Competitive supplier resource allocation -

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Competitive supplier resource allocation -

the use of power and trust by buyers

Master thesis, Supply Chain Management

University of Groningen, Faculty of Economics and Business

16th_{of February 2015}

MARISKA KAPELLE

Contact details: Mauritsstraat 45 9724 BJ Groningen Student number: 1686690 Email: mariska.kapelle@gmail.com

Supervisors

Dr. J. Veldman & MSc N. Ziengs

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Competitive supplier resource allocation -

the use of power and trust by buyers

-

MARISKA KAPELLE

Abstract.

A comparative advantage in supplier resource allocation strengthens the overall competitive advantage and market position of a buyer. Research on the supplier’s perspective of supplier resource allocation showed power and trust as key strategies used by buyers to influence their relative resource position. This paper replicates these findings from a buyer’s perspective, collecting data from buyers in a business-to-business setting. This is a unique methodological contribution to a supplier-perspective dominated research field. Moreover, the paper includes competitive elements (i.e. buyer and supplier market competition) in order to research the impact of competition on buyers’ mechanisms (i.e. power and trust) to influence a supplier’s resource allocation.

Results of the replication study indicate buyer strategies of reward power, goodwill trust and competence trust to relate positively to supplier resource allocation. The effect of coercive power differs, depending on the type of resource. The preliminary results of the multi-group analyses show high buyer and supplier market competition to influence the effect of buyers’ strategies on supplier resource allocation. This impact is larger for physical resources than for the allocation of innovation resources. These results should be cautiously interpreted in light of the methodological limitations of the paper. Two essential recommendations for future research have been made. One is to extent the scope of competition (e.g. by including product market competition or combining buyer and supplier market competition). Additionally, buyer – supplier relationship stages should be included to research whether these have an influence on the used buyer mechanisms to influence their suppliers’ resource allocation.

Keywords.

Supplier resource allocation, coercive power, reward power, goodwill trust, competence trust, physical resources, innovation resources, buyer market competition, supplier market competition.

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1. INTRODUCTION

Have you ever wondered why one buyer is offered better resources than another, and how buyers try to influence their suppliers in their resource allocation decisions? Research has showed that acquiring better supplier resources than competitors is one of the essential parts in achieving a competitive advantage (Ellram, Tate, and Feitzinger 2013) for buying firms. Receiving better resources than competitors does not only directly contribute to the competitive advantage of a buying firm. In addition, these (i.e. better) resources are withhold from competitive buyers (Chatain 2014), which also indirectly contributes to the buyer’s competitive advantage. Buyer firms are therefore expected to try to influence their supplier’s resource allocation decisions in their favor (Pulles et al. 2014). This paper focuses on supplier resource allocation from a buyer’s perspective. It contributes to literature on buyer strategies used to influence a supplier’s resource allocation. Moreover it contributes, exploratory (Malhotra 2009), to literature on the extent to which these strategies are affected by competition.

Attaining a comparative advantage in resource allocation will strengthen the overall competitive advantage of a buyer and most likely its market position (Ellram et al. 2013; Hunt and Davis 2008). This is especially the case in a ‘shared supply chain’ where the buying firm’s rivals source from shared suppliers (Pulles et al. 2014). Markman, Gianiodis, and Buchholtz (2009) describe this as factor-market competition. Both the buyer as well as the supplier could face competition in the factor-market (Ellram et al. 2013). A high level of competition in the buyer market potentially makes buyers decide to change their strategies to influence the supplier’s resource allocation decision, as the buyer firm needs to stand out from competitive buyers. Moreover, a high level of supplier market competition increases the vulnerability of suppliers, as more alternative suppliers are available for buyers in the resource market. In a shared supply chain, buyers will try to influence their supplier’s resource allocation decisions by trying to become the ‘preferred customer’ (Ellis, Henke, and Kull 2012) in the supplier’s customer portfolio. They will encourage their suppliers to commit to and invest resources in their relationship, over the relationship of this supplier with competitive buyers (Baxter 2012). Research on supplier resource allocation from a supplier’s perspective has shown power and trust as key strategies used by buyers to influence their relative resource position (Pulles et al. 2014).

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This paper focuses on buyers’ strategies (i.e. power and trust) which are used to influence the suppliers’ resource allocation decisions. In addition, it researches the impact of competition on these strategies in order to provide a full understanding on competitive resource allocation in shared supply chains (Pulles et al. 2014). Data is collected from buyers via a questionnaire distributed in a business-to-business setting in order to answer the following research question: “To what extent does buyer and supplier market competition influence the effects of power and trust on a buyer firm’s relative resource position?” Theoretically, the paper contributes to literature on supplier resource allocation by replication. Additionally, a first insight is provided in the potential impact of competition on buyer’s mechanisms to influence their supplier’s resource allocation. From a practical perspective, this paper provides useful insights for buyers and suppliers operating in a competitive resource market.

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2. THEORETICAL BACKGROUND

This theoretical background provides insight in the research context of competitive supplier resource allocation. It starts with an introduction on supplier resource allocation in buyer – supplier relationships, its current state of research and its link to social exchange theory. Hereafter, power and trust will be introduced as the two main strategies used by buyers to influence supplier resource allocation. The last section describes the importance of competition. After these elements of supplier resource allocation are described, the hypotheses of the study follow.

2.1 Introduction in supplier resource allocation

In order to obtain a competitive advantage in resource allocation, a buyer firm must ascertain to acquire better resources than its competitors (Dyer and Hatch 2006; Hunt and Davis 2008; Takeishi 2001). This phenomenon is defined as supplier resource allocation which is ‘the extent to which the supplier allocates to a particular buyer better resources than to the buyer’s competitors’ (Pulles et al. 2014: 26). A relatively better resource position additionally strengthens the overall competitive advantage of a buyer firm (Takeishi 2001). Research provided evidence on suppliers’ differentiating their services which they offer to buyers, as a result of the ‘preferred customer concept’ (Baxter 2012; Hüttinger, Schiele, and Veldman 2012). Buying firms might try to use different strategies to stimulate their suppliers in awarding a ‘preferred customer status’ to them. Moreover, as Chatain (2014) argues, resource value in the market is composed of its ‘use value’ as well as its ‘preemption value’ (i.e. the value of denying a competitor access to a resource). Therefore, a buyer is expected to invest in a strong relationship with its crucial supply chain partners. Not only to secure its current and future resource position, but to withhold resources from competitive buyers as well.

