How does your supplier compete?

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How does your supplier compete?

Investigating the effect of supplier competition on obtaining supplier

resources

Master’s Thesis

By:

Mark Lakerveld

Student number: S2554240

E-mail:

m.lakerveld.2@student.rug.nl

December 8, 2018

University of Groningen, Faculty of Economics and Business

MSc. Technology and Operations Management

Newcastle University Business School

MSc. Operations and Supply Chain Management

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Abstract

In today’s business environment, supplier resources are becoming increasingly important for the success of buying firms. Despite the increased interest in the competition for supplier resources from the buyer’s perspective, the competition between suppliers to optimally allocate resources to their buyers has received little attention. By means of a dyadic survey research conducted in the agricultural and maritime industry, this study investigated the effect of supplier competition on the relationship between supplier characteristics and the buyer’s difficulty to obtain physical and innovation supplier resources. Data of 54 buyer-supplier relationships was analyzed by structural equation modeling (PLS-SEM). Key findings are an increased importance of the buyer-supplier relationship strength under low supplier competition. Furthermore, this study shows how different supplier characteristics affect the buyer’s difficulty to obtain supplier resources, which can assist purchasing managers of buying firms in their decision-making regarding purchasing supplier resources, and eventually help to improve the buying firm’s performance. Keywords: Supplier resource allocation, physical resources, innovation resources, buyer competition, supplier

competition, supplier characteristics

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1. Introduction

Supplier resources are becoming increasingly important for a firm’s survival in today’s business environment (Dyer & Hatch, 2006; Hult et al., 2007; Hunt & Davis, 2012; Pulles, Veldman & Schiele, 2016). Besides raw materials and components, suppliers can provide resources such as ideas, innovations, expertise, capabilities and physical capacity. However, competing buying firms often source from the same supplier base (Takeishi, 2002; Dyer & Hatch, 2006). As a consequence, buying firms engage in supplier base competition, i.e., firms compete with other firms that seek similar resources from the same suppliers to acquire superior resources that can be leveraged for firm-level competitive advantages (Pulles & Ellegaard, 2018). However, downstream resource allocation is also affected by competition between suppliers, who aim to achieve the most optimal resource allocation to their buyers (Chu, 2012). In the resource allocation context, this has resulted in a shift from the traditional single buyer-supplier perspective (figure 1a) to a multiple buyer-supplier perspective including buyer (supplier base) and supplier competition (figure 1b). The arrow indicates the flow of resources.

Supplier Buyer Supplier A Supplier B Supplier D Supplier C Buyer A Buyer B Buyer D Buyer C

According to Tanskanen et al. (2017), managing external resources has become a major task for firms. Consequently, a vast amount of literature that addresses buyer competition for supplier resources can be found. However, despite the importance of supplier resources, the effect of supplier competition on the buying firm has received little attention. Therefore, this paper aims to, from the perspective of the buying firm as focal firm, empirically examine the effect of supplier competition on the buyer’s difficulty to obtain physical (tangible) and innovation (intangible) supplier resources.

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position themselves in order to gain a profitable and sustainable position against the forces that determine industry competition. One of these forces is the bargaining power of suppliers, which decreases in a competitive supplier market. However, in today’s business environment, the supplier market should not be perceived as a static collection of companies located in specific area. Fuelled by globalization (Meixell & Gargeya, 2005), buying firms nowadays can have an extremely diverse supplier base resulting in for example suppliers located around the globe and different types of buyer-supplier relationships (Cox, 2004). To account for this effect, this study first identifies supplier characteristics which can affect the difficulty to obtain supplier resources. Hereafter, the effect of supplier competition on these relationships will be examined.

As mentioned above, the literature has explored multiple themes regarding competition for supplier resources. Authors focused on how buying firms can attain preferential resource allocation relative to its competitors (Hüttinger, Schiele & Veldman, 2012; Pulles et al., 2014; 2016a; 2016b; Pulles & Ellegaard, 2018), resource-advantage theory (Hunt & Davis, 2008) and the strategic relevance of supplier resources (Dyer & Hatch, 2006; Hult et al., 2007). Additionally, others explored upstream competition between buying firms in resource (factor) markets (Chatain, 2014; Ellram, Tate & Feitzinger, 2013; Markman et al., 2009), which are markets where firms buy and sell the resources necessary to implement their strategies (Barney, 1986). This study aims to make a contribution to this research field by examining the effect of supplier competition instead of buyer competition on the buying firm.

Although the recent studies contribute to a greater depth of competition for supplier resources literature, the present studies do not consider how the competition between suppliers can affect the buyer’s difficulty to obtain supplier resources. It is interesting to study this topic, because “the share, relative importance, complexity and opportunities of external resources for firms have been multiplied during the latest decades” (Tanskanen et al., 2017, p.1087). Thus, identifying and acting upon the effects of supplier competition could potentially significantly influence a buyer’s performance. This study distinguishes between physical (e.g. raw materials, components) and innovation (e.g. ideas, technological information) resources, because these resources are crucial to a firm’s competitive advantage in almost all industries (Ellram et al., 2013) and are relatively underexplored (Schoenherr et al., 2012). This leads to the following (sub) research questions:

RQ 1: How do supplier characteristics affect the buyer’s difficulty to obtain physical resources? RQ 2: How do supplier characteristics affect the buyer’s difficulty to obtain innovation resources?

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RQ 3: How does supplier competition affect the relationship between supplier characteristics and the buyer’s difficulty to obtain physical resources?

RQ 4: How does supplier competition affect the relationship between supplier characteristics and the buyer’s difficulty to obtain innovation resources?

Since manufacturing firms are generally more reliant on supplier resources than service firms (Pulles et al., 2016b), this study will focus solely on manufacturing firms. The sub research question can be answered by addressing supplier selection literature. For the main research questions, the methodological approach used will be a dyadic survey research. In doing so, the theoretical contribution of this paper is an increased understanding of the supplier characteristics that can affect the difficulty to obtain supplier resources. In addition, the effect of supplier competition on these relationships will be identified. Managerially, identifying the effect of supplier characteristics and competition can contribute to a firm’s supplier strategy development. For example, a firm can more easily identify the suppliers from which it is difficult to obtain resources and devote increased attention to these suppliers to improve their relationship and ensure proper resource allocation in the future.

