The impact of IT implementation on supply chain integration and performance

ARTICLE IN PRESS Int. J. Production Economics 120 (2009) 125–138

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The impact of IT implementation on supply chain integration and performance Gang Li a, Hongjiao Yang a,, Linyan Sun a, Amrik S. Sohal b a

School of Management, The State Key Lab for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xianning West Road 28#, Xi’an, Shann Xi 710049, PR China Department of Management, Monash University, Australia

b

a r t i c l e in fo

abstract

Article history: Received 1 September 2007 Accepted 1 July 2008 Available online 17 October 2008

The implementation of information technology (IT) for supply chain management (SCM) is becoming more and more important in the context of an increasingly globalized and competitive economy. IT, by providing timely, accurate, and reliable information, has greatly improved supply chain performance (SCP). This study aims to investigate the relationship among three factors: IT implementation, supply chain integration (SCI), and SCP. It presents a conceptual structure model in which IT implementation can affect SCP either directly or indirectly, via SCI. Data collected from 182 Chinese companies are analyzed using structural equation modeling. The results suggest that IT implementation has no direct effect on SCP, but instead that it enhances SCP through its positive effect on SCI. These findings highlight the importance for companies to promote SCI and implement IT as an enabler. & 2008 Elsevier B.V. All rights reserved.

Keywords: IT implementation Supply chain integration Empirical study

1. Introduction In recent decades, the development of information technology (IT) has rapidly changed the conditions for doing business around the world. With its power to provide timely, accurate, and reliable information, IT has led to better performance of both the focal firm and the partners in the supply chain (Jin, 2006). IT, as an infrastructure support both inside the organization itself and within its upstream, has been recognized as a critical factor in the improvement of supply chain management (SCM) (Gupta and Capen, 1996; Koh and Saad, 2006). Although there are many definitions in the literature, SCM is primarily concerned with managing relationships with suppliers and customers in order to deliver the best customer value at the lowest cost (Stevens, 1989). SCM emphasizes effective and efficient flows of both information and physical items to meet customer requirements,  Corresponding author. Tel./fax: +86 29 82664643.

E-mail addresses: [email protected] (G. Li), [email protected] (H. Yang), [email protected] (L. Sun), [email protected] (A.S. Sohal). 0925-5273/$ - see front matter & 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2008.07.017

starting from the source of supply of raw materials through to the consumption of the product by the endcustomer. The management of these processes requires close collaboration among the different parties in the supply chain, including raw materials suppliers, manufacturers, distributors, and retailers, in order to achieve the ultimate goal of satisfying customer requirements and reducing costs. By making possible the sharing of large amounts of information along the supply chain, including operational, logistical, and strategic planning data, IT has enabled real-time integration of supply chain partners, provided organizations with forward visibility, and improved production planning, inventory management, and distribution. Thus, virtually all companies in today’s market place either have implemented, or are in the process of implementing IT in order to streamline SCM activities (Olhager and Selldin, 2004; Zhang et al., 2005). Not only are more and more businesses investing in IT, but also, more and more research is being devoted to investigating the impact of IT implementation on supply chain performance (SCP). While the payoff from investing in IT has been a subject of long standing academic research and intense discussion, there is as yet no clear

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consensus reached. Some studies have found that overall IT capability is positively linked to organizational performance (Bharadwaj, 2000; Kearns and Lederer, 2003; Wamba et al., 2008); others have found that investment in IT can give a firm a significant competitive advantage (Earl, 1993; Kathuria et al., 1999). On the other hand, a number of other empirical studies have failed to find a clear IT effect, a phenomenon which has been called the ‘‘IT paradox’’ (see Weill, 1992; Hitt and Brynjolfsson, 1996; Lee and Barua, 1999; Devaraj and Kohli, 2003; Poirier and Quinn, 2003). As a result, despite the widespread implementation of IT, it still remains elusive whether IT implementation has a direct positive effect on SCP. Another open question concerns the manner in which IT implementation affects SCP. Most of the research examining the benefits of IT has focused on broad, overarching firm performance metrics (Bharadwaj et al., 1999; Brynjolfsson and Yang, 1996; Dehning et al., 2003). While these studies do provide insights into the overall benefits of IT implementation, the underlying analyses are affected by a considerable amount of measurement ‘‘noise’’ attributable to (1) the indirect path between IT implementation and these overarching performance metrics and (2) a recognition that these overarching performance metrics are affected by numerous factors other than the focal IT implementation (Dehning and Richardson, 2002). These qualifications notwithstanding, the literature supports the conclusion that supply chain integration (SCI) can greatly improve SCP (Stevens, 1989; Lee et al., 1997; Anderson and Katz, 1998; Hines et al., 1998; Johnson, 1999; Vickery et al., 2003; Stank et al., 2001). These benefits derive from the fact that IT makes possible information sharing and other forms of collaboration between customers and suppliers. Examples include jointly developed demand forecasting (Koloczyc, 1998; Aviv, 2001) and vendor-managed inventory (VMI), also referred to as direct shipment or automatic replenishment (Cetinkaya and Lee, 2000; Kulp et al., 2004). But even if the implementation of IT does not have a direct effect on SCP, it may have an indirect effect via its impact on the processes developed for SCI. This possibility has drawn less attention in the literature. There are few empirical studies that have confirmed the indirect impact of IT implementation on SCP. This study seeks to extend our understanding of how IT implementation impacts on SCI, and SCP. We develop a set of hypotheses based on the literature to empirically test the direct impact of IT implementation on SCP and the indirect impact of IT implementation on SCP mediated by SCI. The remainder of this paper is organized as follows. First, we develop a conceptual model of the relationship

