Impact of collaborative transportation management on logistics capability and competitive advantage for the carrier.
Collaborative transportation management (CTM) is a relatively new collaboration model that addresses inefficiencies in the transportation process in supply chains. This study empirically examines the impacts of CTM on logistics capability and competitive advantage of carriers within a supply chain, and analyzes the relationships between logistics capability and competitive advantage. Based on factor analysis, key factors of CTM practice, key logistics capabilities, and key competitive advantage factors for carriers are identified. The relationships proposed in the framework are tested using structural equation modeling. Analytical results demonstrate that CTM practice enhances logistics capability and competitive advantage, with logistics capability reinforcing competitive advantage. In other words, CTM can have a direct and positive influence on competitive advantage, and an indirect influence through logistics capability. The results also indicate that carriers establish higher levels of integrated relationship with their customers in a CTM model can lead to improved customer-service capability and enhanced cost-leadership advantage.
Supply chain collaboration is the latest paradigm in the fields of transportation, logistics, and supply chain management. Thus, collaboration among supply chain partners has become an important issue of considerable interest to both academics and planners. It is frequently an essential component of strategies for companies operating in extremely competitive markets. A recent collaborative initiative called Collaborative Planning, Forecasting, and Replenishment (CPFR[R]) was developed by the Voluntary Inter-industry Commerce Solutions (VICS) Association. The CPFR initiative has garnered significant attention due to its many benefits (Esper and williams 2003; VICS 2004). One recent extension of the CPFR framework is collaborative transportation management (CTM). Notably, CTM is a holistic process that improves operating performance of all parties involved in a relationship by eliminating inefficiencies in the transportation component of a collaborative supply chain (Bishop 2004; Esper and Williams 2003; Tyan and Wang 2003; VICS 2004). Not only is CTM a new collaborative strategy between shippers and carriers, it is also a new business model. The CTM model includes carriers as a strategic partner for information sharing and collaboration in a supply chain. The application of CTM promises to reduce transit times and total costs for all supply chain partners while increasing asset utilization for the carriers (Tyan and Wang 2003). For carriers, inefficiencies such as deadhead miles, unproductive waiting dwell time, and a lack of critical network mass are potentially addressed by adopting CTM. Increased visibility and improved service levels are also identified as potential benefits of CTM for carriers (VICS 2004).
Studies on CTM principally focused on various research aspects such as business framework, core elements, process models, and the value, performance, and benefits of CTM (e.g., Chan and Zhang 2011; Esper and williams 2003; Feng and Yuan 2007; Tyan and Wang 2003;). Esper and Williams (2003) explored the concept of CTM and discussed the role of information technology in CTM processes as well as its subjective and quantifiable benefits. Also, they employed a descriptive case study of a third-party CTM systems provider to investigate the processes and benefits of CTM. Feng and Yuan (2007) explored the application of CTM to business global logistics and assessed the value of CTM from the perspective of shippers by using a descriptive case study. Chan and Zhang (2011) tested the impact of CTM on retail supply chain performance using a simulation approach. From their simulation results, CTM can significantly reduce the retailer's total costs, improve the retailer's service level, and mitigate the retailer's delivery demand variation.
Notably, CTM is an innovative business model that will provide competitive advantage to those that engage in this new collaborative process (VICS 2004). For carriers, CTM offers an opportunity to improve their capability to serve customers (vies 2004). However, few studies addressed the impact of CTM from the carriers' perspective. Existing literature reveals a gap with regard to an integrated framework, linking CTM practice to both logistics capability and competitive advantage for carriers. Furthermore, it is important for carriers to understand whether their capability and competitive advantage could be enhanced by adopting CTM. This study analyzes the relationships between CTM practice, logistics capability, and competitive advantage for carriers, and examines the impact of CTM on those relationships. This study acquires information for carriers with respect to determinants of CTM practice and impacts of CTM on logistics capability and competitive advantage.
The remainder of this article is organized as follows. The second section provides the theories and definitions underlying CTM practice, logistics capability, and competitive advantage, along with a description of research hypotheses. The third section describes the analysis method. The fourth section gives analytical results, and the last section presents concluding remarks.
Literature Review and Hypotheses
The CTM model includes carriers as a strategic partner for information sharing and collaboration in the supply chain (VICS 2004). However, the measures for CTM have been difficult to find in the existing literature, as Esper and Williams (2003) have noted. They examined the value of CTM and identified that information technology (IT) is an essential enabler of CTM practice. VICS (2004) mentioned that one critical enabler of CTM success is establishment of transportation best practices. It further identified those transportation best practices to include centralizing transportation management, optimizing transportation plans, and implementing shipment visibility and forecasting. VICS also noted key organizational enablers of CTM success, such as common interest, openness, mutual trust, cooperation, and benefit and risk sharing. Feng and Yuan (2007) identified major determinants of CTM practices from the perspective of shippers, including relationship integration, collaborative forecasting and planning, and IT integration. Among these determinants, relationship integration and information technology integration enhance logistics capability and organization performance.
