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The Role of Social Mechanisms in
Promoting Supplier Flexibility
Po-Young Chu a , Kuo-Hsiung Chang b & Hsu-Feng Huang a aDepartment of Management Science , National Chiao Tung University , Hsinchu, Taiwan
b
Department of International Business , Tunghai University , Taichung, Taiwan
Published online: 16 May 2011.
To cite this article: Po-Young Chu , Kuo-Hsiung Chang & Hsu-Feng Huang (2011) The Role of Social Mechanisms in Promoting Supplier Flexibility, Journal of Business-to-Business Marketing, 18:2, 155-187, DOI: 10.1080/1051712X.2010.499835
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Copyright © Taylor & Francis Group, LLC ISSN: 1051-712X print/1547-0628 online DOI: 10.1080/1051712X.2010.499835
The Role of Social Mechanisms in Promoting
Supplier Flexibility
PO-YOUNG CHU
Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan KUO-HSIUNG CHANG
Department of International Business, Tunghai University, Taichung, Taiwan HSU-FENG HUANG
Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan
Purpose: This study explores a conceptual framework for social mechanisms (trust and shared vision) to induce supplier flexibility (i.e., volume, mix, new product, and delivery flexibility).
Design/methodology/approach: The current study is based on marketing research reviews of social mechanisms and supply chain flexibility literature. To explore these issues, the authors developed and tested hypotheses with data from 162 members of the SMIT (Supply Management Institute, Taiwan).
Findings: The results show that trust has a direct impact on sup-plier’s volume flexibility and delivery flexibility. Furthermore, the findings indicate that a shared vision has direct impact on sup-plier’s mix, new product, and delivery flexibility. Finally, shared vision plays a mediating role among trust and mix, new product, and delivery flexibility.
Research limitations/implications: This research considers buyer’s perspective in examining social mechanisms that enhance supplier flexibility. A clear understanding of social mechanisms effects could evaluate competence trust and risk of respective flexi-bility that may affect social mechanism effectiveness.
Practical implications: This article contributes to management guidelines on how to align suppliers to respond quickly to customer demands.
Address correspondence to Hsu-Feng Huang, Department of Management Science, National Chiao Tung University, Hsinchu, Taiwan. E-mail: frank.ms94g@nctu.edu.tw
155
Originality/value: The study provides novel insights into social mechanism impact on suppliers’ respective flexibility.
KEYWORDS supply chain, flexibility, trust, shared vision
INTRODUCTION
R. Sanchez (1995) indicated that a firm with flexibility could respond effectively to a dynamic environment. Relational contracting literature has identified flexibility as an important relational norm (Heide and John 1990; Kaufmann and Dant 1992; Lusch and Brown 1996; Noordewier, John, and Nevin 1990). As supply chain management practices extend beyond the boundaries of a single firm, supplier flexibility enhances buyer capabilities to improve performance. Supplier flexibility refers to a supplier’s capability to manage production resource and uncertainty to meet a specific buyer demand for modifications. Supplier flexibility for a buyer implies the abil-ity to obtain additional services in response to changes in market demands. Chase, Aquilano, and Jacobs (2001) summarized that “recent trends, such as outsourcing and mass customization, are forcing companies to find flexible ways to meet customer demand. The focus is on optimizing core activities to maximize the speed of response to changes in customer expectations.” Accordingly, understanding how a buyer manages supplier flexibility is an important issue for management and practice.
Social capital, encompassing norms and values, facilitates relationships (Coleman 1990) and lowers transaction cost (Chiles and McMackin 1996). In the literature of interorganizational relationships, trust exists when a party has confidence in the exchange partner’s reliability and integrity (Gulati, Nohria, and Zaheer 2000; Morgan and Hunt 1994; Ring and Van de Ven 1992). Tsai and Ghoshal (1998) declared that a shared vision embodies col-lective goals and aspirations of the members of an organization. Following Nahapiet and Ghoshal (1998), shared vision manifests the cognitive dimen-sion of social capital. Fitting the flexibility of interorganizational relational norm strategy requires a firm to extend cognitive resources “not only to become aware of alternatives, but also to be willing to change behavior based upon an assessment of available alternatives” (Griffith and Myers 2005: 258). Relationship marketing refers to all marketing activities directed toward establishing, developing, and maintaining successful relational exchanges (Morgan and Hunt 1994). The core theme of the relationship marketing per-spective is focus on a cooperative and collaborative relationship between firms. Dwyer, Schurr, and Oh (1987) characterized such cooperative rela-tionships as interdependent and long-term orientated rather than concerned with short-term discrete transactions. The main premise of the resource-dependence theory is the need for heightened interfirm coordination when
task uncertainty and complexity increases (Pfeffer and Salancik 1978). Heide (1994) claimed that dependence and uncertainty are the key antecedent variables motivating the establishment of interorganizational relationships.
From a relational contract perspective, trust is an important mechanism for encouraging future exchanges (Hewett and Bearden 2001). Shared vision as a social mechanism facilitates cooperative actions (Li 2005). However, little is known about social mechanism effectiveness to motivate supplier flexibility from either an empirical or a theoretical standpoint. With the growing importance of purchasing as a frontier source of supply chain improvement, this research examines the consequences of social mech-anisms on supplier flexibility, including volume, mix, new product, and delivery flexibility. The remainder of this article is divided into three parts. First, this article reviews the literature on flexibility and social mechanisms and presents the conceptual framework. Next, this study develops specific hypotheses about potential antecedents and outcomes of supplier flexibility. Finally, the conclusions summarize the research findings and implications of this study, and this article discusses limitations and future research directions.
RESEARCH FRAMEWORK AND HYPOTHESIS
Environmental turbulence is the main reason for pursuing manufacturing flexibility (Corrêa 1994). Current market turbulence involving continuous changes in customer preferences or demands (Jaworski and Kohli 1993) and technological turbulence involving the rate of technological change (Calantone, Garcia, and Droge 2003) leads a firm to respond quickly in striving for future business opportunities. In an increasingly dynamic envi-ronment, a buyer’s ability to successfully manage its relationships with suppliers is emerging as a key competence and source of sustainable competitive advantage.