among IT implementation, SCI, and SCP. Second, a brief literature review and a set of three research hypotheses related to the conceptual model are presented. Next, the research methodology and empirical results are discussed. This is followed by a discussion of the results and their implications for managers. Finally, the limitations and conclusions of this study are presented. 2. Literature review and research hypotheses 2.1. Conceptual model development We propose a conceptual model of the relationships among IT implementation, SCI, and SCP (see Fig. 1). According to this model, the implementation of IT can improve SCP not only directly, but also indirectly, via its impact on SCI. The present study takes SCP rather than financial performance as the dependent variable. This is because the direct effect of IT implementation on SCP is likely to be more significant than its effect on financial performance. ‘‘SCP’’ in turn includes the dimensions of cost, quality, flexibility, and delivery (Chen and Paulraj, 2004; Kathuria, 2000). ‘‘IT implementation,’’ as opposed to ‘IT,’ refers specifically to the technical capability to acquire, process, and transmit the information needed for more effective decision making. This definition not only measures the degree of a firm’s proactive adoption and implementation of advanced IT to enforce speed, quality, and quantity of information transferred, but also measures the degree of its embeddedness of IT across the supply chain to coordinate its business processes with its supply chain partners. The third term, ‘‘SCI’’, refers to the ability of a firm to integrate exchange-related activities within functional departments and with supply chain partners. Integration within functional departments requires cross-functional planning, coordination, and sharing of integrated databases. Integration with supply chain partners requires the coordination of operational, logistical, and planning data to improve production planning, inventory management, and distribution. 2.2. SCP and IT implementation The impact of IT on organizational performance has become one of the major preoccupations of both managers and researchers. Studies have ranged from the investigation of the alignment of specific IT applications with the organizational competitive priorities and alignment with strategic objectives (Kathuria et al., 1999; Kearns and Lederer, 2003) to comparisons of the effectiveness of

Supply Chain Integration H3(+) IT Implementation

H2(+) H1(+)

Fig. 1. The proposed conceptual model.

Supply Chain Performance

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specific IT applications (Raghunathan, 1999; Hendricks et al., 2007) and method of IT use (Subramani, 2004). In general, IT has been widely recognized as a critical factor in the supply chain because of the contribution it can make to improve the performance of both the individual firm and the supply chain as a whole. However, research on the direct impact of IT on specific performance measures has yielded inconsistent results (Sanders, 2007). While a considerable number of studies argued that IT implementation has vast potential for improving a firm’s financial performance (Mukhopadhyay et al., 1995; Bharadwaj, 2000; Dehning and Richardson, 2002; Hendricks and Singhal, 2003; Dehning et al., 2006), a few empirical studies have been more equivocal in finding the performance effect of IT (Weill, 1992; Yosri, 1992; Hitt and Brynjolfsson, 1996). This phenomenon has been called the ‘‘productivity paradox of information technology’’ (Lim et al., 2004), and numerous explanations have been offered for it, such as management’s failure to leverage the full potential of IT (Dos Santos and Sussman, 2000), ineffective implementation (Stratopoulos and Dehning, 2000), poor measures of performance (Bharadwaj et al., 1999), and the presence of a time lag between IT investment and its actual impact on performance (Devaraj and Kohli, 2000). Most previous studies have focused primarily on the total level of IT spending by firms over several years and the impact of this spending on financial performance. Few studies have attempted to examine the direct effects of IT on SCP, even though the latter effect is likely to be more significant than the indirect effect on financial performance. In addition, earlier studies have tended to measure IT as a stand-alone resource, focusing on the level of IT spending, without considering the alignment between IT and the overall business processes which are a firm’s strategic emphasis (Wu et al., 2006). These two shortcomings in previous studies may explain the existence of the ‘‘productivity paradox’’. And it is in an attempt to remedy the second that the study reported here takes IT alignment into account in assessing the impact of IT implementation on SCP. Reducing environmental uncertainty has become one of the most important objectives in SCM (Chen and Paulraj, 2004). By providing real-time and accurate information regarding product availability, inventory levels, shipment status, and market needs, IT implementation can reduce environmental uncertainty and improve supply chain efficiency (Radstaak and Ketelaar, 1998). It has come to be widely believed that the implementation of IT along a supply chain is a significant factor determining success in SCM and has increasingly become a necessity for enhancing SCP (Lai et al., 2006; Handfield and Nichols, 1999). On the basis of the foregoing considerations, we propose the following hypothesis: H1. IT implementation has a positive effect immediately on SCP. 2.3. SCP and SCI Mentzer (2001) provides a holistic definition of SCM: SCM is defined as the systemic, strategic coordination of the traditional business functions and the tactics across

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these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole. This view recognizes that the successful management of a supply chain requires going beyond the boundaries of a single company, and requires the integration of business processes among partners along the chain. The theoretical foundation for SCI can be traced to the value chain model (Porter, 1980, 1985), and specifically, its notion of linkages within a firm’s value chain and the linkages among the firms in the value chain. It is believed that all of the individual organizations that comprise the supply chain should ultimately be managed as a single entity or one complete system, which can lead to superior performance (e.g. Tan et al., 1998; Frohlich and Westbrook, 2001). This requires integration and coordination across individual firm functions and throughout the supply chain. Previous studies, both empirical and theoretical, have come to the consensus that SCI can improve firm performance (Stevens, 1989; Lee et al., 1997; Metters, 1997; Anderson and Katz, 1998; Hines et al., 1998; Johnson, 1999; Frohlich and Westbrook, 2001). SCI is enhanced by sharing information about key processing activities. With high degree of SCI, manufacturers can react more flexibly to individual customer demands, to decreased delivery times, and to reduced inventories, all of which can make the supply chain more efficient (Clark and Lee, 2000; Barrat, 2004). In contrast, lack of integration has been shown to create the classic magnification of demand up the supply chain, known as the ‘‘bullwhip effect,’’ with resulting alternations between excess inventory and stock-outs (Lee and Billington, 1992). At present, more and more companies that are directly linked in a supply chain attempt to exploit intensive integration across individual firm functions with their supply chain partners, thereby permitting each company to deliver products quickly and reliably, enhance responsiveness, shorten lead times, improve performance, and eliminate the bullwhip effect (Lee et al., 1997). The development of the channel partnership between P&G and Wal-Mart is a good example. With the channel partnership, both companies have improved profitability and their joint business revenues have grown from $375 million in 1988 to over $4 billion dollars in 2002 (Micheal, 2002). Similarly, the collaborative relationship between Sears and Michelin using CPFR (Collaborative Planning, Forecasting, and Replenishment) has resulted in a 25% reduction in inventories for both companies (Steerman, 2003). And General Motors’ new collaborative relationship with its suppliers has reduced vehicle development cycle times from four years to 18 months (Gutman, 2003). As the preceding examples suggest, enhanced integration across an individual firm’s functions and along its supply chain can be expected to impact many dimensions of performance, including cost, quality, delivery, flexibility, and profits. We therefore propose the following hypothesis. H2. Supply chain integration has a positive effect on SCP.

