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Strengthening the innovation chain: the role of internal innovation climate and strategic relationships with supply chain partners.

INTRODUCTION

Innovation is a key source of competitive advantage (Bowonder, Dambal, Kumar, & Shirodkar, 2010; Duane Ireland & Webb, 2007; Lengnick-Hall, 1992; Tidd, Bessant, & Pavitt, 2005). Despite the many problems of managing it, innovation has become imperative for many companies. Research evidence has identified a range of benefits for those companies that have been able to successfully exploit innovation strategies to realize higher profits and market share (Cho & Pucik, 2005; Damanpour, Walker, & Avellaneda, 2009). The question is no longer one of whether or not to innovate but rather how to strategize for innovation in order to achieve competitive advantage for organizations. In particular, due to the complex nature of innovation strategies, the roles of external constituents such as suppliers and customers have emerged to be vital to the success of the focal firm's innovation strategy (von Hippel, 1986; Vega-Jurado, Gutierrez-Gracia, Fernandez-de-Lucio, & Manjarres-Henriquez, 2008).

The attention of previous studies has been focused on identifying internal antecedents of innovation capability and performance. For example, the influence of leadership styles (Oke, Munshi, & Walumbwa, 2009), organizational processes (Jespersen, 2012; Tyagi & Sawhney, 2010), and human capital (Beugelsdijk, 2008; Chong, Eerde, Rune, & Chai, 2012) on innovation performance has been recognized. However, organizations are becoming increasingly networked with an increase in the number and intensity of collaborations for innovations and new product development (NPD). Accordingly, there is an emerging body of literature focusing on antecedents of innovation in organizational networks, most particularly supply chain networks (Pittaway, Robertson, Munir, Denyer, & Neely, 2004). As such there is a need to investigate the influence of supply chain factors on a firm's innovation performance (Craighead, Hult, & Ketchen, 2009).

Previous studies have investigated the effects of integrating suppliers into a firm's NPD process (Jayaram, 2008; Petersen, Ragatz, & Monczka, 2005; Wagner, 2012). However, beyond full integration of suppliers into the NPD process through their engagement with key suppliers, firms may actually tap or learn from the innovativeness of their suppliers. Similarly, focal firms rely on the expertise of suppliers and customers to weed out failing products. For example, Wal-Mart shares key worldwide sales information with its strategic suppliers such as Procter and Gamble. This helps Procter and Gamble to decide on their own innovation strategies as far as what is likely to sell and what is not likely to sell (Hougland, 2007). Drawing from resource dependency theory (Paulraj & Chen, 2007a; Pfeffer & Salancik, 2003) and the knowledge-based view (KBV) (Grant, 1996b), we argue that firms may depend on and tap from the innovativeness of their supply chain partners to strategize for innovation and enhance their innovation performance (Azadegan & Dooley, 2010). Limited capacity for innovation, shorter time to market, and the need to share risks associated with innovation development are driving firms to seek innovations from their supply chain partners (Koufteros, Rawski, & Rupak, 2010; Koufteros, Vonderembse, & Jayaram, 2005; Wagner, 2010, 2012).

In this study, our focus is on supply chain partners that may include suppliers and customers in a firm's supply chain. We define a supply chain partner's innovativeness in terms of the extent to which the supply chain partner possesses the ability to produce new ideas and innovations. This ability has the potential to channel innovation pathways for focal firms as well. This is so because increasingly the locus of fundamentally innovative ideas stem from the supply chain. For example, the innovations part of the "Intel inside" program benefitted many PC manufacturers. The innovation in the business model of CHEP which revolutionized global pallet management with the "lease anywhere and return anywhere" concept had important implications on supply chain design for its customers. Thus, we argue that tapping innovative knowledge from supply chain partners to strategize for innovation will have an effect on a firm's innovation performance. More importantly, building on the works of Azadegan, Dooley, Carter and Carter (2008), Wagner (2012) and Azadegan (2011), we posit that exploiting supply chain partner innovativeness to strategize for innovation is contingent upon two key factors. First, drawing from social capital theory (Lawson, Tyler, & Cousins, 2008), we posit that building strategic relationships and trust with key supply chain partners will enhance the effective tapping and exchange of knowledge with such firms (Bensaou, 1999). We define "strategic relationship with supply chain partners" in terms of the extent to which the relationship is enduring and on a long-term basis (Choi & Hartley, 1996; Paulraj & Chen, 2007b). Due to the risky nature of innovation efforts and the need to protect intellectual properties in the innovation process, suppliers are more likely to align with customers for innovation if there is a long-term relationship effort in place.

Second, we argue that a culture of innovation provides an environment where supply chain partner innovativeness can be effectively tapped to implement product innovation strategies (Azadegan, 2011). We define innovation climate in terms of the extent to which the firm encourages and builds a climate that supports innovation (Cho & Pucik, 2005; Riordan, Vandenberg, & Richardson, 2005). Finally, product innovation strategy is defined in terms of the extent to which the firm uses new components, new materials, new technologies, and new product features in the development of a product (OECD, 2005), while product innovation performance is defined in terms of the novel products and the number of new products actually produced or developed by a firm.

