The effects of buyer-supplier's collaboration on knowledge and product innovation

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The effects of buyer-supplier's collaboration on knowledge and product innovation Jeanine Chang Shenzhen Audencia Business School - Shenzhen University, 3688 Nanhai Road, Shenzhen 518060, Guangdong, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Channel integration mechanisms Supplier task involvement Joint planning Product knowledge End customer knowledge Supplier incentives Product innovation performance

Drawing on marketing and management literature, this study investigates integration mechanisms between channel members. Specifically, the research framework is built upon the buyer-supplier gray-box integration approach, knowledge-based view, and agency theory. This study identifies and compares the effects of two graybox integration mechanisms, namely supplier task involvement and joint planning, on two kinds of knowledge acquisition. I find that both supplier task involvement and joint planning positively influence manufacturers' product knowledge acquisition and end customer knowledge acquisition. Supplier task involvement has a stronger effect on knowledge acquisition than joint planning. The relationships between integration mechanisms and knowledge acquisition are contingent upon supplier incentives. Furthermore, this study also extends the literature by comparing the effects of two different kinds of knowledge on product innovation performance. Even though both product and end customer knowledge lead to better product innovation performance, end customer knowledge has a stronger effect than product knowledge on product innovation performance. Theoretical and managerial implications are discussed at the end.

1. Introduction Channel collaboration is a topic of interest to both management and marketing researchers. Closely integrated relationships between manufacturers and their channel partners enable firms to gain competitive advantage (Hoegl & Wagner, 2005) and help foster innovation (Inemek & Matthyssens, 2013). Manufacturers can reduce costs and product cycle times as well as improve product quality by working closely with their suppliers (Ragatz, Handfield, & Petersen, 2002; Ragatz, Handfield, & Scannell, 1997). Recently, suppliers, like customers, have come to be regarded as key to successful innovation (Inemek & Matthyssens, 2013). Companies gain competitive advantages by using suppliers' resources, skills, capabilities, and especially their design acumen. Past research on interfirm collaboration and innovation has been well established. For example, different governance mechanisms such as relationship norms have been found to influence interfirm innovations (Mooi & Frambach, 2012). Other factors, such as unilateral governance (Wang, Bradford, Xu, & Weitz, 2008), behavior and output control (Sivakumar, Roy, Zhu, & Hanvanich, 2011) and alliance portfolio (Cui & O'Connor, 2012) have been found to influence interfirm innovation generation. However, there are some research gaps in regarding supplier integration and new product innovation. First, prior research on suppler integration has focused primarily on operational performance, linking supplier integration with operational

achievement (Hoegl & Wagner, 2005; Rothaermel, Hitt, & Jobe, 2006). However, in many industries, manufacturers have given suppliers increasing responsibility for product design, development, and engineering techniques (Wynstra, Van Weele, & Weggemann, 2001). One study showed that automobile manufacturers were able to bring new cars to market faster, with more innovative features, and with less effort by working closely with their suppliers (LI, 2009). Collaboration between business partners is key to knowledge maximization and product innovation because acquiring external resources and knowledge helps firm survive and grow (Batt & Purchase, 2004). For example, Toyota has formed a supplier association to encourage information sharing, and the company holds social events to bring its suppliers together (Gulati, Wohlgezogen, & Zhelyazkov, 2012). The auto manufacturer encourages its suppliers to make frequent small-lot deliveries in order to promote the exchange of production, technical and logistics information (Marksberry, 2012). Toyota's collaboration with its suppliers fosters strong long-term supplier relationships and contributes to the company's reputation as a preferred partner. Yes, the relationships among integration mechanisms, knowledge acquisition and product innovation are unclear. Second, relationships between manufacturers and suppliers require special attention when manufacturers attempt to integrate with their suppliers (Gulati, 2013). The integration literature suggests that there are gray-box and black-box integrations between manufacturers and

E-mail address: [email protected]. http://dx.doi.org/10.1016/j.indmarman.2017.04.003 Received 23 November 2015; Received in revised form 8 March 2017; Accepted 18 April 2017 0019-8501/ © 2017 Published by Elsevier Inc.

Please cite this article as: Chang, J., Industrial Marketing Management (2017), http://dx.doi.org/10.1016/j.indmarman.2017.04.003

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Economic Incentives Supplier Task Involvement

New Product Knowledge

Supplier Joint Planning

Product Innovation Performance

End Customer Knowledge Fig. 1. The benefits of supplier coordination.

end customer knowledge on product innovation performance. Product innovation performance refers to a firm's ability to adopt new ideas, product and processes successfully (Paladino, 2008). I investigate and compare the effects of product and end customer knowledge on product innovation performance. Fourth, this study incorporates economic incentive as a formal governance mechanism and investigates how it interacts with supplier integration mechanisms on knowledge acquisition. Traditionally, by working with upstream suppliers, manufacturers endeavor to cut costs and improve delivery performance (Hoegl & Wagner, 2005; Rothaermel et al., 2006). However, suppliers are increasingly being regarded as important sources of innovation both in academic research and in business practice (Fliess & Becker, 2006). Therefore, it is important to identify the circumstances under which suppliers are most willing to collaborate with manufacturers in the product development process. This research framework (Fig. 1) is built upon three theoretical foundations, i.e., the gray-box supplier integration literature, the knowledge based view (KBV) and agency theory, and investigates the effects of two distinct manufacturer/supplier integration mechanisms on two different kinds of knowledge acquisition. It also looks at the contingent effects of economic incentives provided by manufacturers to suppliers. Importantly, this study distinguishes between the effects of product knowledge and end customer knowledge on product innovation performance.

suppliers in the new product development (NPD) process (Koufteros, Cheng, & Lai, 2007; Petersen, Handfield, & Ragatz, 2005). In the graybox integration, suppliers work with the manufacturer's team members on joint product development and joint decision making. In a black-box integration, suppliers work on their own to fulfill the manufacturer's specifications. The gray-box integration mechanism has been found to facilitate knowledge transfer and product innovation; however, the effect of black-box integration is negligible (Koufteros et al., 2007; Le Dain & Merminod, 2014). Prior studies addressed only the difference between gray-box and black-box integration (Koufteros et al., 2007; Petersen et al., 2005) and there is little extant research that looks into the integration mechanisms within the gray-box realm. Such that research did not identify and differentiate the integration mechanisms within the gray-box realm which might lead to different types of knowledge acquisition. Third, even though prior research studies have established the links between supplier integration and innovation and performance, the results are mixed. Some studies found that supplier integration can facilitate the speed of product development, improve product quality, and reduced production costs (Lau, Tang, & Yam, 2010; van Echtelt, Wynstra, van Weele, & Duysters, 2008). Other research study found that supplier integration may incur coordination cost and put the firms' valuable knowledge at risk (Wagner & Hoegl, 2006). The mix results may due to some boundary conditions. This study makes a few contributions by filling the research gaps mentioned above. First, even though prior studies have linked the graybox integration with knowledge transfer, they did not differentiate the integration mechanisms. In this study, I identify and differentiate two types of integration mechanisms in the gray-box domain, i.e., supplier task involvement and joint planning, that can help manufacturers in the areas of knowledge acquisition and product innovation. Supplier task involvement refers to upstream suppliers that are invited to participate in manufacturers' product development processes (Petersen, Handfield, & Ragatz, 2003). Supplier joint planning is the proactive collaborative setting of goals and tasks with respect to manufacturers' product planning processes (Claro & Claro, 2010). Second, this study compares and tests the effects of the two collaboration mechanisms on product and end customer knowledge acquisition. Although prior research indicates that gray-box integration facilitates knowledge transfer or sharing (Koufteros et al., 2007; Le Dain & Merminod, 2014), it does not differentiate the varying effects on different kinds of knowledge acquisition. Because supplier task involvement is relatively general in nature and joint planning is usually quite specific, they affect the acquisition of different kinds of knowledge in varying ways. Third, prior research studies have not endeavored to differentiate the effects of product and end customer knowledge on product innovation performance. Only a few studies have investigated the differing nature of customer and product knowledge (De Luca & Atuahene-Gima, 2007; Rindfleisch & Moorman, 2001), and none has differentiated their effects on new product development. This study empirically tests and compares the effects of product knowledge and

2. Theoretical background 2.1. Gray-box integration and innovation New product development (NPD) is a firm's ability to introduce new products or features and is a key competitive advantage (Koufteros et al., 2007). NPD increasingly relies on knowledge and technical skills acquired from external resources, such as upstream suppliers and downstream customers (Le Dain & Merminod, 2014). By involving suppliers in product development, manufacturers can maintain focus on building their own core capabilities while depend on the complementary resources of their suppliers (Handfield & Nichols, 2002). As manufacturers turn more and more to their suppliers for knowledge (Wang, Li, & Chang, 2016), supplier integration becomes a critical factor for product innovation performance as well as knowledge acquisition. An intense competitive environment forces firms to continuously innovate and innovation requires firms to integrate internal and external resources to create new knowledge. A firm's capabilities and resources, as well as its organizational learning, influence its innovation processes and outcomes (Crossan & Apaydin, 2010). This study focuses on how manufacturers can acquire knowledge from suppliers by working with them through two gray-box integration mechanisms. The gray-box approach is a basic form of supplier involvement in product development, and it requires suppliers and manufacturers to work together (Koufteros et al., 2007; Petersen et al., 2005). In this approach, suppliers contribute information and suggestions to the 2

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and imitability as central to the formation and sustainability of competitive advantage (Spender & Grant, 1996). In addition to the characteristics of knowledge, the role of alliances in the inter-firm transfer of technological capabilities also depends on the various communication mechanisms between firms as well as industry characteristics and institutional environments. This study focuses on the integration mechanisms that facilitate manufacturers' knowledge acquisition from suppliers and the role of acquired product and end customer knowledge in improving manufacturers' product innovation performance. Knowledge creates platforms upon which firms can expand into new markets as well as maintain and enhance competitive advantage (Kim & Kogut, 1996). Knowledge also provides new ideas for future product development when existing products become commoditized (Ndofor & Levitas, 2004). Knowledge can be categorized in various dimensions, such as explicit and tacit knowledge (Dhanaraj, Lyles, Steensma, & Tihanyi, 2004); knowledge about customers, competitors, suppliers, and regulatory agencies (Kohli & Jaworski, 1990; Li & Calantone, 1998); and product and end market knowledge (Griffith, Noble, & Chen, 2006; Rindfleisch & Moorman, 2001). Product and end market knowledge is especially important for manufacturers because they need this information for NPD. Supply chain partners are important sources for external knowledge acquisition since suppliers possess valuable product and marketing information (such information includes end market knowledge) (Li & Calantone, 1998; Gao et al., 2015). Azadegan (2011) argues that manufacturers acquire both direct and indirect benefits from working with suppliers, including new ideas, processes and technology, and intelligence that can improve product delivery. In addition, manufacturers can also acquire market knowledge from their suppliers through the product development process (Li & Calantone, 1998).

