New product development (NPD) is the lifeblood

Transcription

1 J PROD INNOV MANAG 2012 Product Development & Management Association DOI: /j x Impact of Knowledge Type and Strategic Orientation on New Product Creativity and Advantage in High-Technology Firms* Namwoon Kim, Subin Im, and Stanley F. Slater As different types of knowledge may have different effects on new product positional advantage, knowledge portfolio management in concert with the firm s strategic orientation is indispensable for new product success. However, previous research has not dealt with the knowledge resources and strategic implementations that affect new product development (NPD). To fill in this gap, the current study focuses on two dimensions of knowledge type (knowledge complexity and knowledge tacitness) and two forms of strategic orientation (technological orientation and market orientation), which influence the positional advantages as determinants of NPD outcomes. Drawing on the resourcebased view, this study explains how these knowledge and strategic orientation variables influence new product creativity, which comprised the novel and meaningful characteristics of new products. Finally, it demonstrates how these two dimensions of new product creativity differentially provide product advantages in terms of customer satisfaction and product differentiation, which lead to superior new product performance. A conceptual framework is developed and the related hypotheses provided to incorporate the study variables and to test their relationships in a sample based on data collected from both marketing and project managers from 100 U.S. high-technology firms. The model estimation results from path analysis demonstrate that reliance on knowledge of high tacitness harms meaningfulness, while reliance on knowledge of high complexity increases both novelty and meaningfulness of new product. As expected, market orientation and technological orientation improve the meaningfulness and novelty dimensions of the new product, respectively. New product novelty and meaningfulness are shown to enhance new product advantage in terms of product differentiation and customer satisfaction, both of which contribute to new product performance. It is also found that the combinative use of market orientation and knowledge complexity, and technological orientation and knowledge tacitness positively influence both the novelty and meaningfulness of new products. This study, using the product-level analysis, contributes to the literature by clarifying how the firm s different knowledge properties and strategic orientations both play a role as a source of new product creativity, and how new product creativity, as a valuable and rare resource, enhances new product advantage. The study results suggest that project/product managers should increase the transferability and codifiability of unstructured knowledge by stimulating intraorganizational knowledge sharing among NPD team members, and that they should promote both technology and market-orientated practices to fully develop creativity of new products. Introduction New product development (NPD) is the lifeblood of firms competing in high-technology markets because of relatively short product life cycles. This has led to the development of extensive literature focused on the antecedents to new product success (see Address correspondence to: Namwoon Kim, Department of Management and Marketing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. msnam@polyu.edu.hk. Tel: (852) Fax: (852) * The first author acknowledges the financial support of the Hong Kong Polytechnic University s Central Research Grant (G-YE77) for this study. The second author also acknowledges that this study was partially supported by a research grant from the Association for Research on Consumer Financial Services, Tokyo, Japan. Henard and Szymanski [2001] and Montoya-Weiss and Calantone [1994] for summaries of the meta-analysis results on this relationship). Among these antecedents, two categories of firm resources that have emerged as significant predictors of new product success are the firm s knowledge base and its strategic orientation(s). While the influence of knowledge type (e.g., De Luca and Atuahene-Gima, 2007; Marinova, 2004) and strategic orientation (e.g., Gatignon and Xuereb, 1997; Im and Workman, 2004; Srinivasan, Lilien, and Rangaswamy, 2002; Zhou, Yim, and Tse, 2005) on new product success has been studied independently, no study of which has presented and tested a comprehensive model incorporating both sets of firm resources. This study is grounded in the resource-based view (RBV) of the firm (Barney, 1991; Wernerfelt, 1984).