Recently, buying firms are increasingly dependent on a fewer amount of highly innovative suppliers that would be interesting supply chain partners (Schiele 2012). Furthermore, the relationships with these suppliers have been intensified (Blenkhorn and Banting 1991; Cannon and Perreault 1999; Hunt and Davis 2008). Literature on buyer – supplier interactions in relationship marketing suggests the importance of having a strong relationship with crucial supply chain partners (Whipple, Lynch, and Nyaga 2010) in order for these interactions to provide a competitive advantage. As fewer suppliers are available, the position of the buyer compared to the supplier’s position is weakened. This results in the buyer’s use of strategies to influence their supplier’s resource allocation decisions.

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by sellers, do not qualify as social exchange relationships (Muthusamy and White 2005). Close bonds between buyer and supplier, over a certain period based on repeated interaction and the reciprocity of giving and receiving relational benefits do. Based on these arguments resource allocation strategies are often researched in the scope of SET.

Building on SET, supply chain partners are likely to use different strategies to influence the exchange partner (Pulles et al. 2014) in repeated interactions. As this research focuses on resource allocation from a buyer’s perspective, the two main strategies used by buying firms to influence suppliers; power and trust (Terpend and Ashenbaum 2012) are included. These two mechanisms are chosen as the central strategies in the research of Pulles et al. (2014), which provides the fundamental framework for this research. The following section describes power and trust in more detail.

2.2 Strategies to influence supplier resource allocation: power and trust

Outcomes and benefits of social exchanges between buyers and suppliers are not always (carefully) contracted (Das and Teng 2002). This provides both parties the opportunity to influence the interaction and its outcomes. Building on the framework of Pulles et al. (2014) and SET, power and trust are included as the two main buyer strategies trying to influence its supplier’s resource allocation. More specifically, resource allocation in this research will be two-fold, including both tangible and intangible resources (Hunt and Davis 2008). It is important to include both types of resources in order to have a precise overview of the resource pool, which is not possible when taken into account only one type of resource (Galbreath 2005; Markman et al. 2009). Therefore, power and trust will be linked to the supplier’s allocation of physical (tangible) and innovation (intangible) resources. Power and trust are expected to differ in their effect on the supplier’s allocation of tangible and intangible resources (Chen 1995; Ireland and Webb 2007) which is partly shown in the research of Pulles et al (2014). Therefore, supplier resource allocation and a buyer’s relative resource position will be explained and measured by two types of resources; (1) physical (i.e. tangible) and (2) innovation (i.e. intangible) resources.

2.2.1 Power

Several descriptions of power in SET are constructed. Overall, power is conceptualized as ‘the ability of one partner to have an advantage over the other: it can allow one partner to coerce the other into doing something they otherwise may not do’ (Narasimhan et al. 2009; Powers and Reagan 2007; Wilson 1995). In this research context, power is explained as the ability of the buyer to influence the actions, decisions, and behavior of the supplier in a resource allocation context. Power might be used deliberately (i.e. mediated power), or might not be specifically exercised (i.e. non-mediated) to manipulate the target (Terpend and Ashenbaum 2012). Power could be used to influence the supply chain partner, either by punishment (coercive power) or reward (reward power).

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future order volumes and the power of a buying firm to reward a supplier with additional business or favorable treatment (Terpend and Ashenbaum 2012) respectively. These types of power differentiate in their positive or negative impact on supplier resource allocation (Pulles et al. 2014). This will be explained in the following chapter, which includes the hypotheses and expected effects of power and trust on supplier resource allocation.

2.2.2 Trust

Trust is defined as ‘the willingness to rely on an exchange partner in whom the firm has confidence’ (Powers and Reagan 2007: 1236). Trust in buyer – supplier relationships is an expectation about the exchange partner that results as a combination of expertise, reliability and intentionality (Ganesan 1994). An example: when buyer and supplier trust one another, they are more willing to share resources without the fear of opportunistic behavior of the other party (Pulles et al. 2014). Overall, a higher level of trust in a buyer – supplier relationship tightens the social and structural bonds (Powers and Reagan 2007). Pulles et al. (2014) define two main dimensions of trust that influence supplier resource allocation, namely goodwill trust and competence trust.

Goodwill trust especially develops within long-term relationships through repeated exchanges (Sako 1992). It refers to ‘the degree to which a partner trusts the other to be willing to act in ways that exceed the stipulated contractual agreements without explicitly asking for such help’ (Pulles et al. 2014: 19). An example is a situation where the supplier is convinced of the goodwill of the buyer as the buyer firm takes initiatives for mutual benefits that exceed the contractual agreements; which makes the supplier decide to allocate its best resources to this particular buyer over competitive buyers. Competence trust refers to ‘the belief of a firm that the given partner has the managerial and technical capabilities to properly perform a given set of tasks’ (Ireland & Webb, 2007: 484). It is a source of trustworthiness that supply chain partners seek in their relationships (Powers and Reagan 2007). An example is the supplier’s belief that a buyer is capable to provide value to the supplier in a buyer – supplier relationship. A lack of competence trust could result in negative outcomes in resource allocation as the supplier firm might not be confident that the buyer has the right capabilities to (amongst others) manufacture, distribute, or re-use resources. Literature on trust in buyer – supplier literature mostly describes the positive effect of trust (Pulles et al. 2014). We build further on this assumption in our hypotheses. Before designing the hypotheses of the study, the importance of competition is introduced.

2.3 Competitive resource allocation

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According to Cordón & Vollmann (2008) ‘really good’ suppliers are in short supply. In order for buyers to establish close bonds with their suppliers and receive a competitive advantage in resource allocation, they will try to influence the decisions and actions of these suppliers (Blau 1964). Assuming supply surplus, buyers still want to obtain the best resources, however they are expected to be more powerful as the buyer potentially has more supplier alternatives. Ellram et al. (2013) argue two markets to be vulnerable to competition, as they split the occurrence of competition in the market between product (i.e. buyer) and input (i.e. supplier) markets. Research of Markman et al. (2009) defines factor market rivalry to possibly occur for both supply chain partners, the buyer and the supplier. However, literature on factor market rivalry is relatively limited. This research contributes to this research field by researching the extent to which the use of power and trust are affected by competition in the buyer and supplier market.

