015

.215 .916 .000 .000 .840

.06 ⫺.08 .27 .23 .15

.47 .31 .005 .01 .11

17.0%

.000 (F ⫽ 7.44)

R-square 1

The coding of the familiarity items was reversed. In the original coding (cf. Table 3) a higher score indicated greater newness, i.e. greater unfamiliarity.

familiarity, managers may find it more difficult to assess the feasibility of technologies they are unfamiliar with, and thus may be unlikely to kill a project outside their technological domain based on signals regarding its technological feasibility. The lack of association with the fit dimensions may be due to the great strategic importance of building new marketing and technological resources through product innovation [20], which creates commitment to low fit projects. However, this commitment may be counterbalanced by the tendency to exploit existing marketing and technological resources, which favors high fit projects. Further research is needed to clarify and measure the mechanisms that relate these dimensions of product innovativeness to the decision to continue or terminate a project.

7. The relation of product innovativeness with product performance This section examines the relation between the dimensions of innovativeness and the financial performance of the product. To this end a multiple regression was conducted with the five dimensions of product innovativeness as explanatory variables and the financial performance of the product as the dependent variable. Inspection of VarianceInflation-Factors among the innovativeness measures indicates the highest VIF was 1.975, indicating no problematic multicollinearity is present [40]. Table 6 shows that familiarity with the market and technology, and necessity of new marketing activities have no significant association with product performance, contradicting Hypotheses 4A and 4B. This finding is consistent with the weak support that the empirical literature has found for the familiarity-performance relation. However, both fit with marketing resources and technological resources have a significant association with performance, supporting Hypotheses 5A and 5B. The total explained variance in product performance is 17%, indicating a fairly strong effect of marketing and technological fit on new product success. Of course, this coefficient also suggests that fit is only one of many factors affecting new product performance. We tested for violations of the assumptions of multiple regression in various ways:

partial regression plots to test for nonlinearity, plots of studentized residuals against predicted criterion values to test for heteroscedasticity, and a normal probability plot to test for non-normality. We found no indication that the assumptions of multiple regression are violated.

8. Conclusions and implications The existing literature has used a variety of conceptualizations and measures of product newness. We have attempted to better comprehend these alternative notions by offering a conceptual framework for understanding different ways a product may be innovative. We then used an existing dataset to empirically examine the dimensions of product newness from the firm’s perspective and their relation with new product outcomes. Table 7 presents an overview of the hypotheses that were tested in this article and their outcomes. The data we used suggest that the innovativeness of a product to a firm is multidimensional, and that some elements of product newness relate to newness in marketing terms, and others to newness in technological terms. This finding contributes to the literature because most previous research has treated product innovativeness as a unidimensional construct. The findings presented here clearly show that a product can be new to a firm along several dimensions, and that newness along one dimension does not necessarily imply newness along other dimensions. Our data also suggest that these different dimensions of product innovativeness have different relations with product performance. One of the most striking findings was that financial performance of a product does not so much depend on whether the product stays close to home in terms of the markets it is targeted at or the technologies it uses (i.e., familiarity), but whether it fits with the firm’s existing marketing and technological competences. It is thus not whether the firm aims at new customers that determines performance, but rather whether it can use its marketing skills and resources to address those customers. Similarly, it is not whether the product requires a new technology that determines financial performance,

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Table 7 Overview of hypotheses and findings Hypotheses

Findings

H1A: Product innovativeness, viewed from the firm’s perspective, has a familiarity component and a fit component. H1B: Familiarity and fit both consist of a marketing component and a technological component.

Qualified1 Supported

H2A: The higher the market familiarity of the new product, the more likely the product is to be killed. H2B: The higher the technological familiarity of the new product, the more likely the product is to be killed.

Supported Rejected

H3A: The lower the marketing fit of the new product, the more likely the product is to be killed. H3B: The lower the technological fit of the new product, the more likely the product is to be killed.

Rejected Rejected

H4A: The higher the market familiarity of the new product, the higher product performance will be. H4B: The higher the technological familiarity of the new product, the higher product performance will be.