Based on this review of the literature relating to CTM practices, this study proposes measures for CTM practice from the perspective of carriers, which are associated with the CTM relationship between carriers and their customers. From the aspects of organizational enabler (VICS 2004) and relationship integration (Feng and Yuan 2007), the selected measurement items for CTM practice are as follows: jointly defining the scope of collaboration, sharing cross-organization information, sharing risks and benefits, having a high degree of mutual trust, and having long-term cooperation. From the aspects of transportation practice (VICS 2004) and IT integration (Esper and Williams 2003; Feng and Yuan 2007), the selected CTM measurement items are collaborative planning for transportation, determining joint delivery strategies, forecasting and planning shipments, standardizing information exchange, and applying an integrated information system. Table 1 lists these proposed measures for CTM practice.
Logistics Capability and Competitive Advantage
The concepts of capability and competitive advantage have been emphasized in the strategic management field. Business competitive advantage is typically discussed using resource-based view (RBV) theory (Grant 1991; Prahalad and Hamel 1990; Rouse and Daellenbach 1999; Wernerfelt 1984). The RBV emphasizes that a firm utilizes its resources and capabilities to create a competitive advantage that ultimately results in superior value creation (Prahalad and Hamel 1990). Hafeez, Zhang, and Malak (2002) pointed out that capabilities are formed through the coordination and integration of activities and processes. Capabilities refer to the firm's ability to utilize its resource effectively.
Logistics capabilities have been demonstrated to be a means for enhancing supply chain competitive advantage (Fawcett, Stanley, and Smith 1997; Lynch, Keller, and Ozment 2000; Morash, Droge, and Vickery 1996). Morash, Droge, and Vickery (1996) classified logistics capabilities into demand-oriented and supply-oriented capabilities and identified those logistics capabilities that are sources of competitive advantage to include delivery reliability, presale and postsale customer services, responsiveness to target market, delivery speed, widespread distribution coverage, selective distribution coverage, and low total-distribution cost. Morash and Clinton (1997) mentioned that the supply chain structure defines and drives the transportation capabilities of time compression, reliability, standardization, just-in-time delivery, information systems support, flexibility, and customization. Fawcett, Stanley, and Smith (1997) pointed out that a firm has to establish logistics capabilities focusing on delivery speed, quality service, flexibility, cost, and innovation in order to achieve operational performance. Lynch, Keller, and Ozment (2000) examined the relationships among logistics capabilities, generic business strategies, and logistics performance. From their perspective, process capabilities, value-added service capabilities, and strategies must be combined appropriately to increase firm performance.
Among previous research, Morash (2001) tested twenty-six logistics capabilities to examine supply chain excellence, and Lu and Yang (2006) evaluated twenty logistics capability attributes for international distribution-center operators. Based on the above studies, this study selects twenty logistics capability measurement items for carriers (see table 1 above). These are low logistics cost, on-time delivery and delivery speed, delivery dependability, order cycle time and flexibility, operational flexibility, quick response, quality and reliability, customer service management, cargo tracking system, low cargo damage or loss rate, postsales logistics service, information supportability, employee working experience, employee logistics knowledge, facility utilization, safety and risk prevention, advanced facility and equipment, process review and improvement, new technology and innovative solutions, and high cargo-volume handled and widespread distribution coverage (Lu and Yang 2006; Morash 2001).
The concept of competitive advantage lies at the heart of understanding a firm's performance in competitive markets (Porter 1985). According to Porter, the basis of above-average performance of a firm within an industry is sustainable competitive advantage. Measures of competitive advantage were determined based on Porter's three major competitive strategies, namely cost leadership, differentiation, and focus. Feurer and Chaharbaghi (1994) measured competitiveness quantitatively by profit, ability to raise capital, and cash flow in terms of liquidity. Barney (2002) discussed criteria to measure a firm's competitiveness from the performance perspective. Those criteria include return on investment, market share, profitability, reputation, and service quality. Li et al. (2006) examined the impact of supply chain management practices on competitive advantage and performance. They proposed five dimensions of the competitive advantage constructs and used sixteen measurement items for testing those competitive advantage instruments. The five dimensions include price/cost, quality, delivery dependability, product innovation, and time to market. Kim (2006) discussed four dimensions of the competitive advantage, including cost leadership, customer service, innovation marketing technology, and differentiation, and used twenty measurement items for testing those competition capabilities. Integrating items in the aforementioned literature (Barney 2002; Kim 2006; Li et al. 2006), this study selects twenty-three measurement items for the carrier's competitive advantage (see table 1 above).
