A Meta-Analysis of Supply Chain Integration and Firm Performance


  • Acknowledgment: We would like to thank Senay Demirkan Delice for her help in the initial data collection.


As supply chain activities become more dispersed among customers, suppliers and service providers, there is an increased need for customers and suppliers to work together more closely. Supply chain integration (SCI) has been a highly researched topic during the last 20 years. A meta-analytic approach is used to provide a quantitative review of the empirical literature in SCI, and examines relevant design and contextual factors. Eighty independent samples across 86 peer-reviewed journal articles, yielding a total of 17,467 observations, were obtained and analyzed. While general support exists in favor of the positive impact of SCI on firm performance in the literature, this research helps clarify mixed findings that presently exist. Our results indicate that there is a positive and significant correlation between SCI and firm performance. Additional subgroups and moderators are tested and provide nuanced views of the scope and specific dimensions of SCI, firm performance and their relationships.


As supply chains mature, their complexity increases. Managers are asked to improve productivity while increasing customer service. Shareholders expect profitability to grow quarter over quarter. These internal and external forces have the effect that often tasks that previously were performed internally become outsourced (Williamson, 2008). This results in increased interaction among firms in a supply chain and requires closer relationships to ensure that the flows of product, information and payments operate efficiently (Flynn, Huo, & Zhao, 2010; Frohlich & Westbrook, 2001; Thun, 2010; Wagner, 2003). Managing these relationships requires cross-functional and cross-firm business processes with appropriate levels of information sharing, operational coordination and select close partnerships (Charvet, 2008; Lambert & Cooper, 2000; Rai, Patnayakuni, & Seth, 2006; Sanders, 2007).

The term “supply chain integration” (SCI) has been defined as the extent of engagement with suppliers and customers (Frohlich & Westbrook, 2001). The terms “supply chain collaboration” (Stank, Keller, & Daugherty, 2001) and “supply chain coordination” (Carr, Kaynak, & Muthusamy, 2008) are used to describe elements of SCI. As “collaboration begins with customers and extends back through the firm (…), integration is needed both internally and externally (Stank et al., 2001, p. 29).” In addition, “integration involves coordinating (…) the forward physical flow of deliveries” and “the backward coordination of information technology” (Frohlich & Westbrook, 2001). Therefore, it is believed that collaboration and coordination are elements of SCI (Mackelprang, Robinson, & Webb, 2012).

The focus of this research is on SCI. To integrate all of the studies we collected into one framework, we provide the following definition of SCI for this research. SCI is the scope and strength of linkages in supply chain processes across firms. Information, operational and relational integration facilitate the linkages in supply chain processes between firms. The scope of SCI can be integration with customers, suppliers, internal or external. The overall premise of our research is to test whether tighter integration leads to better firm performance.

A large increase in research investigating SCI has been observed in the SCM discipline, as shown in Figure 1. Until now, only a few qualitative reviews of the SCI literature can be found (Chen, Daugherty, & Landry, 2009b; Fabbe-Costes & Jahre, 2008; Simatupang & Sridharan, 2005; Van der Vaart & van Donk, 2008). While such studies have a substantial contribution to the field, they do have inherent drawbacks because it is challenging to objectively tie together primary research. As the debate between Hanushek (1989, 1994) and Hedges, Laine, and Greenwald (1994a,1994b) has shown, a subjective review of existing literature may be just that. In addition, the results of a meta-analysis can be used subsequently to suggest areas in need of further investigation. SCI has often been operationalized and measured differently, and this adds to the challenge of integrating the findings. Overall, empirical evidence seems to support the positive impact of SCI on firm performance, however mixed findings are not uncommon (Flynn et al., 2010). In addition, the selection of firm performance as the dependent variable is a natural link and has been critical in the literature (Fabbe-Costes & Jahre, 2008; Van der Vaart & van Donk, 2008). The more important decision is how to measure and evaluate firm performance, which is multi-dimensional. It has been show that a single study does not have enough power, due to the relatively small sample size, to explain the magnitude of a statistical relationship (Hunter, 2001; Lipsey & Wilson, 2001). Therefore, aggregating several studies into a meta-analysis is of critical importance in order to draw conclusions that are valid beyond the limited situations in which they were obtained and make empirical generalizations (Leone & Schulz, 1980).

Figure 1.

Published Supply Chain Integration Articles

  • Note: Articles were identified via a keyword search on “supply chain integration” and “supply chain collaboration” among peer-reviewed articles in the EBSCO Business Source Complete database where the search was performed on title, abstract and keywords.