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2.4. IT implementation and SCI More than ever before, today’s IT has permeated the supply chain at every point, transforming exchangerelated activities and the linkages between those activities (Palmer and Griffith, 1998). IT has vast potential to facilitate integration and coordination among supply chain partners through the sharing of information on demand forecasts and production schedules that dictate supply chain activities (Karoway, 1997). A company’s supply chain divides it into a sequence of primary activities: inbound logistics, operations, outbound logistics, marketing and sales, and service, along with support activities. Among these activities, some are internal and some are external to the organization, all with the primary goal of creating value to the endcustomer (Handfield and Nichols, 1999). This goal is accomplished through integration of activities between linked organizations, and should result in reduced costs due to the elimination of operational duplication and resource waste (Andraski, 1998; Stank et al., 2001). In order to achieve this reduction, it requires engaging in integration that is both internal and external to the organization. Many researchers have indicated that SCI needs to be achieved across organization boundaries, linking external suppliers, carrier partners, and customers. Higher levels of integration are characterized by increased logisticsrelated communication, greater coordination of the firm’s logistics activities with those of its suppliers and customers, and more blurred organizational distinctions between the logistics activities of the organization and those of its suppliers and customers (Stock et al., 2000). Successful integration requires fluent flow of accurate and timely information across these supply chain partners. The ability to manage the information flow is one of the critical weapons of today’s leading edge organizations. IT has the potential to manage the information flow and to provide links that support communication and collaboration along the supply chain (Brandyberry et al., 1999). Implementation of IT in SCM can integrate and coordinate the flow of materials, information, and finances among suppliers, manufacturers, wholesalers, retailers and end-consumers. Here, IT serves as a key enabler of SCI through the capture, organization, and sharing of vital information regarding key business processes, both within and outside an organization’s boundaries (Clemons et al., 1993; Frohlich and Westbrook, 2001; Sanders and Premus, 2002; Vickery et al., 2003; Kelle and Akbulut, 2005). The argument that IT improves SCI is further supported by transaction cost economics. Transaction cost economists hold that cooperation and coordination among firms is limited by the transaction cost of managing the interaction (Coase, 1937; Williamson, 1975; Stoeken, 2000). As transaction costs increase, market transaction efficiency decreases, which may result in higher market prices. The determinants of transaction costs are transaction frequency, asset specificity, uncertainty, bounded rationality, and opportunistic behavior. Since IT has the power to provide timely, accurate, and reliable informa-

tion, it provides managers with a convenient, low cost alternative to traditional face-to-face communication, one which decreases information uncertainty and transaction frequency. IT has also proved to be an effective means for decreasing both coordination costs, including the direct cost of integrated decisions (Nooteboom, 1992), and transaction risk, which is the risk of being exploited in the relationship (Clemons et al., 1993). On the basis of the foregoing, we propose the following hypothesis: H3. IT implementation has a positive effect on SCI. 3. Research methodology 3.1. Sampling and data collection Data for this study were obtained through a survey of 182 companies in China, which, as a global factory, plays an important role in many supply chains. Given China’s size, it is extremely difficult to obtain data from all parts of the country. The survey conducted for this study was carried out mainly in three typical large manufacturing cities: Beijing, Shanghai, and Shenzhen. These three cities, which have benefited from a high degree of economic reform and marketization, are representative of the most developed areas in China. For the sake of the accuracy and completeness of the responses, the sampled companies were selected on the basis of recommendations from local universities and government officials. The highest-ranking officers (e.g. president, CEO, vice president, or senior manager) of the targeted companies were contacted first, after which, contact was made with a middle-manager (e.g. supply chain manager, logistics manager, or procurement/ purchasing manager) responsible for the company’s supply chain activities. Since all of our respondents were corporate managers familiar with their company’s supply chain activities, it is reasonable to expect that the respondents could offer a deep insight into the supply chain activities and be knowledgeable about the content of the inquiry. According to Miller et al. (1997), two criteria where subjective data may be reliable and valid are: (a) questions do not require recall from the distant past, and (b) informants are motivated to provide accurate information. We promised confidentiality of data and highlighted the usefulness of the project. In addition, they were informed that they would receive a benchmarking report after the data were collected. Therefore, we attempted to minimize response bias in subjective data obtained from respondents. The survey was conducted from June to September 2005. During the data collection process, we first called every sampled company to inform them of our purpose before sending our questionnaires. The questionnaires were then sent to the respondents via email or postal mail, with a postage-paid return envelope to the complete sample of all 400 companies. Ten days later, follow-up telephone calls were used to remind them of answering the questionnaires. At last, a total of 308 questionnaires

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Table 1 Profile of the respondent companies Characteristics of firms

No. (share in total)

Nature of ownership State-owned Private Joint-venture Unmarked

29 38 58 57

(15.93%) (20.88%) (31.88%) (31.32%)

Number of formally enrolled employee 1–100 101–500 500–1000 Over 1000 Unmarked

54 46 19 48 15

(29.67%) (25.27%) (10.44%) (26.37%) (8.24%)

Level of turnover (in 10 million RMB) Below 2 2–13 13–33 33–66 66–132 132–330 330–660 Above 660 Unmarked

27 61 21 13 7 8 3 26 16

(14.84) (33.52) (11.54) (7.14%) (3.85%) (4.40%) (1.65%) (14.29%) (8.79%)

were returned (effective response rate of 77.0%), but 126 of them were not useable because of significant data missing and incompleteness. The final usable sample contains 182 usable responses, yielding a usable response rate of 45.5%. The profiles of the usable respondent companies and their characteristics are displayed in Table 1. To assess non-respondent bias, we compared the responses of early and late respondents to test for their significant differences (Armstrong and Overton, 1977). The first 75% (n ¼ 136) of the responses were classified as ‘‘early respondents’’. The last 25% (n ¼ 46) of them were classified as ‘‘late respondents’’ and were deemed representative of firms that did not respond to the survey. At the 5% significance level, no differences between the ‘‘early’’ and ‘‘late’’ respondents were detected, suggesting that non-response bias was not a problem with regard to the data collected in this study.