This study makes several contributions. First, this study shows how supplier characteristics have an impact on the buying firm’s attainability of supplier resources. Furthermore, whereas most of the operations management literature only focuses on the buyer’ perspective (van der Vaart & van Donk, 2008), this study acknowledges the perspectives of both parties in the buyer-supplier relationship. In doing so, this study can examine how supplier competition affects the buying firm. Specifically, this study finds for example that the strength of the buyer-supplier relationship is of higher importance in acquiring supplier resources under low supplier competition compared to high supplier competition.

The remainder of this paper is organized as follows. In section 2, the theoretical grounding and hypotheses development is presented. This leads to the conceptual model which will be tested during the research. In section 3, the methodology of the research is discussed. Section 4 provides the results of the research. Thereafter, the results are discussed and a conclusion is drawn in section 5. Finally, section 6 provides the limitations of the research and discusses directions for future research.

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2. Theoretical grounding and hypotheses development

The theoretical background follows the research questions formulated above. However, to provide relevant context, competition in general and competition for supplier resources are discussed first. Hereafter, supplier competition and the supplier characteristics are discussed. In addition, it is hypothesized how the supplier characteristics can affect the buyer’s difficulty to obtain physical and innovation supplier resources, and how the resource type affects these relationships. Then, the effect of supplier competition on the hypothesized relationships will be discussed. Finally, the conceptual model is presented.

2.1 Competition

Wherever firms overlap, coexist, or co-occupy the same space, competition exists (Markman et al., 2009). In general, competition can be defined as the activity of striving to win something that results in supremacy over others. In a business context, this often means that firms strive for increased sales, profit or market share. Competition is often perceived as the overarching logic of business (Ford & Hakansson, 2013). With an exception of monopolistic markets, firms need to cope with the presence of other firms that have similar goals and thus partake in competition.

According to Ford & Hakansson (2013), two conditions should be present in order for competition to exist. First, alternative actors should be available. For example, alternative buyers or alternative suppliers. The more buyers or suppliers there are present, the more competition there will be between those buyers or suppliers. Second, there should be some kind of similarity between the buyers or suppliers on one or more dimensions. A simple example: a supplier of steel is most likely not in competition with a supplier of milk, but can be in competition with a supplier of wood, since both steel and wood can be considered construction materials.

According to Porter (1979, p. 137), “the essence of strategy formulation is coping with competition.” An enhanced understanding of a firm’s competition could therefore help firms to formulate a suitable strategy. This holds for both buyers and suppliers. Therefore, the next sections are devoted to gain an increased understanding of the competition for supplier resources from a buyer’s perspective and the competition between suppliers to optimally allocate resources to their buyers.

2.2 Competition for supplier resources

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comparable assets, and providing similar or substitutable offerings to shared customers in related product markets (Chen, 1996; Ketchen, Snow, & Hoover, 2004; Yu & Cannella, 2007).” However, competition can flare up at any level of a firm’s supply chain (Porter, 1985) and due to the increased importance of supplier resources, buying firms nowadays compete for resources from suppliers as well to achieve competitive advantages (Pulles et al., 2016b).

In the early literature, the traditional resource-based view (RBV) opted that a competitive advantage is mainly a function of internal resources (Barney, 1991; Peteraf, 1993). However, more recent studies argue that resources acquired externally can lead to competitive advantages (Dyer & Singh, 1998; Das & Teng, 2000; Steinle & Schiele, 2008; Hunt & Davis, 2012). This led to the extended resource based view (ERBV), which opts that competitive advantages are derived from both internal and external resources. Adopting this view, it is of importance that firms understand the impact of external resources on their competitiveness and that firms understand the competition for external resources (Pulles et al., 2016b). Researchers have contributed to an increased understanding of the competition for supplier resources by investigating competition in factor markets (Chatain, 2014; Ellram et al., 2013; Markman et al., 2009). In factor markets, firms can buy resources that they do not or cannot develop internally but are still necessary to implement firm strategies (Chatain, 2014). Factor market rivalry explains why firms compete over resources in factor markets, even in the absence of product-market commonality. However, factor markets focus on nonstrategic resources (Ellram et al., 2013) and do not take into account that suppliers can deliberately differentiate their resources amongst competing buyers (Pulles and Ellegaard, 2018). That suppliers differentiate their resources is supported by researchers who have focused on how buying firms can attain preferential resource allocation compared to their competitors (Hüttinger et al., 2012; Pulles et al., 2014; 2016a; 2016b; Pulles & Ellegaard, 2018).

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Hunt & Davis (2008, p.15). This constant struggle is also affected by competition between suppliers, who compete to optimally allocate resources to the buying firms. Therefore, supplier competition and supplier characteristics are discussed next. Moreover, it is hypothesized how these supplier characteristics can affect the buying firm’s difficulty to obtain physical and innovation supplier resources.

2.3 Supplier competition

Whilst buying firms compete with each other for supplier resources, at the same time suppliers are in competition with each other to achieve the most optimal resource allocation to their buyers (Chu, 2012). Supplier competition occurs when multiple suppliers compete to supply their resources to one buyer. A simple explanation for supplier competition is overcapacity. This results in relatively more supply compared to the demand. Then, suppliers compete to sell as many resources as possible to a buying firm to maximize profit. However, it can also be the case that one buyer is simply more attractive than its competitors. For example, a buyer can stand out due to its reputation (Powers & Reagan, 2007) or a buyer can be more financially attractive (Baxter, 2012) than its competitors.

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always preferred when compared to multisourcing. However, Jin & Ryan (2011) only focused on the buying firm’s operational costs and did not consider how difficult it is to acquire all resources from a single supplier. For example, to acquire all resources from a single supplier might require significant relationship-specific investments in this supplier. Besides, sourcing from multiple suppliers has other benefits such as protection against supplier failure, lower risk of not being able to satisfy customer demand and being able to access a wider set of supplier capabilities (Slack, Chambers & Johnston, 2010). Another result of supplier competition is a reduced incentive from the supplier to make relation-specific investments, because suppliers fear that they will lose this investment when they are replaced (Chu, 2012). So suppliers make less relationship-specific investments in a competitive supplier market. Following the same line of reasoning, a supplier in a competitive supplier market might be more careful with choosing to whom to allocate their resources, because they fear of losing a relationship with this buying firm in the future. On the other hand, a supplier in competitive supplier market might need to act fast due to the many alternative suppliers available for the buying firm. The first situation would make it more difficult for the buying firm to acquire supplier resources whereas the second situation would make it easier for the buying firm to acquire resources from that supplier. For the supplier, there is a trade-off between the fear of losing a potentially costly relationship with the buying firm when the supplier is replaced by an alternative supplier and the fear of having no business in the first place. To determine which side dominates, the effect of supplier competition on the buying firm’s difficulty to obtain supplier resources needs further investigation.

