Does the use of knowledge integration mechanisms enhance product innovativeness?

Industrial Marketing Management 46 (2015) 214–223

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Industrial Marketing Management

Does the use of knowledge integration mechanisms enhance product innovativeness?☆ Kuen-Hung Tsai a,⁎, Yi-Chuan Liao a, Teresa Tiaojung Hsu b a b

Department of Business Administration, National Taipei University, 151 University Road, San Shia, Taipei 237, Taiwan Department of Business Administration, Cheng Shiu University, 840 Chengcing Road, Niaosong District, Kaohsiung City 83347, Taiwan

a r t i c l e

i n f o

Article history: Received 9 August 2012 Received in revised form 14 November 2014 Accepted 23 December 2014 Available online 18 March 2015 Keywords: Knowledge integration mechanisms Product innovativeness Technological turbulence Market turbulence Competitive intensity

a b s t r a c t This study draws upon the perspectives of organizational learning and environmental contingency to investigate how the use of knowledge integration mechanisms affects product innovativeness under different levels of technological turbulence, market turbulence, and competitive intensity. Based on a sample of 102 high-tech product projects, hierarchical moderated regression analyses reveal that product innovativeness is related to knowledge integration mechanisms in a curvilinear manner under different levels of competitive intensity, market turbulence, and technological turbulence. Specifically, under a low level of competitive intensity, market turbulence and technological turbulence, the relationship between knowledge integration mechanisms and product innovativeness is an inverted U-shape. By contrast, under a high level of competitive intensity, market turbulence, and technological turbulence, product innovativeness is related to knowledge integration mechanisms in a U-shaped manner. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Managing product innovation has become a significant area of focus in industrial marketing management (Hutt & Speh, 2010). In practice, product innovation usually requires the use of knowledge integration mechanisms in industrial markets (Mohr, Sengupta, & Slater, 2010; Zhang, Di Benedetto, & Hoenig, 2009). Thus, understanding the influence of knowledge integration mechanisms on product innovation outcomes is imperative. Extant studies have examined the effects of knowledge integration mechanisms on product innovation performance (e.g., De Luca & Atuahene-Gima, 2007; De Luca, Verona, & Vicari, 2010; Helfat & Raubitschek, 2000; Koch, 2011) and made contributions to the marketing and innovation literature. In essence, product innovation performance in these studies is described as a new product's market or financial performance. Past literature has suggested that product innovativeness, as market and financial performance, is a key feature of product innovation outcome (McNally, Cavusgil, & Calantone, 2010; Salavou & Avlonitis, 2008). Although product innovativeness has been regarded as a performance dimension in assessing the influence of knowledge utilization in high-tech new products (Molina-Castillo, Jimenez-Jimenez, & Munuera-Aleman, 2011; Tsai,

☆ The work described in this article was supported by a grant from the Ministry of Science and Technology, Taiwan (NSC-101-2410-H-230-025) awarded to the third author. ⁎ Corresponding author. E-mail addresses: [email protected] (K.-H. Tsai), [email protected] (Y.-C. Liao), [email protected] (T.T. Hsu).

http://dx.doi.org/10.1016/j.indmarman.2015.02.030 0019-8501/© 2015 Elsevier Inc. All rights reserved.

Hsieh, & Hultink, 2011), past research has paid little attention toward examining the effects of knowledge integration mechanisms on product innovativeness. Thus, more research is needed for investigating how knowledge integration mechanisms affect product innovativeness.1 The use of knowledge integration mechanisms for new product development in essence is a process of organizational learning. In the process, a product development team would most likely prefer to develop greater competence by exploiting existing technological knowledge bases because they yield more immediate returns than to explore novel technologies that are new to the team (Levinthal & March, 1993). The greater returns associated with exploiting existing technologies encourage KIMs to focus primarily on such exploitation (Levinthal & March, 1993; March, 1991). Path dependency and the reciprocal positive feedback between experience and competence increase the likelihood of the use of knowledge integration mechanisms falling into a learning trap. This makes the use of knowledge integration mechanisms for exploring new technologies less attractive and potentially less rewarding (Ahuja & Lampert, 2001). As such, our first research question arises: Does the effect of knowledge integration mechanisms

1 Knowledge creation, knowledge development, and knowledge sharing are also the important constructs in new product development. However, today, developing a new product, particularly for high-tech products, usually needs to input a variety of knowledge. Knowledge is fragmented throughout a firm, making it difficult to identify and apply; therefore, integrating individual specialized knowledge and applying it to new products and services are imperative (Grant, 1996; Hislop, 2003). Thus, the use of knowledge integration mechanisms is directly related to product innovation performance and has been discussed in extant research (e.g., De Luca & Atuahene-Gima, 2007; Grant, 1996).