2 IMPACT OF KNOWLEDGE TYPE AND STRATEGIC ORIENTATION J PROD INNOV MANAG 137 Resources can be classified into three categories physical capital resources, human capital resources, and organizational capital resources which are a source of competitive advantage when they are valuable, rare, and difficult to imitate (Barney, 1991). Organizational capital includes, among other things, dynamic capabilities that enable managers to adapt, integrate, and deploy physical and human capital to achieve alignment with the changing business environment (Teece, Pisano, and Shuen, 1997). Recent research has shown that marketing capabilities (e.g., DeSarbo, Di Benedetto, Jedidi, and Song, 2006; DeSarbo, Di Benedetto, and Song, 2007; BIOGRAPHICAL SKETCHES Dr. Namwoon Kim is a professor of marketing in the Department of Management and Marketing at Hong Kong Polytechnic University, Hong Kong. He received his Ph.D. from the University of Texas at Austin. His research interests include marketing and innovation strategy, new product-market entry strategy, channel knowledge management, and international services marketing. His research has appeared in Journal of Product Innovation Management, Journal of Marketing, Management Science, International Journal of Research in Marketing, Journal of the Academy of Marketing Science, Journal of Service Research, Industrial Marketing Management, Marketing Letters, and Journal of Business Research, among other journals. Dr. Subin Im an associate professor of marketing at Yonsei University in Korea while he is on leave from San Francisco State University (SFSU). He received his Ph.D. in marketing from the University of North Carolina, Chapel Hill in His primary scholarly interest includes the organizational aspects of innovation, creativity and innovation, new product development for marketing strategy, the consumer side of the innovation adoption process, and research methodology using multivariate statistical techniques including a structural equation model. Dr. Im s recent articles have appeared in Journal of Marketing, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, Journal of Product Innovation Management, Journal of International Marketing, Journal of Business Research, and Psychological Reports, among others. Dr. Im worked as a market researcher at Hynix Semiconductor Inc. in Korea, as well as an international banking officer at California banks. He has been selected for inclusion in Marquis Who s Who in America since Dr. Stanley F. Slater (Ph.D., University of Washington) is the Charles and Gwen Lillis Professor of Business Administration at Colorado State University. Stan s primary research interests are in the areas of market strategy formulation and implementation, strategies for innovation management, and the role of a market orientation in organizational success. He has published more than 50 articles on these and other topics in Journal of Marketing, Journal of the Academy of Marketing Science, Strategic Management Journal, and Journal of Product Innovation Management, among others. A recent study published in the Journal of Marketing found that Stan was one of the ten most influential marketing scholars in the world based on citations to articles that he coauthored from 1990 to He has won Best Paper awards from the International Marketing Review and the Marketing Science Institute. Stan serves on the editorial review boards of the Journal of Marketing, the Journal of the Academy of Marketing Science, and Industrial Marketing Management. DeSarbo, Di Benedetto, Song, and Sinha, 2005; Song, Di Benedetto, and Nason, 2007; Vorhies, Morgan, and Autry, 2009), technology capabilities (e.g., DeSarbo et al., 2005, 2006, 2007; Ramaswami, Srivastava, and Bhargava, 2009; Song et al., 2007), and NPD execution capabilities (Harmancioglu, Droge, and Calantone, 2009) have a positive impact on performance. This study suggests that different types of knowledge assets (e.g., simple versus complex and explicit versus tacit) in concert with appropriate strategic orientations represented by outside-in (e.g., market-oriented) and inside-out (e.g., technology-oriented) capabilities (Day, 1994; Srivastava, Fahey, and Christensen, 2001) comprise a dynamic capability. This capability is valuable when it enables the firm to conduct its business more effectively (i.e., create superior customer value) and/or efficiently (i.e., at a lower cost), which helps the firm maintain alignment with the market by developing creative new products and by taking them successfully to market (e.g., Teece et al., 1997). Such a complementary set of intangible resources is a rare and difficult-to-imitate capability primarily because it is a socially complex phenomenon that is beyond the ability of competitors to thoroughly understand and copy. Because different types of knowledge may have different effects on new product positional advantage, managing the knowledge portfolio that is integrated with the firm s strategic orientation may be required for new product success (Grant, 1996b; Kogut and Zander, 1992). To date, however, our understanding of how these two different dimensions of dynamic capability knowledge type and strategic orientation affect NPD outcomes is still lacking. To remedy this deficiency, this study focuses on two dimensions of the knowledge type knowledge complexity (how many bundles of interdependent knowledge are involved) and knowledge tacitness (how easily knowledge can be codified for transfer) and two forms of strategic orientation technological orientation and market orientation which influence the positional advantages as determinants of NPD outcomes. Technological orientation is a key intangible resource of high-tech firms as it produces the technological knowledge that is required to develop advanced products (Gatignon and Xuereb, 1997), while market orientation (e.g., Kohli and Jaworski, 1990; Slater and Narver, 1998) produces knowledge about extant and latent customer needs, and about competitors capabilities, strategies, and products. To achieve a positional advantage, the firm s product and service offerings must address customer needs more effectively than those of its competitors.