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buying firm’s size will be related to the buying firm’s difficulty to obtain physical and innovation supplier resources. Next, it is explained how the type of resource affects these relationships. Finally, it is hypothesized how supplier competition affects the hypothesized relationships.

2.4 Supplier and buyer characteristics

2.4.1

Number of competitors

An important supplier characteristic is its number of competitors, i.e., the number of competing suppliers that can supply similar resources to the buying firm. The more competing suppliers there are present, the more competition there will be (Ford & Hakansson, 2013). Naturally, the more suppliers available, the more opportunities there are for the buying firm to acquire supplier resources. However, as mentioned above, the fear of losing a costly a relationship in the future might restrain the supplier to establish a relationship with a buying firm in a competitive supplier market. In spite of this, there are several arguments which support the notion that it becomes easier for the buying firm to acquire supplier resources in a competitive supplier market. First of all, a competitive supplier market increases the bargaining power of the buying firm (Chu, 2012; Li & Wan, 2012). In addition, assuming resource similarity between competitive suppliers, the relative value of the resource decreases with an increasing number of suppliers (Markman et al., 2009). This is mainly due to a reduction of the pre-emption value, i.e., the value of denying a competitor access to a resource (Chatain, 2014). When there are many available suppliers and buying firms have multiple alternatives to acquire resources, the pre-emption value of these resources is low because the resources can be obtained easily. Conversely, when there is only one supplier and especially when there is limited supply, the pre-emption value is high. In this case, buying firms compete with each other to establish a relationship with this single supplier to ensure they acquire valuable resources and consequently a competitive advantage. However, the supplier can dictate the contract terms due to its high bargaining power. Finally, when supplier market competition is low, a supplier may charge the buyer a premium price (Walker & Weber, 1987). All in all, it is expected that it becomes more difficult for buying firms to acquire physical and innovation supplier resources when one or a few competing suppliers are present. This leads to the following hypotheses:

H1a: the number of competing suppliers has a negative relationship with the buyer’s difficulty to obtain physical resources.

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2.4.2 Supplier distance

A supplier’s location is an important factor of interest for the buying firm. For example, firms often face the debate of whether they should source locally or globally. Global sourcing has been widely discussed in the literature (e.g. Alguire, Frear & Metcalf, 1994; Trent & Monczka, 2003). By sourcing globally firms seek to benefit from cross-border factor-cost advantages by purchasing larger volumes from low-wage countries (Steinle & Schiele, 2008). However, Steinle and Schiele (2008) argue that global sourcing does not necessarily improve a firm’s competitiveness. Due to social and technical impediments, becoming a preferred customer of a remote supplier is more difficult compared to becoming a preferred customer of a supplier located in the same regional or national cluster. Hence, when a firm is unable to become a preferred customer of a remote supplier, local sourcing might be the better alternative. Moreover, according to Hüttinger et al. (2012), geographical proximity can be considered an antecedent of a preferred customer status. As a result, it could be easier for the buying firm to acquire resources from suppliers that are proximate compared to remote suppliers.

Sourcing locally has several advantages according to Porter (1998): it lowers transaction costs, minimizes the need for inventory, eliminates importing costs and delays and it lowers the risk that suppliers will overprice, because local reputation is important. In addition, communications improve when the buyer and supplier are proximate, and it is easier for the supplier to provide support services to the buying firm. According to Frigant and Lung (2002), another advantage of geographical proximity is a large knowledge exchange due to face-to-face and frequent contacts between employees of buying firm and its suppliers.

In addition, geographical proximity between a buyer and a supplier positively impacts inter-firm trust (Bönte, 2008; Dyer & Chu, 2000). An increased level of trust results in a more cooperative relationship between the buyer and supplier (Johnston et al., 2004). More specifically, a supplier’s trust in the buying firm is strongly linked to cooperative behaviours such as shared planning and flexibility in coordinating activities (Johnston et al., 2004). Thus, geographical proximity can indirectly contribute to a more cooperative relationship between the buying firm and supplier.

Due to the increased chances of becoming a preferred customer, improved communications and a more cooperative relationship between a buyer and a supplier as a result of geographical proximity, it is expected that it is easier for the buying firms to acquire physical and innovation resources from local suppliers compared to remote suppliers, which results in the following hypotheses:

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H2b: the distance between a supplier and the buying firm has a positive relationship with the buyer’s difficulty to obtain innovation resources.

2.4.3 Buyer-supplier relationship strength

In the last decades, the increasing trend of outsourcing non-core activities has resulted in closer inter-organizational relationships between buying firms and suppliers (Scannell, Vickery & Dröge, 2000). Relationships have been characterized in several ways, but common themes that are considered to be central to meaningful relationships were found to be coordination, collaboration, commitment, communication, trust, flexibility, and dependence (Kannan & Tan, 2006). A strong buyer-supplier relationship scores high on these traits. Autry and Golicic (2010) characterize a strong buyer-supplier relationship using a concept developed by Capaldo (2007). This concept is characterized by strong ties based on three dimensions: duration, frequency and intensity of interaction. Following Autry & Golicic (2010), the strength of the buyer supplier relationship can be defined as the extent to which firms are tied to one another on a continuum from strong to weak based on duration, frequency and intensity of interaction. Other things being equal, higher levels of these dimensions involves higher levels of resource commitment (Autry and Golicic, 2010).

In addition, more frequent and intense interaction between the buyer and supplier contribute to easing the process of acquiring supplier resources. Moreover, the duration of the buyer-supplier relationship positively contributes to building relationship-specific investments which allow the buyer and supplier to communicate and collaborate more effectively (Kotabe, Martin & Domoto, 2003). In turn, enhanced communication and collaboration is expected to ease the process of acquiring supplier resources. Besides, a strong buyer-supplier relationship enhances the buying firm’s ability to respond to its customer needs in a timely manner (Martin & Grbac, 2003). This involves fast responses from the supplier as well, including the supply of resources. Thus, a strong buyer-supplier relationship is expected to make it less difficult for the buying firm to acquire physical and innovation supplier resources.

H3a: the strength of a buyer-supplier relationship has a negative relationship with the buyer’s difficulty to obtain physical resources.

H3b: the strength of a buyer-supplier relationship has a negative relationship with the buyer’s difficulty to obtain innovation resources.