Research Framework and Hypotheses
This study aims to analyze the relationships among CTM practice, logistics capability, and competitive advantage for carriers, and to assess the impacts of CTM on a carrier's logistics capability and competitive advantage. Figure 1 shows the expected relationships among CTM practice, logistics capability, and competitive advantage discussed in this study. The framework proposes that CTM will have an impact on competitive advantage both directly and indirectly through logistics capability.
In the CTM model, the focus is clearly on the carrier and incorporating the carrier's input, capabilities, and feedback into the buyer-seller relationship (Browning and White 2000; VICS 2004). Notably, CTM has become a means of addressing issues associated with short planning time-windows, inventory reduction, underutilized carrier equipment, overuse of expedited services, and overall operational performance (Browning and White 2000). Firms using CTM have reduced transaction costs and risks, improved service performance and capability, and streamlined their supply chains (Esper and Williams 2003). Carriers may benefit from CTM by allowing for increased capacity utilization and reducing inefficiency (VICS 2004). VICS (2004) noted that CTM offers an opportunity for carriers to establish business plans with key buyers and sellers to serve their freight requirements and gain visibility to potential future business. Since a carrier's logistics capability refers to its ability to utilize the resource and integrate processes effectively, the above arguments lead to the following hypothesis.
[FIGURE 1 OMITTED]
H1: CTM positively affects a carrier's logistics capability.
The impact of CTM on supply chain performance were explored by Chan and Zhang (2011), who found that CTM can significantly reduce the supply chain total costs, improve service level, and mitigate the delivery demand variation. The case study results in Feng and Yuan (2007) also revealed that the positive impact of CTM on business logistics enables enterprises to gain competitive advantage. Furthermore, VICS (2004) noted that CTM could provide competitive advantage to all trading partners by improving the relationships between shippers and carriers, and strengthening their positions in the market with logistics capabilities and proactive notification. Tyan and Wang (2003) examined CTM implementation by third-party logistics (3PL) providers and showed that results of the implementation included reduced delivery cycle time and total costs. Esper and Williams (2003) indicated that CTM is expected to improve a carrier's performance on transportation and administration costs, on-time performance, and asset utilization. Deducing from their findings, CTM enhances carriers' performance, which in turn leads to enhanced competitive advantage. As mentioned in the literature review, resources and capabilities of a firm are sources of its core competencies to achieving competitive advantage. Consequently, CTM practice is expected to improve a carrier's resource efficiency and enhance its logistics capabilities, whereby the carrier's resources and capabilities together form its distinctive competencies. Therefore, the following hypotheses are proposed.
H2: CTM positively affects the carrier's competitive advantage.
H3: The carrier's logistics capability positively affects its competitive advantage.
The research framework (figure 1) and associated hypotheses were tested using structural equation modeling (SEM). The research steps comprised instrument development, exploratory study, confirmatory study, and structural model testing. Measures of CTM practice, logistics capability, and competitive advantage were used as questionnaire items (see table 1 above). Responses were on a five-point Likert scale. The questionnaire has four sections. In the first section (CTM practice), respondents were asked to indicate the level of CTM practices their firms adopted (1 = very low to 5 = very high). In the second section (logistics capability), respondents were asked to evaluate the logistics capabilities of their firms in relation to their major competitors (1 = worse than competition to 5 = better than competition). In the third section (competitive advantage), respondents were asked to identify the level of importance to the success of their firms (1 = very low emphasis to 5 = very high emphasis). The fourth section acquired basic respondent information. Analysis was carried out using SPSS version 12.0 (2003) for Windows and the AMOS 5.0 (2003) statistical package.
To minimize the self-perceptual bias, this study applied exploratory factor analysis (EFA) to identify key factors for CTM, logistics capability, and competitive advantage constructs and estimated instrument reliability using Cronbach's alpha. Then, this study utilizes confirmatory factor analysis (CFA) to developing the hypothesized measurement model. In CFA, the chi-square value, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), root mean squared residual (RMR), and root mean square of approximation (RMSEA) are used to test model fitness. Convergent validity was examined using the t-value and squared correlation ([R.sup.2]). Furthermore, discriminant validity was tested by comparing average variance extracted with the squared correlations among factors. Construct validity was then tested using composition reliability (CR) and average variance extracted (AVE). Tests of the structural model and hypotheses also used fit indices, that is, [chi square] GFI, AGFI, CFI, RMR, and RMSEA, and the t-values of structural coefficients for significance.