2.3.1 Buyer market competition

In resource allocation, “the few highly innovative suppliers that would be interesting partners for one firm are often exactly the same suppliers that would make interesting partners for the firm’s competitors” (Schiele, 2012: 44). In this case, competition occurs in the buyer market, as more than one buyer, competes for the best (i.e. physical and innovation) resources in the supplier market. This raises resource value, as their value is not only composed of its ‘use value’, but also its ‘preemption value’ – i.e. the value of denying a competitor access to a resource in the product market – (Chatain 2014). Resource sellers have an interest in fuelling rivalry between potential buyers (Chatain 2014) as it increases the value of its supplied resources. Most likely, high competition in the buyer market will trigger more proactive strategies of buyers to acquire the best resources and strengthen their overall competitive resource position. A strong presence of competition in the buyer market is expected to affect the use of power and trust, as these strategies might be increasingly needed to establish and secure a buyers’ relative resource position.

2.3.2. Supplier market competition

Moreover, competition could exist in the supplier market. This occurs when more than one supplier competes for supplying their (total) capacity to one buyer or in one buyer market, assuming resource similarity (Markman et al. 2009). This type of competition could for instance be caused by overcapacity, by a relatively higher supply compared to demand or when one of the buyers stands out in attractiveness to suppliers. This higher attractiveness could be caused by the reputation of the buyer (Powers and Reagan 2007) or its financial attractiveness (Baxter 2012). High supplier market competition is expected to result in a more powerful position of the buyer in the resource allocation market. This could have an impact on the buyer’s use of power and trust strategies. The impact of competition in the supplier market on power and trust strategies will be described in the following section.

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3. HYPOTHESES

The previous section explained strategies, resources and competitive situations which will be translated into hypotheses on supplier resource allocation, based on SET. Replicating the research of Pulles et al. (2014), hypotheses H1a-H4b will link power and trust to supplier resource allocation. Hypotheses H5a-H6d investigate the effect of respectively buyer and supplier market competition on buyer strategies used to influence a supplier’s allocation of physical and innovation resources. The first hypotheses are contributing to the maturity and integrity (Carter 2004; Kaynak and Hartley 2008) of research on supplier resource allocation. The latter contributes to literature on competitive supplier resource allocation by the exploratory (Malhotra 2009) research and analyses on the impact of buyer and supplier market competition on buyer’s strategies used to influence a supplier’s resource allocation decision.

3.1 Coercive power

Coercive power relates to forcing the supplier into decisions and actions that it did not necessarily intended at first. Most studies mainly address the negative effect of coercive power (Brown, Lusch, and Nicholson 1995; Maloni and Benton 2000) as supply chain partners are supposed to be anxious for the outcomes of not complying to the requirements of the party using coercive power. In the context of the present study, we examine the effect of a buyer using coercive power to influence its relative resource position. As suppliers might be afraid for the consequences of coercive power (e.g. declining amounts of order volumes, diminishing future demand and cancellation of joint investments), Pulles et al. (2014) have argued coercive power to have a positive influence on the allocation of physical (i.e. tangible) resources. To avoid possible negative consequences, suppliers are likely to allocate better (physical) resources to buyers using coercive power. On the other hand, as innovation (i.e. intangible) resources typically involve interpersonal exchanges (Chen 1995); coercive power is expected to relate negatively to the allocation of innovation resources. Increasing the strategy of coercive power would therefore have a controversial effect on supplier resource allocation. The results of the study of Pulles et al. (2014) showed nonsignificant values for both hypotheses. However, after the multi-group analyses, i.e. those customers having a large share in the suppliers’ turnover, provided significant support for the hypotheses. Therefore, despite its doubtful significance, both hypotheses will be replicated as suggested by Pulles et al. (2014).

H1a: Coercive power is positively related to the allocation of physical resources H1b: Coercive power is negatively related to the allocation of innovation resources

3.2 Reward power

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collaboration, and adaptation as important factors in successful buyer – supplier relationships, which both the buyer and supplier could benefit from. Pulles et al. (2014) identifies these kinds of rewards used by buyers to positively influence its relative resource position in case of both physical and innovation resources. Their results show significant values for the following hypotheses, which will be replicated in this study.

H2a: Reward power is positively related to the allocation of physical resources H2b: Reward power is positively related to the allocation of innovation resources

3.3 Goodwill trust

Goodwill trust is a certain level of trustworthiness in a buyer – supplier relationship. The buyer uses this as a strategy to make the supplier believe that its intentions and willingness in the relationship are good and could go beyond contractual agreements (Ireland and Webb 2007). It is closely related to the trust that a supplier has in the mutual goals and shared intentions in a buyer – supplier relationship (Powers and Reagan 2007). In case goodwill trust is established, both parties trust each other to not take advantage of their relationship and cooperation. Moreover, it is expected that both parties in this situation are willing to go beyond what is formally contracted (Lindgreen and Wynstra 2005). As they trust the intentions and willingness of the buyer in case of high goodwill trust, suppliers are more likely to allocate the best resources to this buyer. In terms of physical resources, this could lead to better quality resources and lower production costs for the buyer (Li et al. 2007). In terms of innovation resources, a supplier would be more likely to share critical information and innovations (Roy, Sivakumar, and Wilkinson 2004). The results of Pulles et al. (2014) showed nonsignificant values for these hypotheses. However, in their multi-group analyses, significant values are found in the customer group with a large share in the suppliers’ turnover. Therefore, the following hypotheses will be tested in order to be consistent in the replication study, as well as to try to add to the integrity of the findings of Pulles et al. (2014) (Kaynak and Hartley 2008).

H3a: Goodwill trust is positively related to the allocation of physical resources H3b: Goodwill trust is positively related to the allocation of innovation resources

3.4 Competence trust

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H4a: Competence trust is positively related to the allocation of physical resources H4b: Competence trust is positively related to the allocation of innovation resources

The following hypotheses (H5a-H6b) are a first attempt in supply chain management literature to analyze the role of competition in supplier resource allocation. These are exploratory in nature (Malhotra 2009), as current literature on the competitive context in resource allocation is limited. The hypotheses predict the effect of buyer and supplier market competition on the buyer’s use of power and trust strategies to influence its supplier’s resource allocation decisions.

3.5 Buyer market competition

Buyer market competition (BMC) occurs as more than one buyer, compete for the best resources (i.e. physical and innovation) in the supplier market. Although resource dissimilarity (Markman et al. 2009) could occur, this research assumes similarity in the demand of buyers for a specific resource. High BMC then occurs for scarce resources, which are highly valued not only by one buying firm, but also by its competitors1_{(Chatain 2014). In order for a buyer to secure a comparative advantage in resource}

allocation, in high BMC, it is of utmost importance for a buyer to attract and attain a capable supplier firm.