2.4.4 Supplier performance

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to obtain a competitive advantage. To assess whether a supplier performs or will perform according to the buying firm’s conditions, the performance of the supplier can be evaluated. Evaluating suppliers is important, because purchasing supplier resources can constitute a large portion of the total operational costs. For large automotive manufacturers, the cost of purchasing components and parts from suppliers can account for more than 50% of sales (Weber et al., 1991). Supplier performance is often evaluated against multiple criteria but quality, delivery and price/cost are found to be the three most popular criteria (Ho, Xu & Dey, 2010). Suppliers should therefore aim to supply high quality products, deliver these products adequately and ask an acceptable price in order to establish and retain relationships with buying firms.

In much of the buyer-supplier relationship literature, it is argued that a strong buyer-supplier relationship results in better operational performance (e.g. Krause, Handfield & Tyler, 2007; Morgan & Hunt, 1994). However, Autry and Golicic (2010) argue that this relationship in fact has a cyclical nature. They found that higher supplier performance increases the strength of the buyer-supplier relationship, which in turn increases operational performance. Moreover, it is argued that a supplier’s past performance is a prerequisite for the development of relational closeness. For example, prior delivery performance and purchasing experience are highly linked to anticipated future relationship characteristics (Doney & Cannon, 1997). Consequently, a supplier can influence the strength of the buyer supplier relationship by continuously performing effective and efficient on projects (Autry & Golicic, 2010). Thus, supplier performance is not directly linked to the buying firm’s difficulty to obtain supplier resources, but is expected to have an indirect effect as it is expected to increase the buyer-supplier relationship strength. This results in the following hypothesis.

H4: the performance of the supplier has a positive relationship with the strength of the buyer-supplier relationship.

2.4.5 Number of buyers

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Consequently, the number of buying firms that acquire similar resources from the supplier as the focal buying firm can affect the difficulty of obtaining those resources due to different power dynamics in the buyer-supplier relationship. This can be explained as follows. Whether a supplier supplies resources to a single buying firm or many buying firms largely impacts the power distribution in the buyer-supplier relationship. For example, buyer dominance occurs when the supplier has one or few buyers while supplier dominance occurs when the supplier supplies resources to many buying firms (Cox, 2001). A consequence of buyer dominance is the possibility for buying firms to exert power on the supplier, i.e., the ability to influence or control the decisions and behaviour of the supplier (Narashimhan et al., 2009). For example, the buyer’s power attributes can be used to leverage the supplier’s performance on quality and cost improvement and makes sure that supplier receives only normal returns (Cox, 2001). In addition, the buying firm has the possibility to exert coercive power, i.e., the ability to punish the supplier, to induce desired supplier behaviour such as lowering the supplier’s delivery times (Bloom & Perry, 2001). On the other hand, supplier dominance could result in a supplier that wants to renegotiate an existing contract, improve the contractual arrangement when the contract is let or reduce their own performance levels (Lonsdale, 2001), thereby complicating the allocation of resources from the buyer’s perspective.

Although power imbalances in favour of the buying firm could also result in unethical supplier exploitation (Schleper, Blome & Wuttke, 2015), it is expected that the availability of these power mechanisms eases the process of acquiring supplier resources from the buying firm’s perspective. This is the case when the buying firm is dominant and thus few competing buying firms for similar supplier resources are present. Moreover, suppliers are reluctant to compete when there is low customer demand uncertainty or big average customer demand (Qi, Shi & Xu, 2015), which is the case when many buying firms acquire resources from a supplier. As a result, buying firms lose advantages that would otherwise be obtained through supplier competition if the number of buying firms increases, such as higher bargaining power. Thus, it is expected that the number of buying firms that acquire similar resources from a supplier as the focal buying firm has a positive relationship with the buyer’s difficulty to obtain physical and innovation supplier resources.

H5a: the number of buying firms that acquire similar resources from a supplier has a positive relationship with the buyer’s difficulty to obtain physical resources.

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2.4.6 Buying firm’s size

A buying firm’s size can affect the difficulty to obtain supplier resources. According to Udayasankar (2008), who investigated the effect of firm size on the firm’s participation in corporate social responsibility, larger firms have better access to supplier resources than smaller firms. This results in a higher ability for large buying firm’s to implement unique strategies that are difficult imitate for (small) competing buying firms (Barney, 1991). In addition, large firms are often associated with greater resource-slack, which can be operationally defined as excess absolute levels of resources (Nohria & Gulati, 1996), while small firms often have constrained resources (Udayasankar, 2008), which implies that large firms have less difficulties to obtain supplier resources. Moreover, suppliers tend to underbid each other aggressively when a large buyer appears on the market (Snyder, 1998). Consequently, the presence of multiple competing suppliers enhances the bargaining power of the buying firm, making it less difficult for the buying firm to obtain supplier resources. Snyder (1998) explains that the benefits of undercutting when a large buyer enters the market may outweigh the future loss from any punishment for undercutting. As a result, a large buying firm pays a lower price than a small buying firm for similar resources. On the contrary, small firms are expected to have less purchasing power compared to large firms according to Moen (1999), which results in higher resource prices. Moreover, since small firms produce small quantities, small firms are unable to create economies of scale, making it more difficult to build an efficient resource acquisition system due to limited resources. Finally, small firms have limited possibilities to leverage power in the buyer-supplier. As explained above, power can be exerted to influence supplier decisions and behaviour (Narashimhan et al., 2009). All in all, it is expected that a large buying firm has less difficulty to obtain physical and innovation supplier resources than a small buying firm. This leads to the following hypothesis:

H6a: the size of the buyer has a negative relationship with the buyer’s difficulty to obtain physical resources.

H6b: the size of the buyer has a negative relationship with the buyer’s difficulty to obtain innovation resources.

2.5 Supplier’s type of resources

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to be acquired when the buying firm has established a close relationship with the supplier. A close buyer-supplier relationship means that both participants share the risks and rewards and are willing to maintain the relationship over the long term (Shin, Collier & Wilson, 2000). Cox (2004) subdivides the level of involvement in the buyer-supplier relationship into a reactive or a proactive focus. The former entails an arm’s length, non-collaborative and reactive relationship whereas the latter is characterized by a long-term and highly collaborative relationship. In a buyer-supplier relationship with a proactive focus, the buyer and the supplier provide greater transparency regarding their input costs, margins and production techniques in order to create new products and service offerings (Cox, 2004). This information and know-how can be considered innovation resources. Hence, a proactive buyer-supplier relationship results in a higher level of innovation resources sharing from the supplier. However, it is also more difficult to establish such relationships since higher transaction costs make proactive relationships more resource intensive than reactive relationships. Besides, it requires certain competences to develop and manage proactive long-term collaborative relationships (Cox, 2004). As a consequence, it is expected that in general buying firms have more difficulties to acquire innovation resources compared to physical resources. However, as innovation resources are relational by nature, the presence of many competing suppliers does not necessarily affect the difficulty to obtain this type of resources. Thus, it is expected that the number of competing suppliers has more impact on the difficulty to obtain physical resources compared to innovation resources. For the other relationships, it is expected that the hypothesized effect will be stronger for innovation resources. This yields the following five additional hypotheses:

H1c: the hypothesized negative relationship between the number of competing suppliers and the buyer’s difficulty to obtain supplier resources is stronger for physical resources compared to innovation resources.

