Using in
fluence strategies to advance supplier delivery flexibility: The moderating
roles of trust and shared vision
Kuo-Hsiung Chang
a,1, Hsu-Feng Huang
b,⁎
a
Department of International Business, Tunghai University, NO. 181, Section 3, Taichung Port Road, Taichung City, Taiwan
bDepartment of Management Science, National Chiao Tung University, NO. 1001 University Road, Hsinchu, Taiwan
a b s t r a c t
a r t i c l e i n f o
Article history: Received 26 August 2010
Received in revised form 30 August 2011 Accepted 10 September 2011
Available online 18 October 2011 Keywords:
Deliveryflexibility Influence strategies Trust
Shared vision
This study explores trust and shared vision moderate the relationship between the manufacturer's influence strategies and supplier deliveryflexibility. The major components of this study are based on reviews of mar-keting research that focus on influence strategies and literature regarding supply chain flexibility. The results show that the request strategy has a negative effect on supplier deliveryflexibility. The model predicts that trust and shared vision have an asymmetrical effect across recommendations, information exchange, and promises influence strategies. When the relationship contains a highly shared vision, a manufacturer's use of the recommendation influence strongly promotes supplier delivery flexibility, whereas the use of a prom-ise strategy depresses supplier deliveryflexibility. In contrast, an information exchange strategy will have a negative effect, but the promise strategy will have a positive effect on supplier deliveryflexibility when trust is high. This paper contributes to guidelines for management on how to align their suppliers for delivery flexibility to respond quickly to customer demands.
© 2011 Elsevier Inc. All rights reserved.
1. Introduction
Deliveryflexibility is the ability to adjust delivery dates and to ac-commodate rush and special orders (Beamon, 1999; Slack, 2005). Ketokivi (2006)indicated that deliveryflexibility is the order winner over price. Mass customization needs supply chains to meet individ-ual customer demands (Gong, 2008) and outsourcing trends motivate firms to explore supply management as a means to develop greater synergy (Liao, Hong, & Rao, 2010). The essential purpose for buyer and supplier relationship is to create value for customers (Anderson, 1995; Walter, Ritter, & Gemunden, 2001). In business marketing liter-ature, delivery is regarded as a major criterion in supplier evaluation (Hutt & Speh, 2001) and relationship value (Ulaga, 2001). Slack (2005) argued that delivery flexibility becomes more important under increased competition in low volume and high variety custom-er demands. As supply chain management practices exceed the boundaries of a singlefirm, supplier delivery flexibility enhances the capabilities of the manufacturer to improve competitive advantage.
Influence strategies are ‘compliance-gaining tactics’ used to make tar-gets achieve their desired actions (Frazier & Summers, 1984; Payan & McFarland, 2005). Most previous studies have focused on the relation-ships between influence strategies and power (Gelderman, Semeijn, & De Zoete, 2008; Hu & Sheu, 2005; Kale, 1986), satisfaction (Lai, 2007;
Sanzo et al., 2003), relationalism (Boyle, Robicheaux, & Simpson, 1992) and solidarity (Kim, 2000). Researchers have found that the choice of in-fluence strategies has a significant effect on the trading relationship (Boyle & Dwyer, 1995; Frazier & Rody, 1991; Geldermanet al., 2008; Kumar, 2005).However, there are few influence strategies studies in sup-ply chain management (e.g.,Gelderman et al., 2008) and little is known about the effectiveness of influence strategies for promoting supplier de-liveryflexibility. Furthermore, the conditions where the link between in-fluence strategies and supplier delivery flexibility is strengthened (or reduced) have not yet been explored clearly. Although manufacturing firms may use similar influence strategies, not all suppliers will react equivalently in the same position because of different social capital char-acteristics in interorganizational relationships. Social capital is a set of re-lational resources embedded in relationships.Nooteboom, Berger, and Noorderhaven (1997)suggested that trust is a belief that another party will cooperate without any coercion. Since many regard trust as a gover-nance structure (Nooteboom et al., 1997; Zaheer, McEvily, & Perrone, 1998), this enhances the effectiveness of a transaction. Social capital the-ory argues that trust favors greater benefits of knowledge transfer and sharing of risks (Ireland & Webb, 2007) and trust enables the develop-ment of self-enforcing or implicit contracts (Dyer & Singh, 1998; Telser, 1980). In addition, shared vision, as a bounding mechanism for organiza-tional resource exchange and integration (Tsai & Ghoshal, 1998), provides organizational members a sense of purpose and direction, embodies the common value of dyadic relationships, and helps to hold together a loose-ly-coupled system (Wang & Rafiq, 2009). Partners with shared vision can facilitate the pursuit of collective goals. Trust and shared vision may be critical factors as they form a situation conducive to supplierflexibility.
⁎ Corresponding author. Tel.: 886 3 5712121x57103; fax: 886 3 5713796. E-mail addresses:kuo1@thu.edu.tw(K.-H. Chang),hsufeng.huang@gmail.com
(H.-F. Huang).
1
Tel.: + 886 4 23590121x35309.
0019-8501/$– see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2011.09.020
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Few studies on influence strategies have discussed the moderating role in the relationship between influence strategies and supplier delivery flexi-bility. Therefore, the central research question focuses on how trust and shared vision moderate the relationship between the manufacturer's in-fluence strategies and supplier delivery flexibility.