Researchers have conceptualized social capital as embedded resources within cooperative relationships (Burt 1992; Nahapiet and Ghoshal 1998). Nahapiet and Ghoshal (1998) distinguished social capital as structural, rela-tional, and cognitive dimensions. According to Nahapiet and Ghoshal (1998) and Tsai and Ghoshal (1998), the structural dimension includes social inter-action, the relational dimension includes trust and trustworthiness, and the cognitive dimension includes shared vision. From the social exchange the-ory, partners involved in repeated exchange might begin to trust each other. Previous studies have suggested that trust emerges from social interactions (Gulati 1995; Lewicki, McAllister, and Bies 1998). Once trust is built, both partners are more likely to coordinate their efforts because each party does not act only for its own interests (Anderson and Narus 1990; Mohr and Spekman 1994; Morgan and Hunt 1994). This study examines the effects of the relational and cognitive dimension on supplier flexibility. Figure 1
H3 H2 H1 Trust Shared Vision Flexibility: Volume Mix New Product Delivery
FIGURE 1 Conceptual model.
depicts the conceptual model that summarizes the research interests and objectives of this study. Based on the literature reviews, this work generates three hypotheses associated with the model. These hypotheses focus on the interrelationships among trust, shared vision, and respective flexibility of the supplier.
Flexibility
Flexibility becomes a critical order-winning criterion since a firm with flex-ibility gains competitive advantage by rapid response to customer’s volatile demand. Gupta and Goyal (1989: 120) defined flexibility as “the ability of a manufacturing system to cope with changing circumstances or instabil-ity caused by the environment.” Zhang, Vonderembse, and Lim (2003: 178) regarded manufacturing flexibility as “the ability of the organization to man-age production resource and uncertainty to meet various customer requests.” In addition, Upton (1994) described internal flexibility as what the firm can do and external flexibility as what the customer sees. Examples of inter-nal flexibility include machine, material handling, and routing flexibility. External flexibility directly affects a firm’s competitiveness; by contrast, inter-nal flexibility relates to a firm’s operatiointer-nal efficiency (Chang et al. 2003). Examples of external flexibility are volume, mix, new product, and delivery flexibility (Chang et al. 2003). In contrast, internal flexibility relates to oper-ational efficiency instead of market demand (Chang et al. 2003). To achieve customer value (i.e., delivery on time, high quality, and low cost), firms must look beyond their internal flexibility (Lummus, Duclos, and Vokurka 2003; Zhang, Vonderembse, and Lim 2002). From the perspective of buyers, the following external flexibilities significantly relate to supplier response to environmental turbulence.
1. Volume flexibility: the ability to change the level of aggregated output. 2. Mix flexibility: the ability to change the range of products made within a
given time period.
3. Product flexibility: the ability to introduce novel products or to modify existing ones.
4. Delivery flexibility: the ability to change planned or assumed delivery dates.
VOLUME FLEXIBILITY
Volume flexibility is the ability to effectively adjust aggregate production in response to customer demand (Hayes and Wheelwright 1984). Volume flexibility permits the firm to adjust production upwards and downwards within wide limits (Khouja 1998). Vickery, Calantone, and Droge (1999) related volume flexibility to high market share and financial performance, especially in highly cyclical markets. Firms rely on their external supplies as long-term sources of volume flexibility (Jack and Raturi 2002). With chang-ing customer demand, the buyer not only adjusts its own capacity, but also needs its suppliers to meet customer demand quantities. With regard to sup-plier volume flexibility, the buyer is concerned with quantity, cost, time, and quality (Beamon 1999; D’Souza and Williams 2000; Suarez, Cusumano, and Fine 1996) associated with volume change. The strategies for increas-ing volume flexibility include buildincreas-ing slack resources, buildincreas-ing inventory buffers, and training cross-functional workers. Research suggested that sup-pliers reach the volume flexibility requirement through production efficiency (e.g., just-in-time delivery) and resource utilization (e.g., overtime). In addi-tion, reserve capacity and change over time affect volume flexibility (Yang, Lin, and Sheu 2007). In other words, suppliers with the ability to alter equip-ment operating rate and the speed and knowledge of base workers have an internal capacity focus. Tan, Lyman, and Wisner (2002) also suggested that quality, quick response, and volume flexibility are critical criteria in evaluat-ing supplier performance. Buyers will regard suppliers that cannot respond to demand fluctuations and manage effectively to achieve buyer’s require-ments as unqualified. Volume flexibility is an important primary flexibility of the manufacturing system. The buyer is concerned with the supplier’s capacity for volume requirement.
MIX FLEXIBILITY
Mix flexibility refers to the ability to change various products produced within a given period of time economically and effectively without incurring major set-up costs (A. Das 2001; Gerwin 1982; Slack 2005). Mix flexibil-ity implies the capabilflexibil-ity of a firm to respond quickly and economically to different product mix changes in the market (Karuppan and Ganster 2004) to enhance customer satisfaction (Gerwin 2005). A firm with mix flexibility efficiently uses resources and responds to market change (Gerwin 1993). From a buyer’s perspective, a buyer will require its suppliers to produce differentiated products in a certain capacity and change over quickly from
one product to another to respond to a variety of customer preferences without incurring a major cost penalty (e.g., changeover cost). Hutchison and Das (2007) listed capabilities to achieve mix flexibility: manufacturing processes that produce a wide range of products, workforce flexibility, and quick changeover times. Gerwin (2005) also indicated that flexible manufac-turing competencies include machines, labor, material handling, and routing flexibilities.
NEW PRODUCT FLEXIBILITY
Koste and Malhotra (1999) proposed addressing product flexibility by two different dimensions: modification flexibility and new product flexibility. Modification flexibility refers to the ability to make minor design changes into a specific product (D’Souza and Williams 2000; Gerwin 1993). As prod-ucts have a short life cycle, a buyer needs to shorten the lead-time of new product development. Sethi and Sethi (1990) discussed product flex-ibility measurements as either the time or cost required for introducing new products to existing operations. Studies have shown that the early stage of product development involving determining the specifications and designs of a product to be critical to new product success (Bacon et al. 1994; Cooper 1990). Chang et al. (2005) presented that manufacturing involvement, multi-skilled workforce developments, and manufacturing/design integration have significant positive effects on new product flexibility. Kara and Kaysi (2004: 471) described, “Multi-skilled workers and continuous learning are some of the factors enhancing product/new product/modification flexibility.” The new product pre-launch stage includes concept generation, preliminary technical assessment, testing, and marketing plan. All supply chain partners jointly share the responsibility for achieving new product flexibility (Kumar et al. 2006). Suppliers that work closely with the buyer to provide tech-nical or design support during the new product pre-launch stage and the engineering change on existing products could save the buyer time or cost during product development.