3 138 J PROD INNOV MANAG N. KIM ET AL. Knowledge Type Dimensions of New Product Creativity New Product Advantage Knowledge Complexity H2a + Knowledge Tacitness H1a + Novelty of New Product H5 + Product Differentiation H7a + New Product Performance Strategic Orientation Technological Orientation H4 + H1b - H2b + Meaningfulness of New Product H6a + H6b + Customer Satisfaction H7b + Market Orientation H3 + Figure 1. The Conceptual Framework Drawing on the RBV, the current study explicates how these knowledge and strategic orientation variables influence new product creativity, which comprised the novel and meaningful characteristics of new products that are generated in the product development and launch stages. Finally, this study demonstrates how these two dimensions of new product creativity (novelty and meaningfulness) differentially provide product advantages in terms of customer satisfaction and product differentiation, which lead to superior new product performance (Anderson and Mittal, 2000; Day and Wensley, 1988). The model proposed and tested is depicted in Figure 1. Theory and Hypotheses Effects of the Type of Knowledge on New Product Creativity Based on the pioneering work of Amabile (1983, 1988), new product creativity is defined as the degree to which the new product is perceived to provide unique and meaningful differences to customers. For this study, Amabile s (1983) two-dimensional perspective on creativity is adopted, which proposes that creativity is composed of novelty (i.e., the degree of the originality and unique differences of the new product) and meaningfulness (i.e., the degree to which a new product provides appropriate and useful aspects to target customers). A firm s ability to generate creative ideas, convert those ideas into innovative new products, and launch those products into the marketplace constitutes a dynamic capability that gives the firm a competitive edge that is reflected in new product advantage (e.g., Ettlie and Pavlou, 2006; Leonard-Barton, 1992). Based on prior research, knowledge (e.g., Amabile, 1998; Sethi, Smith, and Park, 2001) and strategic orientation (Grinstein, 2008b; Im and Workman, 2004) are asserted to be two of the most important antecedents to new product creativity. Among multiple dimensions and types of knowledge, our study examines the tacitness and complexity dimensions to explain the value of knowledge transfer and integration (see Teece, 1998, for an extensive review of knowledge typologies). Our study focuses on these two dimensions because the knowledge for NPD is nurtured through the search for tacit as well as complex knowledge that is accumulated in the different levels of organizational memory, and that resides in the minds of multiple cross-functional organizational members (Eisenhardt and Santos, 2002). As this study explores the impact of knowledge as a competitive resource that promotes new product creativity, the more ambiguous and difficult-to-interpret knowledge may reveal many promising market opportunities that may stimulate creative minds. In this sense, the fate of a new product depends, to

4 IMPACT OF KNOWLEDGE TYPE AND STRATEGIC ORIENTATION J PROD INNOV MANAG 139 some extent, on how well these two dimensions of knowledge are incorporated and implemented in the NPD process (Doz, Santos, and Williamson, 2001; Hansen, 1999). Tacit knowledge comprised subjective insights, intuitions, and hunches that cannot be written down (Teece, 1998), while codified knowledge is found in manuals as documentation, patents, blueprints, reports, and other accessible sources (Inkpen and Dinur, 1998). Complex knowledge consists of bundles of interdependent knowledge about a strategically relevant phenomenon, while simple knowledge is independent of other bits of knowledge. Complex knowledge is considered a strong foundation for a firm s efforts to generate new sources of competitive advantage (Kogut and Zander, 1992). Link between knowledge tacitness and new product creativity. Knowledge of high tacitness is difficult to imitate, relatively immobile, and unique to the knowledge owner, serving as an intangible asset that differentiates its owner from other competitors (Eisenhardt and Santos, 2002; Gupta and Govindarajan, 2000). Due to its inimitable and creative properties, tacit knowledge is a firm resource that may stimulate creative solutions to market opportunities and problems, and may provide the firm with the opportunity to create and sustain an innovative edge (Berman, Down, and Hill, 2002). In their discussion of the role that organizational knowledge plays in innovation, Hargadon and Fanelli (2002) argue that tacit knowledge provides the firm with novel ideas by allowing it to deviate from its existing patterns of action and to explore new possibilities. Following this stream of logic, it is noted that a firm s efforts to incorporate more tacit knowledge into new product concepts will add to distinctive and unique solutions to customers, and thereby help increase the novel aspect of a new product. Formally put: H1a: The greater the reliance on knowledge of high tacitness, the greater the new product novelty. On the other hand, knowledge of high tacitness is hard to articulate, communicate, and is generally not amenable to codification efforts (Hansen, 1999; Kogut and Zander, 1992; Teece, 1998). Because tacit knowledge is contextspecific, difficult to formalize, and can possibly be transmitted only to employees in the same working unit, sharing such knowledge among different departments is much more difficult than sharing codified knowledge (Grant, 1996a; Nonaka, 1994). It is proposed that reliance on tacit knowledge will have a negative impact on new product meaningfulness. This is because to make a product meaningful to customers, the NPD team members are required to share extensive and explicit information regarding potential customers needs and expectations, as well as the appropriateness and usefulness of the new product for target customers (Amabile, 1983; Im and Workman, 2004). Teece (1998) argues that tacit knowledge, in contrast to codified knowledge, is slow and costly to transfer to different working units in NPD because its ambiguous characteristics hinder communication. Thus, tacit knowledge often prevents NPD team members with different functional backgrounds from communicating and sharing pertinent market and technical information with each other, which inhibits them from developing new product concepts that provide meaningful value to target customers. Day (1994) also argues that insufficient cross-functional