H2c: the hypothesized positive relationship between the supplier distance and the buyer’s difficulty to obtain supplier resources is stronger for innovation resources compared to physical resources.

H3c: the hypothesized negative relationship between the buyer-supplier relationship strength and the buyer’s difficulty to obtain supplier resources is stronger for innovation resources compared to physical resources.

H5c: the hypothesized positive relationship between the number of buyers and the buyer’s difficulty to obtain supplier resources is stronger for innovation resources compared to physical resources.

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2.6 Effect of supplier competition

This section explains how supplier competition impacts the hypothesized relationships between the supplier and buyer characteristics and the buyer’s difficulty to obtain physical and innovation resources. In order to do so, the competitive intensity of the supplier market is related to H1ab, H2ab, H3ab, H5ab and H6ab.

2.6.1 Number of competitors

First of all, it is hypothesized that the number of competing suppliers is negatively related to the difficulty to obtain supplier resources (H1a & H1b). The number of competitors can be considered an antecedent of competitive intensity (Tsaur & Wang, 2011). Therefore, it is expected that the negative relationship between the number of competitors and the difficulty to obtain supplier resources becomes less pronounced in case of high supplier competition compared to low supplier competition. There will be many available suppliers when supplier competition is high, making it easier for the buying firm to obtain supplier resources. Meanwhile, under low supplier competition the number of available suppliers will be low, making it more difficult to obtain supplier resources. This results in the following hypothesis:

H7: The effect of the number of competitors on the buyer’s difficulty to obtain supplier resources will decrease under high supplier competition (compared to low supplier competition).

2.6.2 Supplier distance

Next, the supplier distance is expected to be positively related to the buyer’s difficulty to obtain supplier resources (H2a & H2b). Thus, the further away the supplier, the more difficult it becomes to obtain supplier resources. However, in a highly competitive supplier market, buying firms can (coercively) ensure that their supplier has a proper information exchange system in place, thereby partly diminishing distance related disadvantages such as lack of communication (Porter, 1998), and improving resource related business practices such as just-in-time (Wafa, Yasin & Swinehart, 1996). In addition, in a low competitive intensity supplier market buying firms might need to look abroad to find an appropriate supplier, thereby engaging in cross-cultural communications (Brownell & Reynolds, 2002), making it more difficult to obtain supplier resources. Therefore, it is expected that the positive relationship between supplier distance and difficulty to obtain supplier resources decreases in case of a high supplier competition compared to low supplier competition.

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2.6.3

Buyer-supplier relationship strength

H4a & H4b hypothesize that the buyer-supplier relationship strength is negatively related to the difficulty to obtain supplier resources. In a highly competitive supplier market, a strong buyer-supplier relationship is of lesser importance for the buying firm in acquiring supplier resources compared to a low competitive supplier market, because the supplier can easily be replaced by an alternative supplier. Besides, this effect is strengthened by the fear of suppliers to make relation-specific investments in a competitive supplier market, because they fear of losing this investment when they are replaced (Chu, 2012). Conversely, a strong buyer-supplier relationship is of higher importance in a low competitive supplier market due to the few alternatives for the buying firm. This yields the following hypothesis:

H9: The effect of the buyer-supplier relationship strength on the buyer’s difficulty to obtain supplier resources will decrease under high supplier competition (compared to low supplier competition).

2.6.4 Number of buyers

The number of buyers is expected to be positively related to the difficulty to obtain supplier resources (H5ab), i.e., the more alternative buying firms there are present, the more difficult it becomes to obtain supplier resources. In a highly competitive supplier market, the presence of many suppliers results in greater resource allocations and more aggressive pricing policies (Robertson & Gatignon, 1986) allowing multiple buying firms to be supplied resources satisfactorily. However, in a low competitive supplier market, multiple buying firms compete for the services of the few present suppliers, making it harder to obtain supplier resources under low supplier competition. This yields the following hypothesis:

H10: The effect of the number of buyers on the buyer’s difficulty to obtain supplier resources will decrease under high supplier competition (compared to low supplier competition).

2.6.5 Buying firm’s size

Finally, the buying firm’s size is expected to be negatively related to the difficulty to obtain supplier resources. Especially in a low competitive supplier market, firm size can be the determining factor for the supplier to supply the buying firm, because small firms often lack economies of scale (Nooteboom, 1993) and suppliers tend to supply as many resources as possible to one buyer to prevent high operational costs. Moreover, in a highly competitive supplier market it is expected that the buying firm’s size is of lesser importance because the competitive nature of the supplier market results in less selectiveness of the suppliers in choosing to whom to allocate their resources. This results in the following hypothesis:

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3. Methodology

3.1 Research design

This research employs a quantitative approach to identify how supplier and buyer characteristics affect the buyer’s difficulty to obtain supplier resources and how supplier competition affects these relationships. The unit of analysis is the buyer supplier relationship. In this research, it will be tested whether the hypothesized relationships can be validated. For this type of research, confirmatory research is used (Karlsson, 2016).

Primary dyadic data is collected from manufacturing firms and their suppliers by means of an online survey. By collecting dyadic data, perspectives of both parties in the relationship are acknowledged. Survey research is one of the methods widely used to perform this empirical research in the Operations Management (OM) field (Karlsson, 2016) and it offers several advantages. When compared to other research methods, survey research is considered less costly, achieves higher generalizability and is preferred for sensitive topics because of its anonymity (Nardi, 2015). Anonymity is favourable because this decreases the social desirability of the answers provided in the survey. Finally, an online survey allows for a relatively large sample size in comparison to other research methods such as interviews and case studies (Yin, 2003). This makes an online survey the most a suitable research design for this research.