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on product innovativeness vary with their use, i.e., Is product innovativeness related to knowledge integration mechanisms in a curvilinear manner? Furthermore, the use of knowledge integration mechanisms for product innovation is embedded with the exploration of novel technologies as well as the exploitation of existing technologies. Previous research has suggested that the exploration of novel technologies can help development teams reduce the risk of falling into a learning trap (Ahuja & Lampert, 2001). An organization's willingness to explore novel technologies for product innovation is likely to depend on the opportunities or threats arising from environmental changes (Eisenhardt & Martin, 2000; Gebauer, 2011; Kane & Alavi, 2007; Teece, Pisano, & Shuen, 1997). In other words, environmental context may affect the innovativeness effects of knowledge integration mechanisms. Therefore, the second research question in this study is: Does environmental context affect the knowledge integration mechanisms– innovativeness relationship, required to be investigated in the aforementioned question? To answer the two research questions above, this study investigates the influence of knowledge integration mechanisms and environmental context on the relationship between knowledge integration mechanisms and product innovativeness. Our research advances the industrial marketing literature in two ways. First, this study extends the literature related to knowledge integration mechanisms–performance relationship by posing a contingency viewpoint. Although the importance and role of knowledge integration mechanisms in developing new industrial products has been emphasized in the marketing literature (e.g., De Luca & Atuahene-Gima, 2007; Enz & Lambert, 2012; Garrett, Buisson, & Yap, 2006; Hirunyawipada, Beyerlein, & Blankson, 2010), these studies do not reveal whether the use of knowledge integration mechanisms has an impact on its influence on product innovativeness. Second, this study also extends the perspective with regard to the moderating role of environmental context in business-to-business research. Environmental context has been examined as a moderating role in linking new product performance and its antecedents in marketing literature (e.g., Land, Engelen, & Brettel, 2012; Molina-Castillo et al., 2011); however, its impact on the moderating role of knowledge integration mechanisms in the knowledge integration mechanisms–innovativeness relationship remains unclear. Furthermore, extant industrial marketing literature has paid little attention to the role of environment context while investigating the barriers of high product innovation (e.g., Bessant, Öberg, & Trifilova, 2014; Sandberg & Aarikka-Stenroos, 2014; Story, Daniels, Zolkiewski, & Dainty, 2014). This study also enriches the current research findings on the issue. The remainder of this paper is organized as follows. Section 2 presents theoretical background and research hypotheses. Section 3 addresses the research methods and Section 4 reports the analyses and discussion of the results. Section 5 concludes with theoretical and managerial implications, limitations, and provides direction for future research. 2. Theoretical background and research hypotheses Knowledge resides in organizational members; therefore, integrating individual specialized knowledge is imperative for developing new products and services (Grant, 1996). Knowledge integration for product innovation can be conducted in formal or informal processes (Jansen, Tempelaar, van den Bosch, & Volberda, 2009). In line with the literature, this study is limited to the former. Existing empirical studies center knowledge integration mechanisms on formal processes (e.g., De Luca & Atuahene-Gima, 2007; De Luca et al., 2010; Koch, 2011; Zhou & Li, 2012) as integration mechanisms used in formal processes are easily identified in new product development (Hislop, 2003). Another consideration behind our focus is that ideas from knowledge sharing in informal ways are usually further analyzed and discussed in formal meetings while implementing new product initiatives (Enberg, Lindkvist, & Tell, 2006). Moreover, previous literature suggests that

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informal mechanisms of knowledge integration do not directly affect knowledge creation (Moreno-Luzón & Lloria, 2008). Knowledge integration in formal processes and structures has strategic implications. First, the use of formal mechanisms allows organizational members to articulate knowledge (Zollo & Winter, 2002). By articulating knowledge in face-to-face meetings, team members can analyze past experiences to ensure that individuals express their opinions that challenge each other's viewpoints (Argyris & Schon, 1978). Team members become more aware of the performance implications of their actions and increase understanding of these causal links (Zollo & Winter, 2002). Hence, knowledge residing in individuals is articulated through collective discussions and performance evaluation processes. Second, the use of formal mechanisms enables team members to codify their understandings of performance implications of routines by using written tools, such as memos or formal reports (Zollo & Winter, 2002). These tools can serve as guidelines or directions to coordinate the development of new routines. Furthermore, codifying knowledge can help team members further recognize and understand the causal link between decisions and expected performance outcomes. Documentation can force clarification of action–outcome relationships (Lechner & Floyd, 2007), interrupt the automatic dependence on past experience, and bring newly gained insights into new routines that become better anchored within the group (Eisenhardt, Furr, & Bingham, 2010; Lechner & Floyd, 2007). Group members will have a better understanding of why certain processes succeed or fail by writing a manual or a set of written directions for product innovation (Zollo & Winter, 2002). In addition, formal processes essentially represent the routine storage as a procedural memory because they encode historical inferences and guide individuals and groups as quasi-automatic (Eisenhardt & Santos, 2002). Based on the aforementioned knowledge, this study further draws upon the perspectives of organizational learning and environmental contingency to argue the linkages between knowledge integration mechanisms and product innovativeness. The conceptual framework of this study is illustrated as Fig. 1. In this study, we posit that the effects of knowledge integration mechanisms on product innovativeness vary with the increasing use of knowledge integration mechanisms and that the increasing turbulence of environmental context affects the knowledge integration mechanisms–innovativeness relationship. We present the framework to guide the development of research hypotheses. Specific hypotheses concerning these relationships are detailed in the rest of this section together with their underlying rationale. 2.1. Knowledge integration mechanisms Knowledge integration mechanisms refer to the formal processes and structures that ensure firms to synthesize, integrate, reconfigure, and use different types of knowledge among team members (De Luca & Atuahene-Gima, 2007; Eisenhardt & Martin, 2000; Zahra, Ireland, & Hitt, 2000). These processes and structures, such as regular informationsharing meetings and analysis of successful and failed project reviews, enable team members to understand what has been learned and to articulate knowledge (Zahra et al., 2000; Zollo & Winter, 2002), to combine their varied skills and functional backgrounds, to transfer and recombine resources within the firm (Eisenhardt & Martin, 2000), and to exploit knowledge effectively. By the use of knowledge integration mechanisms, team members can share and codify their specialized knowledge and facilitate more rapid diffusion of newly gained knowledge within the organization. These mechanisms also allow each of the team members to build concrete experiences with others so as to create a common experience base and language that facilitate team members to reach common frames of reference and gain integrated efficiency (Atuahene-Gima, 2005). As a result, team members can effectively integrate and exploit the ideas that challenge existing cause–effect relationships; thus, may