DELIVERY FLEXIBILITY
With regard to supplier’s delivery performance, on-time delivery, lead-time, and reliability are primary metrics (Shin, Collier, and Wilson 2000). Delivery reliability refers to the ability to deliver on or before the promised sched-uled due date (Handfield and Pannesi 1992), and delivery dependability refers to the ability to deliver on time with accurate quantities and kinds of products needed (White 1996). Delivery flexibility is “the ability to accom-modate last-minute changes to order quantities, small-batch deliveries, fast deliveries, and higher on-time delivery rates” Ketokivi (2006: 220). A. M.
Sanchez and Perez (2005) argued that delivery flexibility is the firm’s capa-bility to adapt lead-time to meet changing customer requirements. From the literature, delivery flexibility not only encompasses delivery reliability and delivery dependability, but the ability to cater to changing orders in a very short time (Sawhney 2006). Market demand has previously been more sta-ble and product life cycle longer. Now, customer preferences and demand are difficult to forecast. A firm should be able to change planned delivery dates in meeting customers’ requirements. A buyer’s collaboration practices with suppliers enable it and its partners to act together to improve delivery performance. The supplier that lacks the ability to accommodate rush orders and deliver on promised due dates (Chan 2003) will result in additional cost to the buyer (e.g., line down cost) and negative customer value. Suppliers’ delivery flexibility is the ability to change the product mix and reallocate capacity to accommodate buyers’ rush or special orders. In other words, sup-pliers that operate at different output levels and quickly and easily change production quantities, and quickly change to a different product mix or to producing various products without a major changeover, are more respon-sive to buyers’ demands and deliver on the promised due date. In summary, suppliers with mix and volume flexibilities achieve delivery reliability and dependability and accommodate buyer’s rush orders.
Trust
Researchers have defined trust as the belief that a partner’s word or promise is reliable to fulfill its obligations in the relationship (Schurr and Ozanne 1985) and as a willingness to rely on an exchange partner in whom one has confidence (Moorman, Zaltman, and Deshpande 1992). Trust also refers to one party that believes others to be benevolent and honest (Larzalare and Huston 1980). Trust is the most important variable in relational exchange by social exchange theorists (e.g., Blau 1964; Homans 1958). The social exchange theory assumes that parties maintain a relationship to gain a val-ued outcome. Lambe, Wittmann, and Spekman (2001) suggested that trust building between two parties might start with relatively minor transactions and increase as the number or size of interactions increases. If a party receives increased benefit from the other, it will reciprocate as the benefit increases (Homans 1958). The issue of trust in buyer–supplier relationships is significantly important, since the dyadic relationship often involves a high degree of interdependence. Gao, Sirgy, and Bird (2005: 398) argued, “Based on the principle of reciprocity in exchange theory (Blau 1964), mutual trusting behaviors and bilateral perceptions of trustworthiness must exist for a relationship to become stable and long lasting” (Anderson and Weitz 1992; Smith and Barclay 1997). According to the principle of reciprocity in exchange theory (Blau 1964), “trust entails trust” (cf. McDonald 1981). In the context of buyer–supplier relationships, the supplier’s perceived trust in
the buyer as dependable and benevolent will contribute to joint responsibil-ity, shared planning, and a flexible arrangement (Johnston et al. 2004). This work specifically measures the trust of the buyer in the supplier. According to Doney and Cannon (1997), buyers select reliable suppliers who demon-strate behaviors that consider buyer’s interest to reduce their perceived risk. Morgan and Hunt (1994: 23) defined commitment as “an exchange part-ner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it; that is, the committed party believes the relationship endures indefinitely.” In other words, the causal relationship between trust and commitment results from the principle of generalized reciprocity.
To achieve the flexibility required in the supply chain where there are unforeseen circumstances, buyers and suppliers need to devote high levels of cooperation and joint planning. Research has found that trust significantly and positively relates to commitment (Geyskens, Steeenkamp, and Kumar 1999; Morgan and Hunt 1994) and cooperation (Anderson and Narus 1990; Morgan and Hunt 1994). Trust also facilitates interorganizational communi-cation and information sharing to improve responsiveness (Handfield and Bechtel 2002). According to the social exchange theory, trust is created with reciprocally mutual beneficial actions through manifold interactions over time (Blau 1964; Homans 1958). If previous exchanges have been positive, supply chain partners may anticipate that further exchange will bring pos-itive outcome. Pospos-itive outcome over time increase partners’ trust of each other and commitment to maintaining the exchange relationship (Lambe et al. 2001). Trust increases the probability of maintaining valuable buyer– supplier relationships. Therefore, the supplier will be motivated to increase the value delivered to the buyer by adapting its own products, processes, and procedures to the buyer’s specific needs. This enables suppliers’ willing-ness to make an effort to generate desired outcomes. Hence, it is expected that a buyer’s trust in its supplier positively influences supplier flexibility. Hence, we propose the following hypothesis:
H1: A buyer’s trust in its suppliers has a positive impact on supplier (1) volume flexibility, (2) mix flexibility, (3) new product flexibility, and (4) delivery flexibility.
Shared Vision
Hoe and McShane (2002: 283) indicated, “A shared vision is a clear, com-mon, specific picture of a truly desired future state.” When exchange parties have a shared vision, they have the same perception about how to inte-grate strategic resources and how to interact with one another. Empirical studies have shown that parties in a supply chain with a shared vision have better performance (e.g., Spekma, Kamauff, and Spear 1999). By contrast,
Boddy, Macbeth, and Wagner (2000) found that a lack of shared vision between suppliers and customers causes difficulty in cooperation. Without a shared vision in buyer–supplier relationships, the exchange partners may promote their own interests at the expense of others and further impair cooperative relationships. In other words, a shared vision contributes to relationship continuity. Developing a shared vision between buyers and suppliers helps focus on their strategic goals (Voss 2005) and aligns them in the same direction. Thus, a shared vision helps to create commonality between buyer–supplier relationships and provides coherence in interactive activities.
Developing a shared vision helps each actor in buyer–supplier relation-ships see the potential benefit and understand their expected contribution (Riis 2009). A shared vision aligns goals and values resulting from increased communication, information sharing, and understanding between the part-ners (Young-Ybarra and Wiersma 1999). Buyers and suppliers with a shared vision have a greater perspective toward long-term orientation (Ganesan 1994; Lusch and Brown 1996), which focuses on achieving future goals. Frequent and close interactions allow buyers and suppliers to perceive that they are a team that shares important values and aspirations, in which partners are expected to strengthen cooperative goals. If both buyers and suppliers understand the importance of collaborating and improving the supply chain, they will facilitate cooperative actions (Li 2005) to meet the manufacturer’s flexibility requirements. Hence, we propose the following hypothesis:
H2: Shared vision has a positive impact on supplier (1) volume flexibility, (2) mix flexibility, (3) new product flexibility, and (4) delivery flexibility.