manufacturers' product development process and also assume some responsibility for product development. Since involving suppliers in product development processes involves risk and financial resources from both parties, it calls for a significant bilateral commitment to engage in frequent communication for purposes of exchanging information, tactics, technological skill and knowledge in order to make collective decisions throughout the new product development process (Le Dain & Merminod, 2014). According to the definition of gray-box integration, supplier task involvement and supplier joint planning fit well into this domain (Claro & Claro, 2010; Inemek & Matthyssens, 2013; Le Dain & Merminod, 2014). In supplier task involvement, manufacturers actively invite upstream suppliers to participate in its product development processes (Petersen et al., 2003). In supplier joint planning, both parties proactively working and setting goals and tasks with respect to manufacturers' product planning processes (Claro & Claro, 2010). Both integration mechanisms invite suppliers to work together with manufacturers in the new product development process and requires frequent communications from both parties to set goals and tasks together. All these activities fall in to supplier gray-box approach in which both the manufacturers and suppliers work alongside each other (Koufteros et al., 2007). There are many benefits associated with the gray-box approach to supplier integration. First, knowledge transfer occurs between collaborating parties as they work together, and this may become a regular pattern of interaction between manufacturer and supplier team members for knowledge transfer and acquisition (Grant, 1996). Le Dain and Merminod (2014) found that knowledge sharing and transfer is an ongoing activity in the gray-box approach. Second, gray-box integration with suppliers enhances manufacturers' product innovation capabilities by incorporating the suppliers' complementary competencies (Handfield & Nichols, 2002). NPD is critical for firms to create and maintain competitive advantage in the market; therefore, acquiring external information and resources is essential. Rapid technological development and changing end customer needs make it increasingly important for firms to tap external suppliers for new knowledge and expertise (Phelps, 2010). Third, gray-box integration with suppliers improves a manufacturer's operational performance. In the process of collaboration, manufacturers need to adopt a strategic collaboration mechanism (Heide & John, 1992) which provides a blueprint for achieving mutual goals, cultivating mutual trust, improving contractual understanding, and encouraging efficient communication and conflict resolution (Flynn, Huo, & Zhao, 2010). Such strategic collaboration enables manufacturers to better understand the suppliers operations and production capacity. In summary, gray-box integration, namely supplier task involvement and joint planning, can facilitate manufacturers' knowledge acquisition, enhance its capacity for product development, and improve its operational performance.

2.3. Agency theory Agency theory originally arose in response to agency issues that occur when two cooperating parties have different goals and divisions of labor (Jensen & Meckling, 1976). Agency theory is concerned with resolving two specific problems in principal-agent relationships: first, when the principal and agent have conflicting desires and goals, and the principal is unable to verify what the agent is actually doing; and second, when the principal and agent have different attitudes about risk and therefore take different approaches to risk. Agency theory focuses on determining the most efficient way to govern principal-agent relationships based on these three assumptions (Eisenhardt, 1989): that people are self-interested, bounded by rationality and risk averse; organizational members have conflicting goals; and information is a commodity that can be purchased. Agency theory is a useful lens through which to investigate governance mechanisms in supply chain relationships because the role of these mechanisms is to ensure appropriate agent (i.e. supply chain partner) behavior and determine incentive alignment for cooperative relationships (Agarwal, Croson, & Mahoney, 2010; Conlon & Parks, 1990; Lassar & Kerr, 1996). In the supply chain literature, agency relationships are present when buyer (the principal) relies on supplier (the agent) to undertake action on its behalf. Problems arise in buyersupplier relationships when the parties have different goals, information sets and risk preferences (Bergen, Dutta, & Walker, 1992). Two prevalent governance mechanisms are explicit contracts and monitoring in agency theory. Even though contracts and monitoring serve to align goals in buyer-supplier relationships, it is common for principals (buyers) to offer extra-contract incentives to agents (suppliers) to elicit efforts beyond those contractually specified (Murry & Heide, 1998). To ensure the effectiveness of the value creation process, buyers require some form of “visible hand” or formal control to guarantee the suppliers' cooperation since they are both self-interested business entities and they make decisions according to cost-benefit calculations of the potential consequences (Williamson, 1985). Agency theory has

2.2. The knowledge-based view The knowledge-based view (KBV) has emerged as an extension of traditional strategic management concerns (i.e. strategic choice and competitive advantage) and focuses on knowledge as a firm's most strategically important resource. In order for knowledge to be useful and create value within a firm, knowledge must be transferable, appropriate, and aggregateable (Utterback & Abernathy, 1975). KBV considers knowledge as a critical and unique resource that can help firms gain competitive advantage in a market (Grant, 1996). Kogut and Zander (2003) point out that firms are efficient means for knowledge creation and transfer. Knowledge can be developed and converted into ideas and production through repeated interactions of individuals or groups within a firm. Since the emergence of KBV, many empirical studies have focused on knowledge transfer within and between firms 3

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channels to breed familiarity and trust between buyers and suppliers (Petersen et al., 2005). Supplier gray-box integration reduces transaction costs related to developing, negotiating, monitoring and enforcing formal and informal contracts between buyers and suppliers (Heide & John, 1992; Jung, Wu, & Chow, 2008; Li, Liao, & Yen, 2013; Ragatz et al., 2002).

proposed that economic incentives can be an effective way to prevent governing agents' opportunistic or shirking behaviors (Mishra, Heide, & Cort, 1998). Prior studies have demonstrated ways a visible hand can be deployed, and economic incentives are considered to be a critical factor in controlling inter-firm exchanges (Agarwal et al., 2010; Kumar, Heide, & Wathne, 2011). In the buyer-supplier context, a supplier incentive refers to a price premium that the buyer pays to encourage compliance behavior and prevent the supplier from engaging in short-term shirking or violating behavior (Kumar et al., 2011).

3.1.1. The effect of supplier task involvement on knowledge acquisition The superior value that manufacturers are able to provide to the market relies on efficient processes to coordinate, improve and reconfigure critical resources and capabilities. Many of these resources and capabilities are external. Therefore, manufacturers depend more and more on suppliers to participate in the product development process to enhance their products, performance and quality (Ragatz et al., 2002). Supplier task involvement refers to suppliers' predilection to openly provide production information, market intelligence, and end customer feedback to manufacturers (Carbonell, RodríguezEscudero, & Pujari, 2009). Managing suppliers' participation involves decisions and actions related to cooperation, timing, planning, and information exchange with regard to resources, tasks, and responsibilities of the respective parties. The benefits of supplier task involvement manifest in project performance indicators such as improved product quality, reduction in product development time and cost savings (Chang, Bai, & Li, 2015; Gumusluoğlu & Ilsev, 2009). Supplier involvement in manufacturers' NPD processes can facilitate manufacturers' acquisition of both product and end customer knowledge. First, supplier task involvement boosts the parties' willingness to give and take in the relationship. Such willingness not only sustains the relationship, but also increases the potential value of the affiliation (Emerson, 1962). The capabilities, information, knowledge, and ideas that suppliers provide through the cooperative process are helpful to manufacturers' product development processes (Bass, 1985; van Echtelt et al., 2008). Second, supplier task involvement encourages more robust interactions and better communication between manufacturers and suppliers. Researchers have suggested that sharing critical information and effective communication are key to value creation (Prahalad & Ramaswamy, 2000), and suppliers play a facilitating role in the value creation process that takes place in the manufacturers' sphere (Jansen & Volberda, 2005). Suppliers process the resources that buyers do not have, such as product knowledge, technological acumen, project management ability and market insight. In addition to providing a core product, a supplier can offer insight into the manufacturer's systems, requirements, and the market demand and problem solving processes (Jansen & Volberda, 2005) through the cooperative process. Third, supplier task involvement offers opportunities for manufacturers to more accurately gauge market demand, thereby reducing product design and production timing and making inventory available in anticipation of end customer needs (Flynn et al., 2010). Thus, by involving suppliers in the product development process, manufacturers acquire the necessary product and end customer knowledge to better react to market demand and respond to end customer needs. It is proposed that:

2.4. An overarching framework This theoretical framework (Fig. 1) has integrated the literature from supplier integration, knowledge based view and agency theory. First, this framework proposes that the integration between manufacturers and suppliers has beneficial outcomes including knowledge acquisition and product innovation. Second, KBV proposes that knowledge is one of the most important resources for firms to achieve competitive advantage (Grant, 1996; Kogut & Zander, 1992). This framework identifies knowledge as the most critical resource in the process between integration mechanism and product innovation performance. Third, economic incentive (from agency theory) is a formal governance mechanism that is useful in managing potential opportunistic supplier behavior. Thus, economic incentives function as a contingent factor in the relationship between integration mechanisms and knowledge acquisition. This framework interweaves the relationships among gray-box integration between buyers and suppliers, knowledge acquisition, product innovation performance and economic incentive. 3. Hypothesis development In this section, the hypotheses on the integration mechanisms of knowledge acquisition are developed. I propose that both supplier task involvement and joint planning have positive effects on the acquisition of product and end customer knowledge. However, supplier task involvement has a stronger effect on acquiring both product and end customer knowledge than joint planning. Then I develop the hypotheses on the effects of knowledge on product innovation performance. Even though both product and end customer knowledge have positive effects on manufacturers' product innovation performance, end customer knowledge has a stronger positive effect on product innovation performance than product knowledge. At the end of this section, the moderating effect of economic incentives on the relationships between collaboration mechanisms and knowledge acquisition is discussed. It is proposed that economic incentives strengthen the effects of supplier joint planning on both product and end customer knowledge acquisition, but weaken the effects of supplier task involvement on both product and end customer knowledge acquisition. The summary of these hypotheses is presented in Table 1. 3.1. Gray-box integration and knowledge Supplier integration has become a popular way to improve firm performance as well as develop new concepts. Suppliers possess valuable knowledge that is helpful to a manufacturer's NPD. For example, automotive suppliers possess engineering and manufacturing capabilities which can significantly enhance manufacturers' product development and shorten time-to market (Hong, Doll, Nahm, & Li, 2004). Consumer product manufacturers rely on their suppliers' new product design knowledge and information about user preferences (Fawcett, Jones, & Fawcett, 2012). Suppliers can provide technologies and design expertise to enhance the functionality of final products (Azadegan & Dooley, 2010). In particular, supplier gray-box integration can increase coordination, information exchange, sharing of goals and resolutions, and the use of formal communication and coordination