3.2 Sample and data collection

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The questionnaire was sent to 211 suppliers in total. One week after the initial mailing, a reminder was sent to all the suppliers who had not yet responded. After two weeks the survey was accessed 74 times and then closed. Responses with missing and suspicious answers were removed which resulted in 54 usable surveys. Next, the purchasing managers of the buying firms were asked to fill in a survey about those 54 suppliers. However, to ensure the buyer survey was filled in by the employee with the most accurate knowledge about the supplier, the purchasing manager was allowed to ask colleagues to fill in the buyer survey as well. In practice, all the buyer surveys were filled out by the purchasing managers of the buying firms. This has resulted in an effective response rate of 25.6% and a final sample size of 54 dyads (Table 1).

Company A Company B Company C Total

Suppliers contacted 131 30 50 211

Supplier survey accessed 27 14 33 74

Removed responses 10 2 8 20

Useable surveys 17 12 25 54

Response rate 13.0% 40.0% 50.0% 25.6%

Completed buyer surveys 17 12 25 54

Effective response rate 13.0% 40% 50.0% 25.6%

TABLE 1. Response rates

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Frequency Frequency

Sector

Agricultural 54%

Maritime 46%

Country Annual turnover (in

millions of euros) Czech Republic 1.9% 0-10 31.5% Germany 1.9% 11-50 25.9% Hungary 1.9% 51-100 0.0% Netherlands 88.9% 101-500 9.3% Poland 1.9% >500 3.7%

South Africa 1.9% Unknown 29.6%

United Kingdom 1.9%

Number of employees Relationship length

(in years) 0-10 20.4% 0-5 38.9% 11-50 44.4% 6-10 18.5% 51-100 14.8% 11-25 29.6% 101-500 13.0% 26-50 11.1% >500 7.4% >50 1.9%

TABLE 2. Respondent characteristics

To make sure the purchasing managers of the buying firms were knowledgeable about the relationship with their suppliers, it was asked how long the purchasing managers were personally involved with each supplier. On average, the purchasing managers were personally involved with the suppliers for 6.8 years. Thus, it can be expected that their knowledge regarding the suppliers is accurate. In addition, the questionnaire included a question which indicated the level of confidence of the correctness of the answers provided by the employees from the buying firm. They were asked to indicate their confidence level on a five-point Likert scale ranging from “strongly disagree” to “strongly agree” to the following statement: “I am convinced of the correctness of the answers I have provided in the questionnaire”. With a mean of 4.30 and standard deviation of .964 it can be concluded that the respondents are convinced of the answers they have provided in the questionnaire.

3.3 Measures

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measured using a five-point Likert scale, but the scales are anchored differently. An overview of the constructs and measurement items used in in this study is provided in Tables 3a and 3b for the supplier and buyer survey, respectively.

To measure the first construct, competitive intensity, a measurement developed by Tsaur & Wang (2011) was used. The construct items measure the degree of competition using a three-item scale that was adopted from Ambler et al. (1999). According to Ambler et al. (1999), competitive intensity is manifested in the number of competitors, extent of price competition, and intensity of rivalry. These items are therefore measured on a five-point Likert scale anchored at one end with “very low” and the other end with “very high”. To measure the supplier performance a five-item scale was adopted from Shin et al. (2000). Supplier performance is often evaluated against multiple criteria and the scale from Shin et al. (2000) includes the three most popular criteria which were found to be quality, delivery and price/cost according to a literature review from Ho, Xu & Dey (2010). Each item was measured on a five-point Likert scale anchored at one end with “significant decrease’’ and the other end with “significant increase’’. To measure the strength of the buyer-supplier relationship, a three-item scale was adopted from Martin & Grbac (2003). The five-point Likert scale is anchored at one end with “strongly disagree” and the other end with “strongly agree”. To measure the dependent variables, the difficulty to obtain physical or innovation resources, adapted measures from Pulles et al. (2014) were used. In their study, Pulles et al. (2014) measured the extent to which the supplier allocates the buying firm better physical or innovation resources than its competitors. They developed the items based on resource-based studies of Newbert (2008), Hunt & Davis (2008), and Surroca, Tribó & Waddock (2010). The suppliers were asked to assess their firm’s resource allocation to a buying firm relative to the resource allocation of this buying firm’s competitors. This study adapted the items to measure how difficult it is to obtain supplier physical or innovation resources from the buying firm’s perspective. An example of an item to measure the difficulty to obtain physical resources is: “please indicate how difficult it is for your firm to be granted utilization of this supplier’s production facilities”. Similarly, “please indicate how difficult it is for your firm to be shared key technological information of this supplier” is an item used to measure the difficulty to obtain innovation resources. The items were measured based on a five-point Likert scale anchored at one end with “extremely easy” and the other end with “extremely difficult”.

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supplier the number of customers that buy similar products as the focal buying firm. The buying firm’s size was measured by asking the supplier the contribution in percentage of the buying firm to the supplier’s turnover. In this way, the relative size of the buying firm to the supplier is measured, which gives a more accurate representation of the importance of the buying firm to the supplier. Finally, the supplier distance was determined by measuring the distance of the supplier location to the buying firm’s

location.

TABLE 3a. Measurement items – Supplier questionnaire

(Continued)

TABLE 3b. Measurement items – Buyer questionnaire

Constructs Measurement items Factor

loadings Competitive intensity (CI)

(Tsaur & Wang, 2011) Composite reliability = .86 Average variance extracted = .68

CI1: Number of competitors CI2: Extent of price competition CI3: Intensity of rivalry

1 = very low; 5=very high

.81 .82 .84

loadings Supplier performance (SP)

(Shin et al., 2000)

Composite reliability = .94 Average variance extracted = .75 SP1: Lead times SP2: On-time delivery SP3: Delivery reliability SP4: Quality SP5: Cost

1 = significant decrease; 5 = significant increase

.82 .90 .90 .86 .85 Relationship strength (RS)

(Martin & Grbac, 2003) Composite reliability = .94 Average variance extracted = .84

RS1: We have a strong relationship with this supplier. RS2: We work closely with this supplier on an ongoing basis so we can revise our purchasing to match our customer needs.