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Knowledge integration mechanisms

Environmental context H2 - H4 Knowledge integration

H1 Product innovativeness

mechanisms Fig. 1. A contingency framework linking knowledge integration mechanisms and product innovativeness.

result in developing a highly innovative product (Atuahene-Gima, Slater, & Olson, 2005). However, from the perspectives of organizational learning, the positive effect of knowledge integration mechanisms on product innovativeness may decrease with the increasing use of knowledge integration mechanisms. The increasing returns to the experience of knowledge integration make the refinement of familiar technological knowledge preferable to the exploration of new ones (Levinthal & March, 1993; March, 1991). Experience associated with the use of knowledge integration mechanisms leads to increased competence with the exploitation of familiar technological knowledge. Greater competence with the exploiting familiar technological knowledge increases experience with the use of knowledge integration mechanisms. This mutual feedback between experience and competence enables the organization to build a specialized competence. However, the increased ease of learning and problem solving in specific directions made possible by enhanced competence in those areas makes the adoption of alternate directions of development less attractive and potentially less rewarding. In other words, along the increasing use of knowledge integration mechanism, team members may pay much more attention to the exploitation of familiar and mature technologies instead of exploring novel or pioneering ones. Therefore, we hypothesize that: H1. The positive effect of knowledge integration mechanisms on product innovativeness decreases with the increasing use of knowledge integration mechanisms. That is, product innovativeness is related to knowledge integration mechanisms in an inverted U-shaped relationship.

2.2. Environmental context Environmental context is a broad concept and is not particularly useful (Milliken, 1987; Song & Montoya-Weiss, 2001). This view suggests that environmental context should be studied in relation to specific components in order to properly attribute its effects. Previous research has widely investigated environmental context in terms of three components: competitive intensity, market turbulence, and technological turbulence (e.g., Danneels & Sethi, 2011; Dayan & Basarir, 2010; Tsai & Yang, 2013). In the following, we discuss how these dimensions moderate the curvilinear relationship between knowledge integration mechanisms and product innovativeness. 2.2.1. Competitive intensity Competitive intensity refers to the degree of competition that a firm faces in its industry (Jaworski & Kohli, 1993; Kim & Atuahene-Gima, 2010). In the absence of competition, customers have fewer options for meeting their needs and wants. This lowers the need for firms to

integrate knowledge in order to create innovative products through either explorative or exploitative competence. Even without tracking or responding to customer preferences, a firm might perform well in an environment where the level of competition is low because customers are stuck with that firm's products or services (Jaworski & Kohli, 1993). In the absence of competitive pressure, development teams may focus on familiar knowledge instead of novel knowledge, and thus be more likely to fall into familiarity traps. Hence, the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness may become more prominent with the increase in familiarity traps under the condition of low competitive intensity. However, when competitive intensity increases, firms are often forced to pursue dramatic innovation to distinguish their products from competitor products. Prior research on innovation suggests that breaking away from the status quo and finding diverse knowledge are essential for dramatic product advancement (Bonner & Walker, 2004; Chandy & Tellis, 2000). Highly competitive pressures push NPD teams to create significant differentiation by exploring different possibilities and exploiting accumulated wisdom through the use of knowledge integration mechanisms. Hence, the increasing use of knowledge integration mechanisms may lead to the development of highly innovative products under the condition of high competitive intensity. Further, due to the need to introduce new knowledge for an appropriate response to intense market competition, development teams are less likely to fall into familiarity traps. This may cause the inverted Ushaped relationship between knowledge integration mechanisms and product innovativeness to become less pronounced. Accordingly, we hypothesize that: H2. The moderating effect of knowledge integration mechanisms on the knowledge integration mechanisms–product innovativeness relationship decreases as competitive intensity increases. That is, competitive intensity mitigates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness.

2.2.2. Market turbulence Market turbulence is the rate at which customer composition and customer preferences change. Because customer preferences change infrequently under the condition of low market turbulence, firms have low willingness to continually develop products with dramatic innovations. As such, the use of knowledge integration mechanisms may focus on preexisting experience and knowledge to refine and extend existing products to meet customer needs. The effects of familiarity traps associated with the increasing use of knowledge integration mechanisms may be more significant; thus, the negative relationship between knowledge integration mechanisms and product innovativeness may become steeper. In other words, an inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness

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becomes more significant under the condition of low market turbulence. On the other hand, while operating in a turbulent market environment, firms are more likely to continually develop new products to meet volatile customer preferences (Jaworski & Kohli, 1993; Zahra, 1996). Hence, they need to explore different kinds of knowledge and insights in greater depth. Development team members recognize the need to adopt knowledge integration mechanisms that enable open communication and truthful and efficient exchange of information. Through the use of knowledge integration mechanisms, they can explore new ideas and experiment with novel approaches to solving problems more successfully. Knowledge integration mechanisms also allow development teams to pinpoint and highlight inadequacies within firms, as well as to identify market opportunities that require the further development of new knowledge (Atuahene-Gima, 2005; Li, 2013). The introduction of more novel ideas and knowledge as a response to increasing market turbulence may mitigate the effects associated with familiarity traps. In other words, the slope of the negative relationship between knowledge integration mechanisms and product innovativeness may become flatter, or even vanish, under the condition of high market turbulence. Therefore, the degree of the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness decreases at a high level of market turbulence. According to these arguments, this study hypothesizes that: H3. The moderating effect of knowledge integration mechanisms on the knowledge integration mechanisms–product innovativeness relationship decreases as market turbulence increases. That is, market turbulence mitigates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness.