H1. a & b: Supplier task involvement has a positive impact on manufacturers' acquisition of both (a) product knowledge and (b) end customer knowledge. 3.1.2. The effect of supplier joint planning on knowledge acquisition Joint planning refers to the proactive joint setting of goals that make the future of a relationship foreseeable for buyers and suppliers (Claro & Claro, 2010). Joint planning is the extent to which buyers and suppliers make explicit ex ante of future contingencies the consequential duties and responsibilities in their relationship (Heide & Miner, 1992). Some buyers systematically involve distributors or suppliers in their market planning activities (Hoegl & Wagner, 2005; 4

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Table 1 Summary of hypotheses. Hypothesis

Proposed relationship

Result

H1a & b

Supplier task involvement has a positive impact on manufacturers' acquisition of both (a) product knowledge and (b) end customer knowledge. Joint planning with suppliers has a positive influence on manufacturers' (a) product knowledge acquisition and (b) end customer knowledge acquisition. Supplier involvement has a stronger positive effect on manufacturers' (a) product and (b) end customer knowledge acquisition than joint planning. The greater the product knowledge that manufacturers acquire from suppliers, the greater the level of manufacturers' product innovation performance The greater the end customer knowledge that manufacturers acquire from suppliers, the greater the level of manufacturers' product innovation performance. End customer knowledge has a stronger effect on buyers' product innovation performance than product knowledge. Moderating effects Supplier economic incentives strengthen the positive effects of supplier joint planning on (a) product knowledge acquisition and (b) end customer knowledge acquisition Supplier incentives weaken the positive effect of supplier task involvement on (c) product knowledge acquisition and (d) end customer knowledge acquisition.

Both supported

H2a & b H3a & b H4 H5 H6 H7a & b H7c & d

H2a is supported, H2b is not supported Both supported Supported Supported Supported Both supported H7c is not supported H7d is supported

development, and product forecasting (Pfeffer & Salancik, 2003; Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Even though prior research suggests that these activities can contribute to a firm's manufacturing ability and overall performance (Pfeffer & Salancik, 2003), coordinating and aligning with external resources is challenging. Knowledge is inevitably “imperfectly embedded” in a system and requires a separate exchange device, such as inter-firm collaboration, to be fully understood and internalized by a buyer (Yilmaz, Sezen, & Ozdemir, 2005). Therefore, the effects of supplier task involvement and joint planning on knowledge acquisition should differ. First, supplier task involvement is general in nature, while joint planning is quite specific. In the joint planning process, manufacturers and suppliers tend to focus more on the specific nature of the tasks and goals and overlook the benefits of working and learning together. Second, supplier task involvement pledges the intention of manufacturers and suppliers to work together, while joint planning focuses on how manufacturers and suppliers actually do work together. Third, supplier task involvement focuses more on outcomes while joint planning is largely about procedures. Taken together, supplier task involvement reflects a manufacturers' willingness to collaborate with buyers. It encourages interaction and communication, and more importantly, it encourages the parties to set specific goals and strive to achieve those goals together. Joint planning, however, being specific in nature, focuses on tasks and actions and therefore lost the big picture about working together. Although in the joint planning process manufacturers and suppliers engage in interaction and share aspirations, their interactions are limited and do not include significant exchange of product and end customer knowledge. In conclusion, supplier tasks involvement should facilitate more knowledge acquisition than joint planning does. It is proposed that:

Wagner & Hoegl, 2006). Joint planning is proactive in nature given that the parties are likely to take action to resolve issues at the outset rather than wait for problems to occur. As a result, joint planning can reduce the risk of unexpected problems and the need for continuously monitoring one's partner (Claro, Hagelaar, & Omta, 2003). Joint planning requires buyers and suppliers to set specific goals, product schedules, and production timelines together. It should, therefore, facilitate knowledge acquisition. Joint planning with suppliers enables manufacturers to acquire knowledge from suppliers in three ways. First, joint planning allows both partners to establish and specify mutual expectations and responsibilities prior to collaboration (Claro et al., 2003), while sharing information about production, product design, and short-term plans prior to the actual production process. Second, joint planning requires both parties to set goals, long-term plans, responsibilities and expectations together; therefore, it requires frequent communication between the two partners (Schoenherr & Swink, 2012) and creates mutual learning opportunities. Third, joint planning enables the partners to identify problems and assess market demand during the collaboration process. Thus, manufacturers can adjust production plans and product development schedules using newly acquired information. In sum, joint planning with suppliers is helpful for manufacturers in acquiring both product and end customer knowledge. It is proposed that: H2. a & b: Joint planning with suppliers has a positive influence on manufacturers' (a) product knowledge acquisition and (b) end customer knowledge acquisition.

3.1.3. Comparing the effect of supplier task involvement and joint planning on knowledge Supplier task involvement entails a combination of internal purchasing/manufacturing and external supplier related initiatives. Supplier task involvement is a process that incorporates the supplier as an extension of the buyer's fab through involvement in production and purchasing processes (Das, Narasimhan, & Talluri, 2006). It serves as a channel for the parties to interpret and communicate product plans and product needs as well as a medium for the buyer to maintain awareness of supplier technologies, capabilities, and limitations. According to KBV (Kogut & Zander, 1992), integrating the purchasing/ manufacturing processes facilitates the access, transfer, and use of explicit and tacit firm-specific knowledge in purchasing and production. Joint planning, on the other hand, involves a range of activities such as joint goal setting, attendance at strategy meetings, participation on cross-functional teams, participation in product and process design

H3. a & b: Supplier task involvement has a stronger positive effect on manufacturers' (a) product and (b) end customer knowledge acquisition than joint planning.

3.2. Knowledge to innovation 3.2.1. Product knowledge and product innovation performance Product knowledge is information related to both products and processes, such as a product's underlying components, features, and specifications, as well as the techniques used to develop new products (Rindfleisch & Moorman, 2001). Product knowledge plays an important role in product innovation performance because it informs production tasks and operational performance in a supply chain (Germain, 5

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(Cooper & Kleinschmidt, 1995; Joshi & Sharma, 2004). End customers are the ultimate users of products, and new products can better meet their requirements if end users' preferences are incorporated into the design processes. Second, by identifying and integrating end customers' needs into product design, manufacturers reduce the risk of new product failure. End customer knowledge not only helps firms design and create new products, but it also takes into account end customers' emerging needs and the shortcomings of existing products. Such knowledge can be used to improve product performance and add value for end customers (Cooper, 1992). On the other hand, product knowledge is more related to production and technical processes and cannot reflect what the end users really want. Therefore, I propose that:

Dröge, & Christensen, 2001). Product knowledge is complex, and product-specific knowledge is unique to the set of skills and technologies involved in a given product's manufacture (Modi & Mabert, 2007). By acquiring such knowledge from suppliers, manufacturers can improve their ability to develop and improve their existing products and also create new products (Yli-Renko, Autio, & Sapienza, 2001). Product innovation performance is a firm's ability to adopt new ideas, products, and processes successfully and efficiently (Paladino, 2008), and acquiring up-to-date, relevant information related to products and processes can help firms successfully build innovation capacity. Product knowledge acquired from suppliers can facilitate manufacturers' product innovation performance in two ways. First, manufacturers can use the newly acquired product knowledge from suppliers to meet new market demands (Brown & Eisenhardt, 1995). Second, a firm's ability to manage, maintain, and create knowledge is key to product innovation performance (Cohen & Levinthal, 1990). The product knowledge acquired from suppliers not only helps update manufacturers' knowledge base, but also helps them improve their product innovation performance. In conclusion, it is proposed that:

H6. End customer knowledge has a stronger effect on manufacturers' product innovation performance than product knowledge. 3.3. Moderating role of supplier incentives Previous literature has pointed out that incentives are important governance mechanisms in inter-firm relationships. For example, incentives can be used to motivate resellers to participate in suppliers' promotional programs (Murry & Heide, 1998) and maintain ongoing exchange relationships (Gilliland & Bello, 2001). Incentives is useful channel control mechanisms which serve to govern channel partners' opportunistic behaviors and compliance behaviors (Gilliland, 2003). Although working with supply chain partners for knowledge acquisition or new product development becomes an important part of firms' competitive strategies (Koufteros et al., 2007; Le Dain & Merminod, 2014), there are risks associated with collaboration. First, the boundary between partners is less clear and this makes it difficult to protect intangible resources such as knowledge (Elmquist et al., 2009). Second, there is no best way to share management knowledge and innovation outcomes (Passi et al., 2010). Third, collaboration between partners may incur coordination and monitoring costs (Wagner & Hoegl, 2006). Therefore, appropriate safeguarding mechanisms are needed to protect partners' benefits. An economic incentive is a price premium paid to a supplier that exceeds the marginal cost or the competitive market price for a focal product of a given quality level (Seggie, 2012). Such price premiums create a self-enforcing “contract” tie to the relationship between suppliers and manufacturers that encourages cooperation between them in the value creation process (Rao & Monroe, 1996). Incentives boost suppliers' participation in joint planning activities with manufacturers even when such cooperation is not governed by a formal contract (Kumar et al., 2011), and incentives and joint planning together establish specific requirements and guidelines for both parties to follow. A supplier's decision to support the manufacturer's strategy is positively related to the incentives the manufacturer provides to the supplier. Therefore, I propose that economic incentives strengthen the effects of supplier joint planning on a manufacturer's knowledge acquisition. First, supplier incentives can be viewed as a governance mechanism which can mitigate the risks associated with supplier joint planning (Gilliland, 2003). Since joint planning is specific in nature, manufacturers can align appropriate economic incentives to designated joint planning activities. When economic incentives are high, suppliers are more motivated to fulfill the joint activities and goals with manufacturers and such motivation increase the willingness of suppliers to share relevant knowledge with manufacturers. On the other hand, when economic incentives are low, suppliers may not have such motivation to work and share knowledge with manufacturers. Second, most business partnerships are economic arrangements in which decisions are made based on cost-benefit calculations (Williamson, 1985). Simply exposure to new knowledge derived from joint planning activities doesn't guarantee that the knowledge will be acquired by the manufacturer (Ko, Kirsch, & King, 2005). Economic