understanding and cooperation are a critical barrier to developing useful new product concepts. H1b: The greater the reliance on knowledge of high tacitness, the lower the new product meaningfulness. Link between knowledge complexity and new product creativity. The NPD team s complex deep (i.e., volume of knowledge) and diverse (i.e., heterogeneity among knowledge components) knowledge resource becomes a rich source for distinct and unique new products as it provides an important clue for innovative or outside-thebox new product concepts (Amabile, 1988; De Luca and Atuahene-Gima, 2007; Eisenhardt and Santos, 2002). The complex knowledge for NPD that is deeply rooted in technological and market information will enhance new product novelty because of its great potential for generating new and diverse ideas. Hargadon and Fanelli (2002) argue that such a firm s deeply and diversely embedded technological stock for creating new knowledge is latent knowledge that could produce an innovative outcome. This intraorganizational knowledge heterogeneity reflects a large pool of potential innovative ideas, and thereby helps provide the firm with more opportunities to create unique and novel solutions that seem to be unconventional to other competing firms. H2a: The greater the reliance on knowledge of high complexity, the greater the new product novelty. Prior research shows that a firm s ability to draw on different sources of knowledge facilitates the exploration of unserved market and technological opportunities (e.g., De Luca and Atuahene-Gima, 2007; Kogut and Zander, 1992). The firm s ability to create complex knowledge by combining diverse organizational information is expected to lead to more customer-centered, valuable ideas and

5 140 J PROD INNOV MANAG N. KIM ET AL. increase new product meaningfulness. This is because the multifunctional attributes of complex knowledge will increase the opportunities of the firm s comprehensive understanding and decision making to meet customer expectations, which can result in greater meaningfulness of the new product. H2b: The greater the reliance on knowledge of high complexity, the greater the new product meaningfulness. Effects of Strategic Orientation on New Product Creativity Strategic orientation is the strategic directions implemented by a firm to create the proper behaviors for the continuous superior performance of the business (Gatignon and Xuereb, 1997, p. 78). A strategic orientation is critical to the management of NPD knowledge since it helps a firm determine the focus for knowledge creation, and how knowledge is shared and integrated to become a resource from which to develop and launch new products (Teece, 1998). As noted previously, there exist several different strategic orientations that reflect the focus of the firm s knowledge-creating activities. For example, Gatignon and Xuereb (1997) use market orientation and technological orientation to characterize a firm s strategic orientation. More recently, Grinstein (2008a) also argues that market orientation is a strategic orientation and that it should be studied in combination with other strategic orientation dimensions (e.g., technological orientation in our study) in order to understand how a firm obtains more successful market intelligence and responses. (Please see Grinstein [2008a] for a comprehensive review of different strategic orientations.) Based on this literature, a firm s comprehensive strategic orientation that comprised market orientation and technological orientation will have the greatest influence on new product creativity in high-tech markets. Specifically, it is conjectured that market orientation, as an outside-in process, significantly improves the meaningfulness dimension of the new product, while technological orientation, as an inside-out process, enhances the novelty counterpart. This is because market orientation focuses mainly on understanding customer needs and offering a product as a totality of value for customers or a solution to customer requirements in competitive markets (Slater and Narver, 1998). Therefore, the marketoriented product concept may not be necessarily novel but should be meaningful to customers. On the other hand, technological orientation emphasizes providing new and unique product features and functions derived from technological advancement that increase the innovative product aspects (Zhou et al., 2005). Thus, a technologically oriented new product might be novel but not necessarily meaningful. Link between market orientation and new product meaningfulness. Market-oriented firms place the highest priority on the creation and maintenance of superior customer value through the development of and responsiveness to market information (e.g., Kohli and Jaworski, 1990; Slater and Narver, 1995). A market orientation, as a strategic orientation, may be an intangible resource that leads to positional advantage in the sense that it provides information on how to produce an offering that is well tailored to the preferences of the target market (Hult and Ketchen, 2001; Hunt and Morgan, 1995). Again, it may be so since [c]ompetitive advantage grows fundamentally out of the value a firm is able to create for its buyers (Porter, 1985, p. 16). For the purposes of this study, Narver and Slater s (1990) integrated perspective on market orientation that combines the three components customer orientation, competitor orientation, and interfunctional coordination is adopted. Based on this integrated perspective of a market orientation, Hult and Ketchen (2001) argue that market orientation increases the firm s receptivity to new concepts which leads to better market responsiveness and performance. More recently, Im and Workman (2004) also provide empirical evidence on the close link between the market orientation of firms and new product creativity. The current study argues that a firm s market orientation increases new product meaningfulness because a marketoriented firm engages in the organization-wide development of and responsiveness to information about the expressed and latent needs of customers. From the organizational learning perspective, market orientation enhances new product meaningfulness because it directly relates to generating, disseminating, and responding to market intelligence in order to provide superior customer value (Slater and Narver, 1995). Thus, a highly marketoriented firm, encouraging the integrated effort to respond to customers needs and market trends, tends to develop new products that are more useful and meaningful to customers than its competitors do. Formally stated: H3: The greater the market orientation of a firm, the greater the new product meaningfulness. Link between technological orientation and new product novelty. Gatignon and Xuereb (1997, p. 78)