This study makes several contributions. First, this study divides in flu-ence strategies into three categories: hard coercive influence strategies (i.e. threats, legalistic pleas, and promises), request strategy, and noncoer-cive strategies (i.e. recommendations and information exchange). We separate the request strategy from noncoercive influence strategies. This is because that request strategy is not merely a suggestion and is based on an inferred argument rather than inferred sanctions (Frazier & Summers, 1986; Payan & McFarland, 2005). Second, this study is to pro-vide new perspectives in examining under what conditions influence strategies can promote supplier deliveryflexibility. For a decision to be ef-fective, an organization must match its structure to its contingent factors and thus, to its environment (Galbraith, 1973). Based on contingency the-ory, we argue that the effectiveness of afirm's influence strategies on sup-plierflexibility depends on the firm's ability to match appropriate trust and shared vision with influence strategies, because suppliers respond differently to various social exchange systems. This study extends the framework ofPayan and McFarland (2005)to explore how coercive, re-quests, and noncoercive strategies effect on supplier deliveryflexibility and how trust and shared vision exert distinct effect associated with in flu-ence strategies and affect supplier deliveryflexibility. On the other hand, although influence strategies and social mechanisms (i.e. trust and shared vision) are tools with which manufacturers can influence the behavior of the supplier in buyer–supplier relationships, little is known on the inter-action effects of influence-control mechanism—complementarities or substitution effects—between influence strategies and social mechanisms used to advance supplier deliveryflexibility.Payan and McFarland (2005) argued that the use of coercive influence strategies is ineffective at lower levels of dependence. We suggest that when parties in exchange relation-ships have trust and shared vision, the influence strategy a manufacturer chooses might result in an effective approach to supplier delivery flexibil-ity. Finally, an examination of influence strategies in Chinese societies, whose values differ from those typically found in a Western context, should enhance our understanding of influence strategy effectiveness. Much of research on influence strategies has been conducted in Western context. However, it is unclear whether results from prior studies con-ducted in the West would hold in the Eastern context. We conduct the study in Taiwan, a society with relatively high-context. High-context cul-tures such Taiwan, China, and Japan emphasize a mutual sense of care and view communication as a means to promote smooth, harmonious rela-tionships (Hall & Hall, 1990; Kim, Pan, & Park, 1998). In such a cultural context, information exchange and recommendations are likely to be par-ticularly important. Therefore, we provide an ideal setting in which to test the effects of influence strategies and the moderating role of social mech-anisms on supplier deliveryflexibility. This research not only contributes tofilling the existing theoretical gap but also provides insights into the more effective management of buyer–supplier relationships.
We organize this paper as follows:first, this study reviews the lit-erature on influence strategies, delivery flexibility, and social mecha-nisms, as the basis for presenting the conceptual framework. The next section contains the development of specific hypotheses. The last sec-tion reveals the researchfindings, states the research conclusion, and addresses the theoretical and managerial implications of this study. It includes discussions regarding the study's limitations and suggestions for future research.
2. Theoretical foundation 2.1. Deliveryflexibility
Deliveryflexibility is the ability to change the product mix and to reallocate its capacity to accommodate customer rush or special
orders (Cheng, Simmons, & Ritchie, 1997).Ketokivi (2006, p. 220) de-fined delivery flexibility, “as the ability to accommodate last-minute changes to order quantities, small-batch deliveries, fast deliveries, and higher on-time delivery rates.” As the elements of delivery flexi-bility, reliability and dependability represent key components of agil-ity performance offirms (Paulraj & Chen, 2007). Delivery reliability refers to the ability to deliver on or before the promised scheduled due date (Handfield & Pannesi, 1992). Delivery dependability refers to the ability to deliver on time with accurate quantities and the prod-ucts needed (White, 1996). In addition,Slack (2005, p. 1193)claimed, “Volume and delivery flexibility seemed to be interchangeable to some extent”.Oke (2005)indicated that deliveryflexibility is the con-sequence of volume and mixflexibility. Volume flexibility is the abil-ity to adjust aggregate production in response to customer demands effectively (Hayes & Wheelwright, 1984). Mix flexibility refers to the ability to change various products produced within a given peri-od, economically and effectively without incurring major set-up costs (Das, 2001; Slack, 2005). Therefore, a manufacturer with the ability to operate at different output levels, quickly and easily changes the quantities for production, quickly adapts to a different product mix, or produces various products without a major changeover can be more responsive to customer demands and deliver on the prom-ised due date.
In the past, the market demand was more stable and the product life cycle was longer. Today, the market demand and customer prefer-ences are not as easy to predict. Manufacturers should have the ability to change planned delivery dates for meeting customer requirements. Due to the trend of supplier consolidation in supply chain manage-ment, this has become increasingly important for manufacturers to uphold supplier deliveryflexibility to maintain competitive advan-tage. A customer-oriented manufacturer should have the ability to adjust supply to match customer demands and to offer a large variety of products simultaneously. For manufacturers to stay profitable, it is necessary to meet customer demands without adding significant costs (Gilmore & Pine, 1997).Ndubisi et al. (2005)showed that the supplier management strategies adopted by the manufacturer would help the manufacturer'sflexibility to meet customer needs. Therefore, suppliers with the ability to deliver on the promised due date and adjust their capacity in response to the changes in demand are crucial for manufacturers. From the perspective of manufacturers, the supplier deliveryflexibility significantly relates to the response to environmental uncertainty. In the literature, deliveryflexibility not only encompasses delivery reliability and delivery dependability, but the ability to cater to changing orders quickly (Sawhney, 2006). If the supplier lacks the ability to accommodate rush orders and deliv-ery on the promised due dates (Chan, 2003), the result is generally an increase in the manufacturer's additional cost (e.g. line down cost), and a negative customer value.
2.2. Influence strategies
As commonly defined by the literature on marketing, influence strategies are means of communication in that a sourcefirm attempts to change or modify a channel partner's behavior.Frazier and Summers (1984, 1986)dichotomized influence strategies as either coercive (in-cluding legalistic pleas, threats, and promises), or noncoercive (includ-ing information exchange, recommendations, and requests). In coercive influence strategies, a source firm puts direct pressure on a target firm to perform a specific behavior by stressing noncompliance (Frazier & Rody, 1991). Past studies have empirically illustrated that coercive in-fluence strategies have negative effects on dyadic relationships (Kim, 2000; Kumar, 2005; Sanzo et al., 2003). In addition, Brown, Grzeskowiak, and Dev (2009)found that the use of coercive influence strategies exacerbates opportunism. Conversely, noncoercive strategies primarily center on the beliefs and attitudes of the targetfirm and in-volve little direct pressure from the sourcefirm. Frazier and Rody
(1991, p. 63)found that“suppliers with high power are likely being meaningful enough to use noncoercive strategies effectively.”Molla and Sanchez (1997)pointed out that noncoercive strategies (i.e. recom-mendations and information exchange) are the most prevalent and promises and requests are less preferable according to a survey of com-puter manufacturers in Spain.Venkatesh, Kohli, and Zaltman (1995) di-vided influence strategies into three categories from the coercive intensity perspective. These categories include hard coercive influence strategies (i.e. threats and legalistic pleas), soft coercive strategies (i.e., recommendations and promises), and noncoercive strategies (i.e., re-quests and information exchange).Lai (2009)divided influence strate-gies into three categories: (1) hard coercive stratestrate-gies (including legalistic pleas and threats), (2) promise strategies, and (3) noncoercive strategies (including information exchange, recommendations, and requests).