It is pertinent to note that in our study, product innovation strategy and product innovation performance are distinctly separate constructs. Product innovation strategy relates to what a firm does in terms of obtaining novel products (product innovation performance) including the specific actions and activities that are implemented in order to obtain improved innovation performance. Thus, in our study, we hypothesize product innovation strategy as an antecedent of innovation performance. Taken together, this study aims to investigate (a) the effect of supply chain partner innovativeness on a firm's innovation strategy; (b) the separate and combined moderating roles of the strategic relationship with a supplier and innovation climate in enhancing the effect of supply chain partner innovativeness on innovation strategy; and (c) the effects of implementing a product innovation strategy on a firm's innovation performance. Our study seeks to contribute to the extant literature on developing and building firms' innovation performance through engaging with supply chain partners. Specifically, while much is known about the link between suppliers' innovativeness and performance (e.g., Azadegan & Dooley, 2010; Lau, Tang, & Yam, 2010), little is known about the processes (e.g., product innovation strategy in our study) through which supply chain partners' innovativeness relate to performance. Our study fills this gap in the literature. Further, the investigation of the influence of the combined effects of internal innovation climate and strategic relationships as moderators of the link between supplier chain partners' innovativeness and product innovation strategy represents contributions to and extends the body of work that focuses on tapping innovation and knowledge from external partners such as suppliers (Brown & Eisenhardt, 1995).

In the next section, we discuss the theoretical framework and develop our hypotheses. Next, we present the methodology. Following that we present and discuss the results of the study and conclude by discussing the theoretical and practical implications of the study.

THEORY AND HYPOTHESES

Supply Chain Partner Innovativeness and Product Innovation Strategy

In explicating the relationship between supply chain partner innovativeness and product innovation strategy, we first draw from resource dependency theory (RDT) (Pfeffer & Salancik, 2003). According to resource dependency theory, interorganizational links can be described as a set of power relations based on the exchange of resources (Pfeffer & Salancik, 2003). As firms may not have all the resources needed for competitiveness at their disposal, they will seek to build relationships with others such as firms within the supply chain that possess key needed resources (Azadegan, Napshin, & Oke, 2013).

Innovativeness or the ability to develop new products and innovations is a vital resource that firms seek to gain competitiveness (Avlonitis, Papastathopoulou, & Gounaris, 2001; Cho & Pucik, 2005; Santos-Vijande & Alvarez-Gonzalez, 2007). Firms lacking this vital innovative capability will seek to tap innovative partners. The examples of Intel and CHEP that were highlighted in an earlier section illustrate the connection between supply chain partner innovativeness and a focal firm's innovation strategy. For example, supplier innovativeness has become a key factor in supplier evaluation and the prequalification process (Azadegan, 2011). Indeed, the process of aligning with the innovative capabilities of suppliers is not only an initial process (at the time of screening) but also an ongoing process.

Engaging with innovative suppliers can expose buyers to suppliers' innovations that can increase the buyer's innovative capability (Azadegan, 2011). Innovative suppliers are able to supply buying firms with components at the leading edge of technology which the firms can integrate into their production process (Hoetker, 2005). Similarly, a focal firm's innovative customers can be important resources for innovations (von Hippel, 1986; Napolitano, 1991). The case studies of innovations in the fields of education, medicine, and welfare using IT and multimedia technology in Japan (Kodama, 2002) demonstrate the importance of building strategic partnerships with customers who are highly knowledgeable about the products and services to create new business models as well as reaching out to many other associated customers. Customers are doser to the market and understand the market well; thus, they are able to offer valuable input into a firm's innovative processes. Collectively, this suggests the potential for an orchestration of a knowledge network with each part fulfilling the role assigned to them.

Resource dependency theory (RDT) views organizations as coalitions in which structures and patterns of behavior are molded to acquire needed external resources (Pfeffer & Salandk, 2003). RDT is an appropriate theoretical lens to employ to investigate the tapping of partners' innovativeness because according to RDT, suppliers will try to increase the buyer's dependency on their operations by continuously offering value in the form of innovations. On the other hand, the buyer will attempt to decrease its dependence on the supplier or customers by seeking to develop and increase the vital resource--innovative capability--which these supply chain partners offer. In other words, organizations will alter their patterns of behavior to reduce their dependence on others for vital resources by implementing innovation strategies to enhance their performance. This suggests that focal firms that have neither the technical know-how nor the intention to pursue innovations that are better handled via outsourcing will likely increase their dependence on their supply chain partners. Exposure to supply chain partners that have innovative ideas and offer new technologies and components will not only drive firms to implement and utilize these new technologies in product development (Lau et al., 2010; Tan & Tracey, 2007) but also trigger them to find ways of developing capabilities in innovation.

Second, we draw from the KBV (Grant, 1996b; Nonaka, 1994) to further examine how supply chain partners' innovativeness links with a firm's product innovation strategy. The KBV is an appropriate theoretical lens to use because the transfer and flow of knowledge is at the core of innovative activities and the KBV considers the creation, integration, and application of knowledge as the principal function of the firm (Grant, 1996a; Nonaka, 1994). A key tenet of the KBV is that organizations engage in routines which help to enhance learning through repeated performance (Oke & Kach, 2012). Collaborations with innovative supply chain partners enable a firm to be consistently exposed to innovative behaviors of the partners which the firm can acquire, learn, and internalize to strategize for innovation. In other words, a firm can exploit and reconfigure external knowledge to enhance its own internal capabilities for developing product innovations.

Taken together, the theoretical arguments support the notion that supplier chain partner innovativeness can trigger firms to have innovation strategies, leading to the following hypothesis:

  Hl: Supply chain partner innovativeness has a
  positive relationship with the focal firm's
  product innovation strategy.