6 IMPACT OF KNOWLEDGE TYPE AND STRATEGIC ORIENTATION J PROD INNOV MANAG 141 define technological orientation as the ability and the will to acquire a substantial technological background. Technological orientation includes behaviors such as substantial investment in research and development (R&D); use of the latest, state-of-the-art, sophisticated technologies in NPD; and proactive scanning, acquisition, and rapid integration of new technologies inside and outside the industry (Gatignon and Xuereb, 1997; Han, Kim, and Kim, 2001). More recently, Zhou et al. (2005) argue that technological orientation, reflecting technology push, focuses on concepts that employ pioneering and state-ofthe-art technologies, whereas market orientation, reflecting market pull, nurtures the new product concepts that better satisfy customer needs (Zhou et al., 2005). It is expected that a firm with a strong technological orientation is likely to develop and incorporate distinct and unique new product ideas based on superior technologies in the NPD process. Thus, such a firm involved in keeping up with new technological trends in the market will generate novel new products based on technology push. Hence: H4: The greater the technological orientation of a firm, the greater the new product novelty. Effects of New Product Creativity on New Product Advantage New product advantage is one of the most important determinants of superior new product performance (Cooper and Kleinschmidt, 1987; Song and Parry, 1997). It is defined as a product s perceived superiority over competing products with respect to the product differentiation and customer satisfaction dimensions. The first dimension of new product advantage, product differentiation, represents the degree of distinctiveness of a new product relative to competing products in terms of product image and strategic positioning (e.g., premium brand/pricing, Homburg, Workman, and Krohmer, 1999; Song and Parry, 1997). Customer satisfaction, the second dimension of new product advantage, is the degree to which a new product satisfactorily fulfills customer s needs and expectations (Oliver, 1997). It often reflects the level of customer perception that he/she has obtained a new product that is commensurate with the price paid (Anderson, Fornell, and Lehmann, 1994; Zhang, Vonderembse, and Cao, 2009). Previous research has shown that new product creativity generates new product advantage by enhancing the novel and useful qualities of the product (Kleinschmidt and Cooper, 1991; Song and Montoya-Weiss, 2001). According to Song and Parry (1997), advanced technologies or product designs help solve unusual market requirements more effectively than competing products. It is expected that novel new product offerings based on an advanced technological portfolio will help achieve a premium position for the new product, differentiating it from the competitors products. H5: The greater the new product novelty, the greater the product differentiation of the new product. Previous research on new product advantage has shown that new product creativity may lead to competitive advantage for the new product by meeting unique/ differential market demands in meaningful ways (Hunt and Morgan, 1995; Li and Calantone, 1998). Thus, meaningful new products may enhance product differentiation by including superior designs and unexpected technical advances in response to customers needs (Hargadon and Sutton, 1997). Meaningful new products also maximize product-market fit by adapting and refining existing products and services to the current customer s needs through quality control and improvement (Ettlie, Bridges, and O Keefe, 1984). For example, the House of Quality concept is used to improve the fit between customer needs (e.g., required quality of print output) and engineering characteristics (e.g., maximum resolution capacity provided by a printer) (Crawford and Di Benedetto, 2006). Anderson and Mittal (2000) and Szymanski and Henard (2001) argue that a higher level of meaningful product attributes will increase customer satisfaction for new products. Zhang et al. (2009) also demonstrate that flexibly adjusted new product concepts that meet market needs lead to greater customer satisfaction. In this sense, new product meaningfulness, as it is relevant to fulfilling customer expectations and needs, is expected to enhance customer satisfaction for the new product. Combining these two perspectives suggests that a firm that emphasizes meaningful new product solutions achieves competitive advantage by offering distinctive product attributes that can provide unique customer benefits and, simultaneously, increase customer satisfaction. Thus: H6: The greater the new product meaningfulness, the greater (a) the product differentiation and (b) the customer satisfaction with the new product. Effects of New Product Advantage on New Product Performance It is proposed that both new product advantage dimensions product differentiation and customer satisfaction will increase new product performance in

7 142 J PROD INNOV MANAG N. KIM ET AL. terms of sales, market share, return on investment (ROI), and profit relative to competing products. This is because product differentiation provides a distinctive positioning based on innovative technologies and designs, whereas customer satisfaction creates superior customer-based profitability due to increased customer loyalty and retention, repeat purchases, and/or positive word of mouth that enhance project-level new product performance (Anderson and Mittal, 2000; Szymanski and Henard, 2001). Thus, both product differentiation and customer satisfaction will lead to better new product performance. H7: The greater (a) the product differentiation and (b) the customer satisfaction with the new product, the greater the new product performance. Method Sample and Data Collection The data for this study were collected through a crosssectional survey of firms competing in high-tech manufacturing industries in the United States. We chose high-tech companies that tend to engage in intense product innovation activities and that are generally characterized by short development cycle and product life cycle as well as rapid changes of industry standards (Song and Montoya-Weiss, 2001). The sample includes manufacturing firms in the following fields: computer software and hardware, automation, telecommunications, medical equipment, pharmaceutical and biotechnology, subassembly, industrial electronics, advanced materials, and tests and measurements. In identifying appropriate respondents from the participating firms, a two-stage sampling method was used. First, our research personnel identified the marketing manager who could select a new product and evaluate its performance and assess the constructs represented by the control variables. This manager then identified the project manager who could evaluate the remaining variables of the model. This evaluation was done as a separate second stage. This method is essential to reduce the potential common method bias and perceptual bias across the subjectively measured constructs for independent and dependent variables (Olson, Walker, and Ruekert, 1995). 