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Trust and commitment in relationships among medical equipment suppliers: transaction cost and social exchange theories.

Successful supply chain management provides corporations with competitive advantages. A complete supply chain must be in place to meet the related needs of medical products. In 1994, Taiwan implemented a national health insurance policy. The Bureau of National Health Insurance (BNHI) granted a completely free choice of health care providers and therapies to all insured citizens. Most hospital funding is disbursed from the BNHI. Consequently, the medical industry has gradually become competitive. Building effective ongoing supply chain relationships is critical for hospitals intending to minimize operating costs.

Trust and commitment between and among partners has become a crucial element for supply chain management (Chen, Yen, Rajkumar, & Tomochko, 2011). Effective supply chain planning based on information sharing and trust among partners is an essential requirement of successful supply chain management (Kwon & Suh, 2004; Nyaga, Whipple, & Lynch, 2010; Vijayasarathy, 2010). Commitment involves continuity or long-term cooperation between parties to maintain the relationship, and helps increase the efficiency and effectiveness of relationships among supply chain members (Johnston, McCutcheon, Stuart, & Kerwood, 2004). Morgan and Hunt (1994) found that the presence of both commitment and trust raises efficiency, productivity, and effectiveness.

Two theories that have been adopted to understand, explain, and study supply chain management are transaction cost theory (TCT; Cai, Jun, & Yang, 2010; Chen et al., 2011; Kwon & Suh, 2004) and social exchange theory (SET; Griffith, Harvey, & Lusch, 2006; Kwon, 2008; Kwon & Suh, 2004; Narasimhan, Nair, Griffith, Arlbj0rn, & Bendoly, 2009). The TCT has been applied in vertical and cross-organizational integration, whereas the SET provides a foundation for studying organizational relationships. Both theories offer a comprehensive explanation of the strategic alliances of nonprofit organizations in cross-hospital supplier management studies (Chang, Hwang, Hung, Kuo, & Yen, 2009). Although researchers have focused on the relationship between related variables and the level of trust, empirical studies in the context of supply chains in which the relationship between trust and commitment--the ultimate facilitator of supply chain success--has been examined, are absent. Therefore, in this study we aimed to bridge this gap in knowledge about trust and commitment in supply chain management.

We determined the specific factors that affect the level of trust and commitment in relationships among medical equipment suppliers. Several constructs related to trust have been discussed and tested in the literature, such as those comprising the TCT (asset specificity, behavioral uncertainty, and information sharing) and SET (communication, perceived benefits, and relationship tenure). Kwon and Suh (2004) proposed a research model based on the TCT and SET to investigate the primary determinants affecting trust. Although the current study is similar to that of Kwon and Suh because TCT and SET were adopted in both studies to investigate the primary determinants affecting trust, differences between the studies exist. In this study, we investigated the partnership between hospitals and suppliers, and used communication, perceived benefits, and relationship tenure as variables for the SET. In addition, the primary contributions in this research include an investigation of the integration of TCT, SET, trust, and commitment in explaining the partnership between hospitals and suppliers, and an empirical evaluation of the factors that critically affect this partnership. This study is expected to provide a reference for management decision variables, (referring to the transaction cost theory constructs and social exchange theory constructs mentioned above), for supporting partner relationships, and building successful supplier management.

Literature Review and Hypotheses

Transaction Cost Theory

TCT originates in transaction cost economics (Coase, 1937), which is extensively applied in the economic and social domains (Rindfleisch & Heide, 1991), and is used to explain the reason corporations exist and how corporate boundaries are determined (Coase, 1937; Williamson, 1989). This theory primarily centers on economic aspects of relationships and is useful in explaining why corporations initiate a relationship. The shortcomings of TCT have been identified as emphasizing outcomes and institutional arrangements instead of processes and the environment, and disregarding trust as irrelevant. Williamson (1989) emphasizes the three key characteristics of uncertainty, frequency, and asset specificity, which affect the amount of transaction cost. In TCT studies, Kwon and Suh (2004) investigated the relationships between supply chain partners using transaction cost analysis, according to which it is argued that information sharing has been singled out as the most important factor for successful supply chain management. In this study we borrowed the concept of TCT from Kwon and Suh, who defined TCT as comprising three distinct dimensions: asset specificity, behavior uncertainty, and information sharing.