Rejected Rejected

H5A: The higher the marketing fit of the new product, the higher product performance will be. H5B: The higher the technological fit of the new product, the higher product performance will be.

Supported Supported

1

An additional dimension of “new marketing activities” was found.

but rather whether the firm can use its technological skills and resources to acquire that new technology. We hope that future research will test whether these relationships hold true in other datasets. Previous research has not clearly distinguished the effects of familiarity and fit on performance, and has not estimated their distinct effect by simultaneously entering them in one regression procedure (i.e., simultaneous estimation). The conflation of these dimensions has probably attenuated and confounded the findings regarding the effects of product innovativeness. The managerial relevance of this finding is great. Since familiarity has no association with performance and fit does, it is important to distinguish the two forms of innovativeness. It suggests that managers should evaluate new products on their degree of fit with their firm’s technological and marketing competences. Managers evaluating new product proposals should not be discouraged by products that go after markets with which they have no experience or require technology new to the firm, but should ask whether development of the new product can draw on existing marketing and technological competences. The hypotheses regarding the dimensions of product innovativeness and the relation of fit with performance are strongly supported. Other hypothesized relationships, especially those about the relation between familiarity and performance, are not significant. For the purpose of this manuscript it is very important to report the nonsignificant association between familiarity and performance. Our data thus suggest that different conceptions and measures of innovativeness might have different effects. Product innovation scholars should consider the distinction between fit and familiarity when evaluating past research or when conducting their own. Even though the findings suggest that products venturing outside of the firm’s marketing and technological competences are less financially successful, it should not be implied that a firm should never do so. The measures of success used in this research view the project in isolation; its

potential benefits for later projects are not taken into account. Product development is a tool for exploring new areas and can serve to build new competences in the firm [20]. However, firms doing such exploration should be aware that initial efforts at exploration are likely to be financially unsuccessful, and thus should not be discouraged if they have a serious intent to venture into new areas. In a dynamic environment, a firm should have a portfolio of new product efforts that spans different levels of innovativeness. Less innovative products provide the cash flow in the present to support exploration of new markets and technologies that may lead to organizational renewal and future profits [20]. Finally, the framework discussed in this article allows the product developer to understand the ways in which a particular new product is new, and thus to assess what innovation barriers both the developing firm and the customers are likely to experience. Sheth and Ram [47] provide an excellent overview of suggestions on how to address the barriers to innovation that result from various aspects of product newness.

9. Limitations and further research We started this article by arguing that clarification of the conceptualization and measurement of product innovativeness is necessary and even urgent in our field. Articles using the notion of product innovativeness are published on a regular basis. Most often these articles do not provide much theoretical or empirical foundation for the construct. With this article we hope to provide a basis for further investigation. Future researchers should be very careful about how they conceptualize and measure product innovativeness. Prior research has not clearly distinguished between newness as unfamiliarity, as lacking fit with existing competences, or as implying new types of activity. Often it is not clear if newness is defined from the perspective of the customer or the developing firm. Most prior research has also conflated