H4. The greater the product knowledge that manufacturers acquire from suppliers, the greater the level of manufacturers' product innovation performance. 3.2.2. End customer knowledge and product innovation performance End customer knowledge is the information related to market trends, end user preferences and attitudes about product features, pricing, and distribution channels (Griffith et al., 2006). End customer knowledge, a dimension of market knowledge, consists of a firm's knowledge of end user preferences which enable a firm to respond to market changes efficiently and spontaneously (Li & Calantone, 1998). Understanding end customer preferences is key to maintaining a pipeline of successful new products because the product development stage is where specific new product ideas, concepts, and prototypes can be incorporated (Hamel & Prahalad, 1990). End customer preferences change frequently, and a common cause of product failure is that new products do not align with end customer preferences (Li & Calantone, 1998; Sethi, Smith, & Park, 2001). End customer knowledge can help manufacturers' product innovation performance three ways. First, manufacturers can incorporate newly acquired end customer knowledge into product designs in order to align product attributes with identified needs (Griffin & Hauser, 1991). Second, manufacturers can reduce the potential risk of product failure by identifying and integrating end customers' needs into new product designs. End customer knowledge not only aids in creating new products and improving design, but it also addresses the shortcomings of existing products. Third, the information related to end customers and markets can help manufacturers pinpoint capability deficiencies and develop new capacities targeted to emerging opportunities (Atuahene-Gima, 2005). KBV suggests that existing knowledge influences the extent of new knowledge creation, and new knowledge is formed and converted to existing knowledge in the form of new products and services (Kogut & Zander, 1992). Therefore, end customer knowledge can lead to product advantages and enable manufacturers to be more innovative. Based on this logic, I propose that: H5. The greater the end customer knowledge that manufacturers acquire from suppliers, the greater the level of manufacturers' product innovation performance. 3.2.3. Comparing the effect of product and end customer knowledge on product innovation performance I propose that end customer knowledge has a stronger effect on product innovation performance than product knowledge for two reasons. First, end customer knowledge is a key prerequisite for new product success as well as for product innovation 6

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Chinese firms. Second, as the world's largest emerging economy and with its rapid economic development and unique institutions, China creates both opportunities and challenges for buyer-supplier collaborations (Hoskisson, Eden, Lau, & Wright, 2000). Third, collaboration in emerging markets is distinct from that in developed markets, and it is challenging for manufacturers to involve their suppliers in product development processes and knowledge acquisition due to high uncertainty, market dynamism, and risk associated with weak institutions (Jean et al., 2014). Fourth, supplier collaboration is becoming more and more important for firms to achieve competitive advantage, especially in emerging economies (Nyaga, Whipple, & Lynch, 2010; Zhou, Zhang, Sheng, Xie, & Bao, 2014). Following previously established procedures on questionnaire development (Zhou & Wu, 2010; Zhou et al., 2014), I developed a survey questionnaire to be given to senior managers of manufacturing firms in China. I first conducted in-depth pilot interviews with eight selected managers to understand their relationships and collaboration experiences with suppliers as well as their innovation activities. These interviews revealed the extent of collaboration between manufacturers and suppliers and shed some light on knowledge transfer and innovation activities. Next I reviewed the literature regarding supply chain integration, knowledge transfer, innovation and agency theory, and adopted appropriate measurement scales from the literature. I then developed an English language questionnaire using these scales and made alterations based on the pilot interviews and the study context. Following this, two independent translators translated the questionnaire into Chinese and then back-translated it into English to ensure conceptual equivalence. I then conducted a second pilot study with 30 senior managers in manufacturing firms and revised some questionnaire items based on their feedback. A sample of 800 manufacturing firms was randomly selected from a directory provided by a reputable market research firm. I contacted these firms via telephone to solicit cooperation and to identify key informants. These sample firms covered a broad range of industries, including materials, chemicals, electronics, and textiles. A senior production manager from each firm served as key informant. I hired and trained interviewers to conduct onsite interviews. The interviewers then went to the firms, presented the surveys to the managers and collected the completed surveys. They also answered questions and assured informants of their confidentiality. The interviewers also collected business cards from the managers for record-keeping and later verification. The research company successfully completed interviews at 285 out of the 800 firms contacted, as some key informants were either unwilling or unavailable to participate in the study. After eliminating responses with extensive missing values, I obtained 277 usable questionnaires, a response rate of 34.6%. A comparison of respondents and non-respondents in terms of the number of employees and sales volume revealed no significant differences, indicating there is no evidence of non-response bias. After the fieldwork was complete, I randomly called 30 respondents to confirm that the interviews had been conducted and found no evidence of cheating in the fieldwork. On average, the informants had 10 years of industry experience and 6.6 years of experience in their firms, which indicates that the informants are knowledgeable about both their firms and their industries. The average age of the manufacturing firms was 13.1 years; the average employee count was 236; and the majority (67.4%) were privately owned firms.

incentives, as a form of manufacturers' cost, motivate them to learn from suppliers. When economic incentives are high enough, the manufacturers have higher motivation to learn from their suppliers because such incentives are part of their cost. On the other hand, when economic incentives are low, the cost driven motivation for manufacturers will be lower. Taken together, I propose that: H7. a & b: Supplier incentives strengthen the positive effects of supplier joint planning on (a) product knowledge acquisition and (b) end customer knowledge acquisition. On the contrary, supplier task involvement entails a close working relationship with the manufacturer and therefore requires more interaction and communication than joint planning (Das et al., 2006). Supplier task involvement requires bilateral investments by the partners in the way of relational assets; therefore it reduces the risk of opportunism and increases the expectation of relationship continuity (Heide & John, 1992; Zaheer & Venkatraman, 1995). Supplier task involvement is more general in nature and does not have specific plans or goals compared to joint planning. Therefore, it can be difficult for manufacturers to align the incentives with suppliers' tasks in advance. I proposed that incentives weaken the effect of supplier task involvement on knowledge acquisition for two reasons. First, the prerequisite for supplier incentives to promote knowledge sharing depends on a buyer's ability to define appropriate tasks and performance outcomes and to align the tasks and outcomes with financial rewards (Klein & Leffler, 1981). When incentives are high, manufacturers need to take more effort to plan and align the tasks and outcomes with suppliers in advance. In such situation, high economic incentives might be detrimental to the positive relationship between supplier task involvement and knowledge acquisition because manufacturer are worry about their costs (planning and incentives) than the benefit (knowledge acquisition). When economic incentives are low, the costbenefit connection will be less important. Second, agency theory proposes that it is necessary for economic incentives to be appropriate in order to achieve efficient outcomes (Kim & Mahoney, 2005). However, it becomes difficult for coordinating partners to determine appropriate economic incentives because asymmetric information and conflict often lead to persistence of optimal economic outcomes (Libecap, 1993). Incentives constitute a key aspect of formal governance and are often used in conjunction with monitoring procedures (Gilliland & Bello, 2001). Therefore, incentives themselves may give rise to insidious supplier behaviors that need to be actively monitored and controlled (Kashyap, Antia, & Frazier, 2012). When incentives are high, manufacturers are likely to increase ex post monitoring and controlling behaviors to ensure suppliers remain in compliance with incentive requirements. Therefore, the effort and costs associated with specifying and monitoring supplier incentives may disrupt the collaboration process. Taken together, I propose that: H7. c & d: Supplier incentives weaken the positive effect of supplier task involvement on (c) product knowledge acquisition and (d) end customer knowledge acquisition. 4. Method 4.1. Data collection procedures The empirical setting for this study is manufacturing firms in China. China provides a rich context for this research for several reasons. First, the country's complex and dynamic transitional environment forces innovation to take place at an unprecedented pace (De Luca & Atuahene-Gima, 2007; Zhou & Wu, 2010). To survive and sustain competitive advantage, firms must not only exploit their existing knowledge bases but also continually integrate and develop new knowledge (Li, 2009; Zhou & Wu, 2010). Therefore, up-to-date product knowledge and end customer knowledge are both important for

4.2. Measures The respondents were asked to identify a supplier that they work closely with and answer questions related to this supplier. The measurement items were adopted from prior studies and revised based on my pilot tests and the research context. A seven-point Likert-type scale format was used to measure each item. 7

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4.3. Construct validity

4.2.1. Independent variables The measures for supplier task involvement were adopted from Carbonell et al. (2009), and they capture how suppliers were involved in buyers' early product development process. The three-item measure of supplier task involvement includes questions concerning participation, consultation and communication between manufacturers and suppliers. Joint planning measures the degree to which future contingencies and consequential duties and responsibilities were made explicit ex ante between a manufacturer and supplier and is adopted from Claro and Claro (2010). The four-item measure of joint planning concerns the proactive joint setting of goals and efforts to make the future of the relationship foreseeable.