The Mediating Role of Shared Vision between Trust and Supplier Flexibility
Various studies have identified trust as an essential element of a long-term buyer–supplier partnership (e.g., Anderson and Narus 1990; Rousseau et al. 1998). Prior studies claimed that trust induces joint efforts (Gambetta 1988) or shared resources (Tsai and Ghoshal 1998). Trust facilitates interorganiza-tional communication and commercial or confidential information sharing to improve responsiveness (Handfield and Bechtel 2002). Based on the social exchange theory, if exchange partners realize the benefits of previous trans-actions, the parties may engage in riskier behavior that provides greater benefits to exchange partners while trust increases over time. Growing trust indicates an orientation of parties toward ultimate values rather than immediate rewards (Huston and Burgess 1979). Thus, a buyer with a high level of trust in its suppliers will (1) communicate sensitive information and
(2) provide advance information (Kingshott 2006) about changes to market and customer preference.
Ali and Birley (1998) argued that shared vision is the component of ability, in which shared vision is not just a common value but the ability to achieve a collective goal and align actions accordingly. A shared vision of dyadic relationships likely varies over time in response to opportunities and needs (Lai et al. 2009). Buyer–supplier relationships are difficult to sustain because of different visions, which can result in interorganizational conflicts. As the buyer and supplier frequently interact, both are more likely to per-ceive each other as trustworthy actors (Gabarro 1978), to share important information, and to create a common goal. A positive relationship between trust and a shared vision may be expected, because a trusting relationship between a buyer and its suppliers implies that the buyer and suppliers engage in greater information sharing. Hence, a shared vision requires trust as a prerequisite. In other words, trust helps to convey a sense of identity in interorganizational relationships and may create commitment to collective goals. We propose the following hypothesis:
H3 (a): A buyer’s trust in its suppliers will help to develop a shared vision.
Trust has positive social benefits that draw parties closer together, embedding them in a social framework that promotes cooperation (Stinchcombe 1986; Thibaut 1968) and facilitates a common understanding of aims and objectives (Anderson and Weitz 1989). As in our prior discus-sion, trust helps a buyer and its suppliers to develop a shared vision. This study also proposes that a buyer’s trust in its supplier will affect supplier flexibility. Additionally, if the supplier has a clear picture of mutual goals in the supply chain, it will have a strong intention to integrate resources and engage in productive behaviors to meet the buyer’s flexibility require-ments. In linking this evidence for shared vision on supplier flexibility with our proposition of the influence of trust on shared vision, we can expect a shared vision to mediate in the trust–supplier flexibility linkage. The above arguments lead to the following hypothesis:
H3 (b): Shared vision mediates the relationship of a buyer’s perceived trust and its suppliers’ (1) volume flexibility, (2) mix flexibility, (3) new product flexibility, and (4) delivery flexibility.
Control Variables
A large-scale buyer may have more resources and power on its suppli-ers that lead to supplier flexibility. On the supplier enablement front, large
buyers with available resources can withdraw their demand or offer more orders to compel suppliers to achieve flexibility requirement. The dura-tion of the collaborative reladura-tionship with suppliers may also affect supplier flexibility. According to the relational contracting theory (RCT), the rela-tionship duration will help to develop trust and a shared vision. Following Heikkilä (2002), relationship duration contributes to information flows and cooperation, further leading to high supply chain efficiency. The level of environmental turbulence (market and technological turbulence) might have different effects on social mechanisms of suppliers’ flexibility. The effective-ness of social mechanisms also varies among different industries. In the face of environmental turbulence, buyers in the high-technology industry may prefer interorganizational trust and shared vision building among their sup-pliers to quickly respond to technological turbulence and a dynamic market. Therefore, this study includes the size of the buyer, measured by its total number of employees, duration of relationship, type of industry, market tur-bulence, and technological turbulence as the control variables. These enable us to identify the nature of the relationship between supplier flexibility and social mechanisms more effectively.
METHODOLOGY Sample and Data Collection
This research investigated the relationship between social mechanisms and supplier flexibility in the supply chain. A survey of major Taiwanese firms was conducted. A questionnaire was pre-tested with 25 middle or top managers from different companies not included in the final study. Based on their responses, several questions were eliminated and reworded. We obtained suggestions for adaptations to ensure the clarity and appropriate-ness of items. We revised and eliminated several redundant and ambiguous items accordingly. The revised survey questionnaires were sent out through e-mail to 1,000 members chosen at random from among the 5,000 member-ship of SMIT (Supply Management Institute, Taiwan), which is an institute for purchasing management certification (e.g., Certified Purchasing Professional and Certified Purchasing Manager) training. All the items adapted from English scale were translated into Chinese. Survey questionnaires were sent out through e-mail to the purchasing managers of buyers who are in charge of transactions with suppliers. Purchasing managers were selected as they are often the main point of interaction with their firm’s suppliers. Participants were asked to select one important supply relationship and to answer all questions referring to this one supplier. After two weeks of initial mailing, we sent the follow-up mail to nonrespondents with a copy of question-naire. As a result, 175 returns were received out of 1,000 questionnaires (17.5 percent). After elimination of 13 incomplete questionnaires, the final
TABLE 1 Characteristics of Informant Firms
Characteristics Number in sample % Industry High-tech manufacturing 82 50.62 Traditional manufacturing 80 49.38 Number of employees <1,000 94 58.02 >1,000 68 41.98
Relation duration with supplier
<10 years 83 51.23 >10 years 78 48.15 Not reported 1 0.62 Relation type Purchasing 98 60.49 Outsourcing 18 11.11 Both 46 28.40
sample was 162 questionnaires for analysis (12.2 percent). Table 1 presents characteristics of our final samples.
Rutner and Gibson (2001) reported an expected response rate of 5.7 percent on the data collection by “e-mail-out–e-mail return” method. In addition, their study on logistics information systems indicated that differ-ent survey techniques yield differdiffer-ent rate of return ranging from 3.7 percdiffer-ent to 12.6 percent. Namely, our survey return rate was acceptable from e-mail surveys and supply chain targets. To assess non-response bias, we com-pared early and late respondents (Armstrong and Overton 1977). The results showed that there were no significant differences in terms of number of employees (t = 0.993, p = 0.322) and duration of relationship (t = 1.2, p= 0.231).