A high level of BMC influences several resource market characteristics which in turn are supposed to affect the buyer’s mechanisms used to influence their relative resource position. In case of high BMC, the buyer is limited in its amount of alternative suppliers (Porter 1980). This results in a more powerful and dominant position of the supplier in comparison to the buyer. Moreover, in case of high BMC, attracting the best resources enlarges the entry barriers for competitive buyers to attain and attract resources of a comparable quality and value (Porter 1980). The higher the quality and overall value of a buyer’s resources, the higher the quality and value of a competitor’s resources must be to even or exceed this advantage, especially in case of high BMC. The overall value of a resource is expected to increase when BMC is high. This is the case as not only the ‘use value’ of a resource increases, especially the increase in the ‘preemption value’ of a resource substantially increases the overall value. The more competitive the buyer market, the more valuable it becomes to disable specific resources to competitive buyers. Markman et al. (2009) even explain ‘resource captivity’ as an explicit strategy to hold up resources which are critical to rivals. Lastly, the simple assumption that in case of high BMC total demand is larger than total supply remains valid. Therefore, only one or a couple of buyers are awarded with the best resources in a highly competitive resource market. The above mentioned changes in resource market characteristics result in the urge for buying firms to stand out from competitive buyers in order to enlarge the possibility of receiving the best resources. Thereby, increasing their relative

1_{(Chatain 2014) makes a difference between competitive buyers which compete only in the supply market and which compete in}

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resource position. A buyer could do so by e.g. promising future business and taking initiatives beyond contractual agreements, which are examples of power and trust (Pulles et al. 2014; Terpend and Ashenbaum 2012).

The main argument for using coercive power as a buyer strategy is to increase the supply chain partners anxiousness for the (negative) outcomes of not complying to the requirements of the buyer using coercive power (Brown et al. 1995; Maloni and Benton 2000). As resource values are high and suppliers have more buyer alternatives, they are less anxious of ‘losing’ the buyer firm which uses coercive power if BMC is high. Therefore, the buyer’s use of coercive power would not have the intended effect. No increasing effect is thus expected on the buyer’s use of coercive power in case of high BMC (H5a). As the supplier has more alternative buyer possibilities, the supplier is most likely to choose the buyer which offers rewards in return, over the buyers that offer none or less rewards. When BMC is high, offering more or better rewards to a supplier is most likely to positively influence the supplier’s decision to allocate its best (physical and innovation) resources to this particular buyer. A high level of BMC would therefore result in an increase in the use of reward power as a strategy to stand out from competitive buyers (H5b).

A high level of goodwill trust results in the suppliers’ trust in the willingness and intentions of the buyer. Furthermore the supplier does not expect the buyer to take advantage of their buyer – supplier relationship. Ceteris paribus, in case of high BMC a higher degree of goodwill trust results in a preferred position for the buyer, over its competitors. Buyers could therefore increasingly use goodwill trust as a strategy to attract and attain better resources from its supplier if the buyer market is more competitive (H5c). The same applies for competence trust, which increases trust of the supplier in the competences and capabilities of the buyer as a supply chain partner, and increases trust in positive outcomes of the buyer – supplier relationship. When BMC is high, a buyer most likely increases the strategy of competence trust to influence its supplier in its resource allocation decision (H5d). The hypotheses on the effect of high BMC on buyer mechanisms influencing supplier resource allocation are as follows. H5a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high buyer market competition (compared to low buyer market competition)

H5b: The effect of reward power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition)

H5c: The effect of goodwill trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition)

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3.6 Supplier market competition

Supplier market competition (SMC) is defined as one or more suppliers which compete for supplying their (total) capacity to one buyer or in one buyer market. The plain argumentation for the occurrence of high SMC is that supply is larger than demand. However, this reasoning is too limited as some buyers are preferred by suppliers, for instance due to their financial attractiveness (Baxter 2012), reputation (Powers and Reagan 2007), and opportunities for joint innovations.

High SMC (like high BMC) has an impact on market characteristics which in turn cause changes in the use of power and trust by buyers to influence their relative resource position. In case of high SMC, the number of available suppliers relative to the number of buyers increases (Porter 1980). This could result in suppliers that are not able to allocate their total resource portfolio (i.e. physical and innovation resources) in the buyer market. The number of alternatives for a buyer firm increases relatively to the number of alternatives for a supplier firm. This results in a more powerful and dominant position of the buyer in comparison to the supplier (Porter 1980). In addition, the value of resources relatively decrease, assuming resource similarity between competitive suppliers (Markman et al. 2009). Especially a decrease in the preemption value of resources causes the overall resource value to decline (Chatain 2014). This is the case as alternative suppliers are available when a buyer fails to attract or attain appropriate resources. Additionally, buyers have relatively more alternative suppliers which lowers the preemption value of one particular supplier’s resources.2_{Lastly, the buyer’s risk of losing good supplier}

resources is relatively low due to possible alternative suppliers. The potential risk for a supplier to lose a good buyer is high as the amount of buyer alternatives (in high SMC) is limited. In case no alternative is found, the supplier cannot allocate its resources, which causes losses. Therefore, a natural tendency is expected to occur in which suppliers offer their best resources, under the best conditions on the buyer market, in order to secure allocation and at least a minimum amount of profit. Nevertheless, buying firms use power and trust strategies to influence their relative resource position.

Increasing the use of coercive power is relatively inexpensive and easy for a buying firm as they have relatively low risk of losing potentially good supplier resources. In case the buyer loses a supplier if it does not comply with the coercive power requirements, other supplier firms are available (Porter 1980). Therefore, in case of high SMC it is expected that a buyer increases its strategy of coercive power to make sure a supplier allocates its best resources to them (H6a). Increasing reward power would be relatively costly for a buyer and does not necessarily result in better supplier resource allocation. This is due to the alternatives of a buyer firm and the natural tendency of a supplier to offer the best resources in the resource market. Therefore, it is not expected that high SMC has an increasing impact on the use of reward power on a supplier’s resource allocation (H6b).

Without a certain trustworthiness in the goodwill of the buyer, the supplier (in case of high SMC) might be too vulnerable to become entirely dependent on one (or a couple of buyers). A supplier in this case ideally only decides to allocate its best physical resources and share key information with a buyer

2_{Even in case of high SMC, buyers could use extensive strategies of ‘resource captivity’ to fully limit their competitive buyers in}

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if a certain level of goodwill trust is present (Powers and Reagan 2007). In addition, in the absence of goodwill trust, the buyer would have no exit barrier to exit the buyer – supplier relationship with this (more vulnerable) supplier in case of high SMC. Therefore, increasing goodwill trust as a buyer strategy would be helpful in case of high SMC and higher supplier vulnerability (H6c). As SMC is high and the supplier has a relatively limited amount of alternative buyers, the luxury of allocation both its physical and innovation resource to the most capable buyer is not applicable. Although the buyer is expected to have at least some minimum level of competences and capabilities, the buyer is not expected to increasingly use competence trust as a strategy to influence its suppliers’ resource allocation in case of high SMC (H6d). The following hypotheses on high SMC will be tested in this study.