The Influence of Innovation Climate and Strategic Relationship with Supply Chain Partners on the Supply Chain Partner Innovativeness--Innovation Strategy Link

The relationship between supplier innovativeness and a buyer's manufacturing performance and the moderating roles of interorganizational learning) supplier evaluation, supplier integration, and absorptive capacity have been the focus of recent studies (Azadegan, 2011; Azadegan & Dooley, 2010; Koufteros et al., 2010; Wagner, 2012). Yet, the role of contingency factors such as innovation climate (Bain, Mann, & Pirola-Merlo, 2001) and strategic relationship with supply chain partners as moderators of the relationship has not been studied. To better utilize and tap supply chain partner innovativeness, firms must develop complementary skills and have an orientation toward innovation by providing support for innovation and fostering an environment where innovation thrives (Amabile, 1998). Such an orientation toward innovation increases the learning capability within firms and enables firms to better understand and germinate partners' innovative ideas that translate into more efficient production processes. Indeed, suppliers are more likely to offer innovative ideas to buyer firms that have a culture of innovation and recognize the value of such ideas and new technologies (Kodama, 2002; Lindgreen, Antioco, Palmer, & Heesch, 2009).

A similar argument can be made with respect to the downstream part of the innovation network by involving customers in the product innovation efforts of focal firms (Koufteros et al., 2005). Such an involvement is more likely to take place in focal firms that have a strong internal climate for innovation. Thus, an organizational environment that supports cross-fertilization of ideas and stimulates creativity is likely to provide a good fertile ground for supply chain partners' innovative offerings to firms (Su, Tsang, & Peng, 2009). Indeed, according to the KBV, when outside knowledge is "intertwined" with that residing inside the organization and fostered by an internal innovation climate, it can lead to learning that can enhance innovation performance (Jones & Macpherson, 2006; Morgan, Zou, Vorhies, & Katsikeas, 2003). In sum, the positive effect of supply chain partner innovativeness on a firm's use of innovative offerings to develop new products (product innovation strategy) is likely to be enhanced in an organizational environment where innovation thrives, which leads us to the next hypothesis which is stated below. This posited interaction effect is also shown in Figure 1:

  H2: An innovation climate will positively moderate
  the relationship between supply chain partner
  innovativeness and product innovation strategy.


Social capital theory suggests that firms can more effectively tap knowledge or capabilities from other firms if they can successfully build social capital (in terms of relationship and trust) with those firms (Krause, Handfield, & Tyler, 2007). Under social capital theory, strategic relationships need to be built as a catalyst for tapping the innovative potential of supply chain partners. As relationships with supply chain partners become more enduring and long term, mutual trust is developed and the partners become more conversant with the focal firms' patterns of behavior and product offerings.

According to the KBV, such relationships enable end customers and suppliers to be able to share and offer the required expertise to a firm which the firm can internalize and utilize in its product development. Similarly, the focal firm gains more insight into suppliers' innovative processes and is able to tap from suppliers' knowledge, exploit, and reconfigure it in order to increase its own innovative capability (Knudsen, 2007). Within this relationship model, joint projects may be undertaken that will inevitably lead to the use of innovative offerings to develop new products (product innovation strategy) (Lau et al., 2010). Indeed, Azadegan (2011) argues that established relationships with suppliers enable suppliers' innovativeness to become a relational asset which can be complementary and increase a firm's performance. Similarly, the works of Wagner and Bulai (2005) and Tan and Tracey (2007) have argued for collaborative networks of NPD that involve customers as well. Therefore, we posit here that strategic relationships with key innovative supply chain partners will enable firms to more effectively gain or tap innovative ideas from these partners, thus enhancing the propensity of a firm to implement innovation strategies (Van de Ven, 1986). Thus, we offer the following hypothesis and show this interaction effect in Figure 1:

  H3: Strategic relationships with supply chain
  partners will positively moderate the relationship
  between supply chain partner innovativeness and a
  firm's product innovation strategy.


Synergistic Effects of Innovation Climate and Strategic Relationships

In the following hypothesis, we argue for a joint moderating influence of the interaction between organizational innovation climate and relationship with supply chain partners on the link between supply chain partner innovativeness and product innovation strategy. The premise for this argument is that for firms that have joint investments in both internal organizational innovation climate and external strategic relationships with suppliers, the effect of supply chain partner innovativeness on product innovation strategy is stronger than for firms without these investments. The study by Wagner and Buko (2005) shows that strategic relationships with supply chain partners (customers and suppliers) in the form of knowledge sharing is influenced (moderated) by organizational resource commitment. Implicit in this notion is the interaction between organizational innovative climate and the innovativeness of supply chain partner in influencing innovation strategy of the firm. This argument also stems from the matched domains argument (Koufteros, Vickery, & Droge, 2012) and the innovation chain precepts (Azadegan & Dooley, 2010; Wynstra, von Corswant, & Wetzels, 2010). Both of these precepts argue for an alignment in structures internally and externally to enhance joint innovation progress.

Further, according to the KBV, firms are able to tap from a supply chain partner's innovativeness if they are repeatedly exposed to the partner's knowledge base. Such exposure is possible only through enduring or strategic relationships between the two entities. Internal innovation climate enables a firm to effectively tap, internalize, and exploit such external knowledge to develop and improve innovation internally. Thus, strategic relationships with key supply chain partners will combine with internal innovation climate to enhance the exploitation of the supply chain partner innovativeness to improve a firm's innovation capabilities. Accordingly, we present the following hypothesis and show this interaction effect in Figure 1:

  H4: The joint interaction of innovation climate
  and strategic relationships with supply chain
  partners will positively moderate the relationship
  between supply chain partner innovativeness and
  product innovation strategy.