Social Exchange Theory

SET has received considerable attention in the study of intercorporate relationships, including supplier-buyer relationships (Kwon & Suh, 2004; Narasimhan et al., 2009). In SET it is emphasized that attitudes and behaviors are determined by the rewards of interaction minus the penalty or cost of that interaction (Griffith et al., 2006). This theory helps researchers gain an understanding of the buyer-supplier relationship characterized by lock-in situations where one party is greatly dependent upon another party (Narasimhan et al., 2009). The theory originates from anthropology, and covers many different domains, including sociology, social psychology, behavioral psychology, philosophy, and economics (Griffith et al., 2006; Narasimhan et al., 2009). SET has recently received considerable attention in the literature on interfirm relationships, including manufacturer-distributor relationships, supplier-buyer relationships, and interpartner relationships in strategic alliances (Kwon, 2008). Although there are differing perspectives on exchange relationships, scholars agree that one of the core assumptions of SET is that the benefits given are contingent upon the expectation of a future unspecified benefit (Shiau & Luo, 2012; Zapata, Olsen, & Martins, 2013). In this paper we presented a trust-effected SET to better understand critical factors that affect a medical supplier relationship. The SET can refer to several categories but in this paper we covered only the following social exchange characteristics: communication, partner reputation, perceived benefit fairness, and relationship tenure.

Trust

Trust is one of the most frequently cited dimensions in the supply chain relationship literature and is an important factor in relational exchange, which is considered a central feature of a strategic partnership (Kwon & Suh, 2004). Hausman and Johnston (2010) defined trust as "confidence in the integrity and reliability of another party, rather than confidence in the partner's ability to perform a specific action". Trust exists when one party has confidence in an exchange partner's reliability and integrity (Morgan & Hunt, 1994). Lancastre and Lages (2006) argue that trust is a working relationship, and this fact has repercussions on a firm's activities. These repercussions can be defined as a firm's belief that a partner firm will perform actions that benefit the firm, as well as not act unexpectedly to cause negative outcomes or risks for the firm. Some researchers propose that trust between partners constitutes one of the key factors for becoming long-term partners (Hausman & Johnston, 2010; Ybarra & Turk, 2009).

Commitment

In previous studies on supply chain management researchers have emphasized the important roles that supplier commitment and satisfaction play in successful exchange relationships (Ghijsen, Semeijn, & Ernstson, 2010; Wong, 2000). Morgan and Hunt (1994) defined commitment as "an exchange partner believing that an ongoing relationship with another is so important as to warrant maximum efforts at maintaining it". Several researchers (see e.g., Hausman & Johnston, 2010) support the role of commitment in compliance with requests for joint action or cite it as the most influential of relational elements in its effect on joint action. Kwon and Suh (2004) indicated that commitment is the key to supply chain integration. In the current study we define commitment as partners who are willing to devote themselves to a sustainable partnership and maintain cooperation through temporary compromises.

Hypotheses

We adopted three variables of TCT: asset specificity, behavioral uncertainty, and information sharing, which focus on examining recurring exchanges; an area often ignored in TCT studies. Asset specificity refers to the transferability of assets that support a given transaction. Asset specificity in supply chain partners will reduce partner dissatisfaction and positively relates to commitment for both sides of a partnership (Anderson & Weitz, 1992; Kwon & Suh, 2004). Specific asset investments in supply chain partners will increase the level of trust in the partners. Behavioral uncertainty created by a supply chain partner will decrease the level of trust of its trading partner because this creates a performance evaluation problem. Behavioral uncertainty perceived in relationships with supply chain partners will decrease the level of trust in other partners (Kwon & Suh, 2004). Information sharing has been singled out as the most important factor for successful supply chain management because it will lower the degree of behavioral uncertainty and indirectly improve the level of trust among supply chain partners (Kwon & Suh, 2004). Thus, we proposed three hypotheses, as follows:

Hypothesis 1: In the context of medical equipment supplier relationships, asset specificity will have a positive influence on trust.