I used a three-step approach to assess the uni-dimensionality, reliability and validity of the six latent variables. According to Anderson and Gerbing (1988), exploratory factor analysis (EFA), coefficient alpha, item-total correlations, and confirmatory factor analysis (CFA) are useful to assess uni-dimensionality of multi-item measurements. First, I conducted EFA and obtained six factors. The Cronbach's alpha coefficients for the constructs are as follows: supplier task involvement α = 0.770; joint planning α = 0.841; product knowledge α = 0.911; end customer knowledge α = 0.797; innovation α = 0.885; incentives α = 0.962; reward α = 0.880. All the Cronbach's alpha measurements are higher than the 0.7 benchmark (Lance et al., 2006). Furthermore, the item-total correlations for each construct ranges from 0.539 to 0.912, which points toward a high correlation among items under the same construct (Anderson & Gerbing, 1988). For example, the item-total correlations for product knowledge ranges from 0.664 to 0.738 and for end customer knowledge from 0.539 to 0.673. Second, I subjected all measurement items to a CFA using AMOS. The measurement model fits the data satisfactorily (x2(413) = 629.507, p < 0.001; Goodness-of-fit index [GFI] = 0.884; Comparative fit index [CFI] = 0.962; Incremental fit index [IFI] = 0.962; Root mean square error of approximation [RMSEA] = 0.041). All factor loadings are highly significant (p < 0.001) and fall into the range of > 0.6 (Koufteros, 1999). In addition, the composite reliabilities (CR) of all constructs are greater than the 0.7 threshold, and average variances extracted (AVE) are all > 0.5 (Koufteros, 1999). The above assessments indicate uni-dimensionality and reliability of the scales (see Appendix). Third, I assessed the discriminant validity of all six latent constructs with chi-square difference tests (15 pairs in total). The test was performed for one pair of factors at a time. For example, for the pair of product and end customer knowledge, I compared the fit of the restricted model (correlation fixed to one) with that of a freely estimated model (correlation estimated freely). The differences between each pair are all significant (p < 0.001). The AVE of each construct was much higher than its shared variances with other constructs in support of discriminant validity. Overall, these results indicate that the measures possess adequate uni-dimensionality, reliability and validity (Koufteros, 1999). The measurement items for each construct, standardized factor loadings, Cronbach's Alpha coefficients, CR and AVE are reported in the Appendix. All the measures demonstrate satisfactory psychometric properties. In Table 2, I present the basic descriptive statistics and the correlations of the constructs.

4.2.2. Dependent variables The measures for product knowledge were adopted from Rindfleisch and Moorman (2001), and the items capture the amount of new product-related information acquired from the suppliers. The original scale contained 10 items; one item was removed due to low factor loading. End customer knowledge measurement scales were adopted from Griffith et al. (2006), and they capture the respondents' understanding of existing customers' thoughts regarding items related to the firms' marketing mix (product, advertising, pricing, etc.). This scale initially contained seven items; two items were not included due to irrelevant content. The measures for product innovation performance are adopted from Paladino (2008), and ask the respondents to evaluate features of the firm's new products or services introduced into the market. One item was removed from the scale due to confusing Chinese wording. 4.2.3. Moderator Supplier incentive captures the incentives buyers use in their supplier relationships and were adopted from Kumar et al. (2011). The four-item measure focuses on incentives in the form of price premiums that create ongoing revenue streams for suppliers. 4.2.4. Control variables To account for the influence of extraneous effects, firm size, firm age, years of cooperation between buyers and suppliers, as well as product standardization were included. Firm size is a measure of the number of employees in the manufacturing firm. Age is the number of years the firm has been in operation. Relationship length reflects how many years the manufacturing firm respondents had been working with their focal supplier. Product standardization was assessed by one question indicating the degree of customization of the product provided by the supplier. Table 2 Descriptive statistics and correlations. 1 1. Product innovation 2. Product knowledge 3. Customer knowledge 4. Supplier task involvement 5. Joint planning 6. Supplier incentives 7. Standardization 8. Year of corporation 9. Firm age 10. Firm size MV marker Mean SD

0.561 0.454⁎⁎ 0.520⁎⁎ 0.469⁎⁎ 0.397⁎⁎ 0.172⁎⁎ 0.242⁎⁎ 0.021 − 0.019 − 0.048 − 0.022 5.330 0.837

2

3 ⁎⁎

0.476 0.526 0.568⁎⁎ 0.468⁎⁎ 0.395⁎⁎ 0.037 0.321⁎⁎ 0.027 −0.051 −0.073 0.074 5.256 0.802

4 ⁎⁎

0.542 0.590⁎ 0.500 0.513⁎⁎ 0.320⁎⁎ 0.066 0.286⁎⁎ − 0.005 − 0.035 − 0.034 − 0.072 5.437 0.671

5 ⁎⁎

6 ⁎⁎

0.491 0.490⁎⁎ 0.535⁎⁎ 0.537 0.561⁎⁎ 0.126⁎ 0.208⁎⁎ 0.036 0.008 − 0.047 − 0.082 5.353 0.908

0.419 0.417⁎⁎ 0.342⁎⁎ 0.583⁎⁎ 0.547 0.205⁎⁎ 0.216⁎⁎ 0.110 0.078 −0.034 −0.022 5.310 0.882

7 ⁎⁎

0.194 0.059 0.088 0.148⁎ 0.227⁎⁎ 0.864 − 0.159 0.209⁎⁎ 0.204⁎⁎ 0.142⁎ − 0.233⁎⁎ 4.352 1.774

⁎⁎

0.264 0.343⁎⁎ 0.308⁎⁎ 0.230⁎⁎ 0.238⁎⁎ −0.137⁎ – −0.084 −0.163⁎⁎ −0.149⁎⁎ 0.033 6.379 4.652

8

9

10

0.043 0.049 0.017 0.058 0.132⁎ 0.231⁎⁎ − 0.062 – 0.488⁎⁎ 0.234⁎⁎ − 0.082 5.781 1.287

0.003 −0.029 −0.013 −0.030 0.100 0.226⁎⁎ −0.141⁎⁎ 0.510⁎⁎ – 0.421⁎⁎ −0.179⁎⁎ 12.826 12.178

− 0.026 − 0.051 − 0.012 − 0.025 − 0.012 0.164⁎ − 0.127⁎ 0.256⁎⁎ 0.445⁎⁎ – − 0.164⁎⁎ 369.939 877.905

Notes: sample size = 311. Two-tailed tests. Average Variance Extracted (AVE) is shown on the diagonal of the matrix for latent variables. The correlations above the diagonal is the MV adjusted correlations. ⁎ p < 0.05. ⁎⁎ p < 0.01.

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performance (Table 5). The results indicate that product knowledge has significant influence on product innovation performance (b = 0.182, p < 0.001), in support of H4. Customer knowledge also has significant influence on product innovation performance (b = 0.316, p < 0.001), supporting H5. Hypothesis 3 proposes that supplier task involvement has a stronger effect on product knowledge acquisition than that of supplier joint planning. I used t-test of equality to compare the coefficients of supplier task involvement and joint planning on product knowledge acquisition (Table 3). The results indicate that supplier task involvement has a significantly stronger effect on product knowledge acquisition than that of joint planning (t = 2.015, p < 0.01), in support of H3. Although the effect of joint planning on end customer knowledge acquisition is not significant, I nevertheless calculated the t-test of equality and confirmed that supplier task involvement has a significantly stronger effect on customer knowledge acquisition than that of joint planning (t = 5.357, p < 0.001). Similarly, to test Hypothesis 6, I compare the coefficients of product and end customer knowledge on product innovation performance (Table 5). The result suggests that end customer knowledge has a stronger effect on product innovation performance than that of product knowledge (t = 1.857, p < 0.05), in support of H6. Hypothesis 7 proposes a moderating effect of supplier incentives on the supplier integration mechanisms on knowledge. The results of the moderating effect are presented in Model 3 in both Tables 3 and 4. Supplier incentives positively moderate the effect of joint planning on product knowledge (b = 0.117, p < 0.05), but do not moderate the effect of supplier task involvement on product knowledge (b = − 0.043, p > 0.05). Therefore, H7a is supported while H7c is not supported. To fully examine the moderating effects, I deconstruct the significant interaction terms and compare the impact of joint planning on product knowledge at low and high levels of supplier incentive (Aiken & West, 1991). The low/high levels of the moderating variables are set as one standard deviation below/above their means (Aiken & West, 1991). I calculated the simple slopes and their significance levels associated with the lines in Fig. 2, according to Aiken and West's (1991) suggested approach. The results in Fig. 2 show that supplier incentives increase the positive effect of joint planning on product knowledge. Specifically, joint planning has a stronger positive effect on product knowledge when the supplier incentive is high (b = 0.178, p < 0.01) than when it is low (b = − 0.051, p > 0.05). Supplier incentives positively moderate the relationship between joint planning and end customer knowledge (b = 0.102, p < 0.05) but negatively moderate the relationship between supplier task involvement and end customer knowledge (b = − 0.084, p < 0.05), in support of H7b and H7d. Fig. 3 depicts the results of the interaction between joint planning and supplier incentives on end customer knowledge. In Fig. 3, the results show that joint planning has a positive effect on end customer knowledge when supplier incentives are high (b = 0.217, p < 0.01); however, the effect becomes negative when incentives are low (b = − 0.145, p < 0.05). Fig. 4 depicts the results of the interaction between supplier task involvement and incentives on end customer knowledge. The results show that supplier task involvement becomes less positive when supplier incentives are high (b = 0.137, p < 0.05) but the effect becomes more positive when supplier incentives are low (b = 0.435, p < 0.01).

4.4. Common method bias As with all self-reported data, it is imperative to address the issue of common method bias (CMB). Several approaches have been used to mitigate the possibility of CMB in this study (Malhotra, Kim, & Patil, 2006; Podsakoff et al., 2003). First, I took procedural remedies to avoid CMB by reducing item ambiguity and protecting respondent confidentiality. I then adopted statistical analyses to assess the severity of CMB. Second, I conducted Harmon's one factor test on all of the latent variables, extracting six factors that accounted for 63.4% of the total variance, with the largest factor explaining only 14.5% of the total variance, indicating that CMB is not a major concern in this study (Podsakoff & Organ, 1986). Third, I applied the marker value “MV” method and chose a scale unrelated to at least one measurement in the study as the MV. This scale offered a proxy for CMV (Lindell & Whitney, 2001). I used a three-item scale that measured the reciprocity between the firm and its customer firm (Muthusamy & White, 2005) (Cronbach's α =0.91) and selected the lowest positive correlation (r = 0.01) between the MV and the other variable. All of the significant correlations remained significant after the partial correlation adjustment (see Table 2), suggesting that common method bias is not a concern in my study. 5. Analysis and results I used multiple hierarchal linear regression models to test the proposed hypotheses. I ran three independent regression models to test the effects of predictors and moderators on product knowledge, end customer knowledge, and innovation, respectively. Hierarchical regression analyses were conducted to examine whether the inclusion of predictors and interaction terms would significantly increase the incremental explanatory power of each dependent variable. I used standardized scores for all control variables, predictors and moderators, and used standardized scores to create interaction terms for all the regression models. The standardized scores better alleviate multicollinearity problems and also facilitate the interpretation of path coefficients without altering the form of relationship and results (Echambadi & Hess, 2007). The largest VIF in the moderated regression model is 1.7 which is far below the 3.3 benchmark value (Diamantopoulos & Siguaw, 2006); therefore, multicollinearity is not serious concern in this study. Table 3 presents the results for product knowledge as the dependent variable. Table 4 presents the results for end customer knowledge as the dependent variable, and Table 5 presents the results for innovation as the dependent variable. In tables 3 and 4, the first model is the baseline model with the control variables only. The second model tests the main effects, and the third model tests the moderating effect of incentives. In Table 5, the first model is the baseline model and the second model tests the main effects. The first hypothesis proposes the effect of supplier task involvement on product and end customer knowledge and the results support this hypothesis (Tables 3 & 4). Supplier task involvement is significantly related to manufacturer's product knowledge acquisition (b = 0.247, p < 0.001) and customer knowledge acquisition (b = 0.286, p < 0.001), supporting H1a & b. Hypothesis 2 proposes that supplier joint planning has a positive effect on manufacturers' product knowledge acquisition and customer knowledge acquisition. The results suggest that supplier joint planning has a significant and positive effect on product knowledge acquisition (b = 0.157, p < 0.01) but the effect on customer knowledge acquisition is not significant (b = 0.036, p > 0.05), in support of H2a but not H2b. The reason may be that joint planning activities between manufacturers and suppliers are all focused on technical and procedural aspects of production rather than end customer information. Therefore, manufacturers did not gain any end customer information during in the joint planning process. Hypotheses 4 and 5 propose that manufacturers' products and customer knowledge have significant influence on product innovation