1 Also, it was found that both marketing managers and project 1 As a check for common method bias, an additional analysis regressed new product performance measured by marketing managers on creativity dimensions (novelty and meaningfulness) measured by project managers. The meaningfulness dimension, but not novelty dimension, was significant at a.05 level, consistent with Im and Workman s (2004) study that also used two separate respondents for independent and dependent variables. managers were suitable for this study because of their high level of involvement in the NPD process (i.e., their average levels of new product involvement were over 5.45 on a 7-point scale). 2 A pilot study was conducted first to determine whether the questionnaire was clearly understood. Upon completion of the questionnaire, comments from five academic experts in the related research areas were solicited, and the questionnaire was revised according to their input. A list and detailed information about marketing managers was obtained from the CorpTech Directory of Technology Companies. Thirty-five firms from different high-tech industries were randomly selected, and their cooperation in our study was requested. Then, our questionnaire was sent to the 19 firms that agreed to participate, asking their marketing and project managers to complete the questionnaire and provide comments on its content. After reviewing the results of this pilot test, the researchers confirmed that the questionnaire items were correctly understood in the context of our study. The questionnaire was refined to incorporate feedback from the pilot test and finalized for the main survey. A random sample of 445 firms in different high-tech industries from the above-mentioned directory was obtained. In order to enhance the response rate, all managers in the sample firms were called first, and two follow-up s were sent to them to solicit their participation in our survey. Then, a mail survey was sent to these 445 firm managers. Three weeks later, the same questionnaire was sent to the managers using fax or as a reminder. One hundred nine sets of matched responses from a marketing manager and a project manager in each company were received, resulting in a 24.5% response rate. For each company, it was confirmed that the two respondents replied based on the same new product project. After nine cases were excluded because of incomplete responses, a total of 100 sets of responses were left for further analysis. The participating firms were relatively large and stable, with an average of 1030 employees and average age of business of 6.67 years. On average, the firms have initiated new product projects during the last 3 years. The unit of analysis in our study is the new product project launched by product innovation teams. The first respondents (i.e., marketing managers) were asked to report on the most recently developed and launched new product for which their business unit was responsible and 2 The titles for marketing managers include executive vice president, senior vice president, vice president, director, or manager of marketing and sales, while those for project managers include manager, assistant manager, or project leader.

8 IMPACT OF KNOWLEDGE TYPE AND STRATEGIC ORIENTATION J PROD INNOV MANAG 143 with which they were familiar. To be consistent with current practice (e.g., Song and Parry, 1997), it was required that the product be in the market for at least 12 months, regardless of its level of performance. This approach helped avoid recall bias from the respondents and maximize the variance of the outcome measures. In particular, limiting the selection of projects to those that were at least 12 months old helps respondents avoid selection and social desirability biases toward more successful products (Sethi et al., 2001). Nonresponse bias was also tested. T-test results for the major constructs showed that there are no significant differences between those who returned the survey in 3 weeks from the receipt of the survey and those who returned it after 3 weeks, indicating that nonresponse bias is not a major concern for this study. Multicollinearity diagnostic tests (Belsley, Kuh, and Welsch, 1980) confirm that no serious multicollinearity exists for the analyses (all condition indexes are less than 30, and all variance inflation factors are less than 10). Finally, no violations of the regression assumptions (i.e., normality, linearity, and homoscedasticity) have been found from diagnostic tests and a scatter plot analysis of residuals in regression. Measures The validity and reliability of the measurement instruments were tested following recommendations by Anderson and Gerbing (1988). Existing measures were utilized for the majority of constructs. The measures of some constructs (i.e., knowledge complexity and knowledge tacitness) were developed by adapting content from previous studies to our study context in order to assess these constructs at the product level. Multipleitem, 7-point Likert-type scale measures were used, with 1 for strongly disagree and 7 for strongly agree, for all constructs except for three control variables technology growth rate, market growth rate, and firm size. To confirm scale purity, exploratory factor analyses were performed using varimax rotation, and item-to-total correlations and internal consistency were examined for each factor. Exploratory factor analysis results show that all measurement items load onto the corresponding subject constructs. The scales for each construct showed high internal consistency/reliability based on coefficient alphas of.70 or higher. See the Appendix for the measurement items and their internal consistency. Second, confirmatory factor analysis