H6a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition)

H6b: The effect of reward power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high supplier market competition (compared to low supplier market competition)

H6c: The effect of goodwill trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition)

H6d: The effect of competence trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high supplier market competition (compared to low supplier market competition)

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TABLE 1: Hypotheses

Hypothesis Effect of buyer market competition Effect of supplier market competition

Coercive power

H1a: Coercive power is positively related to the

allocation of physical resources

H1b: Coercive power is negatively related to the

allocation of innovation resources

H5a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high buyer market competition (compared to low buyer market competition)

H6a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition)

Reward power

H2a: Reward power is positively related to the

H2b: Reward power is positively related to the

H5b: The effect of reward power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition)

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Hypothesis Effect of buyer market competition Effect of supplier market competition

Goodwill trust

H3a: Goodwill trust is positively related to the

H3b: Goodwill trust is positively related to the

H5c: The effect of goodwill trust as a buyers’ strategy

to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition)

H6c: The effect of goodwill trust as a buyers’ strategy

to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition)

Compe-tence trust

H4a: Competence trust is positively related to the

H4b: Competence trust is positively related to the

H5d: The effect of competence trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition)

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4. METHODOLOGY

4.1 Research design

The present study focuses on supplier resource allocation from a buyer’s perspective. Partially (H1a-H4b), this study is a replication of Pulles et al. (2014)3_{and partially (H5a-H6d), new insights on}

competition in resource allocation will be analyzed. The main concepts of the study (i.e. supplier resource allocation, power and trust) have been researched previously. However, the hypotheses focusing on the impact of competition on the structural model are relatively new and have not been researched before in the context of supplier resource allocation. This paper therefore is a combination of conclusive and exploratory research (Malhotra 2009). Overall, it provides an answer to the research question: “To what extent does buyer and supplier market competition influence the effects of power and trust on a buyer firm’s relative resource position?”

Primary data is collected from buyers in a business-to-business setting via an online-questionnaire. This is a substantial methodological contribution to research on supplier resource allocation, which has previously been dominated by a supplier perspective. As primary data (in contradiction to secondary data) is collected, the main purpose of this data is to provide a (direct) answer to the research question (Malhotra 2009). A questionnaire has several other advantages. The perceived anonymity is relatively high as no social interaction is needed for the respondent to provide data. This decreases the social desirability of the answers provided in the questionnaire (Malhotra 2009). Moreover, a questionnaire allows for a relatively large sample in comparison to other research methods, like for instance interviews and observations (Yin 2003). Lastly, respondents are allowed to choose the occasion and timing that suits them best for providing data on the questionnaire. An online-questionnaire is therefore the most appropriate research design for this paper.

4.2 Sample and data collection

Data on the study is collected in cooperation with a large dairy company, operating internationally. The scope of data collection is limited to business-to-business buyer – supplier relationships as these are expected to be strongly related to competitive resource allocation (Baxter 2012; Capron and Chatain 2008). Additionally, as the dairy company has a diverse portfolio of buyers, the data is expected to be rich in terms of diverse buyer – supplier relationships (and related buyer strategies) and competitive situations. Managers of the dairy company stimulated its buyers to fill out the questionnaire, informing them that data could not be traced back to an individual level. In addition, employees of the dairy company working in a buyer occupation were requested to fill out the questionnaire. Lastly, data is collected by highlighting the research and requesting for cooperation on several (online) professional supply chain discussion groups.

3_{The methodological approach, however, substantially differs, as Pulles et al. (2014) focused on the perception of suppliers on}

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Before distributing the survey, a pretest has been performed by three young professionals in supply chain management and two PhD candidates. The first were asked to primarily focus on content-related issues and consistency. The latter focused on methodological issues. This resulted in an appropriate combination of evaluation elements which should be included in a pre-test (Malhotra 2009). The outcome of the pretest resulted in minor changes in the invitation and introduction of the survey.

As shown in table 2, a rather diverse group of respondents provided data on the questionnaire. 72% of the respondents considered a buyer – supplier relationship in the survey that had a duration (at this point) of maximum 10 years. Additionally, a relatively small percentage of the total volume is supplied by the focal supplier included in this questionnaire (in 65% of the cases this is lower than or equal to 5% of total volume). Although most of the respondents operate in the food/ dairy industry, overall, the industries are relatively equally distributed.

TABLE 2: Respondent characteristics

Respondent characteristics Frequency Respondent characteristics Frequency

Length of relationship with supplier (in years)

≤ 5 years > 5 years, ≤ 10 years > 10 years, ≤ 15 years > 15 years, ≤ 20 years > 20 years Anonymous

Percentage of suppliers volume compared to total volume (in percentage) ≤ 5 percent > 5 percent, ≤ 10 percent > 10 percent, ≤ 15 percent > 15 percent, ≤ 20 percent > 20 percent Anonymous 39% 33% 9% 6% 9% 3% 65% 9% 3% 11% 9% 3%

Industry of the buyer firm

Food/ dairy Medical Technical Chemical Agricultural

Fast moving consumer goods Energy Electronics Steel Anonymous 24% 18% 12% 12% 6% 6% 6% 6% 3% 6%

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respondents. Two weeks after the first invitation for filling out the questionnaire, a reminder was send to all respondents. As the data is anonymously, no information was available on which respondents already completed the survey. Therefore, the reminder included a kind reminder for the non-respondents and a word of thanks to those who had already provided data on the questionnaire. The final sample included 33 respondents. All of the respondents filled out the questionnaire given two randomly chosen buyer – supplier relationships which allows for analysis based on 66 cases. As the distribution of the survey mainly occurred via managers of the dairy company and (online) discussion groups, no information on the total potential sample size is available. As a result, no response rates could be calculated.

In order to make sure the respondents where knowledgeable about the two buyer-supplier relationships and their answers related to these, the survey included a question concerning their confidence level on a five-point Likert scale, namely: “I am convinced of (the correctness of) the answers I have provided in the questionnaire”. With a mean of 4.07 and standard deviation of 0.58 it can be concluded that the respondents are convinced of the answers they have provided in the questionnaire.