2.2.1. Coercive influence strategies
1. Threats: the source threatens the target with future negative sanc-tions or punishments if the target does not comply with desired performance or behavior.Payan and McFarland (2005)measured the threats including, (1) penalties, (2) discontinuation of specific benefits, and (3) the loss of preferential status for noncompliance. For example, the buyer informs the supplier that if the supplier does not deliver on time, there will be negative consequences, such as no more future orders or a penalty for downtime. Re-searches show that threat strategy results in fewer positive out-comes (Falbe & Yukl, 1992; Scheer & Stern, 1992) and has less of an effect (Payan & Nevin, 2006) than other strategies.
2. Legalistic pleas: the source contends that a legal contract or agree-ment requires the compliance of the target. Gelderman et al. (2008)described that legalistic pleas strategy is a unique case of the threat strategy. The source cites the contract or agreement as the tool to require the target to perform a certain action. For in-stance, a manufacturer may remind suppliers that product delivery within the promised due date is in the contract.
3. Promises: the source offers specific rewards or incentives if the tar-get conforms to the sources stated desires. However, if the tartar-get cannot comply with the source's demands, they will lose the re-wards promised by the source (Venkatesh et al., 1995). Alterna-tively, not to categorize promise strategy as a coercive strategy remains a controversial topic in literature (c.f.Gundlach & Cadotte, 1994; Frazier & Rody, 1991). Ghijsen, Semeijn, and Ernstson (2010) claimed ‘direct strategy’ that includes threats, legalistic pleas, and promises that focus on directly changing the targets specific behavior by employing explicit or implicit rewards or pun-ishments.Frazier and Rody (1991)argued that (1) promise strate-gy is a pressure applied on the target to perform a specific behavior, and (2) there are adverse consequences if they are non-compliant. In addition, noncompliance with the source's desired action considers the depreciated reward an equivalent to the im-position of the sanctions (Gelderman et al., 2008). Therefore, this study adopts the promise strategy as a coercive strategy. 2.2.2. Requests
The source simply informs the target to act without explanation (Gelderman et al., 2008) and directly implying the subsequent sanc-tions or rewards. Past studies often classified requests as a noncoercive strategy based on an inferred argument rather than inferred sanctions (Frazier & Summers, 1986; Payan & McFarland, 2005). In addition, re-quest strategy is a form of communication for stating the desired action for the target to take, without specifically stating the consequences of the target's compliance or noncompliance. In contrast, Lai (2007) claimed that the request strategy explicitly states the desired actions and directly changes the target behavior. He indicated that the request strategy is more coercive than information exchange and recommenda-tions because the targets (e.g. Taiwan car dealers) consider the request
strategy a command, and not merely a suggestion. Therefore, this study separates request strategy from noncoercive strategies.
2.2.3. Noncoercive influence strategies
1. Information exchange: the source supplies general business issues to alter the target's perspectives without stating a request or spe-cific actions with the intent of motivating compliance.Payan and McFarland (2005)claimed that the information exchange strategy tries to alter the target's general perceptions and that the specific desired action remains vague.Boyle et al. (1992)suggested that the information exchange is essential to coordination. An example of an information exchange strategy, if the source says,“Many of suppliers have had great success with Just-in-Time (JIT) delivery.” The intention of the information exchange strategy is to convince the supplier to implement JIT delivery system; however this is not explicitly mentioned.
2. Recommendations: the source stresses that the target will be more profitable if the target achieves specific desired outcomes.Frazier and Summers (1984)indicated that recommendations strategy ex-plicitly states the behavior to be performed by target. Recommenda-tions strategy without offering specific explanations (Gelderman et al., 2008) focuses on the belief and attitudes of the target (Frazier & Rody, 1991). For example, the buyer may highlight that“deliver on time is beneficial to your operation.” Therefore, the recommendation strategy among noncoercive strategies is more focused or directive. If the source applies the recommendation strategy, it will help to nur-ture healthy relationships and increase economic and social satisfac-tion (Lai, 2007).
2.3. Social mechanisms
According toNahapiet and Ghoshal (1998), social capital includes a structural (represented by network position), a relational (repre-sented by trust), and a cognitive dimension (repre(repre-sented by shared vision between units).Inkpen and Tsang (2005)showed that there is substantial variance of knowledge transfer among three network types (i.e. intracorporate network, strategic alliance and industrial district). This study focuses on trust and shared vision because trust and shared vision are interrelated yet different aspects of relational resources, and are two psychological bonding mechanisms in fluenc-ing the level of information sharfluenc-ing (Li, 2005).
2.3.1. Trust
Whitener, Brodt, Korsgaad, and Werner (1998)defined trust as the expectation or belief that the other party will act benevolently. In the literature of interorganizational relationships, trust exists when a party has confidence in the exchange partner's reliability and integrity (Gulati, Nohria, & Zaheer, 2000; Morgan & Hunt, 1994; Ring & Van de Ven, 1992).Zaheer et al. (1998)further defined trust as the expectation that the actor: (1) will be reliable to fulfill obliga-tions; (2) will act and negotiate fairly; and (3) will behave in a pre-dictable manner. A lack of trust will result in higher transaction costs (Beccerra & Gupta, 1999) such as scrutinizing and verifying the exchange behavior. The higher transaction costs will impede ef fi-cient and effective performance and further paralyze responsiveness (i.e., the ability to react to customer dynamic demand and deliver quickly). The issue of trust in manufacturer–supplier relationships is significantly important because the dyadic relationships often involve a high degree of interdependence.Anderson and Narus (1990, p. 45) defined trust, as “the belief that another company will perform ac-tions that will result in positive outcomes for thefirm, as well as not take actions that would result in negative outcomes for thefirm”. 2.3.2. Shared vision
When the exchange parties have a shared vision, they have the same perception about how to integrate strategic resources and
how to interact with each other. Shared vision can reinforce the man-ufacturer–supplier relationships, and a manufacturer can utilize it as the supplier selection criterion (Kamann & Bakker, 2004).Tsai and Ghoshal (1998)declared that a shared vision embodies the collective goals and aspirations of the members of an organization. Empirical studies show that parties in the supply chain with shared vision have better performance (e.g.,Spekman, Kamauff, & Spear, 1999). Boddy, Macbeth, and Wagner (2000)found that a lack of shared vi-sion between suppliers and customers causes difficulty in coopera-tion.Li (2005) claimed that shared vision as a social mechanism facilitates cooperative actions.
3. Hypotheses development
Fig. 1provides a pictorial representation of the hypotheses. In this study, we extend the framework ofPayan and McFarland (2005)to develop hypotheses. Based on a theoretical framework of influence strategies effectiveness, we suggest that promises result in supplier deliveryflexibility only when the source highly trusts in the target, that request strategy is more or less effective only when target has high level of correct inferences, and that noncoercive influence strat-egies (recommendations and information exchange) are more or less effective only when source and target have high level of shared vi-sion. We depict this theoretical framework inFig. 2.