Linking Product Innovation Strategy to Product Innovation Performance

Implementing product innovation strategies through the use of new components and new materials in product development enables the reconfiguration of product architecture and composition of the final product which could result in highly innovative and novel products (Chen, Chen, & Vanhaverbeke, 2011; Oke, Walumbwa, & Myers, 2012; Tsai, Hsieh, & Hultink, 2011). Using new materials and components that have a wide range of applications in product development can also enhance the number of new products produced as well as the overall performance of the final products (Millson & Wilemon, 2008; Song, Song & Benedetto, 2011). Newspaper wood is a set of novel wood products that have resulted in novel furniture and home products. These products that include lamps, chairs, and other home products are developed from new materials--in this case, recycled newspapers (not cut and dried wood) that have been meticulously and individually glued together, rolled into a tight "log" and left to dry, deform and harden just like living trees would do. Thus, a strategy of using new materials in product development can result in novel products.

Similarly, firms can strategize for innovation using new technologies that have a wide range of applications leading to novel products and an increase in the number of new products derivable from those technologies (Judi & Beach, 2010). The introduction of touchscreen technology has revolutionized many products and changed many industries. Through the use of touchscreen technology, novel products such as personal computer tablets and smart phones, including Apple's I-Pad and I-Phone, have been developed. Similarly, the use of gesture-based computer control technology that allows active interaction with a computer through natural human gestures has revolutionized the gaming industry leading to novel products like Microsoft's Xbox Kinect body sensor and Ninten-do's Wii handheld control sensor (Mitra & Acharya, 2007). In other words, implementing innovation strategy through the use of technology in product development offers opportunities for novel products and improved innovation performance.

Taken together, we offer the following hypothesis:

  H5: Product innovation strategy has a positive
  relationship with product innovation performance.


The Mediating Role of Product Innovation Strategy

As we have previously noted, prior studies on tapping supplier innovativeness or integration of suppliers into the NPD process have considered the direct influence of these factors on a focal firm's manufacturing performance (Parker, Zsidisin, & Ragatz, 2008; Petersen, Handfield, & Ragatz, 2003). Similarly, the effect of tapping customer innovativeness on innovation performance has been examined in several studies (Blazevic & Lievens, 2008; Elofson & Robinson, 2007). Largely missing in these studies are the processes by which the innovativeness of these supply chain partners influence other performance outcomes.

Our first hypothesis in this study identifies the implementation of product innovation strategy, comprising the use of new technologies, new product features, and components in product development, as a direct consequence of tapping supply chain partner innovativeness. To develop this hypothesis, we draw from resource dependence theory (Pfeffer & Salancik, 2003) and argue that firms strive to reduce their dependencies on supply chain partners' innovativeness by developing their own product innovation strategies to attain an improved innovation performance. In addition, we argue that when firms implement specific innovation initiatives in product development (product innovation strategy), an improvement in product innovation performance can be attained. Taken together, these arguments suggest that a firm's product innovation strategy is both a consequence of supply chain partners' innovativeness and an antecedent of product innovation performance. The implication of this is that product innovation strategy is a process or a mechanism through which supply chain partners' innovativeness affects a firm's innovation performance. As we noted above, prior research has mainly focused on the direct link between external sources of innovation and a firm's innovation performance (Kodama, 2002; Lau et al., 2010; Oke & Kach, 2012).

The direct link between external sources of innovation and performance may be due to several factors other than product innovation strategy. For example, collaborating with an innovative supply chain partner could enhance a firm's learning and absorptive capacity which may enhance the firm's capability to improve its innovation performance. In other words, apart from product innovation strategy, other mechanisms exist through which supply chain partners' innovativeness relates to a firm's innovation performance. This suggests that product innovation strategy is a partial mediator of the link between a supply chain partner's innovativeness and innovation performance. Accordingly, we offer the following hypothesis:

  H6: Product innovation strategy partially mediates
  the link between supply chain partner innovativeness
  and product innovation performance.


The research model tested in this study is presented in Figure 1.

METHODS

Sample and Procedures

This study utilized a cross-sectional mail survey of a sample of Australian manufacturing firms, encompassing various sectors, including food, electronics, wood, textiles, plastics, metal, and pharmaceuticals within the scope of the Australia and New Zealand Standard Industrial Classification (ANZSIC) codes under Division C (Manufacturing). Table 1 shows the sample distribution of the manufacturing sectors captured in this study. In administering our survey, we specifically requested in the cover letter of the survey that the questionnaire be assigned to middle and senior managers whose primary responsibilities are related to strategic innovation activities of the firms. In total, 1,000 surveys were mailed out, and 207 usable responses were received representing an effective response rate of 20.7%.

TABLE 1 Sample Distribution of the Manufacturing
Sectors Based on Australia and New Zealand Standard
Industrial Classification (ANZSIC) Codes

Manufacturing Sectors    Number  Valid % of
Based on ANZSIC        of Firms  the Sample
Codes

Food, beverage, and         49         23.7
tobacco

Textile, clothing,           7          3.4
footwear, and leather

Wood and paper               9          4.3

Printing, publishing,       11          5.3
and media

Petroleum, coal, and        20          9.7
chemical

Nonmetallic mineral          5          2.4

Metal                       12          5.8

Machinery and equipment     42         20.3

Prefabricated building,     48         23.2
furniture, and others

Construction                 4          1.9

Total                      207          100


Nonresponse Bias

To test for nonresponse bias, we compared the responses of early and late waves of returned surveys based on the assumption that the opinions of late respondents are representative of the opinions of non-respondents (Armstrong & Overton, 1977). Student's t-tests yielded no statistically significant differences between early-wave and late-wave groups with respect to firm's size and several key variables, including business performance. The results of the t-tests suggest that nonresponse bias was not a problem in this dataset.