4.3 Measurement development

Several measurement items are used in order to provide data on the constructs of this study. All constructs are measured on a five-point Likert scale, ranging from “1= no, strongly disagree” to “5= yes, strongly agree”. Table 3 provides an overview of the used measurement items in explaining the constructs of this study. Measurement items on the power and trust dimensions as well as the items on suppliers allocation of physical and innovation resources are replicated from Pulles et al. (2014). However, they are adjusted from a supplier’s perspective towards a buyer’s perspective in order to appropriately measure suppliers’ resource allocation from a buyer’s perspective.

Pulles et al. (2014) used previous scales of Terpend & Ashenbaum (2012) and Maloni & Benton (2000) to design the items which measure coercive power and reward power. The items measure coercive power as ‘the extent to which the buyer forces the supplier into decisions and actions that it did not necessarily intended at first’ (Hinkin and Schriesheim 1989). Reward power is measured as ‘the extent to which the buyer rewards the supplier for decisions and actions undertaken in their buyer-supplier relationship’ (Hinkin and Schriesheim 1989).

The measures of goodwill trust and competence trust in Pulles et al. (2014) are constructed based on a combination of several (conceptual) papers (Ireland and Webb 2007; Miyamoto and Rexha 2004; Roy et al. 2004; Sako and Helper 1998). Goodwill trust measures ‘the degree to which the supplier trusts the buyer to be willing to act in ways that exceed the stipulated contractual agreements’ (Pulles et al. 2014). Competence trust is measures as ‘the extent to which the supplier believes the buyer has the managerial and technical capabilities to properly perform a given set of tasks’ (Ireland and Webb 2007).

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indicate their suppliers’ resource allocation position, relative to the suppliers’ resource allocation position of competitors. ‘This supplier grants to us better utilization of their production facilities, rather than to our competitor(s)’ is an example of an item measuring physical resource allocation. An example for innovation resource allocation is ‘This supplier shares its best ideas with us, rather than with our competitor(s)’.

Finally, measurement items have been designed for measuring buyer and supplier market competition. BMC is measured as ‘the extent to which buyers compete for the best resources in a supplier market’. SMC is ‘the extent to which suppliers compete for supplying their (total) capacity to one buyer or in one buyer market’. A combination of four measurement items is used to explain the competition construct. The first item is the extent to which competition in the buyer or supplier market is directly observable (Porter 1980; Chatain 2014). The second item is the extent to which alternatives/ substitutes are available in the market (Porter 1980). The second last item measures the extent to which buyer and supplier are dependent on each other (e.g. in terms of order volume) (Pulles et al. 2014). The fourth and last item which is included in measuring the construct of buyer and supplier market competition is the extent to which entry barriers exist for competitors (Porter 1980). Buyers are requested to answer whether these four items are relevant in their resource allocation (i.e. buyer) market. In addition, they are requested to answer these questions on their perception of competition in the supplier market. Table 3 displays the constructs and measurement items used in the questionnaire.

4.4 Data validity

Before assessing the validity of the data and data analysis, a check for missing data was performed. In 8 out of 66 cases, missing data occurred. Two respondents (hence, data for four buyer-supplier cases) failed to provide data on the questions related to (buyer and supplier market) competition. Two other respondents provided data on the competition constructs only partially. The latter however have provided data on the constructs necessary for the analysis of the structural model (H1a-H4b). The missing values were coded and case-wise excluded in order to allow for analyses based on unique cases and to not cause any error in the results (Malhotra 2009).

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TABLE 3: Constructs and measurement items

Constructs Measurement Items Factor

loadings Coercive power (CP) Reward power (RP) Goodwill trust (GT) Competence trust (CT) Allocation of physical resources (PR) Allocation of innovation resources (IR)

CP1: If this supplier fails to comply with our supply requirements, we will use penalties against them.

CP2: If this supplier does not agree with our suggestions, we could make things difficult for them.

CP3: If this supplier does not do as asked, we will not provide them with very good treatment.

CP4: If this supplier does not go along with us, we might withdraw certain services they need.

RP1: We offer rewards, if this supplier agrees with our requests. RP2: We offer rewards, in order for this supplier to go along with our wishes.

RP3: We will favour this supplier in other occasions, if they go along with us.

RP4: We will not offer rewards, if this supplier does not do as asked.

GT1: This supplier can rely on us, in ways not required by our (contractual) agreement with them.

GT2: This supplier can rely on us, as we always treat them fairly. GT3: We make sacrifices to support this supplier firm.

GT4: We take initiatives for mutual benefits that exceed the contractual agreements with this supplier.

CT1: We are a highly capable partner for this supplier.

CT2: We are very capable of providing value to this supplier firm. CT3: We have the managerial and technical capabilities to do what we say we do (for/ to this supplier).

CT4: We always provide this supplier with helpful advice.

PR1: This supplier grants to us better utilization of their production facilities, rather than to our competitor(s).

PR2: This supplier prioritizes us in our demand of products in case of extreme events (e.g. natural disasters), rather than prioritizing our competitor(s).

PR3: This supplier allocates to us more scarce material in case of material scarcity, rather than to our competitor(s).

PR4: This supplier dedicates more of its specialized equipment to us, rather than to our competitor(s).

IR1: This supplier is more willing to share key information with us rather than with our competitor(s).

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Buyer market competition (BMC)

Supplier market competition (SMC)

IR2: This supplier shares its best ideas with us, rather than with our competitor(s).

IR3: This supplier dedicates more innovation resources to us, rather than to our competitor(s).

IR4: This supplier spends more of their product development time with us, rather than with our competitor(s).

BMC1: We face a high level of competition from competitive buyers (willing to be supplied by this particular supplier) in attaining…

Physical resources Innovation resources

BMC2: We have a limited amount of alternative suppliers (in comparison to this particular supplier) when attaining...

BMC3: We are dependent (e.g. in terms of volume) on this particular supplier in attaining...

BMC4: Other buyer firms could easily enter the market where we attain the following resource from this particular supplier...

SMC1: We feel this supplier faces a high level of competition from competitive suppliers (willing to supply in this buyer market) in supplying...

SMC2: We feel this supplier has a limited amount of alternative buyers when supplying...

SMC3: We feel this supplier to be dependent (e.g. in terms of volume) on us in supplying...

SMC4: We feel other supplying firms could easily enter the market where we attain the following resource from this particular supplier...