3.1. Coercive influence strategies and delivery flexibility
In regards to coercive influence strategies, a manufacturer may threaten its suppliers that nonconformity to adjusted delivery dates will cause the suppliers to lose future business opportunities. A man-ufacturer may use obligations within the purchasing agreement to claim suppliers to comply with adjusted orders. Threats and legalistic pleas might result in negative consequences if targets fail to comply with the source (Venkatesh et al., 1995). According to Payan and McFarland's (2005)argument, coercive influence strategies are effec-tive only if the target perceives itself to be highly dependent on a sourcefirm. Their findings also suggested that threats are tenable when the target of influence is highly dependent on the source and that promises are totally ineffective.
In the supply chain, manufacturers not only need their suppliers products, but for them to respond quickly. When unforeseen circum-stances occur, manufacturers and suppliers need to apply high levels of cooperation and joint planning to achieve the deliveryflexibility required in the supply chain. If a manufacturer believes that its sup-plier is trustworthy in dealing with their transaction, the manufactur-er may not resort to threats and legalistic pleas to enforce compliance. In contrast, promise strategy refers to a target's perception of a posi-tive sanction (Payan & Nevin, 2006), such as rewards if the target complies with the source (Venkatesh et al., 1995). Under trusting cir-cumstances, a manufacturer believes that suppliers will tend to keep their promises and satisfy their needs. We suggest that trust increases the buyer's willingness to take additional risks and provide more re-ward to motivate supplier delivery flexibility. If a manufacturer adopts the promise strategy, the inspiration of reward will enhance the willingness of suppliers to comply with deliveryflexibility. There-fore, the effectiveness of the promise strategy in advancing supplier delivery flexibility depends on the level of trust in suppliers. The above argument leads to:
H1(a). Promises strategy will have a strong positive relationship with supplier deliveryflexibility when trust is high than when trust is low.
In regards to promises strategy,Venkatesh et al. (1995)claimed that promises strategy is less coercive than threats and legalistic pleas. Frazier and Summers (1984)argued that promises strategy increases the changing base of power from reward to referent. Manufacturers and suppliers with shared vision will gain a wider perspective of the long-term orientation (Ganesan, 1994; Lusch & Brown, 1996). Under a high-level shared vision, parties will expect to maintain long-term coop-erative relationships. Using the promise strategy makes suppliers feel strained with inference to the manufacture's increasing power and fur-ther coming sanction or punishment. Coordination with the manufactur-er facilitates supplimanufactur-er delivmanufactur-ery flexibility that involves suppliers' operation decisions. The promise strategy draws on reward power (French & Raven, 1959) and may induce a supplier to focus on the short-term outcome (Boyle et al., 1992). In the face of various rush or-ders, the supplier may prefer to expedite other profitable orders if the supplier regards its dyadic relationship as unhealthy. The promise strat-egy will have a negative impact on the maintenance of the cooperative relationship. Therefore, this paper proposes the following hypothesis: H1(b). Promise strategy will have a strong negative relationship with supplier deliveryflexibility when shared vision is high than when shared vision is low.
3.2. Request strategy and deliveryflexibility
Frazier and Summers (1984)indicated that request strategy at-tempts to influence the target behavior directly. The effectiveness of request strategy will be in situations in that the value to the target of its compliance significantly exceeds the corresponding costs. A manufacturer may communicate content with the supplier to change a supplier's perceptions, but the content may lack any explanation. Payan and McFarland (2005) claimed that the strong arguments have higher levels of compliance than weak arguments. A manufac-turer informs its desired actions to the suppliers without explanation or statement of consequence. The manufacturer's argument is gener-ally specific and strong. However, if the supplier does not comply, the supplier may infer to the manufacturer's request strategy accompa-nied with a sanction (Payan & McFarland, 2005). A manufacturer that uses request strategy inevitably causes supplier tension. In deliv-eryflexibility, a supplier needs to adjust production plans to accom-modate for rush orders or to deliver accurate quantities on the promised due date. Continuous coordination should exist between manufacturers and suppliers. When the supplier initiate an incorrect
Fig. 1. Effects of influence strategies and social mechanisms on supplier delivery flexi-bility: A.
inference process, the infer explanations for the request may nega-tively impact supplier deliveryflexibility. In addition, asPayan and Nevin (2006)stated, if the supplier perceives that request implemen-tation will take a sizable amount of time, effort, or resources, the sup-plier will be more resistant to the request. Ultimately, the supsup-plier feels anxious and frustrated (Frazier & Rody, 1991). The consequence of the request strategy will damage the relationship between manu-facturer and supplier, and as a result, will compress suppliers' compli-ance. This argument leads to:
H2. The effect of the request strategy negatively impacts a supplier's compliance with deliveryflexibility.
3.3. Noncoercive influence strategies and delivery flexibility
Noncoercive influence strategies are means of communication intended to change the belief or aspiration of another party. The non-coercive influence strategies include recommendations and informa-tion exchange to attempt to alter the target's percepinforma-tion and change its belief. Regarding deliveryflexibility, suppliers need to make capi-tal expenditures on capacity expansion or initiate skilled worker training. However, the information exchange and recommendations strategies have an incomplete argument structure (Payan & McFar-land, 2005) and less pressure. A manufacturer with a high-perceived level of trust has more confidence that the suppliers will act honestly. In the manufacturer–supplier dyadic relationships, if the manufactur-er has a high-level of trust in its supplimanufactur-er, it may decrease the surveil-lance of its supplier's exchange behavior. To achieve the delivery flexibility requirement, suppliers need to become more involved and take riskier actions (e.g. building inventory buffers).Brown et al. (2009)found that noncoercive influence strategies exert less pres-sure could not prevent suppliers from behaving opportunistically. Under competitive pressure, suppliers may decide to serve their own optimal interests and take risk-aversion action when faced
with various rush orders from the manufacturer. Accordingly, the consequence will mitigate supplier's willingness to comply with the manufacturer. Therefore, we hypothesize that:
H3(a). (1) Recommendations and (2) information exchange strate-gies will have a strong negative relationship with supplier delivery flexibility when trust is high than when trust is low.
A manufacturer may predict that the supplier will be more prof-itable if it follows the manufacturer's suggestions (i.e. recommen-dations), or simply discusses general issues with the intent of motivating compliance (i.e. information exchange) (Frazier & Sum-mers, 1986; Payan & McFarland, 2005). When considering delivery flexibility, a manufacturer needs its suppliers to quickly respond and comply with changing market demands.Lai (2007)considered recommendations and information exchange strategies to be per-ception-altered strategies that focus on beliefs and attitudes of the target without a specific explanation. A manufacturer that fre-quently uses recommendations and information exchange as tools to change the supplier's perception will experience positive cooper-ative network relationships.