RS3: This suppliers responds rapidly to our changing needs. 1 = strongly disagree; 5 = strongly agree

.89 .97

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TABLE 3b. Measurement items – Buyer questionnaire (continued)

3.4 Data reliability, validity and common method bias

To assess the reliability and validity of the data, several tests were conducted before analysing the data. First of all, the internal consistency reliability was measured by means of the Composite Reliability (CR). The internal consistency reliability measures whether the different measurement items that probe the same construct produce similar results. CR is preferred over the commonly used assessment of the Cronbach’s alpha because CR takes into account that the factor loadings of the measurement items can differ whereas Cronbach’s alpha assumes that all factor loadings are equal (Hair et al., 2016). Moreover, CR is preferred when using structural equation modelling (SEM) as analysis method (Wong, 2013), which will be introduced in the next section. The CR values are presented in Tables 3a and 3b and range between .86 and .96, and thereby comfortably exceed the recommended threshold of .70 (Fornell & Larcker, 1981). The convergent validity measures the extent to which the measurement items of one construct positively correlate with each other (Hair et al., 2016). To evaluate the convergent validity, the indicator reliability and the average variance extracted (AVE) were evaluated. Since the measurement items for a specific construct intent to measure the same construct, it is expected that they share a high proportion of variance.

loadings Difficulty to obtain physical

resources (PR)

(Pulles et al., 2014) Composite reliability = .96 Average variance extracted = .85

Please indicate how difficult it is for your firm to… PR1: …be granted utilization of this supplier’s production facilities.

PR2: …be prioritised in your demand of products in case of extreme events (e.g. natural disasters).

PR3: …be allocated scarce material from this supplier in case of capacity bottlenecks.

PR4: …be dedicated this supplier’s specialized equipment. 1 = extremely easy; 5 = extremely difficult

.89 .95 .91 .93

Difficulty to obtain innovation resources (IR)

(Pulles et al., 2014) Composite reliability = .95 Average variance extracted = .82

Please indicate how difficult it is for your firm to… IR1: …be shared key technological information of this supplier.

IR2: …be shared the best ideas of this supplier.

IR3: …be dedicated this supplier’s innovation resources. IR4: …be spending product development time on projects with this supplier.

1 = extremely easy; 5 = extremely difficult

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The outer loadings of the indicators range between .81 and .97, well exceeding the recommended threshold from Hair et al. (2016) of .708 (Table 3a & 3b). The AVE values range between .68 and .85 and thereby exceed the recommended threshold of .50 (Fornell & Larcker, 1981). In other words, the constructs explain a minimum of 68% of the variation in the measurement items. As the results of all tests are satisfactorily, no measurement items were deleted.

The discriminant validity measures the extent to which a construct is truly distinct from the other constructs (Hair et al., 2016). To test the discriminant validity, the Fornell-Larcker criterion can be used. The Fornell-Larcker criterion suggests that the square root of the AVE of each construct should be greater than the correlations among the constructs. In Table 4 can be observed that the square roots of the AVE of the constructs (displayed diagonally) are higher than the correlation among the constructs. Thus, all constructs fulfil the requirement for discriminant validity.

Finally, the Harman’s single-factor test was to assess the common method bias. Common method bias can occur if the respondents answer questions in a particular way due to social desirability (Kock, 2015). The unrotated factor solution indicated that the explained variance of the largest factor was equal to 28.0%. Since this value is below 50%, it is not likely that common method bias poses a threat to the validity of the data (Eichorn, 2014).

TABLE 4. Correlations of the constructs (bold elements on the diagonal represent the squared AVE, off diagonal elements represent the correlation between the constructs)

1. 2. 3. 4. 5. 6. 7. 8.

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3.5 Data analysis

As mentioned above, the data will be analysed by means of structural equation modelling (SEM). SEM is a second-generation multivariate data analysis method that allows a set of relationships between one or more independent variables and one or more dependent variables to be examined (Ullman & Bentler, 2012). More specifically, SEM statistically tests theoretically developed causal relationships against empirical data. In addition, SEM allows researchers to include unobservable latent variables that are measured indirectly by indicator variables (Hair et al., 2016). Since the goal of this research is to identify the causal relationships developed in sections 2.4, 2.5 and 2.6, SEM can be considered a suitable data analysis method.

There are two distinct approaches to SEM: partial least squares SEM (PLS-SEM) and covariance-based SEM (CB-SEM). PLS-SEM uses a regression-based approach to maximize the explained variance of the dependent latent constructs. As a consequence, the focus of PLS-SEM is more on prediction rather than on explanation, which makes it highly useful for studies that focus for example on the sources of competitive advantage (Hair et al., 2016). The alternative approach, CB-SEM, tries to minimize the difference between the theoretical covariance matrix and the estimated covariance matrix (Hair, Ringle & Sarstedt, 2011). According to Wong (2013), CB-SEM is the preferred data analysis method when the goal of the research is confirming or rejecting theories through testing hypothesis. However, CB-SEM requires that certain conditions are met which often are difficult to meet in practice. For example, CB-SEM assumes the data is normally distributed, the sample size is large enough and the structural model is not too complex (Hair et al., 2011). In reality, many researchers have difficulties to obtain a data set that meets these requirements (Wong, 2013). When this is the case, PLS-SEM is often a better suited alternative because PLS-SEM results in more robust estimations of the relationships between the latent variables when compared to CB-SEM, especially when the assumptions of CB-SEM are violated (Reinartz, Haenlein, & Henseler 2009). Moreover, PLS-SEM can deal with non-normally distributed data, constructs with few measurement items, a low sample size and higher complexity of the structural model (Hair et al., 2011). Since the sample size of 54 can be considered small and the structural model of this research is relatively complex with many constructs and indicator variables, and some constructs contain only one measurement item, PLS-SEM will be used as the data analysis method in this research.

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H6) whereas the outer or measurement model includes the relationships between the latent constructs and their observed indicators (Wong, 2013). In order to analyse the structural model, the measurement model needs to be trusted. Therefore, the measurement model’s characteristics need to be examined first and measurement items with unacceptable values need to be removed (Hair et al., 2011). This has been done in section 3.4 where the internal consistency reliability, convergent validity and discriminant validity have been evaluated. Next, the structural model can be evaluated, which will be done in the next section. The full sample data set was used to examine the structural model (H1-H6).

Hereafter, the hypotheses H7-H11 are tested for the effect of supplier competition in a more explorative way. First, a multi-group analysis (MGA) will be conducted in SmartPLS 3.0 to observe possible differences in the relationships under high and low supplier competition. Next, the differences are further examined by looking at the scatterplots of the relationships between the independent and dependent variables under high and low supplier competition. In order to so, the continuous variable competitive intensity is split based on the median value into two subsamples: high competitive intensity and low competitive intensity. Although the median split has several disadvantages (Irwin & McClelland, 2003), it enables us to examine the hypothesized effects for two different levels (high and low) of supplier competition by comparing two different subsamples.