3.2. Construct measures We followed the procedures suggested by Churchill (1979) in scale development. First, the domain of each construct was clearly defined in terms of what would be included or excluded. Second, the literature was surveyed to locate any relevant scales. Measures were adopted or adapted from the existing literature where appropriate. If none were available or appropriate, new measures were developed. In this study, the scales for IT implementation were derived from the measurement of IT in Chen and Paulraj (2004), which emphasizes the usage of IT. SCI scales were obtained by modifying the degree of logistics integration

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proposed by Chen and Paulraj (2004). For SCP, measured by six items, scales were adopted from Stank et al. (2001). In addition to adaptation and modification of scales from existing literature, we have also added new items based on interviews with industrial managers. Specifically, we developed three items which emphasize IT alignment to complement the scale for IT implementation. Besides, being different from the traditional five-point Likert scale, we provide five detailed descriptions based on interviews with industrial managers for every item, and each description represents a certain level (marked 1–5). When answering questionnaires, the informants can just draw ‘‘O’’ on the description which is in accordance with the fact of their companies. The survey items are summarized and shown in Appendix A. 3.3. Reliability analysis Reliability is operationalized by the internal consistency method that is estimated using Cronbach’s alpha (Hull and Nie, 1981). Typically, reliability coefficients of 0.70 or higher are considered adequate. Therefore, an alpha value of 0.70 is considered as the critical value. As shown in Table 2, Cronbach’s alpha values of the factors are well above the critical value and ranged from 0.87 to 0.88. These results suggest that the theoretical constructs exhibit good psychometric properties. 3.4. Unidimensionality Assessing unidimensionality means determining whether indicators reflect one, as opposed to more than one, construct (Gerbing and Anderson, 1988). There are two implicit conditions for establishing unidimensionality. First, an empirical item must be significantly associated with the empirical representation of a construct. Second, it must be associated with one and only one construct. Only when a measure satisfies both of these conditions, it can be considered unidimensional. In this study, unidimensionality was established using confirmatory factor analysis (CFA). The CFA results for IT, SCI, and SCP are shown in Table 3. It can be seen from Table 3 that all the measurement models have acceptable fit indices, which prove the unidimensionality of the constructs. Moreover, the convergent and discriminant validities established in the following section, further solidifies the extent of unidimensionality of the constructs. 3.5. Convergent validity In order to perform meaningful analysis of the causal model, the measure needs to display certain empirical properties. The first of them is convergent validity, which measures the similarity or convergence between the individual items measuring the same construct. One way to test convergent validity is to use CFA. In CFA, convergent validity can be assessed by testing whether each individual item’s standardized coefficient from the measurement model is significant, namely greater than twice its standard error (Anderson and

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Table 2 Summary measurement results Factors

Number of items

Mean

S.D.

Cronbach’s alpha

Range of item-to-total correlations

IT Implementation Supply chain integration Supply chain performance

5 5 6

2.91 3.06 2.99

1.02 0.85 0.88

0.87 0.87 0.88

0.66–0.74 0.61–0.75 0.76–0.83

Table 3 Results from confirmatory factor analysis for measurement model Factors and scale items

Standard error

t-Value

IT implementation (w2/d.f. ¼ 3.71, RMSEA ¼ 0.12, GFI ¼ 0.96, NFI ¼ 0.96, NNFI ¼ 0.94, CFI ¼ 0.97, IFI ¼ 0.97) IT1: Electronic data Interchange (EDI) coverage 0.704 IT2: Usage of bar Coding/automatic identification and data capture 0.716 IT3: Effective usage of computers in operations and decision-making 0.771 IT4: Open standards and unique identification codes 0.815 IT5: Decision-making systems and support to supply chain partners 0.775

0.093 0.104 0.075 0.092 0.078

8.143a 8.047a 7.448a 6.721a 7.399a

Supply chain integration (w2/d.f. ¼ 1.90, RMSEA ¼ 0.07, GFI ¼ 0.98, NFI ¼ 0.98, NNFI ¼ 0.98, CFI ¼ 0.99, IFI ¼ 0.99) SCI1: Strategies for optimizing logistics system resources based on DFL 0.664 SCI2: Understanding of market trends and accuracy of demand forecasting 0.703 SCI3: Accuracy and adaptability of SCM planning 0.761 SCI4: Control and tracking of inventory: accuracy and visibility 0.814 SCI5: Process standardization and visibility 0.814

0.076 0.067 0.065 0.063 0.065

8.433a 8.167a 7.598a 6.763a 6.753a

Supply chain performance (w2/d.f. ¼ 1.24, RMSEA ¼ 0.04, GFI ¼ 0.98, NFI ¼ 0.98, NNFI ¼ 0.99, CFI ¼ 0.99, IFI ¼ 0.99) SCP1: Just-in-time 0.741 SCP2: Inventory turnover and cash-to-cash cycle time 0.790 SCP3: Customer lead time and load efficiency 0.702 SCP4: Delivery performance and quality 0.738 SCP5: Supply chain inventory visibility and opportunity costs 0.723 SCP6: Total logistics cost 0.799

0.079 0.048 0.064 0.050 0.074 0.060

8.019a 7.469a 8.329a 8.050a 8.170a 7.345a

a

Standardized coefficient

Significance at the level of pp0.01.