Hypothesis 2: In the context of medical equipment supplier relationships, behavioral uncertainty will have a negative influence on trust.

Hypothesis 3: In the context of medical equipment supplier relationships, information sharing will have a positive influence on trust.

In a high-trust relationship, partners enjoy open communication and are also more inclined to take risks compared to partners in a low-trust relationship (Kwon & Suh, 2004). Kwon (2008) found that, in SET, communication has a significant influence on trust in partnerships. Morgan and Hunt (1994) considered communication and shared values to be the key antecedents of trust, and assessed these values in relation to their effect on improving supplier-retailer relationships. Chang et al., (2009) indicated that the perceived benefits associated with SET have a positive influence on both strategy and trust (Liu, Sia, & Wei, 2008). Gainey and Klaas (2003) investigated the effects of outsourcing and used transaction cost economics, SET, and the resource-based view to identify factors considered to affect client satisfaction with external training vendors. The results showed that long-term relationships between businesses and suppliers lead to increases in positive affect, trust, and satisfaction. Based on the above arguments, we reasoned that these three variables (communication, perceived benefits, and relationship tenure) are all relevant to social exchange in the context of medical equipment supplier relationships and, thus, we included them in the research framework. Therefore, we proposed the following hypotheses:

Hypothesis 4: In the context of medical equipment supplier relationships, communication will have a positive influence on trust.

Hypothesis 5: In the context of medical equipment supplier relationships, perceived benefits will have a positive influence on trust.

Hypothesis 6: In the context of medical equipment supplier relationships, relationship tenure will have a positive influence on trust.

Commitment is a key factor in achieving supply chain integration, and trust is critical to fostering commitment among supply chain partners. Trust can raise the value of trading relationships such that customers make greater commitments to the relationship. Because commitment can be easily harmed, organizations tend to search for partners who are trustworthy (Morgan & Hunt, 1994). In a trading relationship between medical equipment suppliers a framework needs to be developed to link the level of trust and commitment, with the aim of ensuring that certain actions benefiting both parties will be consummated to improve overall relationships. Several researchers have found a positive relationship between trust and commitment (Anderson & Weitz, 1992; Chen et al., 2011; Kwon & Suh, 2004; Morgan & Hunt, 1994). Based on these arguments, we reasoned that trust is a major determinant of relationship commitment. Therefore, we formed the following hypothesis:

Hypothesis 7: In the context of medical equipment supplier relationships, trust will have a positive influence on commitment.

The essence of the research framework in this study was that successful implementation of medical equipment supplier relationships requires a commitment between or among the supply chain partners, and trust is a critical element to sustain such a situation.

Method

Instruments

The instruments were developed after a thorough review of several previous studies, specifically pertaining to TCT and SET in practice and in theory. Measurement items were modified to conform to the adoption context of medical equipment supplier relationships. We adopted standard psychometric scale development procedures recommended by DeVellis (2003). TCT constructs (asset specificity, behavioral uncertainty, and information sharing) were adapted from several previous researchers (Cai et al., 2010; Chang et al., 2009; Gainey & Klaas, 2003; Kwon & Suh, 2004; Ybarra & Turk, 2009), SET constructs (communication, perceived benefits, and relationship tenure) were adapted from several other studies (Chang et al., 2009; Gainey & Klaas, 2003; Kwon & Suh, 2004; Liu et al., 2008; Ybarra & Turk, 2009), trust scales were adapted from several previous researchers (Crosby, Evans, & Cowles, 1990; Kwon & Suh, 2004; Morgan & Hunt, 1994), and commitment scales were adapted from previously used behavior measures (Anderson & Weitz,1992; Kwon & Suh, 2004; Morgan & Hunt, 1994).