6. Discussion and implications This study investigates the influence of two channel integration mechanisms on knowledge acquisition and product innovation performance. The results lend support to most of the hypotheses. First, I differentiate two collaboration mechanisms in the gray-box integration domain. Supplier task involvement has a stronger effect on acquiring both product and end customer knowledge than supplier joint planning. Even though both integration mechanisms are important for inter-firm collaboration, supplier task involvement (i.e., involving suppliers 9

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Table 3 Unstandardized regression coefficients for product knowledge. Model 1

Control variables Year of corporation Product standardization Firm age Firm size

Model 2

Model 3

β (SE)

t-Value

β (SE)

t-Value

β (SE)

t-Value

0.057 (0.050) 0.255 (0.044) − 0.016 (0.053) − 0.027 (0.048)

1.151

0.033 (0.045) 0.169 (0.041) − 0.038 (0.048) 0.008 (0.043)

0.730

0.036 (0.45) 0.159 (0.041) −0.041 (0.048) −0.016 (0.043)

0.811

5.799

⁎⁎⁎

− 0.298 − 0.565

Main effects Supplier task involvement

0.266 (0.047) 0.131 (0.048) − 0.002 (0.041)

Joint planning Supplier incentives

4.078

⁎⁎⁎

− 0.794 − 0.181

5.637⁎⁎⁎ 2.704⁎⁎ − 0.048

Interaction/moderating effects Involvement × Incentives Joint-planning × Incentives

Intercept R2 ΔR2 F

5.256 0.103

5.256 0.291 0.184 26.203⁎⁎

3.851⁎⁎⁎ −0.855 −0.371

0.247 (0.048) 0.157 (0.049) −0.017 (0.043)

5.141⁎⁎⁎

−0.043 (0.049) 0.117 (0.048)

−0.878

3.172⁎⁎ −0.401

2.421⁎

5.237 0.305 0.014 2.944⁎

Notes: N = 311. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

studied in the literature (Chatterji & Fabrizio, 2014; De Luca & Atuahene-Gima, 2007) but end-customer knowledge (Campbell, 2003) has not been well studied in the supply chain literature. This study sheds some lights on the role of end-customer knowledge in the supply chain. Third, agency theory was incorporated to help explain the effects of integration mechanisms on knowledge acquisition. Economic incentive, a formal governance mechanism in inter-firm relationship, has moderating effects on the relationships between integration mechanisms and knowledge acquisition. Economic incentive has not been used a moderating mechanism in the supply chain channel and I extended the channel integration literature by bringing in the perspective of agency theory.

actively in the product development process) brings more benefits than supplier joint planning. Second, economic incentive works as a moderating effect and strengthens the effect of joint planning on knowledge acquisition but weakens the effect of supplier task involvement. Third, I empirically differentiate and compare the effects of product knowledge and end customer knowledge on product innovation performance. Even though product knowledge is critical for innovation, the influence of end customer knowledge is significantly stronger on product innovation performance. 6.1. Theoretical implications In this study, I have integrated three theoretical perspectives into one framework and developed the framework based on integration mechanisms, KBV and agency theory. This study provides three theoretical contributions. First, I identified two important channel integration mechanisms, supplier task involvement and joint planning, which are key collaboration mechanisms of the gray-box integration perspective. As mentioned previously, integrating suppliers in the product development process inevitably involves risk, time, effort and financial resources (Koufteros et al., 2007). This study provides a theoretical base by differentiating the two gray-box integration mechanisms that help manufacturers acquire both product and end customer knowledge. In particular, supplier task involvement has a stronger effect on both types of knowledge acquisition. I extended the literature of channel integration by identifying and differentiating the two gray-box integration mechanisms. Second, while past research has treated knowledge in general as a factor influencing innovation, I differentiated and empirically compared the effects of product knowledge and end customer knowledge on product innovation performance. Product knowledge has well-been

6.2. Managerial implications This study offers important strategic implications for managers. As it becomes critical for manufacturers to involve with their suppliers in the innovation process. More importantly, manufacturers rely more on external sources for product innovation. This study offers some insights on how to work with suppliers and what are benefits. First, I found that supplier task involvement has a strong influence on acquisition of both end customer knowledge and product knowledge. Manufacturing firms may want to involve their suppliers in the NPD process, as suppliers are valuable resources for knowledge and innovation. However, buyers should carefully consider which integration mechanisms they use given that joint planning involves more effort and provides less benefit. Second, in new product development, manufacturers should not just consider product information such as materials, technology, production and process methods, they should also consider end-customer knowledge. In this study, I found that end customer knowledge is significantly more useful than product knowledge for product innovation. When 10

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Table 4 Unstandardized regression coefficients for customer knowledge. Model 1

Control variables Year of corporation Product standardization Firm age Firm size

Model 2

Model 3

β (SE)

t-Value

β (SE)

t-Value

β (SE)

t-Value

0.011 (0.042) 0.193 (0.037) 0.002 (0.045) 0.003 (0.041)

0.272

−0.007 (0.037) 0.129 (0.034) −0.013 (0.040) 0.015 (0.036)

−0.188

− 0.002 (0.037) 0.124 (0.034) − 0.020 (0.040) 0.010 (0.036)

− 0.054

5.173

⁎⁎⁎

0.036 0.063

Main effects Supplier task involvement

⁎⁎⁎

3.735

−0.336 0.412

7.887⁎⁎⁎

0.310 (0.039) 0.010 (0.040) 0.026 (0.034)

Joint planning Supplier incentives

0.257 0.743

Interaction/moderating effects Involvement × Incentives Joint planning × Incentives

Intercept R2 ΔR2 F

5.437 0.082

5.437 0.299 0.217 31.270⁎⁎⁎

3.598⁎⁎⁎ − 0.493 0.290

0.286 (0.040) 0.036 (0.041) 0.023 (0.036)

7.178⁎⁎⁎

− 0.084 (0.041) 0.102 (0.040)

− 2.050⁎

0.871 0.655

2.531⁎⁎

5.426 0.316 0.017 3.787⁎

Notes: N = 311. ⁎ p < 0.05. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

5.5

Table 5 Unstandardized regression coefficients for innovation.

Product standardization Firm age Firm size

β (SE)

t-Value

0.036 (0.053) 0.204 (0.047) 0.009 (0.057) − 0.022 (0.051)

0.670

Main effect Product knowledge

0.154 − 0.419

t-Value

0.017 (0.046) 0.055 (0.043) 0.012 (0.049) −0.017 (0.044)

0.377

0.182 (0.050) 0.316 (0.049)

Customer knowledge

Intercept R2 ΔR2 F

4.325

⁎⁎⁎

β (SE)

5.330 0.061

Product Knowledge

Control variables Year of corporation

Model 2

1.276

Low Incentives High Incentives

5.0

4.5

0.237

Low − 0.376

3.670

High Joint-planning

Fig. 2. Simple slope for the interaction effect of supplier joint planning and incentives on product knowledge.

⁎⁎⁎

6.0

6.476⁎⁎⁎

Customer Knowledge

Model 1

5.330 0.312 0.251 55.527⁎⁎

Notes: N = 311. ⁎⁎ p < 0.01. ⁎⁎⁎ p < 0.001.

5.5 5.0 4.5 4.0

manufacturers work with suppliers, they should focus on what kind of knowledge they hope to obtain from them. Third, by providing incentives to their suppliers, buyers seek to maintain relationships or gain benefits from them. However, I found that supplier incentives only increase the effects of joint planning on

Low Incentives High Incentives

Low

High Joint Planning

Fig. 3. Simple slope for the interaction effect of joint-planning and incentives on customer knowledge.

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Customer Knowledge

6.0

rate with their suppliers. The integration mechanisms investigated in this study may not be suitable for unstandardized products when manufacturers want to learn product and customer from their suppliers. Low Incentives

5.5

High Incentives

7. Limitations and future research

5.0

4.5

A limitation of this study is that data was collected only from the manufacturer's side of the manufacturer-supplier dyad. Future research into the perceptions of both buyers and suppliers will better capture the perspectives of both partners and the overall cooperation/collaboration dynamic. Second, I only investigated two integration mechanisms in this study. There are other integration mechanisms in marketing channels, such as coproduction and joint problem solving, which may influence relationships and collaboration outcomes. Therefore, future research that investigates other integration mechanisms will add to the body of knowledge. Third, this study captures buyers' perceptions at a single point in time. Longitudinal data will better capture the influence of integration mechanisms on knowledge and innovation over the long term.

Low High Supplier involvement

Fig. 4. Simple slope for the interaction of supplier task involvement and incentives on customer knowledge.

product knowledge and end customer knowledge acquisition. By contrast, supplier incentives weaken the effect of supplier task involvement on knowledge acquisition. Therefore, buyers need to carefully consider using incentives to motivate supplier participation in the NPD process. They should avoid using incentives if they are using cooperation mechanisms in the NPD process. However, they are encouraged to use incentives if they are using a coordination mechanism in the NPD process. Fourth, product standardization plays a positive and significant role on both product and customer knowledge (Tables 3 & 4) in this study. Manufacturers should play attention to this factor when they collabo-

Acknowledgements The study was supported by a Strategic Research Grant from City University of Hong Kong (Project No. 7008146).