2.2.3. Technological turbulence Technological turbulence refers to the rate of change in technologies involved in new product development (Song & Montoya-Weiss, 2001). The increase in technological turbulence represents the appearance of new technologies and opens the door to product innovation. In an environment where the level of technological turbulence is low, firms lack alternative avenues to search for diverse knowledge and are limited to the knowledge at hand. As such, team members in pursuit of knowledge integration mechanisms may rely on familiar knowledge and are set in old ways of developing new products. The probability of familiarity traps associated with the increasing use of knowledge integration mechanisms is much higher under such circumstances; in other words, the negative slope of the relationship between knowledge integration mechanisms and product innovativeness is steeper. Thus, the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness appears more significant under the condition of low technological turbulence. In contrast, firms that operate in a more turbulent technological environment are likely to have more opportunities to gain a competitive advantage through technological innovations. In order to minimize the threat of obsolescence, such firms may use knowledge integration mechanisms to introduce innovative skills and knowledge that depart from those used to create existing products (Jansen et al., 2009). The specific environment provides development team members with new possibilities via leveraging a previously unexplored area of knowledge that may result in breakthrough innovation (Ahuja & Lampert, 2001; Feldman & Pentland, 2003). Therefore, under the condition of high technological turbulence, new product development teams introducing new knowledge into the integration process are less likely to fall into familiarity traps. As a consequence, this study argues that the negative slope of the relationship between knowledge integration mechanisms and product innovativeness becomes flatter or even vanishes in the turbulent technological environment, i.e., the degree of the inverted Ushaped curve between knowledge integration mechanisms and product

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innovativeness decreases at a high level of technological turbulence. Therefore, we hypothesize that: H4. The moderating effect of knowledge integration mechanisms on the knowledge integration mechanisms–product innovativeness relationship decreases as technological turbulence increases. That is, technological turbulence mitigates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness.

3. Methods 3.1. Sample and data collection This study focuses on high-technology firms since they usually conduct product innovation activities and developing a highly innovative product is critical for them to gain a competitive advantage (Mohr et al., 2010; Tsai, Chou, & Kuo, 2008). A total of 265 high-tech firms in Taiwan were invited to participate in this study. We send them a questionnaire with a cover letter, a return envelope, and instructions. We asked that the questionnaire has to be filled out by product managers, project managers, R&D managers, and marketing managers because those people typically participate in new product development and interact with each other (Bonner & Walker, 2004; Olson, Walker, & Ruekert, 1995). We also asked these participants to complete the questionnaire by two instructions: (1) a new product that had been in the market for more than 12 months and less than three years to ensure they have sufficient data on the resulting performance and to increase the accuracy of the data; and (2) a new product that is representative of the firm's product development programs. Respondents are asked to complete the survey based on their involvement in the product development process of the selected new product. Three weeks after the first mailing, non-respondents are mailed a follow-up letter with a duplicate copy of the questionnaire and another return envelope. These efforts yielded 105 responses. After eliminating 3 invalid surveys, the final sample included 102 usable questionnaires, representing a 38.5% response rate. F-tests indicate no significant differences in the mean responses on any construct among product, project, R&D, and marketing managers. A time–trend extrapolation procedure is used to test for non-response bias (Armstrong & Overton, 1977). The data set is divided into quartiles based on the number of days between the initial mailing and receipt of the completed questionnaire. This study performed a comparison of early (first quartile) and late (fourth quartile) respondents by using a multivariate analysis of variance (MANOVA). The results reveal no significant differences at the 95 percent confidence level in the mean responses for all of the constructs and the controls (i.e., firm size and firm age) between the two groups. The results suggest that non-response bias is not problematic in this study. Since the data were collected using a survey from a single source, the potential for common method variance was a concern. To address this concern, we applied a procedural method to minimize common method bias. The measures were arranged on the questionnaire in a random order as Podsakoff and Organ (1986) suggested. We assured that the participants' responses would be anonymous. In addition, we adopted several statistical approaches recommended by Podsakoff, MacKenzie, Lee, and Podsakoff (2003). First, we conducted Harman's one-factor test by using an exploratory factor analysis for all the independent (except the demographics of the firms) and dependent variables. The first factor extracted only accounts for 27.97% of the total variance. No single major factor emerged to explain the majority of the variances involved in the model. The results showed that no substantial common method bias existed in the data. Second, we conducted a partial correlation analysis. We ran a factor analysis of all the variables (except the demographics of the firms) and partialled out the variance from the first unrotated factor. The results show that the correlation structure