Gerbing, 1988). In addition, according to Bollen (1989), the larger t-values or the standardized coefficients are, the stronger the evidence that the individual items represent the underlying factors is. The results of CFA reveal that the standardized coefficients for all items greatly exceed twice their standard errors, and that the standardized coefficients for all variables are large (40.6) and significant (all the t-values are larger than 2). Therefore, all items are significantly related to their underlying theoretical constructs.

Table 4 Discriminant validity tests Factors

IT

SCI

SCP

IT implementation (IT) Supply chain integration (SCI) Supply chain performance (SCP)

– 37.78a 84.80a

– 68.73a

–

Chi-square differences between fixed and free models. a Significance at the level of pp0.00.

3.6. Discriminant validity In addition to convergent validity, discriminant validity is another important test to ensure adequacy of the measurement model. Discriminant validity measures the extent to which individual items intending to measure one latent construct do not at the same time measure a different latent construct (DeVellis, 1991). In this study, discriminant validity is established using CFA. Models were constructed for all possible pairs of latent constructs. These models were run on each selected pair, (1) allowing for correlation between the two

constructs, and (2) fixing the correlation between the constructs at 1.0. A significant difference in Chi-square values for the fixed and free solutions indicates the distinctiveness of the two constructs (Bagozzi et al., 1991). For the three constructs of IT, SCI, and SCP, a total of three different discriminant validity checks were conducted. As shown in Table 4, all the three Chi-square differences between the fixed and free solutions in Chisquare are significant for statistical significance at po0.00 confidence level. This result provides strong evidence of discriminant validity among the theoretical constructs.

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SCI1

SCI2 0.73

IT1 IT2

SCI3

0.69

0.74

131

SCI4 0.80

SCI5

0.81

SCI 0.69

0.89*

0.70

IT3

0.77

0.99*

IT

0.83

IT4

-0.10

0.79

SCP

IT5 0.76

SCP1

SCP2

0.77 0.68

SCP3

0.72

0.73

SCP4

0.82

SCP5

SCP6

Fig. 2. Standardized results of structural equation model. Significant at: *po0.01.

4. Results In the study, we use structural equation modeling to estimate the causal relationship among the different constructs with linear structural relations (LISREL) program. The results of the structural equation model tested are shown in Fig. 2. The overall model fit indices are as follows: w2 d:f : ¼ 152:54=101 ¼ 1:51, root mean square error of approximation (RMSEA) ¼ 0.048, goodness of fit index (GFI) ¼ 0.90, normed fit index (NFI) ¼ 0.92, nonnormed fit index (NNFI) ¼ 0.97, and comparative fit index (CFI) ¼ 0.97. These indices are better than the threshold values suggested by Hu et al., 1992. Therefore, the structural equation model is well within the suggested range and can be supported. Since the structural equation model is satisfactory, it can be served as the basis of evaluation for our hypotheses. The results indicate that SCI has a positive and significant effect on SCP (b ¼ 0.99, t ¼ 12.74, po0.01), and IT implementation has a positive and significant effect on SCI (b ¼ 0.89, t ¼ 9.39, po0.01). Thus, hypotheses H2 and H3 are supported. However, our analysis found no significant effect of IT implementation on SCP therefore, hypothesis H1 is not supported (b ¼ 0.10, t ¼ 0.62, po0.01). To assess the mediation effect of SCI on the relationship between IT implementation and SCP, two alternative models are estimated (Venkatraman, 1989). First, the construct of SCI is removed and only the direct effects of IT implementation and SCP are estimated. The results, with its fit indices of w2 =d:f : ¼ 1:43, RMSEA ¼ 0.049, GFI ¼ 0.94, NFI ¼ 0.95, NNFI ¼ 0.98, and CFI ¼ 0.98, indicate that the direct effect of IT implementation on SCP is positive and significant (b ¼ 0.85, t ¼ 9.91, po0.01). Second, the path between IT implementation and SCP is removed from the original model, where only the indirect effect of IT implementation on SCP via SCI integration is remained. From the results of this specific model, with its fit indices of w2 =d:f : ¼ 1:50, RMSEA ¼ 0.053, GFI ¼ 0.90, NFI ¼ 0.92, NNFI ¼ 0.97, and

CFI ¼ 0.97, it can be indicated that both the direct effects of IT implementation on SCI (b ¼ 0.89, t ¼ 9.33, po0.01) and SCI on SCP (b ¼ 0.97, t ¼ 10.56, po0.01) are positive and significant. Hence, we conclude that the effect of IT implementation on SCP is mediated by SCI. 5. Discussion and implications 5.1. Discussion The purpose of this study was to propose and test a model of the relationship among three factors: IT implementation, SCI, and SCP. A number of important findings emerge that have both theoretical and managerial implications. First, a significant contribution of this study is the empirical test of theoretical assumptions in the existing literature on the impact of IT implementation on SCI and its performance. Although some empirical studies have been conducted to test the relationship between IT and SCP (Devaraj and Kohli, 2003; Jin, 2006; Aaker and Jacobson, 1994, 2001; Allenby and Rossi, 1991), as far as we are aware, this is the first study that explores the antecedents of IT implementation, SCI and SCP. While more and more companies are seeking efficient ways to improve SCP and often make large investments in IT systems, it is not clear whether IT implementation has a direct effect on SCP. This study proposes a new model where SCI bridges IT implementation and SCP. This model helps to reveal the impact of IT implementation on SCP and verifies the general finding that SCI has a positive effect on SCP. Second, this study provides answers to two questions: (1) Does IT implementation have a direct effect on SCP? As hypothesized in H1, IT implementation has a direct, positive effect on SCP. It is perhaps surprising that we found there to be no significant relationship. However, this may be explained by the answer of the second question: (2) How the implementation of IT affects SCP. As