Considering the changing environment and necessity for a quick response to dynamic customer demands, shared vision is the prereq-uisite of supply chain partnerships. Without a shared vision between the manufacturer and supplier, the exchange partners may promote their own interests at the expense of the other partners. In a shared vision approach, manufacturers and suppliers believe they are on the same team and share a commitment based on mutual benefit. Therefore, a manufacturer's noncoercive influence strategies can eas-ily alter suppliers' perception toward the common goal. Under this condition, suppliers have a clear understanding of the supply chain mutual goals and will have a strong intention to take action to meet the manufacturer's deliveryflexibility requirements. Therefore, this paper hypothesizes that:
H3(b). (1) Recommendations and (2) information exchange strate-gies will have a strong positive relationship with supplier delivery flexibility when shared vision is high than when shared vision is low. 4. Methodology
4.1. Measures
The measurements for each construct in this study are listed in the Appendix. Informants responded tofive-point Likert-type scales for all variables from‘strongly disagree’ (1) to strongly agree’ (5). 4.1.1. Influence strategies
Frazier and Summers (1984, 1986) used a single-item scale to measure influence strategy and asked informants to estimate the per-centage of sourcefirm representatives tried to influence.McIver and Carmines (1981, p. 15) claimed,“It is very unlikely that a single item can fully represent a complex theoretical concept or any specific attribute for that matter”. Thus, this study adapted and modified the influence strategies outline developed byBoyle et al. (1992). For coer-cive influence strategies, informants were asked about the extent which the manufacturer used promises (PR), threats (THT), and legal-istic pleas (LP) and noncoercive strategies about recommendations (REM), information exchange (IE), and request strategy (RQ). Thus, there were 15 items for coercive strategies, 8 items for noncoercive strategies and 4 items for requests subscale.
4.1.2. Deliveryflexibility
In the measurements of deliveryflexibility, we modified the mea-surements in reference to related researches (Chan, 2003; Duclos, Vokurka, & Lummus, 2003; Krause, Pagell, & Curkovic, 2001; Sawhney, 2006). There are 5 items for deliveryflexibility subscale.
4.1.3. Trust and shared vision
To examine the interaction effect of trust (TRST) and shared vision (SHV), we further employ the construct from prior researches (e.g. trust taken from Kumar, Scheer, & Steenkamp, 1995; Kozak & Cohen, 1997; Spekman et al., 1999and shared vision fromLi & Lin, 2006). There are 9 items for trust and 3 items for shared vision. 4.1.4. Control variables
Size of the manufacturer was measured by employee headcounts 1—more than 1000 and 0—less than 1000. Duration (DUR) was mea-sured by more than 10 years of cooperative experience with 1 and less than 10 years with 0. In regard to industry type (IND) measure-ment, 1 represented high-techfirms and 0 represented traditional manufacturingfirms.
4.2. Sample and data collection
This research investigated the relationship among influence strate-gies, social mechanisms and supplier deliveryflexibility. A question-naire was pretested with 25 middle or top managers from different companies not included in thefinal study. Based on their responses, several questions were eliminated and reworded. All the items adapted from English scale were translated into Chinese. The revised survey questionnaires were sent out to 1000 members chosen at random from among the 5000 membership of SMIT (Supply Management Insti-tute, Taiwan) which is an institute for purchasing management certi fi-cation (e.g. Certified Purchasing Professional and Certified Purchasing Manager) training. The subjects were the purchasing managers of man-ufacturers who are in charge of transactions with suppliers. Purchasing managers were selected as they are often the main point of interaction with theirfirm's suppliers. Participants were asked to select one impor-tant supply relationship and to answer all questions referring to this one
supplier. The reasons for choosing the Taiwanese manufacturers at the target samples for the questionnaire survey are summarized as follows. First, there were few studies on influence strategies focused on Taiwan-esefirms (e.g.Hu & Sheu, 2005; Lai, 2007, 2009). Previous studies focus on the marketing research instead of operation and production side. Second, due to higher manufacturing costs, efficacy disadvantages and non-core technologies, Taiwanese manufacturingfirms support Origi-nal Equipment Manufacturer (OEM) components for large corporations (e.g. Apple, HP, Nokia, and Nike). Most of those Taiwanese manufactur-ingfirms tightly cooperate with their suppliers to achieve those large corporations requirement. Third, Taiwanese culture is rated as relative-ly high on the dimensions of collectivism and long-term orientation, and also scored as high-context culture as determined byHall and Hall's (1990)cultural dimensions. Therefore, there may be significant interest-induced interaction in the corresponding manufacturer–supplier relationships, which may help to investigate the interrelationships be-tween social mechanisms and influence strategies, as well as the corre-sponding effects on deliveryflexibility.
After 2 weeks of initial mailing, we sent the follow-up mail to non-respondent with a copy of questionnaire. As a result, 128 returns were received out of 1000 questionnaires (12.8%). Of these, 6 were removed for incompleteness, yielding a final sample size of 122 (12.2%).Rutner and Gibson (2001)reported an expected response rate of 5.7% on the data collection by “e-mail-out-e-mail return” method. In addition, their study on logistics information systems sur-vey that different sursur-vey techniques yielded different rate of return ranging from 3.7% to 12.6%. Namely, our survey return rate was ac-ceptable from E-mail survey method and supply chain targets. We compared the response group before and after the follow-up mail, and there were no significant differences in terms of cooperative du-ration relationship with key suppliers and number of employees. Table 1presents characteristics of ourfinal samples.
4.3. Reliability and validity
This study conducted an exploratory factor analysis on the in flu-ence strategies, social mechanisms and deliveryflexibility measures. On the basis of a baseline Eigenvalue of 1.0,Table 2showed that all measures have a good factor structure. In examining all constructs, the minimum Cronbach's alpha was .618 (promises strategy) and the maximum Cronbach's alpha was .936 (shared vision). Only prom-ises and information exchange strategies do not meet Cronbach's alpha of 0.7 cut off recommended byNunnally and Bernstein (1994) but all items reached the basic threshold for reliability (Cronbach's alphaN.6;Sakakibara et al., 1997). We further tested the validity of construct measures by confirmatory factor analysis (CFA) of all first-order constructs (Gerbing & Anderson, 1988).The reliability for all scales all exceeds the following criteria composite reliability (CR)N.70 (Fornell & Larcker, 1981), and average variance extracted (AVE)N.50 (Anderson & Gerbing, 1988; Fornell & Larcker, 1981; Hair, Anderson, Tatham, & Black, 1998). All the variables are success-fully measured, and the statistics summary and correlation matrix are presented inTable 3. According toHair et al. (1998), for confirmatory
Table 1
Characteristics of informants'firms.