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involving product innovativeness and knowledge integration mechanisms still existed, as all significant zero-order correlations remained significant. We then conducted a post hoc analysis. Based on the items loaded together on the first factor of the previous factor analysis, we trimmed the items that overlapped for different measures. We again conducted a Harman's one-factor test and a partial correlation analysis, and found that the results remained unchanged. Third, based on existing research (McFarlin & Sweeney, 1992; Sanchez & Brock, 1996), we tested for common method bias by applying a confirmatory factor approach. From a confirmatory factor examination, if common-method bias poses a serious problem for data analysis, a single latent factor will account for all manifest variables (Podsakoff et al., 2003). The result of this one-factor model test showed a bad fit, with a large chi-square value (χ2 = 2010.47 with 527 degrees of freedom), indicating that no single latent factor accounted for all manifest variables. Overall, these statistical evaluations suggested that common method bias was not a serious problem in this study. 3.2. Measures The measures for all constructs of this study are grounded on past literature. This study adopts the measures from prior research (De Luca & Atuahene-Gima, 2007; Zahra & Nielsen, 2002; Zahra et al., 2000) to assess knowledge integration mechanisms. This seven-item scale assesses the extent to which a firm attempts to use a set of formal processes. All items are scored on a seven-point scale, ranging from “strongly disagree” to “strongly agree.” Competitive intensity, market turbulence, and technological turbulence are measured by the widely used scales developed by Jaworski and Kohli (1993). Market turbulence scale items assess the extent to which customer composition and preferences of a firm change over time. Competitive intensity scales assess the behavior, resources, and ability of competitors to differentiate. The technological turbulence scale items assess the extent to which a firm perceives that technology in an industry is in a state of flux. All items are scored on a 7-point scale, ranging from “strongly disagree” to “strongly agree.” Based on Song and Xie (2000), this study measures product performance by seven items that asked respondents to indicate the extent to which the firm achieved its product development objectives. For example, is this product one of the first of its kind introduced into the market? All items are scored on a 7-point scale, ranging from “strongly disagree” to “strongly agree.” In addition, this study includes three important controls, technological level, firm size, and firm age, to reduce the possibility of alternative explanations. Technological level affects product innovativeness and also is correlated closely with the use of knowledge integration mechanisms (Eisenhardt & Martin, 2000). We measure this variable by the work of Langerak, Hultink, and Robben (2004). Firm size represents the resources of a firm to exploit existing competencies, build new ones, and develop innovations (Chandy & Tellis, 1998). Firm age represents older firms that may be unwilling to engage in product innovativeness because inertia and ongoing operation costs might inhibit the ability of these firms to explore a novel product outside their boundaries (Zahra, 1991). 3.3. Reliability, validity, and descriptive statistics Confirmatory factor analysis (CFA) is used to assess the dimensionality, reliability, and validity of the scales, consisting of six latent variables. Appendix A shows the results of measurement analyses, including loadings, composite reliabilities (CR), and fit indices. The CFA model results in a reasonable fit to the data (χ2/df = 2.21, NNFI = 0.90, CFI = 0.90, IFI = 0.90). Composite reliability (CR) was calculated using the procedures suggested by Fornell and Larcker (1981). The CR values for the five of six constructs exceed 0.7 except for the technological turbulence construct, which is the acceptable CR

Table 1 Means, standard deviations, and correlations (n = 102). Variable

KIMs

PI

CI

MT

TT

TL

FS

FA

1 2 3 4 5 6 7 8 Mean SD

1.00 0.46 0.06 0.29 0.62 0.43 0.02 0.08 5.26 1.05

1.00 −0.06 0.44 0.51 0.74 −0.16 0.04 4.28 1.25

1.00 0.25 0.46 −0.23 −0.14 −0.29 5.10 1.06

1.00 0.67 0.51 −0.03 0.01 5.07 0.98

1.00 0.51 −0.11 0.02 5.60 0.96

1.00 −0.22 0.16 5.17 1.08

1.00 0.43 5.32 1.40

1.00 5.25 2.07

Note: KIMs: knowledge integration mechanisms. PI: product innovativeness. CI: competitive intensity. MT: market turbulence. TT: technological turbulence. TL: technological level. FS: firm size. FA: firm age.

level suggested by Bagozzi and Yi (1988). All item loadings are significant at the 1% significance level, indicating convergent validity (Bagozzi, Yi, & Phillips, 1991). Discriminant validity in all scales is assessed by two different techniques. All pairs of constructs in the two-factor CFA models are analyzed (Anderson & Gerbing, 1988). Each model is run twice: one time to constrain the correlation between the constructs to unity and the other time to free the parameter. A chi-square test on the nested models assessed whether the chi-squares are significantly lower for the unconstrained models. All combinations result in a higher critical value (Δχ2(1) = 3.84 at the 5% significance level), indicating acceptable discriminant validity for each scale. This study also examines discriminant validity to determine whether the confidence interval (plus and minus two standard errors) around the correlation estimate between the two factors includes 1.0 (Anderson & Gerbing, 1988). The result suggests that the confidence intervals of all pairs of factors do not include 1.0. Thus, this work concludes that the measures are valid and reliable. Table 1 presents the standard deviations of means, and the correlations of the constructs used in the following analyses.