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hypothesized in H2 and H3, SCI mediates the relationship between IT implementation and SCP. The results reported that IT implementation affects SCI directly and SCI has a positive effect on SCP. This is partly in line with previous scholars (e.g., Devaraj et al., 2007) who have suggested that supplier integration leads to better SCP. It indicated that SCI is not synonymous with IT implementation. Rather, IT implementation is a separate construct that improves SCI. Supply chain integration is a result of human interactions which can be supported, but not replaced by IT (Sanders, 2007). This is an important point for managers when they consider investing in various IT initiatives. Based upon the findings of this study, priority should be given to IT investments that improve SCI. Any competitive advantage gained from IT will result from the improvement of SCI, but not from the IT investment per se. The third contribution of this study relates to the measurement of IT implementation. Most previous studies measured IT by measuring financial investment in IT; in addition, they measured IT facility usage from secondary data, which did not take account of the impact of IT adaptation, implementation and use by supply chain partners. The lack of IT alignment among supply chain partners often frustrates the potential beneficial effects of IT implementation by individual companies (Kearns and Lederer, 2003; Seggie et al., 2006). This study sought to integrate the usage of IT and IT alignment into a comprehensive framework, which allows better capture of the nature of IT implementation and its affect on SCM.

5.2. Managerial implication The findings of this study provide insights into the design of effective strategies for IT implementation and SCI in order to enhance SCP. A key finding is that SCI is affected by IT implementation, and SCI mediates the relationship between IT implementation and SCP. This finding has a number of implications for managers. First, it underscores the important role IT implementation plays in the functioning of supply chain organizations. Based upon this finding, IT efforts that promote SCI should be given higher priority. The proactive adoption, implementation, and utilization of IT systems, such as electronic data interchange (EDI), bar coding, enterprise resource planning (ERP), customer relationship management (CRM) and decision support systems (DSS), and the alignment of IT philosophies, patterns and practices among supply chain partners should result in better SCI. Second, although the finding that SCP can best be improved by the integration of the supply chain partners is not new, it validates the important role played by SCI. The significant impact of SCI on SCP suggests that companies should invest in strategies that promote collaboration and integration across the members of the supply chain. Since IT implementation has been shown to promote SCI, companies should also consider implementing these types of ITs. Third, this finding suggests that IT is not an actual source of competitiveness but a source of

competitive necessity. SCM emphasizes the global and long-term benefits to all entities participating in the chain through cooperation and information sharing. A company’s efficient communication with downstream and upstream business entities is a necessity, rather than a source that can boost its competitive advantage. Today, more and more companies are deploying and utilizing IT to improve communications and decrease the response time to market fluctuations. Implementing IT has become a necessity, not a choice (Jin, 2006). Companies should invest in IT capability if they want to enhance the SCI intensity. At the same time, companies should not seek to justify investments in IT in terms of their potential direct impact on SCP. Another contribution of this study is the IT measurement, which focus on the adoption, implementation and IT alignment among supply chain partners. The implication is that managers should not assume that all investments are equally effective. The same level of investment does not guarantee the same result. In the complex environment of the supply chain, the successful implementation of SCI projects is not so much a technological problem as it is a management problem, requiring a thorough study of the business conditions for all companies involved. Companies should have the processes and procedures in place to capture the full potential of IT implementation. The different business contexts of the individual supply chain partners have to be aligned to the supply chain. Participants in the supply chain have to transform their collaboration patterns and build an open and uniform IT framework to support IT implementation throughout the supply chain. In the long run, as supply chain members align their philosophies on SCM, adopt a win–win collaboration pattern for their business interaction, and adopt an open framework (e.g. architecture, standard) for IT implementation, IT implementation will be better able to enhance SCI and boost SCP. 6. Limitations and future research Several limitations of this study need to be noted, as well as some directions for future research. This study focused on the impact of overarching ITs, and not on any specific IT system. Also, this study did not classify the various supply chains in the samples, as has been proposed by Fisher (1997), who suggested that supply chains could be categorized into efficient supply chains and responsive supply chains, and that supply chains facing different environmental dynamism should use different supply chain practices. Furthermore, previous studies have proposed many different classifications of ITs (Barki et al., 1993; Kendall, 1997). But regardless of classification, it can be assumed that some ITs have a more direct and significant impact on integration and performance for some kinds of supply chains than others. Given the high cost of IT implementation, it may be important for future work to consider the impact of different types of ITs on different types of SCI and SCP. A model for matching ITs with supply chain characteristics is needed, so that managers can more easily find the best form of IT implementation.

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Second, in a web of dyadic supply chain relationships, there are numerous factors that can contribute to SCI and SCP. Furthermore, it remains unanswered whether investment in IT implementation will lead to greater enhancements in SCI and SCP outcomes than will other investment alternatives such as vertical integration. Future research should explore such alternatives to help managers find the best way to enhance SCI and SCP. Third, the data for this study came from companies located in the developed areas of China. Companies in other parts of the country may have different business conditions, different cultures, different leadership styles, and so on. All of which might affect company strategies on IT and SCM. Therefore, future research is needed to determine whether the findings reported here are valid in other areas (e.g. the underdeveloped areas of China). Finally, there are several limitations on the survey procedures. Like many other studies, this study used a single informant from each company. A dataset with multiple informants could enhance the validity of the findings. Also, this study uses cross-sectional data which is static in nature. Although the causal interrelations were analyzed and could imply temporal aspects, collecting data over time from informants can offer richer implications. Future research might be undertaken to test the findings of this study using time-series data. The topic of SCI is still in its infancy. There are many opportunities for future research in this area. We call upon managers and researchers to take up these challenges. 7. Conclusions With the accelerated development of science and technology, the capability of IT has dramatically increased.