Appendix

Construct

Description and source

Supplier task involvement α = 0.770 CR = 0.776

Please indicate your working relationship with the selected supplier: 1. There were extensive consultations with this supplier. 2. Representative of this supplier was invited to join the project as team members. 3. This supplier discusses its needs openly to deliver the best possible product. (1 = strongly disagree, 7 = strongly agree) (Carbonell et al., 2009) 1. Our company provides this supplier with sale forecasts for the products our company buys from them. 2. Our company shares long-term plans of our products with this supplier. 3. This supplier is willing to make production adjustment based on our product innovation. 4. This supplier is willing to respond to the changing market situations. (1 = strongly disagree, 7 = strongly agree) (Claro & Claro, 2010) Please identify a major product that this supplier provided you and evaluate the following statements: 1. The price we pay this supplier is higher than what competitors pay for similar products. 2. This supplier earns gross margins that are higher than normal. 3. The price we pay this supplier exceeds what is warranted based on this supplier's manufacturing performance. 4. The price we pay this supplier is higher than the competitive market price. (1-Strongly disagree, 7- strongly agree) (Kumar et al., 2011) Please rate the amount of the following types of information that your firm has acquired from other participants (1 = low amount, 7 = high amount) 1. Information about participants' R & D projects. 2. Research findings related to the development of new products. 3. Information about key product specifications. 4. Information about competitors' technology. 5. Information about new manufacturing processes. 6. Insights into new ways to approach product development. 7. Information about new ways of combining manufacturing activities. 8. Insights about key tasks involved in the production process. 9. Insights into new ways to streamline existing manufacturing processes. (Rindfleisch & Moorman, 2001)

Joint planning α = 0.841 CR = 0.828

1. Supplier incentives α = 0.962 2. CR = 0.962

Product knowledge α = 0.911 CR = 0.909

Standard factor loadings

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0.728 0.785 0.682 0.735 0.798 0.719 0.703

0.928 0.931 0.936 0.922

0.721 0.672 0.765 0.713 0.733 0.725 0.708 0.763 0.720

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End customer knowledge α = 0.797 CR = 0.801

Innovation α = 0.880 CR = 0.884

In cooperating with participants, our firm learned a great deal of knowledge about … (1 = low amount, 7 = high amount) 1. how our end customers perceive our products 2. how our end customers perceive the pricing of our products 3. the importance of distributions related to our end customers 4. our existing end customers 5. the demand trend and change of end customer preferences (Griffith et al., 2006) 1. The quality of this new product is superior to that of our competitors. 2. This product design (in terms of functionality and features) is superior to that of our competitors. 3. Overall, we have an advantage over our competitors in terms of this new product we offer our customers. 4. This new product is minor improvements in a current technology. 5. This new product incorporates a large new body of technological knowledge. 6. The applications of this new product is totally different from the applications of our main competitors' products. (1 = strongly disagree, 7 = strongly agree) (Paladino, 2008)

0.760 0.655 0.642 0.632 0.649 0.833 0.778 0.754 0.695 0.688 0.735

Note: χ2 = 629.507 (df = 413, p < 0.001); GFI = 0.884, CFI = 0.962, IFI = 0.962, RMSAE = 0.041.

Cui, A. S., & O'Connor, G. (2012). Alliance portfolio resource diversity and firm innovation. Journal of Marketing, 76(4), 24–43. Das, A., Narasimhan, R., & Talluri, S. (2006). Supplier integration—Finding an optimal configuration. Journal of Operations Management, 24(5), 563–582. De Luca, L. M., & Atuahene-Gima, K. (2007). Market knowledge dimensions and crossfunctional collaboration: Examining the different routes to product innovation performance. Journal of Marketing, 71(1), 95–112. Dhanaraj, C., Lyles, M. A., Steensma, H. K., & Tihanyi, L. (2004). Managing tacit and explicit knowledge transfer in IJVs: The role of relational embeddedness and the impact on performance. Journal of International Business Studies, 35(5), 428–442. Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282. Echambadi, R., & Hess, J. D. (2007). Mean-centering does not alleviate collinearity problems in moderated multiple regression models. Marketing Science, 26(3), 438–445. Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of Management Review, 14(1), 57–74. Elmquist, M., Fredberg, T., & Ollila, S. (2009). Exploring the field of open innovation. European Journal of Innovation Management, 12(3), 326–345. Emerson, R. M. (1962). Power-dependence relations. American Sociological Review, 27(1), 31–41. Fawcett, S. E., Jones, S. L., & Fawcett, A. M. (2012). Supply chain trust: The catalyst for collaborative innovation. Business Horizons, 55(2), 163–178. Fliess, S., & Becker, U. (2006). Supplier integration—Controlling of co-development processes. Industrial Marketing Management, 35(1), 28–44. Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71. Gao, G. Y., Xie, E., & Zhou, K. Z. (2015). How does technological diversity in supplier network drive buyer innovation? Relational process and contingencies. Journal of Operations Management, 36, 165–177. Germain, R., Dröge, C., & Christensen, W. (2001). The mediating role of operations knowledge in the relationship of context with performance. Journal of Operations Management, 19(4), 453–469. Gilliland, D. I., & Bello, D. C. (2001). Channel incentives as unilateral and bilateral governance processes. Journal of Marketing Channels, 8(1–2), 5–31. Gilliland, D. I. (2003). Toward a business-to-business channel incentives classification scheme. Industrial Marketing Management, 32(1), 55–67. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109–122. Griffin, A., & Hauser, J. R. (1991). The voice of the customer. Marketing Science, 12(1), 1–27. Griffith, D. A., Noble, S. M., & Chen, Q. (2006). The performance implications of entrepreneurial proclivity: A dynamic capabilties approach. Journal of Retailing, 82(1), 51–62. Gulati, R. (2013). Reorganize for resilience: Putting customers at the center of your business. Harvard Business Press. Gulati, R., Wohlgezogen, F., & Zhelyazkov, P. (2012). The two facets of collaboration: Cooperation and coordination in strategic alliances. The Academy of Management Annals, 6(1), 531–583. Gumusluoğlu, L., & Ilsev, A. (2009). Transformational leadership and organizational innovation: The roles of internal and external support for innovation. Journal of Product Innovation Management, 26(3), 264–277. Hamel, G., & Prahalad, C. K. (1990). Corporate imagination and expeditionary marketing. Harvard Business Review, 69(4), 81–92. Handfield, R. B., & Nichols, E. L. (2002). Supply chain redesign: Transforming supply chains into integrated value systems. FT Press.

References Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411–423. Agarwal, R., Croson, R., & Mahoney, J. T. (2010). The role of incentives and communication in strategic alliances: An experimental investigation. Strategic Management Journal, 31(4), 413–437. Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions. London, UK: Sage. Atuahene-Gima, K. (2005). Resolving the capability-rigidity paradox in new product innovation. Journal of Marketing, 69(4), 61–83. Azadegan, A. (2011). Benefiting from supplier operational innovativeness: The influence of supplier evaluations and absorptive capacity. Journal of Supply Chain Management, 47(2), 49–64. Azadegan, A., & Dooley, K. J. (2010). Supplier innovativeness, organizational learning styles and manufacturer performance: An empirical assessment. Journal of Operations Management, 28(6), 488–505. Bass, B. M. (1985). Leadership and performance beyond expectations. New York and London: Free Press. Batt, P. J., & Purchase, S. (2004). Managing collaboration within networks and relationships. Industrial Marketing Management, 33(3), 169–174. Bergen, M., Dutta, S., & Walker, O. C., Jr. (1992). Agency relationships in marketing: A review of the implications and applications of agency and related theories. Journal of Marketing, 56(3), 1–24. Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of Management Review, 20(2), 343–378. Campbell, A. J. (2003). Creating customer knowledge competence: Managing customer relationship management programs strategically. Industrial Marketing Management, 32(5), 375–383. Carbonell, P., Rodríguez-Escudero, A. I., & Pujari, D. (2009). Customer involvement in new service development: An examination of antecedents and outcomes. Journal of Product Innovation Management, 26(5), 536–550. Chang, J., Bai, X., & Li, J. J. (2015). The influence of leadership on product and process innovations in China: The contingent role of knowledge acquisition capability. Industrial Marketing Management, 50, 18–29. Chatterji, A. K., & Fabrizio, K. R. (2014). Using users: When does external knowledge enhance corporate product innovation? Strategic Management Journal, 35(10), 1427–1445. Claro, D. P., & Claro, O. B. O. (2010). Collaborative buyer–supplier relationships and downstream information in marketing channels. Industrial Marketing Management, 39(2), 221–228. Claro, D. P., Hagelaar, G., & Omta, O. (2003). The determinants of relational governance and performance: How to manage business relationships? Industrial Marketing Management, 32(8), 703–716. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. Conlon, E. J., & Parks, J. M. (1990). Effects of monitoring and tradition on compensation arrangements: An experiment with principal-agent dyads. Academy of Management Journal, 33(3), 603–622. Cooper, R. G., & Kleinschmidt, E. J. (1995). Benchmarking the firm's critical success factors in new product development. Journal of Product Innovation Management, 12(5), 374–391. Cooper, R. G. (1992). The NewProd system: The industry experience. Journal of Product Innovation Management, 9(2), 113–127. Crossan, M. M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.