Measures
We followed the procedures suggested by Churchill (1979). First, we defined the domain of each construct. Second, we searched the literature for appro-priate scale. The measurements for each construct in this study are listed in the appendix. Informants responded to five-point Likert-type scales for all variables from 1 (strongly disagree) to 5 (strongly agree).
FLEXIBILITY
In regard to flexibility, the measurements of volume and mix flexibility were adapted from Zhang et al. (2003). There were five items for volume flexi-bility and six items for mix flexiflexi-bility measurement. For delivery flexiflexi-bility and new product flexibility, scales were adapted from previous researches (cf. Chan 2003; Duclos, Vokurka, and Lummus 2003; D’Souza and Williams
2000; Krause, Pagell, and Curkovic 2001; Koste and Malhotrar 1999; Sawhney 2006). There were five items for delivery flexibility and four items for product flexibility measurements.
TRUST AND SHARED VISION
To examine the effect of trust and shared vision, we further employed the construct from prior researches. We adapted scales from Kumar et al. (1995), Kozak and Cohen (1997), and Spekman et al. (1999) to measure trust. For shared vision, scales were adapted from Li and Lin (2006). There were nine items for trust and three items for shared vision.
CONTROL VARIABLES
Size of the buyer was measured by employee headcounts 1- more than 1,000 and 0- less than 1,000. Duration was measured by more than 10 years of cooperative experience with 1 and less than 10 years with 0. In regard to industry type measurement, 1 represented high-tech firms and 0 represented traditional manufacturing firms. Market turbulence measurement items were adopted from Jaworski and Kohli (1993) and technological turbulence items were from (Calantone et al. 2003).
Reliability and Validity
This research conducted confirmatory factor analyses (CFA) using AMOS 7.0 to assess the reliability and convergent and discriminant validity for our measurement models (Bagozzi and Yi 1988) and to drop some items that possessed low factor loadings. To assess model fit, this article used the over-all model chi-square measure (χ), root mean square error of approximation (RMSEA), root mean square residual (RMR), comparative fit index (CFI), normed fit index (NFI), and goodness-of-fit index (GFI). Because the sam-ple sizes were not large, this study estimated two measurement models: the two independent variables, trust and shared vision (χ2(7) = 8.098, p > 0.05;
RMSEA= 0.031; RMR = 0.01; CFI = 0.99; NFI = 0.99; GFI = 0.984); and the second for supplier’s flexibility (χ274= 90.792, p > 0.05; RMSEA = 0.038;
RMR = 0.026; CFI = 0.986; NFI = 0.929; GFI = 0.933). The results of these models are presented in the Table 3-1 and Table 3-2.
The convergent validity of the scales was tested in two ways. First, the results on indicator loadings were significant (p< .001). The composite reliability (CR) and Cronbach’s alpha of each factor ranged from 0.7 to 0.9 (Nunnally 1978). Second, this study checked the average variance extracted (AVE) for each construct to evaluate the discriminant validity of the focal constructs. The results showed that the AVE for each factor is higher than
TABLE 2 Means, Standard Deviation, and Correlation Matrix of Constructs
Construct VOL DLV MIX NP TST SHV
Volume flexibility (VOL) 1.00
Delivery flexibility (DLV) 0.351∗∗ 1.00
Mix flexibility (MIX) 0.484∗∗ 0.503∗∗ 1.00
New product flexibility (NP) 0.397∗∗ 0.449∗∗ 0.599∗∗ 1.00
Trust (TST) 0.233∗∗ 0.328∗∗ 0.251∗∗ 0.256∗∗ 1.00
Shared vision (SHV) 0.176∗ 0.401∗∗ 0.321∗∗ 0.395∗∗ 0.511∗∗ 1.00
M 3.673 3.877 3.601 3.671 3.895 4.008
SD 0.550 0.521 0.624 0.628 0.484 0.640
Cronbach’sα 0.709 0.847 0.866 0.834 0.881 0.932 Composite trait reliability 0.790 0.850 0.848 0.845 0.884 0.905 Average variance extracted (AVE) 0.559 0.588 0.530 0.648 0.718 0.760
∗∗Correlation is significant at the 0.01 level (two-tailed).
∗Correlation is significant at the 0.05 level (two-tailed).
TABLE 3 Fit Statistics—Confirmatory Factor Analysis for Constructs TABLE 3-1 Result of CFA on Social Mechanisms
Construct Measurement Standardized loading
TST TST4 0.776 TST5 0.939 TST6 0.83 SHV SHV1 0.815 SHV2 0.956 SHV3 0.95 χ2(7)= 8.098.
RMSEA= 0.031, CFI = 0.99; NFI = 0.99, GFI = 0.984, RMR = 0.01.
TABLE 3-2 Result of First-order CFA on Flexibility
Construct Measurement Standardized loading
VOL VOL1 0.604 VOL2 0.641 VOL4 0.632 DLV DLV1 0.689 DLV2 0.766 DLV4 0.685 DLV5 0.834 NP NP2 0.807 NP3 0.814 NP4 0.761 MIX1 0.611 MIX2 0.702 MIX3 0.728 MIX4 0.875 MIX5 0.793 χ2(74)= 90.792.
RMSEA= 0.038, CFI = 0.986, NFI = 0.929, GFI = 0.933, RMR = 0.026.
TABLE 3-3 Result of Second-order CFA on Flexibility
Construct Standardized loading
VOL 0.684
MIX 0.919
DLV 0.667
NP 0.778
TST= trust; SHV = shared vision; VOL = volume
flex-ibility; MIX= mix flexibility; VOL = volume flexibility;
DLV= delivery flexibility; NP= new product flexibility.
χ2 (76)= 92.81, RMSEA = 0.037, CFI = 0.986, NFI =
0.927, GFI= 0.931, RMR= 0.025.
TABLE 4 Results of Discriminant Validity Tests
Constrained model Unconstrained model χ2
Factors χ2 df χ2 df χ2 (1) MIX-DLV 230.519 27 93.065 26 137.454 MIX-NP 131.332 20 54.792 19 76.54 MIX-TST 300.032 20 49.578 19 250.454 MIX-SHV 451.683 20 61.314 19 390.369 MIX-VOL 112.003 20 61.642 19 50.361 DLV-VOL 92.983 14 20.778 13 72.205 DLV-NP 164.55 14 38.058 13 126.492 DLV-TST 269.228 14 27.572 13 241.656 DLV-SHV 259.42 14 33.999 13 225.421 VOL-NP 70.526 9 10.011 8 60.515 VOL-TST 96.871 9 10.788 8 86.083 VOL-SHV 95.895 9 5.917 8 89.978 NP-TST 177.077 9 7.203 8 169.874 NP-SHV 167.876 9 18.395 8 149.481 TST-SHV 211.436 9 15.698 8 195.738
TST= trust; SHV = shared vision; VOL = volume flexibility; MIX = mix flexibility; VOL = volume
flexibility; DLV= delivery flexibility; NP = new product flexibility. All values were significant at
the p< 0.01 level.