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Hereafter, Cronbach’s alpha (α) is used to test for the internal consistency of all items used in the structural model (testing H1a-H4b) and the multi-group model (testing H5a-H6d) respectively. Both models showed satisfactory results on the F-test. Additionally, the results of Cronbach’s alpha (α) are 0.845 and 0.780 respectively. Which both exceed the threshold of 0.7 (Malhotra 2009). It can therefore be concluded that both models (including all measurement items) are internally consistent and the created constructs for both the structural and the multi-group model are valid.

Lastly, the adjusted Rsquare shows the variance in the dependent variable which is explained by its explanatory (independent) variables. In other words, it test whether differences (i.e. variance) in the power and trust dimensions are appropriate for explaining the difference (i.e. variance) in the supplier’s resource allocation of physical and innovation resources. If the threshold of 0.6 (Malhotra 2009) is exceeded, the model has a high explanatory power. However, this paper includes a relatively small sample (n=66) in comparison to a fair amount of exploratory variables. Therefore, the adjusted Rsquare values are exceptionally high (i.e. especially in the multi-group analysis) and do not result in reliable values to conclude the explanatory power of the multi-group model on.

4.5 Data analysis

The data analyses will be performed in two steps. The first step is performed in order to provide results on H1a-H4b, the structural model. The second step includes a multi-group analysis in order to conclude on the extent to which buyer and supplier market competition influence the effects of power and trust on a supplier’s resource allocation. The analyses have been done by using SmartPLS version 3.0. 4.5.1 Step 1: Analysing the structural model

Partial least-squares (PLS) structural equation modelling (SEM) is a regression-based approach which tries to maximize the linear relationship between the independent and the dependent variable(s). It allows for robust analysis based on relative small sample sizes. The minimum sample size needed in PLS modelling has not been confirmed by academic literature yet. However, a rule of thumb is that it should be ten times the number of maximum arrowheads pointing on a latent variable. In addition, PLS modelling allows multivariate variables to be tested and is appropriate to use in case of many explanatory variables. Additionally, PLS modelling could be used in case of (multi)collinearity and it does not require variables to be normally distributed (Reinartz, Haenlein, and Henseler 2009). In this study, the full sample (which will be used in step 1) includes multivariate items and correlations are significant. Furthermore, the sample size is relatively small and collinearity between items could occur. Therefore, PLS modelling, over other methods such as for instance multiple linear regression (Malhotra 2009), is the appropriate method to analyse the structural model (H1a-H4b).

4.5.2 Step 2: Analysing the multi-group models

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in the ‘high’ BMC and SMC groups. This resulted in two models (see table 4), one representing high and low BMC, one representing high and low SMC. The PLS regression of the structural model is repeated for these groups, providing insight in the effect of buyer and supplier market competition on the buyer’s use of power and trust in supplier resource allocation.

TABLE 4: Multi-groups based on median split

Buyer market competition (n= 58) Supplier market competition (n=58)

Low < 3.25 N = 24 cases High ≥ 3.25 N = 34 cases Low < 2.75 N = 26 cases High ≥ 2.75 N = 32 cases

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5. RESULTS

This section includes the results of the analyses. Firstly, the effects of coercive power, reward power, goodwill trust and competence trust on the supplier’s allocation of physical and innovation resources using the full sample are analysed, resulting in the outcomes for hypotheses H1a-H4b. These are then compared to the results of Pulles et al. (2014), providing results for the replication of their paper. Hereafter, the multi-group analyses will follow. These results provide a first exploratory result on the impact of buyer and supplier market competition on the structural model (i.e. H5a-H6b).

5.1 Results of the structural model (H1a-H4b)

Figure 1 shows the outcome of the analyses on hypotheses H1a-H4b, the structural model of this paper. Adjusted Rsquares (R²) and collinearity analysis (VIF) showed satisfactory results (physical resources: 0.620, innovation resources: 0.780 and VIF statistics all < 5) supporting the explanatory power of the model.

FIGURE 1: Structural model (H1a-H4b)

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The analysis on goodwill trust provides significant results for the positive effect on the allocation of physical (β = 0.605) as well as innovation (β = 0.643) resources. These effects are as expected and H3a and H3b are thereby supported. The hypotheses on competence trust expected a positive effect on both physical and innovation resource allocation. Although the path coefficients are positive (physical resources: β = 0.142 and innovation resources: β = 0.140), no significant support is found for H4a and H4b.

In this sample, it can be cautiously concluded that the effects of trust strategies are stronger (on both the suppliers’ allocation of physical as well as innovation resources) than the effects of power strategies. In addition, the effects of the trust strategies appear rather consistent between both types of supplier resources. It is therefore not expected that a buyer would use different trust strategies based on the type of resource. The effects of the power strategies however, do differ based on the type of resource. Therefore, a buyer should take into account the type of resource when deciding on using either coercive power or reward power. The findings in this paragraph should be cautiously considered because most of the effects are not supported by significant statistics.

5.2 Results of the replication study

The following section includes a comparison of the results of this paper to the results of Pulles et al. (2014) and thereby the results of the replicating and extension of their study (Kaynak and Hartley 2008). Table 5 presents the results of both studies.

TABLE 5: Replication outcomes

Outcomes of Pulles et al. (2014)

Outcomes of current research

H1a: Coercive power is positively related to the

β = 0.17 nonsignificant β = 0.178 nonsignificant

H1b: Coercive power is negatively related to the

β = - 0.18 nonsignificant β = - 0.258 significant

H2a: Reward power is positively related to the

β = 0.34 significant β = 0.271 nonsignificant

H2b: Reward power is positively related to the

H3a: Goodwill trust is positively related to the

β = 0.18 nonsignificant β = 0.605 significant

H3b: Goodwill trust is positively related to the

β = - 0.02 nonsignificant β = 0.643 significant

H4a: Competence trust is positively related to the

H4b: Competence trust is positively related to the

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studies, it can be concluded that the replication results provide satisfactory support for all except two hypotheses (H2b and H3b).

The analysis of the structural model in this paper resulted in a Beta value of 0.000 for the effect of reward power on the allocation of innovation resources. In the multi-group analysis (which is described in the following section), the effect of reward power on innovation resource allocation is positive, independent of the type and level of competition. Comparing the positive effect of hypothesis H2b in the study of Pulles et al. (2014) (β = 0.64) to the multi-group analysis of this paper, despite relationships that are not significant, the Beta values provide support for the replication of this hypothesis. Considering hypothesis H3b, the current study found a positive effect of goodwill trust on the allocation of innovation resources. In the study of Pulles et al. (2014), only the multi-group representing buyers that account for a ‘large’ share in the supplier’s turnover showed in a positive effect of goodwill trust on the allocation of innovation resources. The structural model and the buyers that accounted for a ‘small’ share in the supplier’s turnover resulted in a negative effect. Therefore, no satisfactory support is found for the replication of hypothesis H3b.