We conducted an initial pilot study to ensure that the survey questionnaire was worded in a concise and understandable style. Seven researchers who were interested in hospital management and who specialized in medical supply chain management reviewed the initial questionnaire, which was then revised based on their comments. The face and content validities of the questionnaire were also verified based on in-depth interviews with these professionals, and the generation of constructs based on an extensive study of prior literature in related fields (TCT, SET, trust, and commitment). Adaptation of measurement items had been validated in previous empirical studies.

To improve questionnaire validity further, we conducted a pilot study prior to the actual test. The initial questionnaire was distributed to 60 respondents who were medical equipment procurement staff (including executives). The reliability scores, based on the report of Cronbach's alphas ranging from 0.652 for relationship tenure and 0.944 for communication, show that the scales in this study were appropriate for measurement of the constructs of interest. Participants rated items using 5-point Likert-type scales ranging from 1 (strongly disagree) to 5 (strongly agree). To reduce the potential ceiling (or floor) effects, we induced monotonous responses to the items designed for measuring the same construct.

Sample and Descriptive Statistics

Data were collected from 128 hospitals in Taiwan (including medical centers, regional hospitals, and district hospitals). As a preliminary step, we contacted a manager in each hospital to ensure his/her cooperation. We used a census sample in this study and utilized mail surveys for hospitals that were willing to distribute the survey. All participants in the study were volunteers. A total of 512 respondents accessed the survey link and the demographic details are provided in Table 1.

Results

Structural equation modeling was used in a comprehensive, combined analysis of both the measurement and structural model. Structural equation modeling using the maximum likelihood estimation method was applied to the sample data using LISREL version 8.72. We also analyzed the psychometric properties of the variable measurement scales and handled missing data using listwise deletion.

The measurement model was applied to specify the relationships between observed variables (manifest variables or indicators) and latent variables (constructs measured). The assessment of the measurement model included three indices: reliability coefficients (Cronbach's alpha), average variance extracted (AVE), and composite reliability (CR) coefficients (Bagozzi & Yi, 2012; Fornell & Larcker, 1981; Hair, Black, Babin, & Anderson, 2010; Joreskog & Sorbom, 2005).

Measurement Model Evaluation

In this study we used Cronbach's alpha coefficients to assess internal consistency. The results presented in Table 2 show that the values of the Cronbach's alpha coefficient ranged from 0.676 to 0.931. All variables except relationship tenure displayed a higher coefficient than the 0.10 benchmark recommended by Nunnally (1918). The composite reliability coefficients range from 0.747 to 0.932 (see Table 2). The constructs also exhibited a higher composite reliability than the benchmark of 0.6 (Fornell & Larcker, 1981). Convergent validity and discriminant validity were evaluated by calculating the AVE for each factor within each model. An AVE exceeding the benchmark of 0.5 also indicated convergent validity and acceptability of the construct (Fornell & Larcker, 1981). All constructs had AVE values ranging between 0.578 and 0.775 (see Table 2). Moreover, all constructs exhibited a higher AVE than the 0.5 benchmark and, thereby, reached adequate convergent validity. Trust was valued the lowest (0.578) among the eight variables.

Structural Model Estimation

The structural equation modeling obtained from the theoretical model includes chi square, chi square/df, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root mean square error of approximation (RMSEA), and comparative fit index (CFI; Bagozzi & Yi, 2012; Hair et al., 2010; Joreskog & Sorbom, 2005). The indices for measurement of fit of the structural model are provided in Table 3. The chi square p value did not meet the recommended .000 because of the relatively large sample size employed (283 respondents). A ratio of chi square to degrees of freedom which is smaller than 5:1 is considered an acceptable fit, and the chi square value should be as small as possible. The GFI and AGFI values should preferably exceed 0.90. The more liberal cut-off point of 0.80 has been used to indicate good model fit (Hair et al., 2010). An RMSEA value of .08 or below is preferable. However, Raykov (2001) argued that .08 to .1 represents a moderate value. Most indices in our structural model exhibit a reasonably high level of goodness of fit.