4. Analyses and results Table 2 shows the results of the regression analysis.2 Each construct is composed of a summated index of the items that constitute the construct. Control variables, including firm size, firm age, and technological level are entered in Model 1, followed by knowledge integration mechanism and moderator variables in Model 2. To test for curvilinear relationship, the study adds the quadratic term of knowledge mechanisms to the regression equation in Model 3. The interactions between knowledge integration mechanisms and three environmental context variables are individually added in Models 4–6. Notice that the signs of the regression coefficients for the squared terms represent the direction of curvature produced by the effects of knowledge integration mechanisms on product innovativeness. The regression-coefficient estimate of the quadratic terms for knowledge integration mechanisms is the key to whether a relationship is an inverted U-shape. The coefficients of the interaction terms associated with the coefficients of the squared terms depict how nonlinear relationships between the knowledge integration mechanisms and product innovativeness are moderated by competitive intensity, market turbulence, and technological turbulence, respectively. In Table 2, Model 3 shows that the estimated coefficient of KIMssquared terms fails to achieve a statistical significance level even though its direction is as expected (β = −0.02, p N 0.05), providing no support 2 The tests for firm size reveal that this variable is not normally distributed (t-values for kurtosis and skewness are 4.61 and −8.03, respectively), but the tests for firm age do not reject null hypothesis (t-values for kurtosis and skewness are −1.66 and −1.37, respectively). Before running the models, we transformed the variables by the parametric power transformation proposed by Box and Cox (1964).

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Table 2 Results of hierarchical moderated regression analyses. Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

VIF

FS

0.01 (0.15) −0.08 (−0.83)⁎⁎ 0.65 (8.09)

−0.02 (−0.20) −0.07 (−0.70) 0.51 (5.08)⁎⁎ −0.07 (−0.68) 0.15 (1.59) 0.10 (1.00) 0.08 (0.90)

−0.02 (−0.20) −0.07 (−0.70) 0.51 (5.00)⁎⁎ −0.07 (−0.68) 0.15 (1.58) 0.10 (0.99) 0.08 (0.75) −0.02 (−0.04)

−0.07 (−0.68) −0.04 (−0.39) 0.51 (5.23)⁎⁎ −0.28 (−2.36)⁎⁎

−0.01 (−0.14) −0.07 (−0.68) 0.52 (5.31)⁎⁎ −0.10 (−1.04) −0.02 (−0.19) 0.12 (1.16) 0.07 (0.65) −0.03 (−0.45)

−0.01 (−0.10) −0.05 (−0.53) 0.51 (5.12)⁎⁎ −0.10 (−1.06) 0.12 (1.23) 0.02 (0.15) −0.04 (−0.30) −0.04 (−0.67)

1.47

FA TL CI MT TT KIMs KIMs2 KIMs × CI 2

KIMs × CI

0.11 (1.19) 0.12 (1.21) 0.02 (0.18) −0.04 (−0.06) 0.18 (2.09)⁎ 0.15 (2.56)⁎⁎

KIMs × MT

2

KIMs × TT 0.43 0.41 24.15⁎⁎ –

0.45 0.42 11.63⁎⁎ 1.72

0.46 0.42 10.07⁎⁎ 0.02

0.51 0.46 9.52⁎⁎ 4.37⁎⁎

1.49 1.50 2.33 2.67 1.66

2.19

KIMs × TT

R2 Adjusted R2 F-value F-value for ΔR2

1.88

1.40

0.16 (1.97)⁎ 0.12 (2.82)⁎⁎

KIMs2 × MT

1.43

0.51 0.46 9.42⁎⁎ 4.12⁎

1.56 2.14 0.11 (1.05) 0.17 (1.86)⁎ 0.49 0.43 8.57⁎⁎ 1.83⁎

1.34 2.89

t-Values are in parentheses. FS = firm size. FA = firm age. TL= technological level. KIMs = knowledge integration mechanisms. CI = competitive intensity. MT = market turbulence. TT = technological turbulence. ⁎ p b 0.05. ⁎⁎ p b 0.01.

for H1 that the relationship between knowledge integration mechanisms and product innovativeness is an inverted U shape. Model 4 reveals that the regression coefficient of the interaction term between KIMs-squared and competitive intensity is positive and significant (β = 0.15, p b 0.01). This result supports H2 that competitive intensity positively moderates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness. Likewise, Model 5 shows that the estimated coefficient of the interaction term between KIMs-squared and market turbulence is positive and significant (β = 0.12, p b 0.01), supporting H3 that market turbulence positively moderates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness. The statistical evidence in Model 6 also provides a support for H4 that technological turbulence positively moderates the inverted U-shaped relationship between knowledge integration mechanisms and product innovativeness (β = 0.17, p b 0.05). To check the robustness of the results, we rerun the models of Table 2 by using a three-stage hierarchical moderated regression technique suggested by Luo, Rindfleisch, and Tse (2007). Because the results are rather similar to those found above we do not report them in this study for the sake of brevity. To gain further insight into these interaction relationships, this study examines the nature of the interactions by using the procedure suggested by Aiken and West (1991). This procedure is adopted to test the significance of regression-coefficient estimates for the KIMssquare variable at one standard deviation above and below the means of the three environmental moderators. Significantly positive relationships are found between KIMs-squared and product innovativeness at a high level of competitive intensity (β = 0.11, p b 0.05), market turbulence (β = 0.09, p b 0.05), and technological turbulence (β = 0.13, p b 0.05), respectively. In contrast, at a low level of competitive

intensity, market turbulence, and technological turbulence, the relationship between KIMs-squared and product innovativeness is respectively significant and negative (β = − 0.19, p b 0.01; β = − 0.15, p b 0.01; β = − 0.21, p b 0.05). As a further aid to understanding, we plot the KIMs–innovativeness relationships as Figs. 2–4 by using these slope analyses. These figures clearly illustrate that knowledge integration mechanisms and product innovativeness have different curvilinear relationships under different levels of competitive intensity, market turbulence, and technological turbulence.