Characteristics Number in sample Percentage Industry High-tech manufacturing 64 52.46 Traditional manufacturing 58 47.54 Number of employees b1000 68 55.74 N1000 54 44.26
Relation duration with supplier
b10 years 59 48.36
factor analysis (CFA), it is recommended that the data set must meet at leastfive observations per estimated parameter threshold. Because the sample sizes are not large in this study, we estimated two mea-surement models respectively: thefirst for influence strategies, and the second for social mechanisms (trust and shared vision). A six-factor (recommendations, information exchange, requests, legal-istic pleas, promises and threats) and 3-factor (trust, shared vision
and deliveryflexibility) confirmatory factor analyses were used to es-timate the goodness-of-fit. Model fit exceeded the standard cutoffs for acceptable fit: trust, shared vision and delivery flexibility (X2(24) = 32.95, pN0.05; root mean square error of approximation (RMSEA) = 0.018; root mean square residual (RMR) = 0.056; com-parativefit index (CFI)=0.988; Tucker and Lewis index (TLI)=0.982) and trust, shared vision and delivery flexibility influence strategies (X2(109) = 125.1, pN0.05; RMSEA=0.035; RMR=0.045; CFI=0.984; TLI = 0.978). From the result of CFA factor loadings, the magnitudes of the factor loadings and levels of statistical significance provide evidence of the measures convergent validity. Finally, we used the AVE exceeded 0.5 as the criterion to assess discriminant validity (Fornell & Larcker, 1981).Table 3also showed that all AVE exceeded the squared correla-tion between any pair of constructs support a satisfactory level of dis-criminant validity.
5. Results
This study used hierarchical multiple regressions to test the hy-potheses (Cohen et al., 2003). To avoid problems with multicollinear-ity, we mean-centered the independent variables, asCohen et al. (2003)recommended. To test the control variables effects, we en-tered the control variables in thefirst stage. In the second stage, we entered the main effects of individual coercive, noncoercive and re-quest strategies into the model. To test the interaction hypotheses, we conducted an interaction regression in the third stage in which the interaction was conducted among trust, shared vision, promises, information exchange and recommendations strategies respectively. Following the above steps, there are three models in the regression results shown inTable 4. To test H2, we conducted a main effect
model, which we specified inTable 4Model 2. To testH1and H3,
we specified an interaction model in Model 3.
InTable 4, Model 2 accounts for 29.5% of the variance in delivery flexibility (F (11, 109)=4.15, pb.01). The standardized regression co-efficients indicated that requests has a negative effect on supplier de-liveryflexibility (standardized β=−.176, t-value=−1.971, pb.05). Thus,H2was supported. The results also imply social mechanisms
ac-count for the primary impact on supplier deliveryflexibility. In regard to promises, the interaction item of promise × trust showed positively significantly positive (standardized β=.298, t-value=2.214, pb.05). In contrast, the promises × shared vision (standardized β=−.311, t-value =−2.328, pb.05) indicated a negative effect on delivery flex-ibility. In Model 3, the interaction item information exchange × trust (standardizedβ=−.157, t-value=−1.691, pb.1) is negatively sig-nificant. The interaction between shared vision and recommendation
Table 2
Exploratory factor analysis of construct. Factors
Items Loadings Cronbach's alpha
Deliveryflexibility 0.807 DLV1 0.722 DLV2 0.834 DLV4 0.668 DLV5 0.831 Threats 0.817 THT4 0.799 THT5 0.831 THT6 0.868 Legal pleas 0.850 LP1 0.861 LP2 0.798 LP3 0.825 LP4 0.755 Promises 0.618 PR1 0.620 PR3 0.839 Requests 0.838 RQ1 0.860 RQ2 0.806 RQ3 0.850 Information exchanges 0.625 IE1 0.832 IE2 0.650 Recommendations 0.917 REM1 0.874 REM2 0.891 REM3 0.845 REM4 0.828 Trust 0.894 TRST4 0.884 TRST5 0.883 TRST6 0.836 Shared vision 0.936 SHV1 0.798 SHV2 0.868 SHV3 0.864 Table 3
Correlation matrix and summary statistics.
DLV THT LP PR RQ IE REM TRST SHV DLV 1.00 THT −0.037 1.00 LP 0.012 .334⁎⁎ 1.00 PR 0.16 0.166 0.029 1.00 RQ −.238⁎⁎ 0.14 .228⁎ −0.146 1.00 IE 0.172 −0.084 0.032 .324⁎⁎ −0.143 1.00 REM 0.167 0.117 0.147 −0.146 −0.14 .340⁎⁎ 1.00 TRST .324⁎⁎ −0.070 0.089 0.055 −0.104 .250⁎⁎ .237⁎⁎ 1.00 SHV .410⁎⁎ −0.120 0.007 .189⁎ −.306⁎⁎ .403⁎⁎ .367⁎⁎ .445⁎⁎ 1.00 Mean 3.922 3.123 3.387 3.488 2.443 3.467 4.125 3.918 4.016 Standard deviation 0.459 0.820 0.748 0.691 0.791 0.700 0.599 0.492 0.665 AVE 0.588 0.694 0.657 0.544 0.704 0.557 0.739 0.753 0.712 Cronbach's alpha 0.807 0.817 0.850 0.618 0.838 0.625 0.917 0.894 0.936 Composite reliability 0.850 0.872 0.884 0.700 0.877 0.713 0.919 0.902 0.881 Notes: DLV = deliveryflexibility; THT = threats; LP = legal pleas; PR = promises; RQ = requests; IE = information exchanges; REM = recommendations; TRST = trust; SHV = shared vision.
⁎⁎ Correlation is significant at the 0.01 level (two-tailed). ⁎ Correlation is significant at the 0.05 level (two-tailed).
strategy presented recommendations × shared vision (standardized β=.263, t-value=2.59, pb.05) is positively significant. The recom-mendations × trust (standardizedβ=−.245, t-value=−2.5, pb.05) indicated a negative effect on delivery flexibility. Following the above statistic results,H1(a), H1(b),H2, and H3(a)are all supported
butH3(b)is partially supported. Our results showed that trust and
promise strategy interact to positively impact deliveryflexibility but trust and noncoercive strategies interact to negatively impact delivery
flexibility. In contrast, the interaction between shared vision and rec-ommendations has positive impact on deliveryflexibility, while the interaction with promises presents a negative impact. Finally, size and duration as the control variables, revealed no significant effect on dependent variable. However, the industry shows a significantly positive effect on deliveryflexibility in Model 2 and Model 3. The re-sults imply high-technology industries require more supplier delivery flexibility than do traditional manufacturing industries (Fig. 3).