Interpretation of Structural Model Results

The model path coefficients (loading and significance) and [R.sup.2] value for each variable are shown in Figure 1, and these support the hypothesized model. The model also explains a substantial portion of variance for all the endogenous variables: 68.6% for trust and 68.1% for commitment.

Standardized beta coefficients from the estimated structural model are reported in Figure 1. All seven causal paths are specified in the hypothesized model and five of these were found to be statistically significant and these results supported our model. As posited in H1, the estimated positive coefficient estimates for the paths from asset specificity to trust are significant. As proposed in H2, the negative relationship between behavioral uncertainty and trust is significant. However, H3 was not supported as no significant relationship was found between information sharing and trust. Trust was influenced by communication and perceived benefits, supporting both H4 and H5. Contrary to the

expectation, relationship tenure did not influence trust so H6 was not supported. From H7, we expected that trust would be positively related to commitment. According to the results of a direct relationship between trust and commitment, H7 was supported.

Discussion

In this investigation we employed two models for medical equipment supplier relationships, TCT and SET, to understand their predictive power in regard to trust and commitment in relationships between and among medical equipment suppliers. Five of the seven paths specified in the integrated model were found to be significantly affected by trust. TCT constructs (asset specificity and behavioral uncertainty) and SET constructs (communication and perceived benefits) accounted for 68.6% of the variance in trust, and trust accounted for 68.1% of the variance in commitment, which demonstrates the efficacy of the model. The results indicate that the combined TCT and SET models provide a complete explanation for the variance in medical equipment supplier relationships. Our analysis combining the two models provided a better explanation of the relationship than analyses using only one of the two models.

In the integrated model, three independent variables associated with TCT (behavioral uncertainty, asset specificity, and information sharing) remain important contributors in explaining trust. Two paths (behavioral uncertainty and asset specificity) were found to be positively significant, which is consistent with results in previous studies (Anderson & Weitz 1992; Chen et al., 2011; Kwon & Suh, 2004). However, in this study information sharing was not found to be an antecedent of positive trust for medical equipment supplier relationships. This difference may result from a difference in contexts under study. Information sharing sometimes requires releasing guarded financial and other operating information to partners who might have been, or will be, competitors, because effective information sharing is heavily dependent on trust, beginning within the corporation and ultimately extending to supply chain partners (Bowersox, Closs, & Stank, 2000). The medical industry is intensely competitive and companies typically do not share information. A greater amount of information sharing and a longer partnership build trust between or among partners. Because hospitals seldom provide information to partners (financial data, supply chain data, and profit sharing), hospitals limit their information sharing to suppliers, and information sharing has a nonsignificant effect on trust. To conclude, success will only be achieved in transparent relationships based on trust and commitment established over a period of time (Handfield, Krause, Scannell, & Monczka, 2000). For implementing trust in supply chain management, many conditions affecting the level of trust in various perspectives have yet to be identified and tested.

Based on the existing SET literature, in the current study we examined the significant, direct, and positive effect of antecedents on trust for perceived benefits and communication. As predicted, perceived benefits and communication had positive and significant effects on trust, and these results are consistent with those in previous studies (Chang et al., 2009; Kwon & Suh, 2004; Liu et al., 2008). However, differing from the results of previous researchers, we found that the effect of relationship tenure on trust was nonsignificant. Hospitals in Taiwan have recently adopted the build, operate, and transfer (BOT) equipment purchasing process to reduce expenses and many hospitals have instituted centralized purchasing systems to lower costs. These cost-saving policies alter the relationship tenure between hospitals and partners and lower levels of trust. Therefore, trust had a nonsignificant effect. Finally, the result for the trust and commitment dimensions indicates that trust had a significant influence on commitment, which is an important factor in achieving supply chain integration. This finding is in line with numerous studies relating to the influence of trust (Anderson & Weitz, 1992; Chen et al., 2011; Kwon & Suh, 2004; Morgan & Hunt, 1994).