0.5 and larger than the squared correlation between the factor pair (see Table 2). These results support the convergent validity of the scale items (Anderson and Gerbing 1988; Fornell and Larcker 1981).
To further assess the validity of supplier’s flexibilities as a second-order construct, this research further conducted a second-order CFA to examine the underlying unidimensionality of flexibility constructs. The model exhib-ited an excellent model fit, with a ratio of chi-square to degree of freedom of 1.221, RMSEA of 0.037, RMR of 0.025, CFI of 0.986, and GFI of 0.931. The result revealed all four first-order factors loaded on the second-order factor strongly (>0.67). The second-order confirmatory factor analysis supported the view of flexibility as a single overall construct composed of four distinct
sub-dimensions (see Table 3-3). Finally, the evaluation of discriminant valid-ity was checked by chi-square difference test between each pair of construct (Anderson and Gerbing 1988). In all cases, combining each of flexibility and social mechanism dimensions with another resulted in a significant increase in the chi-square statistic (p < 0.01). The results of Table 4 support the discriminant validity.
RESULTS
Hypotheses tests were examined by using structural equation model. Because this study posited that shared vision mediates the effects of trust on four flexibility dimensions (i.e., volume, mix, new product, and delivery flexibility), tests were conducted by examining whether mediated models fit significantly better than the direct effect model. In the direct effect model, trust and shared vision were modeled to have independent effects on four flexibility dimensions. The model fit indices indicate less good fit for direct effect model: χ2161= 240.825, p < 0.01; RMSEA = 0.055; RMR = 0.056;
CFI = 0.96; NFI = 0.892; GFI = 0.884. Next, the mediated model was estimated and resulted the good fit of indexes:χ2160= 192.246, p < 0.05;
RMSEA= 0.036; RMR = 0.035; CFI = 0.984; NFI = 0.913; GFI = 0.905. Chi-square difference tests indicate that mediated model is significantly better fit, χ2(1) = 12.579, p < 0.01.
According to Baron and Kenny (1986) and Kenny et al. (1998), this research conducted four steps to determine whether the shared vision medi-ates the effect of trust on suppliers’ respective flexibility dimensions, four conditions must hold: (1) the predictor variables (trust) must affect the dependent variables in the predicted direction; (2) the predictor variables (trust) must affect the mediator (shared vision) in the predicted direction; (3) the mediator (shared vision) must affect the dependent variables (i.e., volume, mix flexibility, new product, and delivery flexibility) in the pre-dicted direction; and (4) the impact of the predictors on the dependent variables must be not significant (full mediation) or reduced (partial medi-ation) after controlling for the mediator (shared vision) (Baron and Kenny 1986; Holmbeck 1997). Table 5 contains the analyses necessary to exam-ine the mediated hypothesis. First, the estimates on the direct effect of trust on four flexibility dimensions are all significant at the 0.01 level (Model 1). Second, the direct effect of shared vision on volume flexibility is signifi-cant at p < 0.05 and other flexibilities are all significant at p < 0.01 level (Model 2). Third, in Model 3, the direct effects of trust on volume, mix, new product, and delivery flexibility were added to the original model, includ-ing the indirect effects, as mediated by shared vision. The results reveal that direct effect of trust on volume flexibility at p< 0.05 and delivery flexibility is
TA B L E 5 The E ff ect o f S ocial M echanisms o n S upplier Flexibility Dependent variables V OL NP MIX D LV Model 1 Standar dized B (t-V a lue) Control variables Size − .019 (0.084) .012 (0.96) .023 (.095) .092 (.076) DUR .065 (86) .032 (.429) − .019 (− .252) .008 (.113) IND − .057 (− .754) − .007 (− .092) − .059 (− .778) − .074 (− MTU − .057 (− .578) − .064 (− .656) .034 (.344) − .165 (− TTU .132 (1.342) .182 ∗(1.857) .119 (1.221) .239 ∗∗ (2.529) Independent variables TST .225 ∗∗∗ (2.964) .239 ∗∗∗ (3.161) .236 ∗∗∗ (3.129) .308 ∗∗∗ Model 2 Control variables Size − .015 (− .199) − .004 (− .05) .014 (.187) .081 (1.15) DUR .052 (.681) .001 (.012) − .044 (− .598) − .023 (− IND − .059 (− .763) − .028 (− .386) − .073 (− .985) − .092 (− MTU − .068 (− 0.677) − .084 (− .885) .017 (.177) − .186 ∗∗ (− TTU .136 (1.366) .153 (1.643) .102 (1.066) .218 ∗∗ (2.386) Independent variables SHV .164 ∗∗ (2.126) .384 ∗∗∗ (5.325) .31 ∗∗∗ (4.178) .389 ∗∗∗ (Continued) 171
TA B L E 5 (Continued) Dependent variables SHV VOL N P M IX DL V Model 3 Standar dized B (t-V a lue) Control variables Size .054 (.805) − .023 (− .302) − .006 (− .087) .009 (.127) .075 (1.075) DUR .083 (1.243) .06 (.79) .004 (.049) − .04 (− .542) − .018 (− .251) IND .063 (.939) − .061 (− .81) − .029 (− .4) − .075 (− 1.012) − .094 (− 1.342) MTU .047 (.547) − .06 (− .611) − .081 (− .866) .022 (.228) − .18 ∗∗ (− 1.987) TTU .092 (1.063) .126 (1.283) .15 (1.608) .096 (1.01) .21 ∗∗∗ (2.323) Independent variables TST .495 ∗∗∗ (7.376) .193 ∗∗ (2.185) .067 (.805) .113 (1.313) .155 ∗(1.908) SHV .066 (.454) .35 ∗∗∗ (4.179) .253 ∗∗∗ (2.946) .311 ∗∗∗ (3.831) TST = trust; SHV = shared vision; VOL = volume flexibility; MIX = mix fl exibility; VOL = volume flexibility; DL V = delivery fl exibility; NP = new p roduct fl exibility; DUR = duration; IND = industry type m easurement; MTU = market turbulence measurement ; TTU technological turbulence; T ST = trust; SHV = shared value. ∗p < .10. ∗∗p < .05. ∗∗∗ p < .01. 172
significant at the 0.1 level, and there is no effect of trust on mix flexibility and new product flexibility. In addition, the effect of shared vision on vol-ume flexibility is nonsignificant, and new product flexibility, mix flexibility, and delivery flexibility are all significant at p < 0.01 level. Further, details of the result also show that the effect of trust on shared vision is signifi-cantly supported (β = 0.495, p < 0.01). Additionally, we used Sobel’s (1982) test to verify the mediated effect of shared vision on volume flexibility. The result supports that there is no mediated effect of shared vision on volume flexibility (z = 0.604, p < 0.05). Furthermore, the relation between trust on mix flexibility and new product flexibility controlling the mediator (shared vision) is zero, suggesting that the effects of trust on mix flexibility and new product flexibility is fully mediated through shared vision. When the mediator was controlled, the effect of trust on delivery flexibility was signif-icant (β = 0.193, p < 0.05). The relation between trust on delivery flexibility through shared vision is ascertained by analyzingβ for trust on delivery flex-ibility added shared vision (β = 0.155) to model is significantly smaller than direct effect of trust on delivery flexibility in Model 1 (β = 0.308). The data suggest that shared vision is a partial mediator between trust and delivery flexibility. Therefore, the effect of trust on mix and new product flexibility is fully mediated, and delivery flexibility is partial mediated by shared vision (Baron and Kenny 1986; Venkatraman 1989). The finding showed trust is the main drive of volume flexibility instead of shared vision. Finally, size, duration of relationship, and industries as the control variables revealed no significant effect on dependent variable. In contrast, market turbulence has negative effect on supplier delivery flexibility (β = −0.18, p < 0.05) and technological turbulence has positive effect on supplier delivery flexibility (β = 0.21, p < 0.01). The possible explanation is customer-changing prefer-ences may constrain supplier accommodation to rush orders or adjustment of production planning. Under higher technology change rate, suppliers might promote their delivery flexibility to reduce risk of obsolete inventories. However, our findings reveal shared vision plays a mediator between trust and delivery flexibility with market turbulence and technological turbulence as control variables.