Measures

All measures used in this study were derived from previous studies to secure their content validity. The measure for supply chain partner innovativeness was derived from the studies by Azadegan and Dooley (2010) and Wang and Ahmed (2004). In addressing this scale, we specifically asked respondents to focus on the firm's most valuable supply chain partner (i.e., the buyer or supplier with whom their firm conducts maximum business in dollar terms). The measure for strategic relationship was derived from the supply chain management literature (Chen & Paulraj, 2004; Fynes, Voss, & de Burca, 2005; Li, Rao, Ragu-Nathan, & Ragu-Nathan, 2005) which emphasized the importance of building long-term relationships and strategic collaborations with the firm's most valuable supply chain partners for mutual benefits. In measuring innovative climate, we adopted the scale by Prajogo and Ahmed (2006) which includes several key practices such as providing resources for employees to generate innovative ideas and to facilitate knowledge and idea sharing through open communications. The scale for product innovation strategy was derived from Akgun, Keskin and Byrne (2009), Gunday, Ulusoy, Kilic and Alpkan (2011), and Yamin, Mavondo, Gunasekaran and Sarros (1997), and it reflects several key practices that firms employ to develop new and innovative products, including the use of new components, new materials, and new technologies.

Product innovation performance is measured in terms of the quantity and innovativeness of new products developed and introduced to the market (Gopalakrishnan, 2000; Prajogo & Sohal, 2006; Spanjol, Qualls, & Rosa, 2011), for which we asked respondents to assess their performance relative to the industry average using 7-point Likert-type scale from 1 (well below) to 7 (well above). The items used to capture each scale are presented in Table 2.

RESULTS

Scale Validity and Reliability

As a first step, we performed validity and reliability tests on the five measures used in this study using confirmatory factor analysis (CFA). The results of the CFA and measures of Cronbach's alpha are presented in Table 2. The normed chi-square value ([x.sup.2] = 201.01; df = 142) is <3. The fit indices (NFI = .937, NNFI = .974, CFI = .978, CFI = .907) are also above the cutoff score for an acceptable model (.90) with most of the indices even exceeding the cutoff score for a good model (.95). The RMSEA value is .045, well below the recommended cutoff point of .08. The overall model fit results suggest acceptable unidimensionality and convergent validity for the measures (Bagozzi, Yi, & Philips, 1991; Bollen, 1989; Carmines & Mclver, 1981; Hoskisson, Hitt, Johnson, & Moesel, 1993). The results also show that all items loaded significantly on their respective constructs with standardized path loadings above .5. The five scales also showed a strong reliability as indicated by Cronbach's alpha values which surpassed the threshold point of .70 (Nunnally, 1978). We also computed composite reliability using the standardized loadings and the measurement error of each indicator, and the results show that the five constructs have composite reliabilities above 7 (Hair, Black, Babin, Anderson, & Tatham, 2006). Next, we computed the average variance extracted (AVE) for each construct using the squared standardized loadings and the measurement error of each indicator to further confirm the scale validity; these results show that the AVEs for the five scales are greater than the recommended value of .50 (Fornell & Larker, 1981).

TABLE 2

Scale Validity and Reliability

Scales             Items                 Standard  Cronbach's
                                           Factor       Alpha
                                          Loading

Supply chain       Our supply chain           .81         .90
partner's          partner adopts
innovativeness     innovation as their
                   primary strategy.

                   Our supply chain           .77   (.91) (a)
                   partner uses cutting
                   edge technologies in
                   the industry.

                   Our supply chain           .86   (.66) (b)
                   partner continuously
                   produces new ideas.

                   Our supply chain           .74
                   partner gains a
                   large portion of
                   revenue from new
                   products.

                   Our supply chain           .88
                   partner is
                   recognized as being
                   innovative.

Organizational     Our company provides       .75         .78
innovative         time and resources
climate            for employees to
                   generate,
                   share/exchange, and
                   experiment with
                   innovative
                   ideas/solutions.

                   Our employees are          .83     (.84) *
                   working in diversely             (.57) (b)
                   skilled work groups
                   where there is free
                   and open
                   communication among
                   the group members.

                   Our employees              .75
                   frequently encounter
                   nonroutine and
                   challenging work
                   that stimulates
                   creativity.

                   Our employees are          .69
                   recognized and
                   rewarded for their
                   creativity and
                   innovative ideas.

Strategic          We expect our              .76         .78
relationships      relationship with
with supply chain  the key supply chain
partners           partners to last a
                   long time.

                   We collaborate with        .70   (.81) (a)
                   the key supply chain             (.51) (b)
                   partner to improve
                   performance in the
                   long run.

                   The supply chain           .81
                   partner sees our
                   relationship as a
                   long-term alliance.

                   We view our supply         .57
                   chain partner as an
                   extension of our
                   comoany.

Product            We develop or use          .78         .85
                   new components.

innovation         We develop or use          .68   (.85) (a)
strategy           new materials
                   (including
                   green/recycled
                   materials).

                   We develop or use          .79   (.59) (b)
                   new technologies in
                   our products.

                   We develop or use          .81
                   new product
                   features.

Innovation         Novelty of new             .78         .78
performance        products

                   Number of new              .82   (.78) (a)
                   products introduced              (.64) (b)

(a) CR composite reliability.

(b) AVE, average variance extracted.

[x.sup.2] = 201.01, df = 142, RMSEA = .045, NFI = .937, NNFI
= .974, CFI = .978, GFI = .907.