Limitations and Directions for Future Research

The results in this study provide a strong theoretical foundation for understanding medical supplier management. The model's developmental and empirical testing demonstrate that a hospital can positively influence the trust outcomes of its medical equipment partners through asset specificity, behavioral uncertainty, perceived benefits, and communication. However, in the structural model estimation index, the GFI and AGFI had only a moderate level of fit in this study for the following reasons: Few previous researchers have examined the management of medical supply chains, therefore, the index fit obtained in this study might be acceptable. Additionally, satisfaction is an antecedent of relationship quality and has a critical effect on it. Trust and commitment were the critical variables in this study. In future research, by integrating a broader theoretical framework (i.e., satisfaction and loyalty) with literature on supplier management, we expect to provide a reference for the management decision variables defined in the introduction, and the support of partner relationships in supplier management.

http:dx.doi.org/ 10.2224/sbp.2013.41.7.1057

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CHENG-MIN CHAO, CHENG-TAO YU, BOR-WEN CHENG, AND PEN-CHEN CHUANG

National Yunlin University of Science and Technology

Cheng-Min Chao and Cheng-Tao Yu, Department of Industrial Engineering and Management; Bor-Wen Cheng, Department of Industrial Engineering and Management and Department of Institute of Global Operations Strategy and Logistics Management; Pen-Chen Chuang, Department of Institute of Global Operations Strategy and Logistics Management, National Yunlin University of Science and Technology.

Correspondence concerning this article should be addressed to: Cheng-Min Chao, Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, 123 University Road, Sec. 3, Yunlin 640, Taiwan, ROC. Email: g9521807@yuntech.edu.tw

Table 1. Profiles of Respondents

Factor/ Level      N    %    Factor/Level                     N    %

Gender                       Formal Education
Male              104  36.7  High school/technical school     23   8.1
Female            179  63.3  Faculty degree/bachelor degree  221  78.1
Tenure                       Master's degree or above         39  13.8
f
<1 year            12   4.2  Type of Hospital
1-3 years          45  15.9  Local hospital                  127  44.9
3-5 years          51  18.0  Regional hospital               101  35.7
5-10 years         80  28.3  Medical center                   55  19.4
10 years or more   95  33.6

Note. N = 283.

Table 2. Construct Reliability Results

Construct                Cronbach's [alpha]       CR       AVE

Asset specificity                 .854           .860     .607
Behavioral uncertainty            .862           .863     .613
Information sharing               .925           .923     .668
Communication                     .931           .932     .775
Perceived benefits                .841           .860     .674
Relationship tenure               .676           .747     .598
Trust                             .869           .872     .578
Commitment                        .801           .829     .713

Table 3. Measures of Model Fit and Reported Values for the Structural
Model

Fit index                    Recommended      Model        Model fit
                               values        values

Chi square goodness-of-fit   p [??] .05   values 697.52     Poor fit
  test                                       (p = .000)
Chi square/degrees of          [??] 5       3.03 (df        Good fit
  freedom (df)                                 = 230)
Goodness-of-fit index (GFI)    [??] .9         .829       Moderate fit
Adjusted goodness-of-fit       [??] .9         .777       Moderate fit
  index (AGFI)
Root mean square error of      [??] .08        .085         Good fit
  approximation (RMSEA
Comparative fit index (CFI)    [??] .9         .958         Good fit
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Author:Chao, Cheng-Min; Yu, Cheng-Tao; Cheng, Bor-Wen; Chuang, Pen-Chen
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9TAIW
Date:Aug 1, 2013
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