CONCLUSIONS AND IMPLICATIONS
How the B2B buyer promotes supplier flexibility through its relationships is critically important and has been unexplored. The buyer teams up with its suppliers to establish long-term collaborative relationships for a sustain-able and competitive supply chain. Long-term supply chain success requires trust to develop a shared vision of the future. A customer-oriented buyer should be able to adjust suppliers’ capacity to match dynamic customer demand. Findings from this study provide important insights into how
social mechanisms lead to supplier flexibility for responsiveness. We suggest that the buyer leverage supplier flexibility to meet customer requirements through social mechanisms. Exchange partners with trust will also ensure their shared vision development. Partners with a shared vision will view their goal as cooperative instead of competitive. A shared vision helps facili-tate group actions that benefit the whole supply chain. Concerning the effect of social mechanisms on flexibility, although trust induces supplier flexibil-ity, this study finds shared vision as the mediator between trust among mix, new product, and delivery flexibility. On the other hand, trust has direct impact on volume flexibility without a mediator.
Trust and Supplier Flexibility
Flexibility is the willingness to alter conditions to meet an unanticipated situation (Johnston et al. 2004). Buyer–supplier collaboration strengthens the buyer’s responsiveness (Squire et al. 2009). Suppliers need to reallo-cate their capacity and change over to meet volume flexibility requirements from buyers. Achieving mix flexibility and new product flexibility need more investments [e.g., human resources or research and development (R&D) expenditures]. Slack (2005: 1193) claimed, “volume and delivery flexibility seemed to be interchangeable to some extent.” A buyer not only deliv-ers to customdeliv-ers on time, but also has the ability to change the planned delivery date (Sawhney 2006). According to Johnston et al. (2004), higher levels of buyers’ perceived trust of suppliers lead suppliers to involve and facilitate performance. From the social exchange theory, trust building is a gradual process through increased exchange and positive outcomes. Joshi and Stump (1999) suggested that trust strengthens the effect of supplier asset specificity on their joint action relationships. While a supplier tries to meet a buyer’s requirements (i.e., quickly change quantities, produce various product combinations, minimize the time to implement new product devel-opment and accommodate special orders), the supplier needs to change over its capacity and production plans and devote efforts in R&D and human resources. If a supplier benefits from cooperating with the buyer, it will be willing to maintain the relationship and commit to the buyer with the expec-tation for future benefit. Hence, trust positively relates to supplier flexibility for responsiveness to a buyer’s needs.
Shared Vision as the Mediating Role on Supplier Flexibility
Shared vision is regarded as a necessary condition (Li 2005) and a bonding mechanism (Tsai and Ghoshal 1998) for exchange partners to combine or integrate resources. Shared vision means that the buyer and supplier have similar objectives and a shared understanding of the importance of collabo-ration. Ratnasingham and Kumar (2000) characterized trust by an increased
level of open communication and information sharing. A buyer with high-perceived trust will have more confidence that the suppliers will act hon-estly. Under this circumstance, the buyer is willing to share more strategic and sensitive information with its suppliers, thus the buyer–supplier relation-ships possess common goals and perceive the dyadic relationship as a whole team. This research found that trust facilitates buyer–supplier shared vision. Volume flexibility enables a firm to meet customer satisfaction by quickly providing volume in response to unanticipated demand and quickly reducing volume to eliminate excess and obsolete inventories. Additionally, Ndubisi et al. (2005) showed no significant relationship between cost, tech-nology consideration, and volume flexibility. They concluded that the level of supplier involvement is not as high as other flexibility dimensions. A buyer that highly trusts the supplier to keep its commitment and perform internal capacity adjustment for meeting volume change enhances supplier volume flexibility. Suppliers gain mix flexibility through both direct labor and indi-rect labor to design and implement the expanded product mix. Suarez et al. (1996) described that skilled workers or sophisticated equipment to achieve mix flexibility increases additional cost. Suppliers’ involvement in new prod-uct development promotes new prodprod-uct flexibility (Narasimhan and Das 1999). A. M. Sanchez and Perez (2003) argued that supplier development sig-nificantly contributes to new product time and cost minimization. Suppliers’ involvement, including R&D, marketing, and manufacturing, is essential to new product development. The new product introduction process also involves more people in the decision-making process and greater uncer-tainty. With regard to mix and new product flexibility, suppliers need greater involvement and more investments to achieve the buyer’s requirement. Investment risks include additional cost and holdup between buyer–supplier transactions. Thus, a buyer should develop tighter relationships with suppli-ers to drive them to make risky investments. Findings from this study suggest that shared vision mediates the relationship between trust and mix/new product flexibility. In other words, a buyer with a high level of trust in its suppliers builds a shared vision to promote its suppliers’ mix/new product flexibility. Oke (2005) indicated that delivery flexibility is the consequence of volume and mix flexibility. Kandemir, Yaprak, and Cavusgil (2006) presented the concept of “alliance coordination,” and Miller, Besser, and Malshe (2007) further claimed that shared vision generates alliance coordination. From this perspective, closely coordinating with the buyer facilitates suppliers’ deliv-ery flexibility, involving suppliers’ operation decision. Hence, shared vision influences suppliers’ delivery flexibility so that suppliers act responsively.