During the years 2003-2008, the Department of Commerce and Industrial Technology Research Institute (ITRI) in Taiwan have embarked on the "CTM Integration and Promotion Plan" and successfully developed CTM key technology, promoted logistics alliance models, and subsequently engaged 625 affiliated logistics firms to adopt e-logistics applications. The sample for this study was based on the carriers and transportation service providers among these 625 affiliated logistics firms. This study sought to choose respondents who can be expected to have the best knowledge about the logistics operation and management in their organizations. Based on literature and recommendations from practitioners, it was decided to choose managers who are at higher managerial levels as respondents for the current study. Relevant project managers or planning engineers who take charge of CTM related tasks were also chosen as respondents.
Although few questionnaires were mailed, most were administered face to face with the respondents in carrier firms and transportation service providers during March, April, and May of 2008. Overall there were 147 respondents in the sample. A total of 106 usable and complete responses were obtained for an overall response rate of 72.1 percent. Table 2 lists respondent characteristics. Of all respondents, 11 percent were vice presidents or above, and 49 percent held a position title of manager. The major commodity types were electrical and electronic products (29%), and retail goods (24%). Roughly 37 percent of respondents rated their firms' annual revenue at lower than NT$50 million, 20 percent in the range of NT$100-200 million, and 17 percent in the range of NT$50-100 million. Approximately 30 percent of respondents represented domestic firms operating in Taiwan; 20 percent in Hong Kong, Macau, and Mainland China; 20 percent in Japan; 13 percent in Korea; 13 percent in North America; and 7 percent in Southeastern Asia. A comparison of the first-wave respondents and the second-wave respondents was made, using the [chi square] statistic and P < 0.05. It was found that there were no significant differences between the two groups in firm's operational type, annual revenue, and respondent's position title. An absence of nonresponse bias is therefore inferred.
Results and Discussions
This study used EFA to identify key factors associated with CTM practice, logistics capability, and competitive advantage for carriers. The Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy and Bartlett's test of sphericity were utilized to test the factorability of intercorrelation matrices for these three constructs. For the CTM construct, KMO = 0.882 (>0.8) and Bartlett's test [chi square] = 465.904 (P < 0.05); for logistics capability construct, KMO = 0.891 (>0.8) and Bartlett's test [chi square] = 1449.527 (P < 0.05); and for competitive advantage construct, KMO = 0.888 (>0.8) and Bartlett's test [chi square] = 1534.143 (P < 0.05). Thus, the intercorrelation matrices of the three constructs are factorable. Principal components analysis with varimax rotation was employed to identify key factors. Eigenvalues >1 were employed to determine the number of factors in each dataset. To assist with interpretation, only items loading on each factor at 0.50 or higher were extracted (Hair et al. 1998). However, four items, namely, shipment forecasting and planning (X8), quick response (X24), delivery costs (Y3), and market share (Y20), simultaneously loaded on two factors at 0.50 or higher loading. Thus, these four items were eliminated and the remaining items were factor analyzed. Table 3 lists the factor analysis results. The EFA results indicate that three factors accounted for approximately 71.05 percent of total variance in representing CTM practice; four factors accounted for 70.99 percent of total variance in representing logistics capability; and three factors accounted for 60.17 percent of total variance in representing competitive advantage. The key factors for each construct are described as follows.
1. Factor C1 (Collaborative planning) accounted for 25.93 percent of total variance, and consisted of four items, namely long-term cooperating, collaborative transportation planning, jointly defining the collaboration scope, and jointly determining delivery strategies. These items are closely associated with factors for collaborative planning and strategies. Long-term cooperation had the highest factor loading on collaborative planning.
2. Factor C2 (Information integration) accounted for 24.33 percent of total variance and consisted of two items: standardized information exchange and integrated information system application. These items are closely associated with information integration factors. Integrated information-system application had the highest factor loading on information integration.
3. Factor C3 (Relationship integration) accounted for 20.79 percent of total variance and consisted of three items--sharing cross-organization information, sharing risks and benefits, and developing highly mutual trust. These items are closely associated with relationship integration factors. Sharing risks and benefits had the highest factor loading on relationship integration.
1. Factor L1 (Internal operation) accounted for 25.35 percent of total variance, and consisted of seven items--operational flexibility, new technology and innovative solutions, safety and risk prevention, employee logistics knowledge, advanced facility and equipment, high cargo-volume handling and widespread distribution coverage, and process review and improvement. These items are closely associated with internal operation factors. Advanced facility and equipment had the highest factor loading on internal operation.