As the data were provided by a single respondent in each responding firm, we needed to check whether the responses would be affected by common method bias using the Harmann's single-factor test (Paulraj, 2011; Podsakoff, MacKenzie, Jeong-Yeon, & Podsak-off, 2003). This test was run by loading all 19 items into one factor as a competing model for the CFA model. The common model produced a poor fit to the data (chi-square ([x.sup.2]) = 1,828.88, df = 152, RMSEA = .231). In addition, a large portion of the indicators has poor factor loadings (.3 or below). These results suggest that common method variance was not a significant problem in the dataset.

A discriminant validity test was performed to examine whether constructs were distinct from each other. We followed the method used by Ahire, Golhar and Waller (1996) by pairing each of the constructs and subjecting them to two (CFA) measurement models. All of the tests results passed the criterion for discriminant validity (p < .01).

The second step was to generate the composite scores for the five scales which were used in the regression analysis, using the mean score (Hair et al, 2006). The normality of the five composite scores was tested, and we found no violation of normality as indicated by skewness and kurtosis values which are within the accepted range ([+ or -]1 and <7, respectively).

As the data were obtained from different manufacturing sectors, we conducted a preliminary test using MANOVA to check whether there was any difference across the above five composite variables between different manufacturing sectors. The MANOVA was statistically significant (Pillai's Trace F = 1.01, p < .05, Wilks' Lambda F = 1.01; p < .05, and Hotelling's Trace F = 1.01, p < .01). Following Tabachnick and Fidel' (2007), follow-up MANOVAs were conducted using the Bonferroni test, and the results showed that none of the five variables was significantly different (at p < .05) between manufacturing sectors. Overall, the results of MANOVA test suggest that it is appropriate to pool the data in the analysis.

Prior to conducting the regression analysis, we performed Pearson bivariate correlations between all variables included in this study (including organizational size as a control variable). These correlations are presented in Table 3. Overall, the correlation coefficients suggest there is no multicollinearity among these variables which could affect the regression.

TABLE 3

Correlation Matrix

                   Mean    SD      1     2    3    4    5

Firm size       1  3.76  1.77

Organizational  2  4.57  1.09  -.0.4    --
innovative
climate

Supply chain    3  3.96  1.14   -.15  .15*   --
partners                           *
innovativeness

Strategic       4  5.61   .81   -.17   .12  .27   --
relationship                       *         **

Product         5  4.79  1.15   -.01   .47  .30  .20   --
innovation                              **   **   **
strategy

Product         6  4.08  1.23    .06   .33  .27  .10  .40
innovation                              **   **        **
performance


ANALYSIS

Structural equation modeling (SEM) was used to test the hypotheses relating to direct and indirect effects. We employed SEM because this approach allows estimation of all the relationships simultaneously to account for any potential measurement error as opposed to testing the model in a piecemeal fashion. It is also pertinent to note that SEM approach provides the best balance of type I error rates and statistical power when testing direct and indirect effects (James, Mulaik, & Brett, 2006; MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002). We used hierarchical moderated regression to test for interaction effects. This approach helps to provide effect size assessment and has been used in a similar way in other research studies that include investigation of moderator variables.

Structural Equation Modeling (Tests for Hi, H5, and H6)

We used SEM to test the relationships among the supply chain partner's innovativeness, strategic relationship, innovative climate, product innovation strategy, and product innovation performance. The focal firm's size was used as a control variable for both product innovation strategy and product innovation performance. The results displayed in Figure 2 show a good fit ([x.sup.2] = 73.40, df = 49, RMSEA = .049, NFI = .959, NNFI = .979, CFI = .985, and GFI = .944). Supply chain partner's innovativeness has a positive effect on product innovation strategy ([beta] = .34 at p < .01). Thus, H1 is supported. Furthermore, product innovation strategy shows a positive relationship with product innovation performance ([beta] = .44 at p < .01), supporting H5. The results also show that a supply chain partner's innovativeness has a direct, positive effect on product innovation performance albeit relatively weaker than the effect of product innovation strategy ([beta] = .18 at p < .05). This direct effect suggests that product innovation strategy partially mediates the influence of supply chain partner innovativeness on innovation performance, which indicates support for H6.

A further test was conducted by comparing this partially mediating model with another model where the direct paths between the three independent variables (supply chain partner's innovativeness, innovative climate, and strategic relationship) and the dependent variable (product innovation performance) were removed (i.e., a fully mediating model). This model produced a slightly higher chi-square value (78.18 with df = 50). The chi-square difference between the partial and the fully mediating models is 4.68 (df = 1, p < .05). Thus, H6, which posits that product innovation strategy partially mediates the association between supply chain partners' innovativeness and innovation performance, is supported.

Hierarchical Moderated Regression Analysis (tests for H2, H3 & H4)

In testing the hypotheses, we ran three different sets of multiple regression models. We consider organizational size as a control variable for all regression models tested in this study. The first regression model aims to confirm the results of the SEM analysis presented previously. These results are presented in Table 4. Supply chain partner innovativeness shows a positive effect on product innovation strategy (b = .23 at p < .01).

The second regression model tests the moderation hypotheses included in this study (H2, H3, and H4). In this regard, we calculated three interaction variables that comprise two two-way interactions (i.e., between innovative climate and supply chain partner innovativeness and between supply chain partner innovativeness and strategic relationship) and one three-way interaction (i.e., between innovation climate, strategic relationship, and supply chain partner innovativeness). Prior to this process, all variables involved were standardized to minimize potential multicollinearity between the independent variables and their product terms (Tabachnick & Fide11, 2007). We also checked for multicollinearity by examining variance inflation factors (VIFs) for all the variables. All VIF values were lower than 2, thus well below the recommended cutoff of 10, confirming that multicollinearity is not a problem in our results.