13

Industrial Marketing Management xxx (xxxx) xxx–xxx

J. Chang

Modi, S. B., & Mabert, V. A. (2007). Supplier development: Improving supplier performance through knowledge transfer. Journal of Operations Management, 25(1), 42–64. Mooi, E. A., & Frambach, R. T. (2012). Encouraging innovation in business relationships—A research note. Journal of Business Research, 65(7), 1025–1030. Murry, J. P. J., & Heide, J. B. (1998). Managing promotion program participation within manufacturer-retailer relationships. The Journal of Marketing, 58–68. Muthusamy, S. K., & White, M. A. (2005). Learning and knowledge transfer in strategic alliances: A social exchange view. Organization Studies, 26(3), 415–441. Ndofor, H. A., & Levitas, E. (2004). Signaling the strategic value of knowledge. Journal of Management, 30(5), 685–702. Nyaga, G. N., Whipple, J. M., & Lynch, D. F. (2010). Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? Journal of Operations Management, 28(2), 101–114. Paladino, A. (2008). Analyzing the effects of market and resource orientations on innovative outcomes in times of turbulence. Journal of Product Innovation Management, 25(6), 577–592. Paasi, J., Luoma, T., Valkokari, K., & Lee, N. (2010). Knowledge and intellectual property management in customer–supplier relationships. International Journal of Innovation Management, 14(04), 629–654. Petersen, K. J., Handfield, R. B., & Ragatz, G. L. (2003). A model of supplier integration into new product development. Journal of Product Innovation Management, 20(4), 284–299. Petersen, K. J., Handfield, R. B., & Ragatz, G. L. (2005). Supplier integration into new product development: Coordinating product, process and supply chain design. Journal of Operations Management, 23(3), 371–388. Pfeffer, J., & Salancik, G. R. (2003). The external control of organizations: A resource dependence perspective. Stanford University Press. Phelps, C. C. (2010). A longitudinal study of the influence of alliance network structure and composition on firm exploratory innovation. Academy of Management Journal, 53(4), 890–913. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544. Prahalad, C. K., & Ramaswamy, V. (2000). Co-opting customer competence. Harvard Business Review, 78(1), 79–87. Ragatz, G. L., Handfield, R. B., & Petersen, K. J. (2002). Benefits associated with supplier integration into new product development under conditions of technology uncertainty. Journal of Business Research, 55(5), 389–400. Ragatz, G. L., Handfield, R. B., & Scannell, T. V. (1997). Success factors for integrating suppliers into new product development. Journal of Product Innovation Management, 14(3), 190–202. Rao, A. R., & Monroe, K. B. (1996). Causes and consequences of price premiums. Journal of Business, 511–535. Rindfleisch, A., & Moorman, C. (2001). The acquisition and utilization of information in new product alliances: A strength-of-ties perspective. Journal of Marketing, 65(2), 1–18. Rothaermel, F. T., Hitt, M. A., & Jobe, L. A. (2006). Balancing vertical integration and strategic ousourcing: Effects on product portfolio, product success, and firm performance. Strategic Management Journal, 27(11), 1033–1056. Schoenherr, T., & Swink, M. (2012). Revisiting the arcs of integration: Cross-validations and extensions. Journal of Operations Management, 30(1), 99–115. Seggie, S. H. (2012). Transaction cost economics in international marketing: A review and suggestions for the future. Journal of International Marketing, 20(2), 49–71. Sethi, R., Smith, D. C., & Park, C. W. (2001). Cross-functional product development teams, creativity, and the innovativeness of new consumer products. Journal of Marketing Research, 38(1), 73–85. Sivakumar, K., Roy, S., Zhu, J., & Hanvanich, S. (2011). Global innovation generation and financial performance in business-to-business relationships: The case of cross-border alliances in the pharmaceutical industry. Journal of the Academy of Marketing Science, 39(5), 757–776. Spender, J. C., & Grant, R. M. (1996). Knowledge and the firm: Overview. Strategic Management Journal, 17, 5–9. Utterback, J. M., & Abernathy, W. J. (1975). A dynamic model of process and product innovation. Omega, 3(6), 639–656. van Echtelt, F. E. A., Wynstra, F., van Weele, A. J., & Duysters, G. (2008). Managing supplier involvement in new product development: A multiple-case study. Journal of Product Innovation Management, 25(2), 180–201. Wagner, S. M., & Hoegl, M. (2006). Involving suppliers in product development: Insights from R & D directors and project managers. Industrial Marketing Management, 35(8), 936–943. Wang, J. J., Li, J. J., & Chang, J. (2016). Product co-development in an emerging market: The role of buyer-supplier compatibility and institutional environment. Journal of Operations Management, 46, 69–83. Wang, Q., Bradford, K., Xu, J., & Weitz, B. (2008). Creativity in buyer–seller relationships: The role of governance. International Journal of Research in Marketing, 25(2), 109–118. Williamson, O. (1985). The economic institutions of capitalism. New York: Free Press. Wynstra, F., Van Weele, A., & Weggemann, M. (2001). Managing supplier involvement in product development: Three critical issues. European Management Journal, 19(2), 157–167. Yilmaz, C., Sezen, B., & Ozdemir, O. (2005). Joint and interactive effects of trust and (inter) dependence on relational behaviors in long-term channel dyads. Industrial Marketing Management, 34(3), 235–248. Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition,

Heide, J. B., & John, G. (1992). Do norms matter in marketing relationships? Journal of Marketing, 56(2), 32–44. Heide, J. B., & Miner, A. S. (1992). The shadow of the future: Effects of anticipated interaction and frequency of contact on buyer-seller cooperation. Academy of Management Journal, 35(2), 265–291. Hoegl, M., & Wagner, S. M. (2005). Buyer-supplier collaboration in product development projects. Journal of Management, 31(4), 530–548. Hong, P., Doll, W. J., Nahm, A. Y., & Li, X. (2004). Knowledge sharing in integrated product development. European Journal of Innovation Management, 7(2), 102–112. Hoskisson, R. E., Eden, L., Lau, C. M., & Wright, M. (2000). Strategy in emerging economies. Academy of Management Journal, 43(3), 249–267. Inemek, A., & Matthyssens, P. (2013). The impact of buyer–supplier relationships on supplier innovativeness: An empirical study in cross-border supply networks. Industrial Marketing Management, 42(4), 580–594. Jansen, J. J. P., & Volberda, H. W. (2005). Managing potential and realized absorptive capacity: How do organizational antecedents matter? Academy of Management Journal, 48(6), 999–1015. Jean, R. J., Sinkovics, R. R., & Hiebaum, T. P. (2014). The effects of supplier involvement and knowledge protection on product innovation in customer–supplier relationships: a study of global automotive suppliers in China. Journal of Product Innovation Management, 31(1), 98–113. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. Joshi, A. W., & Sharma, S. (2004). Customer knowledge development: Antecedents and impact on new product performance. Journal of Marketing, 68(4), 47–59. Jung, D. D., Wu, A., & Chow, C. W. (2008). Towards understanding the direct and indirect effects of CEOs' transformational leadership on firm innovation. The Leadership Quarterly, 19(5), 582–594. Kashyap, V., Antia, K. D., & Frazier, G. L. (2012). Contracts, Extracontractual incentives, and ex post behavior in franchise channel relationships. Journal of Marketing Research, 49(2), 260–276. Kim, D.-J., & Kogut, B. (1996). Technological platforms and diversification. Organization Science, 7(3), 283–301. Kim, J., & Mahoney, J. T. (2005). Property rights theory, transaction costs theory, and agency theory: An organizational economics approach to strategic management. Managerial and Decision Economics, 26(4), 223–242. Klein, B., & Leffler, K. B. (1981). The role of market forces in assuring contractual performance. The Journal of Political Economy, 89(4), 615–641. Ko, D.-G., Kirsch, L. J., & King, W. R. (2005). Antecedents of knowledge transfer from consultants to clients in enterprise system implementations. MIS Quarterly, 29(1), 59–85. Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397. Kogut, B., & Zander, U. (2003). Knowledge of the firm and the evolutionary theory of the multinational corporation. Journal of International Business Studies, 34(6), 516–529. Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1–18. Koufteros, X. A., Cheng, T. C., & Lai, K.-H. (2007). “Black-box” and “gray-box” supplier integration in product development: Antecedents, consequences and the moderating role of firm size. Journal of Operations Management, 25(4), 847–870. Koufteros, X. A. (1999). Testing a model of pull production: a paradigm for manufacturing research using structural equation modeling. Journal of Operations Management, 17(4), 467–488. Kumar, A., Heide, J. B., & Wathne, K. H. (2011). Performance implications of mismatched governance regimes across external and internal relationships. Journal of Marketing, 75(2), 1–17. Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria what did they really say? Organizational Research Methods, 9(2), 202–220. Lassar, W. M., & Kerr, J. L. (1996). Strategy and control in supplier–distributor relationships: An agency perspective. Strategic Management Journal, 17(8), 613–632. Lau, A. K., Tang, E., & Yam, R. (2010). Effects of supplier and customer integration on product innovation and performance: Empirical evidence in Hong Kong manufacturers. Journal of Product Innovation Management, 27(5), 761–777. Le Dain, M. A., & Merminod, V. (2014). A knowledge sharing framework for black, grey and white box supplier configurations in new product development. Technovation, 34(11), 688–701. Li, E. Y., Liao, C. H., & Yen, H. R. (2013). Co-authorship networks and research impact: A social capital perspective. Research Policy, 42(9), 1515–1530. Li, T., & Calantone, R. J. (1998). The impact of market knowledge competence on new product advantage: Conceptualization and empirical examination. Journal of Marketing, 62(4), 13–29. Li, Z. (2009). Analysis of the dynamic relationship between the emergence of independent Chinese automobile manufacturers and international technology transfer in China's auto industry. Annals of Business Administrative Science, 8(0), 21–42. Libecap, G. D. (1993). Contracting for property rights. Cambridge university press. Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in crosssectional research designs. Journal of Applied Psychology, 86(1), 114–121. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883. Marksberry, P. (2012). Investigating “the way” for Toyota suppliers: A quantitative outlook on Toyota's replicating efforts for supplier development. Benchmarking: An International Journal, 19(2), 277–298. Mishra, D. P., Heide, J. B., & Cort, S. G. (1998). Information asymmetry and levels of agency relationships. Journal of Marketing Research, 277–295.

14

Industrial Marketing Management xxx (xxxx) xxx–xxx

J. Chang and knowledge exploitation in young technology-based firms. Strategic Management Journal, 22(6–7), 587–613. Zaheer, A., & Venkatraman, N. (1995). Relational governance as an interorganizational strategy: An empirical test of the role of trust in economic exchange. Strategic Management Journal, 16(5), 373–392.

Zhou, K., Zhang, Q., Sheng, S., Xie, E., & Bao, Y. (2014). Are relational ties always good for knowledge acquisition? Buyer–supplier exchanges in China. Journal of Operations Management, 32(3), 88–98. Zhou, K. Z., & Wu, F. (2010). Technological capability, strategic flexibility, and product innovation. Strategic Management Journal, 31(5), 547–561.

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