2. Factor L2 (Cost and service quality) accounted for 19.16 percent of total variance, and consisted of five items, including low logistics costs, delivery dependability, low cargo damage rate, quality and reliability, and order cycle time and flexibility. These items are associated with costs and service quality factors. Low logistics costs had the highest factor loading on cost and service quality.
3. Factor L3 (Customer service) accounted for 15.61 percent of total variance, and consisted of four items--postsales logistics service, customer service management, cargo-tracking system, and information supportability. These items are closely associated with customer service factors. Postsales logistics service had the highest factor loading on customer service.
4. Factor L4 (Productivity) accounted for 10.87 percent of total variance and consisted of three items, namely, employee working experience, facility utilization, and on-time delivery and delivery speed. These items are related to productivity of logistics services. Employee working experience had the highest factor loading on productivity.
1. Factor CA1 (Differentiation) accounted for 22.31 percent of total variance and consisted of seven items--customer service, flexibility of operational processes, wide range of services, specialized services, customer satisfaction, reputation, and overall competitive position. These items are closely related to the factors for differentiated and specialized services; therefore, this factor was identified as a differentiation factor. Ability to provide specialized services had the highest factor loading on differentiation.
2. Factor CA2 (Cost leadership) accounted for 21.23 percent of total variance and consisted of eight items, namely, profitability, growth rate, above-average return on investment, operating costs, transportation planning costs, holding costs, better-trained employees, and competitive freight fare. These items are closely related to influence on costs; thus, this factor was deemed a cost leadership factor. Operating costs had the highest factor loading on cost leadership.
3. Factor CA3 (Uncertainty control) accounted for 16.63 percent of total variance and consisted of six items--delivery quality and reliability, forecasting ability for freight demand, level of customer relationship, time-to-market, on-time delivery, and response to market changes. These items are closely associated with controlling and managing uncertainty in demand and delivery; therefore, this factor was categorized as an uncertainty control factor. Delivery quality and reliability had the highest factor loading on uncertainty control.
A reliability test based on Cronbach's alpha was employed to determine whether the dimensions of each factor were consistent and reliable. The coefficient [alpha] is a summary measure of intercorrelation among a set of items. Table 4 presents Cronbach's alpha value for each factor dimension. The reliability values of each measure markedly exceeded 0.70, which is considered satisfactory for reliability.
Before testing the structural model and hypotheses, the measurement model was assessed by CFA. However, the initial calibrated model was unfit. Although all measurements had significant loadings, the squared correlations value for factor L4 ([R.sup.2] = 0.176) did not meet the 0.3 criterion. From interview results, respondents from carrier firms indicated that items belonging to productivity factor (L4) somehow have been seen as basic requirements in the logistics industry, not competitive capabilities. Thus, factor L4 was eliminated to generate a modified model. The overall fit of the modified model was adequate, with all fit indices ([chi square] = 39.775, [chi square]/df = 1.657, GFI = 0.922, CFI = 0.968, and RMR = 0.019) meeting the recommended criteria. Notably, AGFI = 0.853 was close to the recommended level of 0.90, and RMSEA = 0.082 had a higher than recommended level at 0.05. Statistical results indicate that no standardized residual value exceeded 2.58 in absolute terms, providing additional evidence of model fit and of no apparent misspecifications.
Table 5 lists calibration results of the proposed model. All measurements had significant loadings, and the [R.sup.2] values for all factors exceeded the 0.3 criterion, indicating that analytical results are marginally acceptable. Moreover, all critical ratios exceeded 1.96, proving convergent validity, CR values were all >0.7, and all AVE values were >0.5. Additionally, the AVE for each construct was substantially higher than the squared correlation between the construct and all other constructs. These analytical results are evidence of the construct validity and discriminant validity of study variables. To summarize, the overall analytical results for model goodness-of-fit and measurement model assessment provide substantial support for the proposed model.
Table 6 lists SEM results for all hypotheses. Figure 2 also illustrates SEM results and structural relationships. Analytical results support hypothesis 1, which states that CTM positively affects the carrier's logistics capability. The standardized coefficient was 0.535, which is statistically significant at P < 0.05 (t = 4.473). The statistical significance of hypothesis 1 confirms that implementation of CTM may directly strengthen a carrier's logistics capabilities. Hypothesis 2, which indicates that CTM positively affects the carrier's competitive advantage, was also supported. The standardized coefficient was 0.224, which was almost significant at P = 0.055 (t = 1.915). Implementation of CTM may improve a carrier's competitive advantage over the long term. Hypothesis 3, which states that the carrier's logistics capability positively affects its competitive advantage, was also supported. The standardized coefficient was 0.592, which was statistically significant at P < 0.05 (t = 5.009). The statistical significance of hypothesis 3 confirms that a carrier's logistics capabilities can strengthen its competitive advantage.