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technological and marketing aspects of product innovativeness. The different conceptualizations and measurements of product innovativeness in prior research may have led to inconclusive findings. The findings in this article imply that depending on how researchers define and measure the innovativeness concept, they will obtain different results. The data for this article were collected over a decade ago. Practices in NPD have changed in those years. However, our focus was not on best practices, rather we wanted to examine the underlying structure of innovativeness, and its effects. It is not clear why these would not hold up over time. Analogously, Rogers developed dimensions of innovativeness from the adopter’s perspective (compatibility, trialability, . . . ) decades ago, and these dimensions and their effects on adoption decisions have remained current [44]. Even though there is no obvious reason why the patterns revealed here would no longer be valid, the findings derived from our data should be treated as tentative and should replicated with other samples. The data are retrospective, and are thus susceptible to memory loss and attribution bias: respondents may not remember the exact characteristics of the project and their memory may be colored by their knowledge of project outcomes. A recent empirical study of retrospective biases supported retrospective reporting as a viable research methodology [36]. However, only a longitudinal study that measures project characteristics at the beginning of each project and measures the project performance after the product has been in the market for a while, can avoid such distortions. Unfortunately no such longitudinal study of projects has yet been conducted. Because the current study used an existing dataset, it was limited in the extent to which it could examine the relationship between product innovativeness and its outcomes. In this article we only examined direct effects. Future research could build on the present findings by specifying mediator and moderator variables and developing a theoretical rationale for their inclusion. We made a first step by highlighting different dimensions of product newness and how they may be related to project termination and product performance. We hope that future research will build on this foundation by specifying more fine-grained causal links between product newness and its outcomes. Also because this study used existing data, it was limited to examining the product newness items present in those data. Future research aiming to improve our measures of product innovativeness might follow the measurement development procedure prescribed by Churchill [11], which would involve building items measuring product newness “from the ground up.” We hope that our theoretical framework will help researchers to specify the domain of the “product innovativeness” construct, and generate an even broader range of items to tap that domain. The empirical section of the present article has only looked at innovativeness from the firm’s perspective. Future research should examine the dimensions and effects of the

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newness of products to their prospective customers. Despite the volume of research that has already been done in this area, Gatignon and Robertson [22] concluded that the dimensions of innovativeness to customers are still unclear. Research into innovativeness from the customer’s perspective will be methodologically challenging since the proper judges of innovativeness to the customer can only be customers. The perceptions of firm informants of how new their product is to potential customers may be inaccurate. Consequently, researchers will have to collect data from customers as well as from innovating firms if they want to study the relation between product newness to customers on the one hand and product performance and firm development processes on the other hand. The question as to what determines the decision to go ahead with a project or to terminate it is important. The set of decisions to allocate development funds and managerial attention to some projects and not others together constitute the firm’s product portfolio, and ultimately shape its future direction and prosperity. This study found that only market familiarity had a significant association with the Go/No Go decision, but that technology familiarity, marketing and technological fit, and the necessity of new marketing activities did not. A limitation of the current study is that it did not make a fine-grained distinction as to when the new project was terminated. Several Go/No Go decisions are made throughout the development process. Ronkainen [45] and Hart et al. [27] found that managers use different screening criteria at different gates. It may well be that product newness has different effects on termination depending on the stage in the process at which the decision is made.2 To the best of our knowledge, the current article provides the first in-depth conceptual and empirical look at product innovativeness, a topic of great interest to all in the new product field. We hope that other authors will be able to draw on the distinctions between types of innovativeness made in this manuscript and use them to inform their own constructs and measures of innovativeness. Our findings regarding project termination and product performance suggested different effects for different aspects of product newness. We hope to intrigue scholars into conducting further research to verify these findings, and to explicate the mechanisms through which innovativeness affects new product outcomes.

Acknowledgments We are indebted to Hans Baumgartner, Sundar Bharadwaj, Jennifer Chang, Jack Matson, Susan Mohammed, Liza Rivera, Jeff Schmidt, Christophe Van den Bulte, Dave Wilson, and anonymous reviewers for their insightful sugges-

2

We thank a reviewer for this suggestion.

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tions. Special recognition goes to Robert Cooper for his willingness to share the data which this article re-examines. This research was supported by the Institute for the Study of Business Markets at Penn State University.

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Biographical sketches Erwin Danneels is Assistant Professor of Marketing at the Worcester Polytechnic Institute in Worcester, Massachusetts. He obtained his Ph.D. in Marketing from Penn State University. His research area of interest includes the growth and renewal of corporations through product innovation, the nature and consequences of product innovativeness, the characteristics of corporations with innovative new product programs, and the performance effects of innovative new product programs. Elko J. Kleinschmidt is Professor of Marketing and International Business and Director of the Engineering and Management Program at McMaster University. He holds a Mechanical Engineering degree, as well as an M.B.A. and Ph.D. in Business Administration. He is a leading researcher in the field of new product development, innovativeness, and the impact of the international dimension on new products. He has world-wide teaching, research, and consulting experiences in the area of new product development and marketing.