Fig. 2. The relationship between knowledge integration mechanisms and product innovativeness under different levels of competitive intensity.

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contribute novel ideas to the product project; thus, may lead to the development of a highly innovative product. 5.1. Theoretical implications

Fig. 3. The relationship between knowledge integration mechanisms and product innovativeness under different levels of market turbulence.

5. Discussion This study advances the marketing and innovation literature by untangling the curvilinear and contingent effects of knowledge integration mechanisms on product innovativeness. Overall, the results suggest that the increasing competitive intensity, market turbulence, and technological turbulence lead to a change of the inverted U-shaped relationships between knowledge integration mechanisms and product innovativeness. More specifically, knowledge integration mechanisms have an inverted U-shaped linkage with product innovativeness when the degrees of competitive intensity, market turbulence, and technological turbulence are low. By contrast, the knowledge integration mechanisms–product innovativeness relationship is approaching a U shape under the conditions of high competitive intensity, market turbulence, and technological turbulence. These results suggest that the increasing environmental turbulence may force firms to pay more attention to exploring novel, emerging, or pioneering technologies by the use of knowledge integration mechanisms for product innovation to create a competitive advantage. However, during the process, the routines and directions associated with knowledge integration mechanisms are required to be adapted for exploring and integrating new technologies. Thus, the use of knowledge integration mechanisms may not be significant or even have a negative effect on product innovativeness in the period of adaptation. After this period, increasing use of knowledge integration mechanisms may increase the exploration of new technological knowledge and

Fig. 4. The relationship between knowledge integration mechanisms and product innovativeness under different levels of technological turbulence.

Our study fills the knowledge gap in the industrial marketing literature concerning the development of new industrial products by showing how the use of knowledge integration mechanisms affects product innovativeness under different levels of market conditions. As extant empirical evidence suggests that the use of knowledge integration mechanisms enhances the new product market of financial performance (De Luca & Atuahene-Gima, 2007), our findings signify that the use of knowledge integration mechanisms has a curvilinear impact on product innovativeness, a key dimension of product innovation outcomes. Moreover, considering that the use of knowledge integration mechanisms lies at the heart of cross-functional integration for new product development (De Luca & Atuahene-Gima, 2007; Hirunyawipada et al., 2010; Tsai & Hsu, 2014), from a broader perspective, this study offers implications with respect to cross-functional integration for product innovation. There are two perspectives of literature regarding the use of cross-functional integration. Some works argue that high crossfunctional integration achieves a high level of product innovation outcome (e.g., De Luca & Atuahene-Gima, 2007; Song, Thieme, & Xie, 1998). Other studies consider that over-cross-functional integration does not increase, even harm, product innovation outcomes (e.g., Kahn, 2001; Lovelace, Shapiro, & Weingart, 2001). Thus, in terms of product innovativeness, our findings may provide possible explanations for reconciling these two perspectives by demonstrating the moderating roles of environmental turbulence. Furthermore, the findings of this study also have implications on some theoretical viewpoints widely used in industrial marketing literature with respect to knowledge utilization for product innovation. First, this study sheds new light to organizational learning theory. The perspectives of organizational learning argue that mutual positive feedback between experience and competence may lead development teams to favoring familiar and mature knowledge and searching for solutions in the neighborhood of existing solutions in the process of knowledge integration for product innovation (Levinthal & March, 1993; Martin & Mitchell, 1998); thus, expect that product innovativeness is related to the use of knowledge integration mechanisms in an inverted U-shaped manner. Our findings suggest that an inverted U-shaped relationship holds in a stable environmental context rather than an environmental context of high change, which reveals a contingency in such a curvilinear linkage. This contingency may also lend weight to Ahuja and Lampert's (2001) finding, which suggests that established firms can still create breakthrough inventions. In addition, our findings reveal that the use of knowledge integration mechanisms increases product innovativeness in a curvilinear manner while environmental turbulence increases. This result echoes Grant's (1996) work which argues the value of knowledge integration mechanisms in a highly competitive environment. Second, this study highlights the role of environmental context to the organizational ambidexterity's paradigm, which emphasizes the value of balancing exploitation and exploration for innovation (O'Cass, Heirati, & Ngo, 2014; Raisch, Birkinshaw, Probst, & Tushman, 2009). From the perspectives of organizational learning, knowledge integration mechanisms for product innovation are embedded in the process of exploitation or exploration (Levinthal & March, 1993; March, 1991). Our results suggest the contingent effects of knowledge integration mechanisms on product innovativeness, which implies that the balance of exploitation and exploration for product innovation should not ignore the role of environmental changes. Third, the product development contingency theory proposes that the relationships between organizational activities and new product success are contingent upon environmental turbulence (e.g., Calantone, Garcia, & Dröge, 2003; Calantone, Schmidt,

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& Di Benedetto, 1997). Since knowledge integration mechanisms play a crucial role in developing new products, this study confirms the contingency theory of product development by suggesting that knowledge integration mechanisms contingently affect product innovativeness by a curvilinear linkage. 5.2. Managerial implications In industrial markets, the argument that the use of knowledge integration mechanisms enhances product innovativeness has gained wide acceptance among practitioners. Our research qualifies this conclusion by providing a more fine-grained view for managers. This study suggests that the intensive use of knowledge integration mechanisms does lead to the development of highly innovative products in a high change of environmental context. This result means that managers can explore novel and pioneering knowledge by increasing the use of knowledge integration mechanisms to improve product novelty under circumstances of high market turbulence, technological turbulence, or competitive intensity. Our results also suggest that the intensive use of knowledge integration mechanisms may harm the development of highly innovative products in a stable environmental context. This result calls managers to adopt strategies, such as introducing novel technologies, to reduce the possibility of falling into familiar and mature traps while developing a new product with a high degree of novelty.