Based on the standardized coefficients of the three hypotheses (table 6 and figure 2), CTM practice may have a greater direct impact on logistics capability ([beta] = 0.535) than on competitive advantage ([beta] = 0.224). This conception may hold true as competitive advantage is typically influenced by many factors, and determining whether any one factor, such as collaboration, determines overall competitive advantage of a carrier is difficult. Analytical results also demonstrate that competitive advantage is influenced more by logistics capability ([??] = 0.592) than by CTM practice ([??] = 0.224). This analytical finding indicates that CTM initially enhances logistics capability for a carrier, and logistics capability will, in turn, result in improved competitive advantage. The standardized coefficient of the indirect effect of CTM on competitive advantage was 0.317 (i.e., 0.535 x 0.592 = 0.317). Thus, the indirect effect (0.317) of CTM on competitive advantage was greater than its direct effect (0.224). Analytical findings suggest the presence of an intermediate measure of logistics capability between CTM and competitive advantage. Analytical results (table 6) thus show that CTM can directly and positively influence competitive advantage, and indirectly influence competitive advantage through logistics capability. Table 6 also shows the relative significance of factors for each construct. Factor C3, relationship integration, had the most significant influence on CTM practice. Factor L3, customer service, was considered as the strongest factor in logistics capability, and factor CA2, cost leadership, was considered the factor most significant to competitive advantage. This finding implies that the carrier adopts CTM practice through high levels of integrated relationship with supply chain partners can improve its customer service capability, and then enhance its cost-leadership advantage.
[FIGURE 2 OMITTED]
Collaborative transportation management is defined as collaboration between trading partners and carriers in a supply chain to avoid inefficiencies in distribution and improve supply chain efficiency. Few studies explored the impact of CTM from the perspective of carriers. This study analyzed empirically the relationships among CTM practice, logistics capability, and competitive advantage for the carrier and how CTM influences those relationships.
Via factor analysis, key factors of CTM practice for the carrier were identified, namely collaborative planning, information integration, and relationship integration. Key logistics capabilities for carriers were found to include internal operation, cost and service quality, customer service, and productivity. Key competitive advantage for carriers--differentiation, cost leadership, and uncertainty control--were also identified. The SEM results demonstrate that CTM practice positively affects the carrier's logistics capability and competitive advantage, and the carrier's logistics capability positively affects its competitive advantage. Analytical results also demonstrate that CTM may have a greater direct impact on logistics capability than on competitive advantage, and CTM initially enhances the carrier's logistics capability that will, in turn, improve its competitive advantage. Moreover, carriers establish higher levels of integrated relationship with their customers in a CTM model, which can lead to improved customer service capability and enhanced cost-leadership advantage.
One major contribution of this study is that it is the first attempt to explore the CTM impacts on a carrier's logistics capability and competitive advantage. This study provides empirical evidence to support conceptual and prescriptive statements in the literature regarding the impact of CTM for carriers and the relationship between logistics capability and competitive advantage. The conceptual framework and CTM impact presented in this study can serve as a foundation for carriers considering the implementation of CTM practices. Study results also suggest that carriers should develop core logistics capabilities and apply a CTM system to enhance their competitive advantage.
Research limitations of this study are further acknowledged, leading to suggested directions for future research. Since CTM is a relatively new model, the number of effective observations was limited for the current study; the revalidation of constructs was not carried out in this research. Lack of systematic confirmatory research impedes general agreement on the use of instrument. Future research should revalidate measurement scales developed through this research. This study is limited to unidimensionality assumption in SEM, thus future research could further formulate a more complicated structural model with multidimensional constructs. The future study should examine which carriers' logistics capability will impact competitive advantage and hypothesize the impact on each logistics capability individually. Since the analysis used in this study was static in nature, future research should conduct a longitudinal analysis to examine how determinants of CTM adoption might change over time. Additionally, while the current study focused on the framework of CTM impact, the future research could further focus on quantifying the benefits and impacts of CTM for carriers and supply chain partners.