5.3 Results of the multi-group models (H5a-H6d)

The median split of the full sample resulted in multi-group samples as displayed in table 4 in the methodology section. Although these samples are relatively small and significant effects are most likely not able to be found (Irwin and McClelland 2003; Pulles et al. 2014; Qureshi and Compeau 2009), between-group differences can be identified based on the effects (β) which are represented in the following section. The hypotheses on the effects of buyer and supplier market competition are exploratory (Malhotra 2009) in nature. A preliminary identification of between-group differences could be used as a starting point for future research on this topic, and is therefore sufficient in this type of research. The results of the analyses on buyer and supplier market competition will be described separately in the following paragraphs.

5.3.1 Buyer market competition

As shown in figure 2, the overall models testing for respectively low and high BMC are appropriate with adequate results on the collinearity statistics (VIF). As explained in the methodology section, the adjusted Rsquare values are exceptionally high and not reliable in their explanation of the explanatory power of the multi-group analyses. Results for these analyses are represented in figure 2 and table 6.

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decreasing extent. High BMC increases the effect of reward power on physical resource allocation and decreases the effect on innovation resource allocation. Hypothesis H5b is therefore only partially supported.

For physical resources, goodwill trust has a strong positive (β = 1.082) effect in case of low BMC. This effect remains positive (β = 0.271) for high BMC, however decreases in comparison to low BMC. The opposite effect occurs for the allocation of innovation resources. In case of low BMC, this effect is positive (β = 0.470), however this positive effect increases in case BMC is high (β = 0.859). Hypothesis H5c is only partially supported, as the effect of goodwill trust in the allocation of physical resources decreases when BMC is high. In case of high BMC, the effect of competence trust on the allocation of physical resources indeed increases (β = 0.555) in comparison to low BMC (β = -0.342). In

the allocation of innovation resources, this effect decreases, as the effect for low BMC is positive (β = 0.553) and the effect in case of high BMC is still positive (β = 0.064), however decreases in

comparison to low BMC. High BMC increases the effect of competence trust on the allocation of physical resources, however, decreases the effect on the allocation of innovation resources. Therefore, hypothesis H5d is only partially supported.

5.3.2 Supplier market competition

The values for testing the overall appropriateness of the models analysing the effect of SMC are satisfactory, as shown in figure 3. Results from the analysis on SMC are represented in figure 3 and table 7.

When SMC is high, the effect of coercive power on the allocation of physical resource allocation increases from β = -0.366 (low SMC) to β = 0.253. For the allocation of innovation resources, the effect of coercive power decreases from β = 0.051 (low SMC) to β = -0.352 (high SMC). As an increase in the effect of coercive power in case of high SMC is only partially found, hypothesis H6a is partially supported. The effect of reward power on the allocation of both physical and innovation resources increase in case of high SMC compared to low SMC. The increase in case of physical resources is larger (difference in β = 0.21) than the increase for innovation resources (difference in β = 0.02). As the hypothesis expected no increase in the effect of reward power in case of high SMC, hypothesis H6b is rejected.

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TABLE 6: Effect of buyer market competition (BMC) (H5a-H5d)

Results on low buyer market competition (BMC)

Results on high buyer market competition (BMC)

H5a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high buyer market competition (compared to low buyer market competition) Coercive power Physical resources Innovation resources β = - 0.217 nonsignificant β = 0.231 nonsignificant β = 0.284 nonsignificant β = 0.009 nonsignificant

H5b: The effect of reward power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition) Reward power Physical resources Innovation resources β = - 0.100 nonsignificant β = 0.313 nonsignificant β = 0.286 nonsignificant β = 0.083 nonsignificant

H5c: The effect of goodwill trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high buyer market competition (compared to low buyer market competition) Goodwill trust Physical resources Innovation resources β = 1.082 significant β = 0.470 nonsignificant β = 0.271 nonsignificant β = 0.859 significant

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TABLE 7: Effect of supplier market competition (SMC) (H6a-H6d)

Results on low supplier market competition (SMC)

Results on high supplier market competition (SMC)

H6a: The effect of coercive power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition)

Coercive power Physical resources Innovation resources β = - 0.366 significant β = 0.051 nonsignificant β = 0.253 nonsignificant β = - 0.0352 nonsignificant

H6b: The effect of reward power as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high supplier market competition (compared to low supplier market competition)

Reward power Physical resources Innovation resources β = 0.141 nonsignificant β = 0.139 nonsignificant β = 0.351 nonsignificant β = 0.159 nonsignificant

H6c: The effect of goodwill trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will increase in case of high supplier market competition (compared to low supplier market competition) Goodwill trust Physical resources Innovation resources β = 0.740 nonsignificant β = 0.472 nonsignificant β = 0.835 significant β = 0.585 significant

H6d: The effect of competence trust as a buyers’ strategy to influence the suppliers’ allocation of physical and innovation resources will not increase in case of high supplier market competition (compared to low supplier market competition)

Competitive supplier resource allocation -

Competitive supplier resource allocation -

the use of power and trust by buyers

MARISKA KAPELLE

Supervisors

Dr. J. Veldman & MSc N. Ziengs

Competitive supplier resource allocation -

the use of power and trust by buyers

-

MARISKA KAPELLE

Abstract.

Keywords.

TABLE OF CONTENTS

1. INTRODUCTION

2. THEORETICAL BACKGROUND

2.1 Introduction in supplier resource allocation

2.2 Strategies to influence supplier resource allocation: power and trust

2.3 Competitive resource allocation

3. HYPOTHESES

3.1 Coercive power

3.2 Reward power

3.3 Goodwill trust

3.4 Competence trust

3.5 Buyer market competition

3.6 Supplier market competition

TABLE 1: Hypotheses

4. METHODOLOGY

4.1 Research design

4.2 Sample and data collection

TABLE 2: Respondent characteristics

4.3 Measurement development

4.4 Data validity

TABLE 3: Constructs and measurement items

4.5 Data analysis

TABLE 4: Multi-groups based on median split

5. RESULTS

5.1 Results of the structural model (H1a-H4b)

FIGURE 1: Structural model (H1a-H4b)

5.2 Results of the replication study

TABLE 5: Replication outcomes

5.3 Results of the multi-group models (H5a-H6d)

TABLE 6: Effect of buyer market competition (BMC) (H5a-H5d)

TABLE 7: Effect of supplier market competition (SMC) (H6a-H6d)