The results of the moderated regression analysis are presented in Table 4. The interaction between supply chain partner innovativeness and innovative climate has no effect on product innovation strategy (b = -.04 at p> .05); thus, H2 is not supported. On the other hand, the interaction between supply chain partner innovativeness and strategic relationship has a positive effect on product innovation strategy (b = .21 at p < .01); thus, H3 is supported. Finally, the three-way interaction between supply chain partner innovativeness, innovative climate, and strategic relationship has a positive effect on product innovation strategy (b = .14 at p < .05); therefore, H4 is supported.

TABLE 4

Hierarchical Moderated Regression Analysis

                             Product Innovation
                                       Strategy

Organizational size                         .04     .01

Supply chain partner                      .3 **  .23 **
innovativeness

Organizational innovative                        .45 **
climate

Strategic relationships                             .09
with supply chain partners

SC partner innovativeness x                        -.04
innovative climate

SC partner innovativeness x                      .21 **
strategic relationships

SC partner innovativeness x                       .14 *
innovative climate x
strategic relationships

R2                                          .10     .34


DISCUSSION AND CONCLUSION

The precepts of resource dependence theory, the KBV, and social capital theory were used to argue for the links between supply chain partner innovative-ness, the implementation of product innovation

strategy, and product innovation performance of a firm. This is consistent with the work of Tan and Tracey (2007) who found that involving customers and suppliers in collaborative NPD projects had a positive influence on performance. We find support for the notion in this study that supply chain partner innovativeness positively relates to product innovation performance through product innovation strategy. Firms depend on their supply chain partners for innovative input. The dependency on supply chain partners' innovativeness triggers firms to implement product innovation strategies of their own in order to reduce the dependency. Further, a firm's exposure to and dependency on innovative partners enable the firm to develop routines and garner relevant knowledge that can be exploited and intertwined with internal knowledge to implement its own product innovation strategies including using new components, materials, and new technologies in product development.

While the theoretical arguments justify the key relationship that ties supply chain partner innovativeness to the focal firm's innovation strategy, we extend this idea by addressing the "how" of this phenomenon. For example, we argue that an appropriately designed internal climate for innovation will further strengthen the innovation chain that links supply chain partner innovativeness to the innovation strategy of the focal firm. Next, building strategic relationships with supply chain partners to tap into the innovative base of these supply chain partners sets the right external climate. This is so because the foundation of knowledge sharing across network partners cannot take place without a climate of cooperation and trust across the entire innovation chain (Wagner & Buko, 2005).

Taken together, our study is perhaps the first to complement the innovation chain with the notion of a climate chain. In other words, the internal climate for innovation should align and blend well with efforts made externally with key suppliers for tapping into ideas for new innovations. However, these claims were modified when our results found no support for the moderating influence of internal innovation climate but found full support for the moderating influence of building strategic relationships with supply chain partners. An explanation for the insignificant finding is that at the margin, an appropriately designed internal innovation climate is a "given" and therefore does not further explain the positive influence of supply chain partner innovativeness on focal firm's innovation strategy. In contrast, building trust and relationships with external constituents serves as a catalyst for tapping the innovative potential of supply chain partners. Strategic relationships enable focal firms and their partners to become more conversant with each other's organizational processes and patterns of behaviors, and more willing to share ideas and innovative practices. This enhances the positive influence of supply chain partners' innovativeness on a focal firm's innovation strategy.

It is interesting to note that the joint investment in internal innovation climate and building relationships with supply chain partners moderates the relationship between supply chain partner innovativeness and product innovation strategy. In other words, the influence of innovative climate on supply chain partner innovativeness will be more effective when there is a strategic relationship between the focal firm and its innovative supply chain partner. As we have noted above, building strategic relationships appears to be a key ingredient in enhancing a firm's innovative capability from supply chain partner innovativeness. It is conceivable that for many product and process technologies, the locus of innovation, at the margin, lies with the external supply chain partners, which means that building strategic relationships with partners is more important than having an internal innovative climate. Although an investment in internal innovative climate by itself does not strengthen the innovation chain, we find that such an investment is equally not detrimental. In other words, both investments in internal innovation climate and strategic relationships with partners (external climate) are needed to strengthen the innovation chain.

This study contributes to knowledge by extending the notion of supply chains and value chains to specifically understand what happens in innovation chains. In particular, what mechanisms substantially enhance supply chain partners' efforts on the focal firm's efforts to innovate? Our results point to the powerful combinatorial influence of two factors--one internal and another external--that further strengthen the innovation chain. Internal innovative climate factors specifically motivate firms to encourage "out of the box" and lateral thinking, and promote employees to engage in high-risk innovative ideas. For example, firms such as Google, 3M, and AT&T have a longstanding tradition of creating workplaces that foster creativity. At the firm level, the use of appropriate metrics motivates such behavior. For example, firms such as Motorola and 3M specifically track innovations (and sales from those innovations) from products that did not exist five years ago. This creates a rejuvenating cycle of innovation that constantly challenges existing models, modes, and processes. However, while these may be effective in generating innovations from internal constituents, these efforts must be combined with external efforts such as building strategic relationships with supply chain partners in order for a firm to effectively tap supply chain partner innovativeness.