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Table 1/Items for Model Measurement Items Previous Studies Support CTM Practice X1: Jointly define the scope Feng and Yuan 2007; VICS 2004 of collaboration X2: Cross-organization Feng and Yuan 2007 information sharing X3: Sharing risks and benefits VICS 2004 X4: High degree of mutual trust X5: Long-term cooperation X6: Collaborative transportation planning X7: Jointly determine delivery strategies X8: Shipment forecasting and planning X9: Standardized information Esper and Williams 2003; exchange Feng and Yuan 2007 X10: Integrated information system application Logistics Capability X11: Low logistics costs Morash 2001 X12: On-time delivery and delivery speed X13: Delivery dependability X14: Order cycle time and flexibility X15: Customer service management Lu and Yang 2006 X16: Cargo tracking system X17: Low cargo damage rate X18: Post-sales logistics service X19: Information supportability X20: Operational flexibility Morash 2001 X21: Employee working experience Lu and Yang 2006 X22: Facility utilization X23: New technology and Lu and Yang 2006; Morash 2001 innovative solutions X24: Quick response Morash 2001 X25: Quality and reliability Lu and Yang 2006 X26: Safety and risk prevention X27: Employee logistics knowledge X28: Advanced facility and equipment X29: High cargo volume handled Lu and Yang 2006; Morash 2001 and widespread distribution coverage X30: Process review and Lu and Yang 2006 improvement Competitive Advantage Y1: Lower transportation Li et al. 2006 planning costs Y2: Better trained and skilled employees Y3: Lower delivery costs Y4: Lower holding costs Y5: Competitive freight fare Li et al. 2006; Kim 2006 Y6: Higher delivery quality Li et al. 2006 and reliability Y7: Better customer service Kim 2006 Y8: Flexibility of operational processes Y9: Ability to offer a wide range of services Y10: Ability to provide Li et al. 2006 specialized services Y11: Forecasting ability for Kim 2006 freight demand Y12: Level of customer relationship Y13: Time-to-market lower than Li et al. 2006 industry average Y14: On-time delivery ability Li et al. 2006; Kim 2006 Y15: Ability of response to market changes Y16: Higher profitability Barney 2002 Y17: Higher growth rate Li et al. 2006 Y18: Lower operating costs Y19: Above-average return Barney 2002 on investment Y20: Higher market share Y21: Higher customer satisfaction Y22: Firm reputation Y23: Overall competitive position Li et al. 2006 Table 2/Respondent Characteristics Characteristics Respondents Position Titles Vice-president or above 11% Manager or director 49% Engineer or planner 11% Other 29% Operational Types (commodity types) Electric and electronic products 29% Metal mechanics 18% Retailing 24% Specialized 18% Other 11 Annual Revenue (million NT$) Less than $50 37% $50-100 17% $100-200 20% $200-300 11% More than $300 15 Operating Locations Taiwan 30% Hong Kong, Macau, and China 20% Japan and Korea 13% Southeastern Asia 7% Europe 5% North America 13% Mid-South America 5% Australia and New Zealand 5% Other 2% Table 3/Factor Analysis Results for CTM Practice, Logistics Capability, and Competitive Advantage CTM Practice Factor Items C1 C2 C3 X5: Long-term cooperation .723 -.168 .425 X6: Collaborative transportation planning .699 .420 .185 X1: Jointly define the scope of collaboration .692 .311 .062 X7: Jointly determine delivery strategies .655 .471 .211 Factor Items C1 C2 C3 X10: Integrated information system application .065 .819 .156 X9: Standardized information exchange .331 .791 .212 X3: Sharing risks and benefits .063 .310 .835 X4: High degree of mutual trust .499 .003 .705 X2: Cross-organization information sharing .229 .408 .636 Eigenvalue 2.59 2.43 2.08 % of variance 25.93 24.33 20.79 Cumulative % of variance 25.93 50.26 71.05 Logistics Capability Factor Items L1 L2 L3 L4 X28: Advanced facility and .829 .278 .222 .163 equipment X27: Employee logistics knowledge .765 .740 .365 .079 X30: Process review and .761 .390 .179 .161 improvement X29: High cargo volume handled .715 .214 .342 .110 and widespread distribution coverage X26: Safety and risk prevention .686 .428 .169 .187 X23: New technology and .578 .434 .337 .146 innovative solutions X20: Operational flexibility .569 .206 .406 .282 X11: Low logistics costs .241 .820 -.001 -.045 X17: Low cargo damage rate .174 .657 .412 -.031 X25: Quality and reliability .348 .639 .274 .227 X13: Delivery dependability .352 .598 .383 .197 X14: Order cycle time and .489 .594 .297 .110 flexibility X18: Post-sales logistics service .347 .250 .802 .071 X15: Customer service management .191 .391 .687 .038 X16: Cargo tracking system .324 .403 .650 .980 X19: Information supportability .480 .261 .599 .173 X21: Employee working experience .252 -.097 .024 .818 X22: Facility utilization .329 .490 .088 .799 X12: On-time delivery and -.236 .479 .160 .699 delivery speed Eigenvalue 5.07 3.83 3.12 2.17 % of variance 25.35 19.16 15.61 10.87 Cumulative % of variance 25.35 44.51 60.12 70.99 Competitive Advantage Factor Items CA1 CA2 CA3 Y10: Ability to provide specialized .787 .720 .158 services Y9: Ability to offer a wide range of .723 .220 .312 services Y7: Better customer service .674 .281 .318 Y22: Firm reputation .669 .409 .128 Y8: Flexibility of operational processes .648 .170 .186 Y21: Higher customer satisfaction .638 .204 .385 Y23: Overall competitive position .609 .372 .177 Y18: Lower operating costs .396 .748 .161 Y17: Higher growth rate .363 .719 .209 Y16: Higher profitability .370 .703 .284 Y19: Above-average return oninvestment .489 .627 .210 Y1: Lower transportation planning costs .540 .625 .281 Y2: Better trained and skilled employees .498 .582 .118 YS: Competitive freight fare .067 .546 .393 Y4: Lower holding costs .314 .520 .196 Y6: Higher delivery quality and .172 .185 .767 reliability Y13: Time-to-market lower than industry .300 .347 .725 average Y14: On-time delivery ability .289 .203 .690 Y15: Ability of response to market changes .431 .139 .603 Y12: Level of customer relationship .421 .206 .594 Y11:Forecasting ability forfreight demand .120 .415 .508 Eigenvalue 5.13 4.88 3.82 % of variance 22.31 21.23 16.63 Cumulative % ofvariance 22.31 43.54 60.17 Table 4/Reliability Analysis Result Factors Number Means Cronbach's of [alpha] Items CTM practice C1: Collaborative 4 3.854 0.782 planning C2: Information 2 3.838 0.752 integration C3: Relationship 3 3.822 0.755 integration Logistics L1: Internal operation 7 3.846 0.927 capability L21. Cost and service 5 3.929 0.859 quality L3: Customer service 4 3.990 0.842 L4: Productivity 3 3.758 0.728 Competitive CA1: Differentiation 7 4.277 0.887 advantage A:2: cost leadership 8 4.246 0.893 CA3: Uncertainty control 6 4.315 0.858 Table 5/Calibration Statistic Result Factors Standardized Standard t-value [R.sup.2] Factor Loading Error CTM practice C1 0.767 -- -- 0.589 C2 0.676 0.182 5.983 0.457 C3 0.795 0.155 6.583 0.633 Logistics L-1 0.913 -- -- 0.833 capability L-2 0.812 0.088 10.245 0.659 L-3 0.930 0.086 10.609 0.689 Competitive CA1 0.834 -- -- 0.695 advantage CA2 0.844 0.112 9.930 0.712 CA3 0.826 0.102 9.177 0.683 Factors CR AVE CTM practice 0.791 0.559 Logistics 0.917 0.786 capability Competitive 0.873 0.697 advantage Table 6/ Results of SEM and Structural Coefficients Relationship Standardized Standard Coefficient Error H1: CTM Practice [right arrow] Logistics 0.535 0.177 capability H2: CTM practice [right arrow] Competitive 0.224 0.122 advantage H3: Logistics capability [right arrow] 0.592 0.083 Competitive advantage SEM Model CTM practice [right arrow] C1 0.767 -- CTM practice [right arrow] C2 0.676 0.182 CTM practice [right arrow] C3 0.795 0.155 Logistics capability [right arrow] L1 0.913 -- Logistics capability [right arrow] L2 0.812 0.088 Logistics capability [right arrow] L3 0.930 0.086 Competitive advantage [right arrow] CA1 0.834 -- Competitive advantage [right arrow] CA2 0.844 0.112 Competitive advantage [right arrow] CA3 0.826 0.102 Relationship t-value p H1: CTM Practice [right arrow] Logistics 4.473 *** capability H2: CTM practice [right arrow] Competitive 11.915 0.055 advantage H3: Logistics capability [right arrow] 5.009 *** Competitive advantage SEM Model CTM practice [right arrow] C1 -- -- CTM practice [right arrow] C2 5.983 *** CTM practice [right arrow] C3 6.583 *** Logistics capability [right arrow] L1 -- -- Logistics capability [right arrow] L2 10.245 *** Logistics capability [right arrow] L3 10.609 *** Competitive advantage [right arrow] CA1 -- -- Competitive advantage [right arrow] CA2 9.930 *** Competitive advantage [right arrow] CA3 9177 *** Fit indices: [chi square] = 39.775, [chi square]/df = 1.657, GFI = 0.922, AGFI = 0.853, CFI = 0.968, RMR = 0.019, RMSEA = 0.082 *** P value < 0.01
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|Title Annotation:||Industry Notes|
|Date:||Sep 22, 2012|
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