Table 4
Results of multiple regression analyses. Dependent
variable
Deliveryflexibility
(Model 1) (Model 2) (Model 3)
Standardizedβ t-value Standardizedβ t-value Standardizedβ t-value Control variables IND 0.200⁎⁎ 2.148 0.292⁎⁎ 3.356 0.279⁎⁎ 3.224 Size 0.033 0.352 0.116 1.337 0.132 1.558 DUR 0.019 0.210 −0.008 −0.097 0.002 0.027 Main effects THT 0.031 0.342 0.022 0.232 LP 0.041 0.458 0.021 0.233 PR 0.102 1.106 0.024 0.227 RQ −0.176⁎⁎ −1.971 −0.179⁎⁎ −2.005 IE −0.016 −0.162 0.002 0.025 REM −0.084 −0.851 −0.029 −0.284 TRST 0.208⁎⁎ 2.256 0.224⁎⁎ 2.436 SHV 0.320⁎⁎ 3.109 0.284⁎⁎ 2.752 Interaction effects TRST × PR 0.298⁎⁎ 2.214 SHV × PR −0.311⁎⁎ −2.328 TRST × REM −0.245⁎⁎ −2.500 TRST × IE −0.157+ −1.691 SHV × REM 0.263⁎⁎ 2.590 SHV × IE 0.147 1.465 R² 0.040 0.295 0.378 Adjusted R² 0.015 0.224 0.275 F-statistic 1.626 4.15⁎⁎⁎ 3.678⁎⁎⁎
Notes: DLV = deliveryflexibility; THT = threats; LP = legal pleas; PR = promises; RQ = requests; IE = information exchanges; REM = recommendations; TRST = trust; SHV = shared vision.
+ pb0.1.
⁎⁎ pb0.05. ⁎⁎⁎pb0.01.
6. Discussion and suggestions 6.1. Discussion
This study explores trust and shared vision moderate the relationship between the manufacturer's influence strategies and supplier delivery flexibility in the buyer–supplier relationships with the integration influ-ence strategies and social capital theory. For promises, the results show that promise strategy doesn't have significantly positive impact on suppli-er delivsuppli-eryflexibility. In particular, the effectiveness of the promise strat-egy in advancing supplier deliveryflexibility depends on the level of trust in suppliers. Trust building involves a mutual reciprocation of beneficial actions through manifold interaction over time (Blau, 1964; Homans, 1958). The basic assumption of the social exchange theory is that“parties enter into and maintain relationships with the expectation that doing so will be rewarding” (Blau, 1964; Homans, 1958; Lambe, Wittmann, & Spekman, 2001, p. 4). Using the promise strategy will increase economic satisfaction because the source offers reward to encourage the target's compliance (Busch, 1980; Wilkinson, 1979). Therefore, only after the es-tablishment of a high-level of trust with the suppliers does the promise strategy appear effective. However, if the manufacturer and supplier have a high-level of shared vision, they are more willing to generate syn-chronized activities and to invest in long-term relationships (Jarillo, 1988; Jarillo & Ricart, 1987). Therefore, the use of the promise strategy under a high-level of shared vision may change the supplier's mind-set as unbal-anced power base and reduce the willingness to coordinate with the manufacturer to achieve deliveryflexibility.
Additionally, this study separates the request strategy from non-coercive influence strategies (Lai, 2007). As expected, the request strategy exacerbates supplier deliveryflexibility. The reason for this is that suppliers see request strategy as a command that spoils coop-erative relationships. This causes the supplier to behave in a manner opposite of what the manufacturer desires. According toFu et al. (2004, p. 286), persuasive strategy includes ‘rational persuasion’ (using logical arguments) and ‘consultation’ (seeking the targets input or participation in task). The request strategy is a strong and specific communication tool to seek the target's desired actions. Therefore, this study considers request strategy to be a persuasive strategy. In addition, the uncertainty avoidance dimension of cultural differences may be particularly important in this study. Taiwanese culture has a high uncertainty avoidance structure (Schmidt & Yeh, 1992) in situations where people“feel threatened by uncertainty or unknown situations” (Hofstede, 1997, p. 113) and lowers people's confidence in influencing others through logical arguments. In a high uncertainty avoidance culture, people perceive persuasive strat-egies as ineffective (Fu et al., 2004). Therefore, using the request strategy causes a negative impact on supplier deliveryflexibility.
Our research involved the moderating effects of social mecha-nisms on the use of the noncoercive influence strategies. The signif-icant interaction between shared vision and the recommendations strategy sheds additional light on the efficacy of the recommenda-tions strategy on supplier deliveryflexibility. The recommendation strategy explicitly communicates to the target with specific desired actions (Heide & John, 1990). Deliveryflexibility requires the coor-dination of dyadic production plans and the ability to adjust opera-tions. Shared vision facilitates the pursuit common goals between the manufacturer and supplier. Whenfirms have a high-level of shared vision, using the recommendation strategy may drive the supplier toward compliance to achieve compatible goals. According to the argument structure theory, the influence strategies with complete argument structure are more effective than influence strategies with a less complete argument structure (Payan & McFar-land, 2005). In addition, the information exchange strategy has a less complete structure is an unfocused strategy. Therefore, the in-formation exchange strategy with shared vision shows no signi fi-cant effect on a supplier's compliance.
Interestingly, the interaction between trust and information ex-change strategy displays a negative effect on supplier delivery flex-ibility.Das and Teng (1998)defined trust as a positive expectation regarding the target's motive, and should not influence the target's behavior. To meet customer demands by using information ex-change strategy without further reward and surveillance of sup-pliers seems ineffective since the supsup-pliers may act to protect their own interests under competitive pressure. Additionally, there is a negative interaction effect between trust and recommen-dations, suggesting that recommendations lead to low supplier de-liveryflexibility under high level of trust. This might occur because of the“dark side” of social capital in buyer–supplier relationships (Villena, Revilla, & Choi, 2010).Villena et al. (2010)claimed that, as relational capital increases, it can create occasions for opportu-nistic behavior (Granovetter, 1985). Excessive levels of trust may lead the buyer to reduce its efforts of monitoring and put the suppli-er in a bettsuppli-er position to take greatsuppli-er advantage of the buysuppli-er. Thsuppli-ere- There-fore, using the recommendations strategy associated with trust may lead to a supplier's adverse actions.