Product innovation strategy shows a positive effect on product innovation performance. This result is consistent with the study of Azadegan and Dooley (2010). The use of new technologies and components in product development (product innovation strategy) may offer various options and possibilities of developing novel products and increasing the number of new products developed. For example, new technologies can enable platform products to be built which then offer the possibility of building derivative products based on the platform product, thus increasing the number of new products. Finally, it is important to note that supply chain partner innovativeness had a direct positive effect on product innovation performance (see Figure 2). This suggests that product innovation strategy represents only one mechanism through which supply chain innovativeness affects innovation performance in firms. The identification of a mediating variable (product innovation strategy) that links supply chain partner innovativeness to innovation performance is a contribution to the literature that has largely focused on the direct link between external sources of innovation and performance. For example, supplier innovativeness and performance (Azadegan, 2011; Azadegan & Dooley, 2010); outsourcing and operational innovation performance (Oke & Kach, 2012), and customer innovative-ness and performance (Kodama, 2002; Lau et al., 2010). Indeed, our finding suggests that in addition to innovation strategy, supply chain partner innovativeness could lead to other factors including increased organizational learning, absorptive capacity, joint product development efforts, innovative process implementation, and the like, which in turn could lead to improved product innovation performance.

In terms of managerial implications, managers must recognize the importance of supply chain partner innovativeness as a key ingredient in fostering innovative capability and strategy. Specifically, our study shows that it is not sufficient to assume that a focal firm will enjoy improved innovation performance if its partners are innovative. What matters are the actions that the focal firm takes as a result of being involved with innovative partners. We show that partners' innovativeness are likely to trigger the focal firm to implement innovation strategies (e.g., to develop or use new components in product development) which will in turn lead to improved innovation performance (novel products). Thus, managers must recognize the potential added benefits of supply chain partner innovativeness and must put mechanisms in place to implement specific product innovation strategies (e.g., the use of new components, materials, and technologies in product development) to exploit partners' innovative capabilities. Another important implication of our study is that while it is important to build a culture and a climate that supports innovation internally, managers must invest in strategic relationships with supply chain partners because such investments help firms to better tap partners' innovativeness. These relationships, combined with internal innovation climate, help to effectively tap and utilize partners' innovative knowledge. Thus, organizational processes and structures that facilitate the development of such relationships are encouraged.

LIMITATIONS .AND FUTURE RESEARCH

We offer several suggestions to expand upon this study. First, it could be argued that our innovativeness measure (supply chain partner's innovativeness) which captures the entirety of the relationships, a firm possesses with its supply chain partners, is a limitation of the study. We specifically asked the respondents to focus on the firm's most valuable supply chain partner (i.e., the buyer or supplier with whom the firm conducts maximum business in dollar terms). In line with Frohlich and Westbrook (2001), our intention was to increase variance and widen the scope of these relationships, unlike previous studies that focus specifically on either supplier innovativeness or customer innovativeness. Indeed, we were not interested in the specific source of innovation but whether an external source of innovation triggers innovation activities within the firm that lead to innovation performance in the firms. Nevertheless, future studies can separate these supply chain partners between upstream (suppliers) and downstream (customers) to gain deeper insights.

Second, our findings are admittedly limited as the information is exclusively based on the focal firms' (manufacturers') perspectives, and we relied on single respondents in each manufacturing firm. Future studies might expand this study (albeit in a methodologically challenging fashion) using triadic data which includes a supplier, the focal firm, and a customer. Third, future studies can also improve the quality of the data by incorporating secondary data rather than perceptual data. Data on other control variables such as the level of R&D expenditure and industry sector should be collected in future studies to investigate the effect of these constructs on the variables examined in this study. Also, new contextual variables like the stage in the product life cycle, research and development expenditure, and country of origin (of supply chain partners) effects on the innovation chain relationships can be examined.

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ADEGOKE OKE

Arizona State University

DANIEL I. PRAJOGO

Monash University

JAYANTH JAYARAM

University of South Carolina

Adegoke Oke (Ph.D., Cranfield University) is an associate professor of supply chain management in the Department of Supply Chain Management at the W. P. Carey School of Business, Arizona State University in Tempe, Arizona. His current research interests include innovation management in horizontal networks, supply chains and supply networks, the relative effectiveness of different open innovation strategies, and risk management strategies in supply chains. Dr. Oke's work has been published in the Journal of Organizational Behavior and the Supply Chain Management Review, among other outlets, and he received the 2007 Best Reviewer Award from the Journal of Operations Management.

Daniel I. Prajogo (Ph.D., Monash University) is an associate professor in the Department of Management at Monash University in Victoria, Australia. His research focuses primarily on operations and supply chain management, quality management and innovation management. Currently, Dr. Prajogo's work examines inter-firm collaborative innovations within supply chain networks. He also is building research collaborations with industry partners in Australia. Among the publications in which his articles have appeared are the International Journal of Operations & Production Management, the International Journal of Production Research, the International Journal of Production Economics, Supply Chain Management: International Journal, the European Journal of Operational Research, Technovation, R&D Management, the Journal of Small Business Management, and the Journal of Cleaner Production.

Jayanth Jayaram (Ph.D. Michigan State University) is a professor of management science, and a Moore Research Fellow, in the Department of Management Science of the Moore School of Business at the University of South Carolina in Columbia, South Carolina. His research interests are in the areas of sustainability, supply chain integration, new product development, performance measurement and strategic purchasing. Currently, he is working on projects related to sustainability. Dr. Jayaram currently is serving, or has served, as an Associate Editor for the Journal of Operations Management, Decision Sciences, the Journal of Supply Chain Management, and the Journal of Business Logistics. He also serves as an editorial board member for IEEE Transactions on Engineering Management and the Journal of Integrated Supply Management.
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Author:Oke, Adegoke; Prajogo, Daniel I.; Jayaram, Jayanth
Publication:Journal of Supply Chain Management
Geographic Code:1USA
Date:Oct 1, 2013
Words:10393
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