Managerial Implications and Theoretical Implications
Suppliers can display flexibility toward buyer-requested adjustments (Noordewier et al. 1990). With respect to flexibility, buyers who quickly
respond to customers’ product requirement or change technical specifica-tions cultivate a closer connection to customers (Homburg 1998). While organization and marketing studies have already discussed trust and shared vision, this study focuses on the effects of these two social mechanisms on supplier flexibility. The developed conceptual model gives business managers insightful assessment of interorganization relationships and man-agement practices in supply chains. The key contributions of this study include a profound understanding of the buyer’s roles for suppliers’ respon-siveness and identifying how the social mechanisms of trust and shared vision influence their expectation of suppliers’ compliance to respective flex-ibility. This research demonstrates two specific managerial and theoretical implications and gives a few ideas for future research.
MANAGERIAL IMPLICATIONS
First, the results highlight that shared vision is the critical determinant on suppliers’ mix, new product, and delivery flexibility. From the resources-based view, managers of buyer firms need to build new capabilities, transform their resource base, and reconfigure processes to leverage new valuable resource combinations to sustain competitive advantage in chang-ing environments. Powell (1990) argued that firms engagchang-ing in fast-movchang-ing industries with short product cycles are likely to engage in network partner-ships to reposition products rapidly and respond quickly to changing market conditions. In today’s turbulent business environment, firms are teaming up with each other due to technological complexity and diverse customer needs. In the new business model, competitors would rather be individual firms than an entire supply chain. Interfirm relationships with a shared vision have collective goals and aspirations, and strategically align with mutual interests. Specifically, this value centers on the belief that collaboration leads to better mutual benefit. To achieve buyers’ flexibility requirement, suppli-ers should commit and be willing to allocate their resources. We suggest that managers be involved in shared vision development between interfirms rather than using a buying–selling approach.
Second, research has regarded trust as a catalyst in the buyer–supplier relationship, since it provides an expected successful exchange. T. K. Das and Teng (2001) argued that trust is a state of mind that reduces perceived relation risk. When trust exists between exchange parties, they are more willing to increase information sharing. In addition, when buyers trust in suppliers, they are inclined to provide critical or confidential information to suppliers. Although our findings suggest that trust alone advances sup-plier volume flexibility, trust is still the important element of buyer–supsup-plier relationships. To advance supplier flexibility requirements, managers should frequently interact with suppliers to involve in mutual trust as an integral part of relationships and then develop a shared vision through communication and information sharing.
THEORETICAL IMPLICATIONS
Trust is the crucial element in the industrial marketing relationship. For instance, Johnston et al. (2004) empirically showed that supplier’s perceived trust has significant impact on joint responsibility and flexibility arrangement. Handfiel and Bechtel (2002) also found out that higher levels of buyer trust relate to higher levels of supplier responsiveness. Trust significantly influ-ences the relationship commitment in which partners maximize their efforts to maintain relationships (Morgan and Hunt 1994). The social exchange the-ory suggests that causal relationship between trust and commitment result from the principle of generalized reciprocity (McDonald 1981). Suppliers that are willing to make specific asset commitments develop higher level of trust (Handfiel and Bechtel 2002). Trust attracts and secures partner commit-ments (Kingshott 2006). Our finding is consistent with the previous studies that trust significantly impacts supplier flexibility.
Our framework provides helpful guidance for identifying and examin-ing relationships between buyers and suppliers. Despite the strong linkage between trust and supplier flexibility, our model suggests that shared vision plays a crucial role among trust, mix, new product, and delivery flexibility. As prior discussions in our study, suppliers require high levels of involve-ment and idiosyncratic asset investinvolve-ments to achieve mix, new product, and delivery flexibility. The risk of those prerequisites is higher than volume flexibility achievement. Although trust provides a motivation for trustee com-mitment, whether that commitment manifests in actions depends on the risk of involvement and investments. However, trust leads to a high level of sensitive information (Handfiel and Bechtel 2002) and critical and pro-prietary information sharing (Lambe et al. 2009). Shared vision develops through communication and information sharing. While a buyer perceives its suppliers as trustworthy, increased strategic or critical information-sharing facilitates the same team identification and whole goal understanding. Thus, suppliers are more willing to make adaptations for buyer needs. In contrast to most previous studies, which suggest that trust always leads to desir-able outcomes (Dirks and Ferrin 2001), we demonstrate that shared vision building effectively extends trust and the commitment theory.
LIMITATIONS AND FURTHER RESEARCH
Future research can address several limitations of this study. First, because our samples only consist of buyers, the results of a single investigation may have limited generalizability. However, this limitation should be somewhat tempered because every respondent was from a different firm. Second, this study empirically demonstrates social mechanisms: (1) Trust has significant effect on supplier flexibility; (2) trust helps buyers and suppliers to evolve a shared vision; and (3) shared vision is the mediating role on supplier
flexibility (i.e., mix, new product, and delivery flexibility). However, we do not measure the risk to suppliers of providing respective flexibility in detail. Future studies might examine perceived risk on respective flexibility from the supplier’s side. Finally, this study focused on the effect of trust, which refers to the firm’s intention to make things work rather than the ability to perform (T. K. Das and Teng 2001; Nooteboom 1996). Following Singh and Sirdeshmukh (2000), goodwill trust and competence trust may provide more insight into exchange relationships. How does a buyer’s perceived competence trust in suppliers affect suppliers’ actions in terms of flexibil-ity? Theoretically intriguing and practically important questions such as this, deserve further study.
ACKNOWLEDGMENTS
The authors thank Dr. J. David Lichtenthal and the anonymous JBBM review-ers for comments on earlier vreview-ersions of the manuscript. The second author is grateful to the National Science Council, Taiwan (NSC-98-2410-H-029-021-MY2) foundation for research support.
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