was performed. Confirmatory measurement models were estimated to examine the unidimensionality and convergent validity of each construct using maximum likelihood (ML) estimation in AMOS (IBM Software, New York, USA) (Anderson and Gerbing, 1988; Bagozzi, Yi, and Phillips, 1991). As recommended by Bentler and Chou (1987), two confirmatory measurement models were run one with knowledge type, strategic orientation, and creativity dimensions of the new product, and another with new product advantage, performance dimensions, and control variables. The goodness-of-fit indices for the first group are as follows: normed fit index (NFI) =.91, incremental fit index (IFI) =.96, relative fit index (RFI) =.90, Tucker Lewis index (TLI) =.95, and comparative fit index (CFI) =.96. Those for the second group are as follows: NFI =.93, IFI =.97, RFI =.92, TLI =.96, and CFI =.97, demonstrating a reasonable fit of the measurement model to data and no interpretational confounding from the measures. The significant factor loadings for all of the measure items (l s) in the Appendix provide evidence of convergent validity of the measure (Bagozzi et al., 1991). In addition, discriminant validity was assessed by comparing chi-square estimates for pairs of the significantly correlated constructs in Table 1. The significant results from chi-square difference tests (3.7 < Dc 2 < 40.6, Dd.f. = 1, all p-values <.05) in favor of the unrestricted models (i.e., correlations are freely estimated) over the restricted models (i.e., correlations are fixed at 1) confirmed discriminant validity (Anderson and Gerbing, 1988). For a more stringent verification of discriminant validity, the procedure based on the average variance extracted (AVE) was used, following Fornell and Larcker (1981). In Table 1, the AVE value for each construct is larger than the squared correlation between a pair of constructs, showing that discriminant validity is achieved. In all, these results suggest good construct validity and unidimensionality for all measures. Measurement scales. Knowledge tacitness is operationalized in terms of the degree to which the knowledge involved in a new product project is not structured, organized, or documented as the knowledge base of the business unit. Based on the studies by Eisenhardt and Santos (2002) and Hansen (1999), a 4-item Likert-type scale measure was developed for knowledge tacitness with a good internal consistency (composite reliability [CR] =.87). Knowledge complexity refers to the degree to which the knowledge involved in a new product project requires combining and integrating diverse types of knowledge. Based on the expert interviews, as well as the extant studies by Amabile (1988) and Hansen (1999), a 3-item Likert-type scale was created for this

9 144 J PROD INNOV MANAG N. KIM ET AL. Table 1. Correlations among Major Constructs AVE Number of Items Mean (SD) 1. Knowledge tacitness 2. Knowledge complexity 3. Market orientation 4. Technological orientation 5. New product novelty 6. New product meaningfulness 7. Product differentiation 8. Customer satisfaction 9. Technology growth rate 10. Market growth rate.91 a *** ***.50*** ***.25**.48*** ***.21**.31***.20**.38*** (1.30) (1.15) (1.02) (1.55) (1.46) (0.83) ***.23***.30***.34***.27*** (1.25) * *.27***.35*** (1.25) **.31*** **.23** (1.40) -.23** ***.23**.21***.20** (2.09) 11. Firm size (3676.0) 12. Resource ***.54***.24**.14.49***.09.33***.26*** deployment capability 13. Firm innovativeness 14. Research and development expenditure 15. New product performance ***.57***.42***.42***.34***.40*** *** (.81) ***.11.55***.35*** ***.21** *** (1.62) ***.51***.17*.29***.07.22**.13.22** (1.60) (1.06) * Significant at the.10 level (two-tailed). **Significant at the.05 level (two-tailed). ***Significant at the.01 level (two-tailed). a Diagonal elements represent coefficient alpha values. AVE, average variance extracted; SD, standard deviation.

10 IMPACT OF KNOWLEDGE TYPE AND STRATEGIC ORIENTATION J PROD INNOV MANAG 145 construct. The measure has a good internal consistency as reflected by the CR of.78. For market orientation, Narver and Slater s (1990) measure was adopted, which combines customer orientation, competitor orientation, and cross-functional coordination into a single construct. Fourteen items were used to measure this construct, and they exhibited good internal consistency (CR of.92). For technological orientation, a 6-item scale was employed from Gatignon and Xuereb (1997) and Han et al. (2001). The measure exhibited a high internal consistency (CR of.93). Following Im and Workman (2004), this study adapted 4-scale items each to measure the novelty and meaningfulness dimensions of new product creativity. These items, along with their CRs (.88 and.80, respectively), are reported in the Appendix. New product advantage refers to a product s perceived superiority relative to competitive products, encompassing product differentiation and customer satisfaction (Cooper, 1979; Song and Montoya-Weiss, 2001). Product differentiation represents the degree of distinctiveness of a new product relative to competitive products in terms of premium product, brand image, and price. A 3-item scale was adapted from Homburg et al. (1999) that had good internal reliability (CR of.88). Customer satisfaction reflects a customer s pleasurable level of fulfillment of his/her needs and expectations with use of a new product compared with competing products and original goals (Oliver, 1997). Based on this conceptual definition, the current study measured customer satisfaction using Im and Workman s (2004) 3-item scale (CR of.75). New product performance is measured as the degree to which a product performs well in the market relative to its major competitors in terms of sales, market share, ROI, and profit (e.g., Kleinschmidt and Cooper, 1991; Song and Parry, 1997). As recommended by Montoya-Weiss and Calantone (1994) and Song and Parry (1997), a 4-item measure of new product performance was used to assess different perspectives of new product performance that combine market measures (i.e., relative market share, relative sales) and financial measures (i.e., relative ROI, relative profitability). Relative subjective measures (i.e., performance relative to a firm s other competing products) were used because objective measures (i.e., financial data) are often inaccurate or unavailable for new products (Han, Kim, and Srivastava, 1998; Song and Parry, 1997). The measure had a high internal consistency (CR of.91). Control variables. Two environmental variables (i.e., technology growth