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Because IT has been proved to have the potential to improve SCP, more and more practitioners invest in IT. With the growing awareness of the benefits of IT implementation, it is important to understand how it impacts on SCP. Earlier studies have viewed IT as an enabler of integration, which is the foundation of SCM. IT implementation has had a particularly profound impact on supply chain organizations. Its power to provide timely, accurate, and reliable information has created the expectation that IT will always improve SCI and SCP. The present study focused on the relationship among IT implementation, SCI, and SCP, using empirical data collected in China. Our findings show that IT implementation has no direct effect on SCP, but rather, that it contributes to the improvement of SCP through its positive impact on SCI. These findings contribute to deepening our understanding of the impact of IT implementation on SCP. In addition, these findings should highlight the importance for managers to promote SCI and implement IT as an enabler.

Acknowledgments The authors wish to thank the anonymous referees for their valuable suggestions, and in particular, the authors acknowledge the contributions of professor De-bi Cao of Keio University. This research was supported by the National Natural Science Foundation of China (Nos. 70433003, 70701029), the National Social Science Foundation of China (No. 08XJY016) and the Research Fund for the Doctoral Program of Higher Education of China (No. 20070968063).

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Appendix A. Construct measurement

Level 1

Level 2

Level 3

Level 4

IT implementation (IT)

IT1: Electronic data interchange (EDI) coverage

Company is not electronically linked to any customer or supplier.

EDI links are set up with some customers or suppliers at their request.

EDI is used with over 50% of customers or suppliers. Proprietary EDI standards are used in most cases.

IT2: Usage of bar coding/automatic identification and data capture (AIDC)

Bar codes or other forms of automatic identification and data capture (AIDC) are not utilized.

IT3: Effective usage of computers in operations and decision-making (ERP, supply chain planning software, etc.)

PCs are not utilized anywhere in the business.

In addition to Level 3, EDI is integrated with the company’s internal systems so that manual re-entry of data is not necessary in most cases. Extending the scope of Level 3, bar codes are used as a means to accelerate innovation of the logistics system, in addition to synchronizing the material and information flow. In addition to Level 3, decision support systems and other IT tools are utilized for logistics planning and optimization.

IT4: Open standards and unique identification codes

Company has no awareness of open standards and unique identification codes.

IT5: Decision- making No knowledge or interest in systems and support to the decision-making supply chain partners processes and systems used by suppliers or customers.

Supply chain integration (SCI)

Level 5

EDI is used for nearly all transactions and is integrated with internal systems. Open standards for EDI are adopted or in-process of adoption. Bar codes are utilized in Bar codes are utilized in some The best mix of bar codes, 2some activities such as activities, such as inspection, dimensional symbols, IC tags and inspection, but the data are and the data are shared with other AIDC methods is linked not used for other purposes. internal systems to with EDI, and used to support synchronize the material and innovation of the logistics system information flow. at the supply chain level. PCs are used to support Most routine business ERP, SCP, CRM and other IT tools some business operations operations and activities are are utilized for planning and and activities. computerized (e.g. accounting, optimization of the entire supply production, etc.) but are not chain. Outsourcing and other integrated with each other. means are considered for increasing the effective use of IT and related resources. Company understands the To exploit the potential of IT, In addition to Level 3, usage of In addition to Level 4, unique importance of open unique identification codes are unique identifiers is extended to identification codes are extended standards and unique used within the company and suppliers and/or customers. Open to both suppliers and customers. identification codes for process simplification is also standards for EDI and other IT Company is actively working improving the efficiency of carried out. applications are adopted or under towards adoption of open logistics processes. consideration. standards for EDI and other IT applications. Has a general Understands the systems used Exploring ways to modify or Have succeeded in implementing understanding of how a by a supplier or customer, but integrate the systems of the a win-win solution with supply supplier or customer makes has made no proposals or company and its suppliers or chain partners, and actively its decisions, but does not efforts to bring about a wincustomers in order to realize win- provide proposals and support to know the details of the win solution. win solutions. partners to improve their systems systems used. and innovate the supply chain.

SCI1: Strategies for optimizing logistics system resources based on DFL

Efficient utilization of logistics facilities and resources is not seen as a problem. No improvement strategy exists.

Importance of optimizing logistics system resources is recognized, but there is no strategic plan or review.

Strategic plan exists for review of transportation modes and inventory allocation among plant, distribution center, transfer center. Optimization efforts are making progress.

In addition to Level 3, suppliers and customers are involved in efforts to optimize logistics sterns resources.

Clear strategy exists for collaboration and optimization across the supply chain, including product re-design based on design for logistics, and use of other approaches such as joint distribution and category management.

SCI2: Understanding of market trends & accuracy of demand forecasting

Rely on the experience and judgment of the sales department to predict market trends and forecast demand.

Demand forecasting for certain products is based on a quantitative sales history combined with the judgment and experience of the sales department.

Demand forecasting for key products is based on an analysis of market trends and quantitative sales history, and includes the input of sales and related departments.

Level 3 approach is extended to all products, and forecasts for key products are broken down into items or categories. Demand forecasting system is in place.

Level 4 approach is carried out jointly with supply chain partners. Demand forecasts can be revised dynamically for changing market conditions.

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Construct

SCI4: Control and tracking of inventory (product/parts/WIP): accuracy and visibility

No tracking or visibility of inventory/WIP status. Management action is taken after-the-fact.

SCI5: Process standardization and visibility

Little standardization of work methods or use of unit loads. Some process activities are treated as a ‘‘black-box’’.

Work methods are mostly standardized, but the overall work flow is not completely visible.

SCP1: Just-In-Time (elimination of idle time and setup time through information sharing and synchronization of material and information flow) SCP2: Inventory turnover & cash-tocash cycle time

JIT philosophy is not part of the company’s approach or practices.

Plans for sales, replenishment and delivery are intended to be coordinated with each other on a monthly basis, but in practice tills is only partially achieved. For most items, inventory status is tracked on a daily basis, and supply is adjusted to meet demand on a monthly basis.

Plans for sales, replenishment and delivery are supposed to be coordinated with each other on a weekly basis, but individual departments may make their own adjustments during the week. A system is in place which enables the company to manage and track its own inventory and replenishment activities on a daily basis.