6.2. Theoretical implications
The theoretical implications of this study are as follows. First, this study offers important contributions to the literature, not only because previous research only focused on the influence strat-egies and ignored the nature of the request strategy, but more im-portantly because the social mechanisms have a stronger impact on compliance than the unique influence strategy. By integrating influence strategies and social capital theory, this study adds to our understanding of the complex nature of supplier delivery flexi-bility. The results offer new insights into the interaction effects of influence-control mechanism—complementarities or substitution effects—between influence strategies and social mechanisms used to advance supplier deliveryflexibility. The results show that prom-ises with trust, and recommendations with shared vision have a complementary effect, and that information exchange and recom-mendations respectively with trust, and promises with shared vi-sion have a substitutive effect on supplier deliveryflexibility.
Second, this study extendsPayan and McFarland's (2005)work and explores the determinants of supplier deliveryflexibility in the buyer–supplier relationships.Payan and McFarland (2005) investi-gated the effectiveness of influence strategies by drawing on argu-ment structure theory from the consumer behavior literature and on dependence theory from the marketing channels literature. According to argumentation theory, influence strategies reliant on changing the target's perception will be more effective if the com-municated content has a more complete argument structure. Therefore, if the communicated content lack of a complete argu-ment structure, the target's perception will not be changed. In ad-dition, if the target perceives itself to be highly dependent on a source and the source attempts to communicate that it will apply sanctions in an influence attempt (i.e., threats or legalistic pleas), the effect on compliance will be amplified (Payan & McFarland, 2005). However, they found that the interaction between promises and dependence on compliance is not significant. Drawing on so-cial capital theory, we argue that trust increases the buyer's will-ingness to take additional risks and provide more rewards to motivate supplier delivery flexibility. Therefore, promises result in supplier deliveryflexibility only when source has higher level of trust in suppliers. On the other hand, the results of our research show that noncoercive influence strategies (i.e. information ex-change and recommendations) don't have significantly positive impact on supplier deliveryflexibility. According to social capital theory, we argue that shared vision provides a referent frame of be-havioral norms and common understanding of collective goals that
increases more complete argument structure to change the target's perception.
6.3. Managerial implications
Our research also has several managerial implications. First, the finding that request has a negative effect on supplier delivery flexi-bilities holds important implications for purchasing managers. A purchasing manager should consider the efficacy of request in influ-ence attempts and anticipate the supplier's perception of a request when assessing the probability of compliance. A supplier is more likely to comply with requests that are uniquely relevant to achiev-ing the supplier's goals than with requests that are not to advance their goals. It may be wise for a purchasing manager to alter a re-quest to be more important or be relevant to the supplier's goals to enhance supplier deliveryflexibilities.
Second, even though purchasing managers may use the similar promise strategy, not all suppliers will react equivalently in the same position. Thefinding shows that the use of the promise strat-egy under a high-level of trust may enhance the supplier's econom-ic satisfaction and willingness to cooperate with the manufacturer to achieve deliveryflexibility. We suggest that a purchasing manag-er should add promise strategy undmanag-er high trust levels. Howevmanag-er, when shared vision is high, promise strategy may reduce supplier deliveryflexibilities. The findings suggest to purchasing managers that a mismatch of promise strategy and shared vision deters sup-plier from achieving deliveryflexibility.
Third, our results indicate that the use of recommendation under high shared vision levels is effective. The recommendations focus on altering the target's perception and the manufacturer may provide recommendation actions that facilitate mutual advantages. Manageri-ally, a purchasing manager should add this strategy to their repertoire under high shared vision. On the other hand, the use of recommenda-tion under high trust levels is ineffective. A purchasing manager should understand that a mismatch of recommendation and trust might be possible to reduce supplier deliveryflexibility.
Fourth, the use of noncoercive influence strategies to increase supplier deliveryflexibility can't be beneficial under high level of trust. An unexpected, yet interestingfinding is the negative associ-ation between deliveryflexibility and noncoercive influence strate-gies (recommendations and information exchange) associated with trust. It appears that trust may hinder a manufacturer's information exchange and recommendations to prompt suppliers to coincide with the manufacturer's desired actions on delivery. Trust does not encourage the use of coercive influence strategies (Kim, 2000). Therefore, purchasing managers should understand that noncoercive influence strategies and trust have a substitutive effect on supplier deliveryflexibility.
6.4. Limitations and further research
Future research could address several limitations of this study. First, because the research samples only consist of manufacturers, the results of a single investigation may have limited generalizabili-ty. However, this limitation should be tempered since every respon-dent was from a differentfirm. This study shares the manufacturers' perspective in examining the influence strategies used to attempt to enhance supplier deliveryflexibility. The extent to that the supplier's perspective would yield similar results remains unknown. A clear understanding of the effects of the influence strategies on delivery flexibility could entail collecting data from manufacturers and sup-pliers. Second, the samples of this study were only Taiwanese firms. Taiwanese culture is high-context different from low-context culture of Western countries. Future studies could concentrate in greater detail on the difference of culture types and effects of in flu-ence strategies. Finally, this paper did not study the power position
between buyer and supplier relationships.Kale (1986)claimed that the greater the power of a manufacturer is, the more frequently it uses coercive strategies. By contrast, when the supplier has high power in a dyadic channel relationship, the manufacturer will at-tempt to avoid the use of coercive strategies (Frazier & Rody, 1991). Researchers justify the use of coercive influence strategies when the target is highly dependent on the source (Hu & Sheu, 2005; Payan & McFarland, 2005). Future studies can thoroughly ex-amine the interfirm power structure and dependence level between the manufacturer and supplier. From a reciprocal action theory per-spective, we can further consider the dyadic interplay of influence strategies (Kim, 2000). This means that the manufacturer's influence strategies may stimulate the dyadic exchange partner's use of the same ones. How would a supplier's reciprocal strategies affect the ef-fectiveness of a manufacturer's influence strategies? Theoretically intriguing and practical questions such as this merit further study.
Acknowledgments
The authors thank Dr. P. La Placa and the anonymous IMM re-viewers for comments on earlier versions of this research. Thefirst author gratefully acknowledges the National Science Council, Taiwan (NSC 100-2410-H-029-045-MY2) foundation research support.
Appendix A. Supplementary data
Supplementary data to this article can be found online atdoi:10. 1016/j.indmarman.2011.09.020.
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