environmental factors in the curvilinear relationship between knowledge integration mechanisms and product innovativeness. However, other extraneous factors, such as knowledge creation and development, may be other potential moderators which can be considered in future research. Finally, this study defines product innovativeness of technology, with highly innovative measures. However, product innovativeness is a multidimensional construct characterized by diverse dimensions such as incremental innovation and radical innovation (Chandy & Tellis, 1998). Future studies should explore the relationship complexities between knowledge integration mechanisms and alternative dimensions of product innovativeness. Appendix A CFA of measures Measures and sources

Descriptions

SFL* t-Value

Product innovativeness (Song & Xie, 2000) CR = 0.86

This product relied on technology that has never been used in the industry before This product caused significant changes in the whole industry This product was one of the first of its kind introduced into the market This product was highly innovative—totally new to the market The nature of the manufacturing process was totally new to our company The technology required to develop the product (R&D) was totally new to our company The technology required to develop the product (experiences and technology) was totally new to our company Regular formal reports and memos that summarize learning Regular formal reports and memos that share information Information sharing meetings Face-to-face discussions by cross-functional teams Formal analysis of failing product development projects Formal analysis of successful product development projects Use of experts and consultants to synthesize knowledge In our kind of business, customers' product preferences change quite a bit over time Our customers tend to look for new product all the time Sometimes our customers are very price-sensitive, but on other occasions, price is relatively unimportant We are witnessing demand for our products and services from customers who bought them before New customers tend to have product-related needs that are different from those of our existing customers. Competition in our industry is cutthroat There are many promotion wars in our industry Anything that one competitor can offer, others can match readily

0.84 11.74

5.3. Limitations and directions for future research This study includes some limitations, and suggests some directions for future research. First, this study, as past empirical research for product innovation, limits the use of knowledge integration mechanisms to the formal forms; however, informal forms may also facilitate knowledge sharing (Lawson, Petersen, Cousins, & Handfield, 2009). Thus, to fertilize the research in relation to knowledge integration, future study may include informal mechanisms in examining the influence of knowledge integration mechanisms on product innovation outcomes. Second, the generalizability of these findings is limited because these findings are based on a sample of new product projects from Taiwanese high-tech firms. The findings of this study should be validated through further research in other contexts because the use of knowledge integration mechanisms is a process of knowledge sharing and the sharing activities may be affected by national culture (Garrett et al., 2006). Third, this study acknowledges that other characteristics of environmental variables may also moderate the link between knowledge integration mechanisms and product innovativeness that may be important to study environmental characteristic gestalts in the form of interactions among variables, such as environmental uncertainty, complexity, and munificence (Aragón-Correa & Sharma, 2003). Fourth, this study is conducted at the project level only. Firmlevel constructs, such as environmental context, may interact with project-level determinants of product innovativeness outcomes. Other project-level constructs should be incorporated. Building on our findings, future researchers should examine additional variables, such as absorptive capacity or manager attitudes. Fifth, the environmental variables modeled in this study as moderators may act in tandem to increase the importance of knowledge integration mechanisms for product innovativeness. The limited sample size in this study precludes an analysis of this joint effect. Future research could consider this type of analyses to clarify these conditions. As mentioned previously, absorptive capacity and organizational culture may have an effect on product innovativeness. Therefore, the joint moderate effects of environmental context and internal condition on relationships between knowledge integration mechanisms and product innovativeness development should be assessed. Sixth, this study only focuses on investigating the moderating effects of three

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KIMs (De Luca & Atuahene-Gima, 2007; Zahra & Nielsen, 2002; Zahra et al., 2000) CR = 0.88

MT (Jaworski & Kohli, 1993) CR = 0.76

CI (Jaworski & Kohli, 1993) CR = 0.72

0.65 8.23 0.76 10.18

0.65 8.18

0.57 6.99

0.76 10.09

0.55 6.63

0.74 9.99 0.73 9.74 0.60 7.67 0.67 5.66 0.89 13.32 0.92 14.07 0.71 9.46 0.75 9.71

0.87 12.01 0.55 5.28

0.64 9.97

0.53 6.28

0.57 5.43 0.84 11.08 0.60 4.62

(continued on next page)

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Appendix A (continued) (continued) Measures and sources

TT (Jaworski & Kohli, 1993) CR = 0.57

TL (Langerak et al., 2004) CR = 0.90

Descriptions

SFL* t-Value

Price competition is a hallmark of our industry One hears of a new competitive move almost every day The technology in our industry is changing rapidly Technological changes provide the big opportunities in our industry A large number of new product ideas have been made possible through technological breakthroughs in our industry The new product offered unique benefits for customers The new product solved problems for customers The new product was highly innovative The new product was radically different from competitor products The new product provided higher quality than competing products The new product offered solutions not possible with existing products The new product replaced inferior products

0.53 4.94 0.85 11.27 0.70 8.03 0.56 6.40

0.58 5.39

0.88 12.83 0.79 10.91 0.75 1017 0.83 11.80

0.64 8.22 0.68 8.91

0.65 6.77

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