Linkage of weekly plans between departments is done on a rolling basis. Plan adjustments for customers can be done on a daily basis.

Linkage of daily plans between departments is done on a rolling basis. Plan adjustments for suppliers or customers can be done on an hourly basis.

A system is in place which enables the company to manage and track inventory and replenishment activities for itself and its suppliers on a daily/ hourly basis Work methods are Work flow, including interface standardized and unit loads are activities with suppliers and used but interface activities customers, is standardized and with suppliers and customers made visible. There is continuous are not made sufficiently improvement of work activities visible. within the company

Inventory and replenishment activities are managed and tracked throughout the entire supply chain, including suppliers and customers. Information is strategically shared. In addition to Level 4, partnerships are established for each business unit and the entire supply chain is made visible. Process innovation is continually pursued.

Company recognizes the importance of JIT philosophy, but has not implemented JIT practices in production, replenishment, material handling, or delivers.

JIT practices such as setup time reduction, lot-size reduction, load consolidation or floorready merchandise are implemented, but they are not synchronized with other activities.

Some JIT activities are synchronized (e.g. picking sequence is determined from delivery plan, delivery trucks allocated based on picking sequence, etc).

JIT activities are synchronized throughout the material flow and involve suppliers and customers.

Neither inventory turnover nor cash-to-cash cycle time is measured. Inventory turnover is low, and cash flow is poor.

Inventory turnover is known at the aggregate level for each facility, but inventory management is not linked to cash flow.

Inventory turnover for each supplier and individual product is measured with accuracy at the week-level and actual performance level of less than 12 turns/yean

Inventory turnover for each supplier and SKU is measured with accuracy at the day-level and actual performance level of 12+ turns/year. Inventory management is linked with cash flow.

Exceeds Level 4, with inventory measured with accuracy at the hours-level and actual performance of 24+ turns/year. Cash-to-cash cycle time is less than 10 days.

SCP3: Customer lead time (from order placement to receipt) and load efficiency

Lead time from order placement to receipt is long. Company receives frequent requests from customer to shorten lead time.

Lead time is known and managed for each customer or item category, and is linked to truck allocation planning to increase load efficiency.

In addition to Level 3, average lead time is less than 2 days. Continuous efforts made to further reduce lead times.

In addition to Level 4, achieves load efficiency of 80% or higher.

SCP4: Delivery performance and quality

On-time delivery rate (ontime deliveries/total orders) and order fulfillment accuracy (accurate deliveries/total orders) are not known. Company faces many customer complaints.

Lead times for different customer categories are known, but orders with short lead time are covered by on-hand inventory. Little effort made to reduce lead times. On-time delivery rate and order fulfillment accuracy are measured, but actual performance level is less than 95%.

Performance is between 95 and 99% for both rates. To improve performance, efforts are made to collect data on the root causes of late deliveries, stock outs, miss deliveries, damage, etc.

Performance exceeds 99% for both rates. Based on data about root causes, error prevention measures such as mistakeproofing are implemented on an ongoing basis.

In addition to Level 4, suppliers and customers are involved in improvement efforts. While maintaining high performance, efforts to improve efficiency, such as elimination of incoming inspections, are promoted.

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Planning for sales, replenishment and delivery is carried out separately, without consideration of inventory availability.

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Supply chain performance (SCP)

SCI3: Accuracy and adaptability of SCM planning

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SCP6: Total logistics cost (transportation costs, inventory holding costs, order management costs, administrative costs, etc.)

Only on-hand inventories within one’s own facility or company are known. Opportunity cost of lost sales is not known or estimated.

Inventory levels within the company are known. Some estimation is made of the opportunity cost of lost sales.

Inventory levels are known for the company and its immediate suppliers or customers. Some estimation is made of opportunity cost of lost sales for the company only. Order management costs and Most logistics-related costs In addition to Level 2, logisticsproduct manufacturing costs for the company are known related costs are broken down are known, but logisticsat an aggregate level (e.g. to individual supplier and related costs are not wellown transportation costs, customer level well enough defined or separated out. freight payments to outside that they can be utilized in carriers, inventory holding revenue management. costs).

Inventory levels are known for the company and its immediate suppliers and customers. Some estimation is made of opportunity cost of lost sales for the company only. Total logistics costs (transport, inventory holding, order mgmt, admin, costs, etc) are broken down for each supplier and customer. Using activity-based costing approach, this information is used in revenue management and system improvement and innovation.

Inventory levels are known throughout the entire supply chain. Estimation is made of opportunity cost of lost sales at the end demand level.

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SCP5: Supply chain inventory visibility & opportunity costs

In addition to Level 4, total logistics costs throughout the supply chain are known and shared among supply chain members. Win-win scenarios for cost reduction are developed from the viewpoint of supply chain optimization.

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Note: Electronic data interchange (EDI): The computer-to-computer exchange of structured information, by agreed message standards, from one computer application to another by electronic means and with a minimum of human intervention. There are two types of EDI standard. Proprietary standard and open standard. UN/EDIFACT is used as the international open standard. Recently, Web EDI and XML EDI are also used as a simple standard. Automatic identification and data capture (AIDC): The methods of identifying objects, collecting data about them, and entering that data directly into computer systems for the synchronization of material and information flow. Technologies typically considered as part of AIDC includes bar codes, QR codes, biometrics, OCR, RFID (IC tags). Enterprise resource planning (ERP): Management information systems that integrate and automate many of the business practices and information associated with the operations and accounting of a company. Supply chain planning (SCP): Management information system for planning and optimization of the entire supply chain. It integrates and supports many of the business planning and decision making by synchronizing material and information flow of the supply chain. Unique identification codes: Unique code for cargos and products through departments, organizations, and the whole country, which prevents re-entering and re-handling, IT plays an important role for utilizing EDI or AIDC technologies. Design for logistics (DFL): General term of a measure/approach for product and load redesign that goes up to replenishment and distribution processes restructuring, in order to enhance efficient logistics while coping with diversification and constant changes.

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