3.6 Control variable

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4. Results

4.1 Structural model: H1-H6

As shown in figure 3, the number of competitors is as hypothesized negatively related to the difficulty to obtain physical resources as well as the difficulty to obtain innovation resources. However, only the effect on the difficulty to obtain physical resources is significant (p < .05), thus H1a is supported and H1b is rejected. The supplier distance has a nonsignificant negative effect on both the difficulty to obtain physical resources as well as innovation resources whereas a positive effects were expected. Therefore, H2a and H2b are rejected. The relationship strength is negatively related to physical resources and positively related to innovation resources. However, both effects are nonsignificant, thus H3a and H3b are rejected. H4 is supported as the supplier performance has a significant (p < .01) and positive effect on the buyer-supplier relationship strength. The number of buyers has a significant (p < .05) positive effect on the difficulty to obtain physical resources and a significant positive effect on the difficulty to obtain innovation resources (p < .10), thereby supporting both H5a and H5b. Finally, the buying firm’s size is nonsignificant and positively related to the difficulty to obtain physical resources and a significant positive effect (p < .10) was found for innovation resources, whereas negative relationships were expected. Therefore, H6a and H6b are rejected. The results of the structural model are summarized in Table 5.

Hypothesis Beta (β) t-value Significant (p < .10) p-value 95% confidence intervals

H1a. Number of competitors  Difficulty to obtain PR -.28 1.98 Yes .05 [-.57, -.05]

H1b. Number of competitors  Difficulty to obtain IR -.04 .28 No .78 [-.34, .23]

H2a. Supplier distance  Difficulty to obtain PR -.03 .24 No .81 [-.20, .31]

H2b. Supplier distance  Difficulty to obtain IR -.31 1.54 No .12 [-.58, .16]

H3a. Relationship strength  Difficulty to obtain PR .04 .23 No .81 [-.25, .39]

H3b. Relationship strength  Difficulty to obtain IR -.17 1.05 No .29 [-.47, .16]

H4. Supplier performance  Relationship strength .55 6.40 Yes .00 [.39, .72]

H5a. Number of buyers  Difficulty to obtain PR .26 2.54 Yes .01 [.04, .45]

H5b. Number of buyers  Difficulty to obtain IR .29 1.96 Yes .05 [-.01, .53]

H6a. Buying firm’s size  Difficulty to obtain PR .03 .33 No .74 [-.12, .20]

H6b. Buying firm’s size  Difficulty to obtain IR .17 1.76 Yes .08 [-.00, .36]

TABLE 5. Results of the structural model

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relationship strength. According to Chin (1998), R2 values of 0.67, 0.33, or 0.19 are described as substantial, moderate or weak for endogenous latent variables. Therefore, the structural model possesses a weak to moderate level of explanatory power. Finally, to assess hypotheses H1c-H6c, the path coefficients of the structural model can be compared for physical and innovation resources (Table 5). Consequently, H1c, H3c and H5c are supported and H2c and H6c are rejected.

β = -.28**

.55***

β = -.03

β = -.17

Figure 3. Results of the structural model. *p < .10, **p < .05, ***p < .01, dashed paths indicate nonsignificant results.

4.2 Multi-group analysis

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conducted to observe whether the between group differences are likely to be significant. Hereafter, the scatterplot of the relationship will be created to examine whether the between group differences can also be observed in a visual representation of the data.

To make sure that the differences between the low and high supplier competition were not caused by differences in the measurement model, the measurement invariance was calculated first. If there is no measurement invariance, the power of the statistical tests can be reduced providing misleading results (Hair et al., 2016). The measurement invariance was assessed by looking at the significance of the outer loadings after conducting a MGA factor analysis with a bootstrapping of 2000 subsamples. No items were found to be significant (p-values < .05). Thus, the potential differences are expected to result from trait differences instead of measurement differences. The path coefficients for the full-sample, sub-sample with high supplier competition, sub-sample with low supplier competition and the between group differences are summarized in Table 6.

Hypothesis Full sample Beta (β) Low supplier competition Beta (β) High supplier competition Beta (β) Difference high and low

competition

Significant difference

H1a. Number of competitors  Difficulty to obtain

PR

-.28 -.27 .00 .27 Unlikely

H1b. Number of competitors  Difficulty to obtain

IR

-.04 -.04 .62 .66 Likely

H2a. Supplier distance  Difficulty to obtain PR -.03 -.13 -.14 .01 Unlikely

H2b. Supplier distance  Difficulty to obtain IR -.31 -.53 -.21 .32 Unlikely

H3. Supplier performance  Relationship strength .55 .46 .74 .27 Unlikely

H4a. Relationship strength  Difficulty to obtain

PR

.04 -.22 .32 .54 Unlikely

H4b. Relationship strength  Difficulty to obtain

IR

-.17 -.52 .25 .76 Likely

H5a. Number of buyers  Difficulty to obtain PR .26 .28 .50 .22 Unlikely

H5b. Number of buyers  Difficulty to obtain IR .29 .36 .21 .14 Unlikely

H6a. Buying firm’s size  Difficulty to obtain PR .03 -.02 .23 .25 Unlikely

H6b. Buying firm’s size  Difficulty to obtain IR .17 .09 .61 .52 Unlikely

TABLE 6. Results of the Multi-group analysis

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size. Consequently, the significance levels of the individual path coefficients are not shown. When looking at the difference levels of the number of competitors in the two different groups, it can be observed that for physical resources the path coefficient increases with .27 under high supplier competition compared to low supplier competition. Similarly, for innovation resources the path coefficient increases with .66 under high supplier competition compared to low supplier competition. Moreover, for innovation resources, the difference is likely to be significant. This suggests partial support for H7, which opted that the (negative) effect of the number of competitors on the difficulty to obtain supplier resources decreases under high supplier competition compared to low supplier competition. To assess whether H7 is truly partially supported, the scatterplot for this relationship will be created in the next section. Second, the effect of the relationship strength increases with .54 under high supplier competition compared to low supplier competition for physical resources. For innovation resources a similar effect is observed. The path coefficient increases with .76 when supplier competition is high compared to low supplier competition. In addition, the difference for innovation resources is likely to be significant. H9 hypothesizes that the (negative) effect of the relationship strength on the difficulty to obtain supplier resources decreases under high supplier competition compared to low supplier competition. Thus, partial support is found for this hypothesis. To validate this relationship, it will be further examined in the next section. Finally, the other relationships did not show large enough differences between low and high supplier competition for both physical and innovation resources and could therefore not be assessed with the desired level of confidence. Therefore, H8, H10 and H11 are not taken into consideration for further analysis and are thus neither supported or rejected.

4.3 Scatterplots

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Figure 4a. The effect of the number of competitors on the difficulty to obtain innovation resources

How does your supplier compete?