rate and market growth rate) were used as control variables to account for external influences on new product creativity. The munificence of these environmental conditions may influence new product creativity (novelty and meaningfulness) because NPD activities must appropriately reflect the changes in the technological and market situations (Brown and Eisenhardt, 1995). Firm size defined as the number of employees of a firm was also included since large firms would be more likely to have the financial and technical capabilities to develop, introduce, and manage creative new products (Chandy and Tellis, 2000). Three additional control variables resource deployment capabilities, firm innovativeness, and R&D expenditure that are presumed to influence new product performance were also tested. Resource deployment capability is defined as the degree to which a business unit has the ability to acquire, allocate, and outsource financial, physical, and human resources from within and outside the company. Based on the results from previous studies by Kor and Mahoney (2005) and Maritan (2001), resource deployment capabilities, as an implementation/ coordination aspect of organizational capabilities, are expected to contribute to the firm s new product performance. Firm innovativeness reflects the extent to which a firm is seeking or readily adopting innovative ideas (Hurley and Hult, 1998). Hurley and Hult posit that a firm s innovativeness increases its capacity to innovate for better organizational performance. It is expected that such firm innovativeness will eventually contribute to better product performance. Finally, R&D expenditure refers to the degree to which a firm emphasizes R&D activities and invests in them relative to major competitors in the industry. According to Hurley and Hult (1998) and Kor and Mahoney (2005), greater R&D expenditure may lead to superior R&D capabilities, creating greater customer value and product performance. Responses to the control variable items were provided by the marketing manager of the firm. In many strategic business units, the marketing manager holds an executive position such as executive vice president, senior vice president, and vice president. Accordingly, these respondents should have good access to various company-specific internal information and reports (Johnston and Woodward, 1988). The measurement items and CR of these control variables are provided in the Appendix. Analysis and Results Before the results of our hypothesis tests are discussed, a correlation matrix and descriptive statistics (means

11 146 J PROD INNOV MANAG N. KIM ET AL. Table 2. Estimation Results from Path Model: Standardized Coefficients (t-value) New Product Novelty New Product Meaningfulness Product Differentiation Customer Satisfaction New Product Performance Independent variables Knowledge tacitness -.08 (-.83) -.26** (-2.86) Knowledge complexity.26** (2.67).17* (1.68) Market orientation -.03 (-.15).21** (2.16) Technological orientation.38** (3.63) -.01 (.06) New product novelty.45** (5.27).11 (1.10) New product meaningfulness.23** (2.73).23** (2.31) Product differentiation.16* (1.88) Customer satisfaction.45** (5.17) Control variables Market growth rate -.03 (-.30).26** (2.91) Technology growth rate -.07 (-.95) -.10 (-1.12) Size.01 (.01).10 (1.01) Resource deployment.15 (1.07) Firm innovativeness -.02 (-.01) Research and development expenditure -.02 (-.07) Squared multiple correlation (R 2 ) * Significant at the.10 level (two-tailed). ** Significant at the.05 level (two-tailed). Fit statistics for main effects path model: c 2 (d.f.) = (70) p <.05**, NFI =.95, RFI =.91, IFI =.96, TLI =.93, CFI =.96. NFI, normed fit index; RFI, relative fit index; IFI, incremental fit index; TLI, Tucker Lewis index; CFI, comparative fit index. and standard deviations) for composite scales are demonstrated in Table 1. In general, signs of the correlation matrix appear to be consistent with the hypothesized relationships, and the means and standard deviations of the major constructs indicate adequate variability. Hypothesis Test Results In order to estimate the suggested framework, an ML estimation was performed using path analysis in AMOS, since the model examines the causal ordering of the knowledge type and strategic orientation factors, new product creativity, and new product outcomes (i.e., new product advantage and performance). 3 The overall model fit was examined first. Although the chi-square statistic (c 2 = , d.f. = 70) is significant at the.05 level, the overall baseline comparison indices (NFI =.95, RFI =.91, IFI =.96, TLI =.93, CFI =.96) are greater than.90, which suggests that the model fits the data well. In Table 2, the ML estimation results are reported based on standardized coefficients. First, the estimation results for the influence of the control variables show that market growth rate is 3 Seemingly unrelated regression (SUR) estimation method was also used to analyze interaction effects between knowledge type and strategic orientation on the dimensions of new product creativity. The SUR model estimation results are discussed in the Additional Exploratory Analysis section. positively related to new product meaningfulness (g=.26, p <.05) but that the other control variables do not have significant influence on the related dependent variables. After controlling for the effects of the control variables, the current study examined the following relationships as hypothesized. H1a b proposed that reliance on knowledge of high tacitness improves novelty, while it harms meaningfulness of the new product. The estimation results show that reliance on knowledge of high tacitness has no significant impact on novelty (g=-.08), while it harms meaningfulness (g=-.26) at the.05 level. Thus, H1b is supported, but H1a is not supported. H2a b hypothesized that reliance on knowledge of high complexity positively influences both novelty and meaningfulness. The empirical findings indeed support both H2a (g=.26 for novelty, p <.05) and H2b (g=.17 for meaningfulness, p <.10). H3 posited a positive effect of market orientation on meaningfulness. Our results support H3 (g=.21, p <.05). It is also found that technological orientation enhances novelty as expected (g=.38, p <.05), supporting H4. H5 predicted that new product novelty positively influences product differentiation. H5 is empirically supported (g=.45, p <.05). A positive impact was hypothesized from new product meaningfulness on product differentiation (H6a) and customer satisfaction (H6b). The estimation results are consistent with both