The primary purpose for this study is thus to provide the first comprehensive, quantitative and integrative review of empirical research linking SCI to overall firm performance. The methodological advantage of a meta-analytic study is that statistical artifacts such as sampling or measurement error can be accounted for (Hunter & Schmidt, 1990). Another advantage is the ability to examine how various study design factors may affect the relationship between SCI and firm performance: (1) Is there evidence of a positive correlation between SCI and firm performance? (2) Does the correlation between SCI and firm performance vary across different dimensions and operationalizations of SCI? and (3) Does the correlation between SCI and firm performance vary across different performance dimensions?

The remainder of the study is organized as follows. The theoretical background and research hypotheses are developed in the following section. Following that section, the research methodology is described and results of the meta-analysis are reported. Last, conclusions are presented, including theoretical implications, managerial implications, limitations and recommendations for future research.

Theoretical Development

The process of achieving and maintaining higher levels of integration is complex and may demand unwarranted resources. To add structure to the relationship of SCI to firm performance, researchers have grounded their studies in a variety of organizational theories. An overview of the most commonly used theoretical bases is provided next and highlighted in Table 1. Our primary theoretical focus in this meta-analysis is on the resource-based view (RBV) of the firm with the extensions of resource-advantage (R-A) theory and the relational view (RV) of the firm. We also use secondary but important theoretical lenses, which are described later.

Table 1. Theoretical Bases for Supply Chain Integration
TheoryRelevant ThemesSample Empirical Studies

Resource-based view (RBV)

Barney (1991), and Wernerfelt (1984)

Firms can develop a unique capability and excel in integrating with firms in the supply chain. Supply chain integration as a strategic resource can lead to a sustained competitive advantage and superior firm performance.Chen et al. (2009b), and Mesquita et al. (2008)
Relational view (RV)Dyer and Singh (1998), Lavie (2006), and Lorenzoni and Lipparini (1999)Strategic resources can also arise at the inter-firm level. The achievement of a competitive advantage via supply chain integration is dependent on the generation of relational rents between multiple firms.Deveraj, Krajewski, and Wei (2007), and Mesquita et al. (2008)

Knowledge-based view (KBV)

Argote (1999), Grant (1996), and Kogut and Zander (1992)

Supply chain integration helps deploy knowledge resources by exchanging valuable information (operational and strategic information) across the organizational boundary with supply chain partners.Rosenzweig et al. (2003), Paulraj et al. (2008), Swink et al. (2007), and Rai et al. (2006)

Social exchange theory (SET)

Blau (1964), Emerson (1962), Dwyer et al. (1987), MacNeil (1980), and Morgan and Hunt (1994)

Relational governance mechanisms such as trust and commitment can be used to achieve a higher degree of integration between supply chain partners. Relational exchange relationships can be more effective and efficient, though, the risk of opportunism can dampen these benefits.Johnston et al. (2004), Prahinski and Benton (2004), Golicic and Mentzer (2005),Griffith, Harvey, and Lusch (2006), Gulati and Sytch (2007), and Nyaga, Whipple, & Lynch (2010)

Transaction cost economics (TCE)

Coase (1937), Rindfleisch and Heide (1997), and 1975, 2008Williamson (1975)

Supply chain integration may help firms reduce the burden of transaction cost, and implement safeguard mechanisms to mitigate the threat of opportunism. Asset specificity and uncertainty are important factors to consider when selecting the most appropriate inter- organizational governance form.Lee, Kwon, and Severance (2007)

Information processing theory (IPT)

Lawrence and Lorsch (1967), Thompson (1967), Galbraith (1973), and Huber (1991)

Increased flow and quantity of information can lead decision-makers to be able to improve the performance of the firm itself and has positive effects on the performance of the supply chain.Swink et al. (2007), Wong, Boon-itt, and Wong (2011)

Resource-Based View of the Firm

The RBV posits that firms can be viewed as collections of resources, some of which can be considered strategic resources (Penrose, 1959; Wernerfelt, 1984). Strategic resources are valuable, rare and imperfectly imitable and substitutable (Barney, 1991). As they are distributed heterogeneously across firms, they can result in a sustained competitive advantage (Barney, 1991; Peteraf, 1993). Supply chain scholars have acknowledged that internal/cross-functional and external integration with customers and suppliers can be complex and requires unique capabilities that may be difficult or costly to implement (Barney, 2012; Chen, Daugherty, & Roath, 2009a; Chen et al., 2009b). SCI can be seen as an internal strategic resource that could result in a competitive advantage and improved firm performance (Barney, 2012).

Resource-Advantage Theory and the RV of the Firm

An extension of RBV, R-A theory, focuses not just on resources per se, but more specifically on advantageous resources, which give firms a competitive advantage (Hunt & Davis, 2008). Resources under R-A theory are tied to their contribution in producing a market offering that has value as perceived by customers and the degree to which they are available are used to create a competitive advantage (Hunt & Davis, 2012; Priem & Swink, 2012). Another extension of the RBV, the RV, postulates that firms can benefit from inter-firm integration and strategic partnerships to acquire valuable resources they lack in-house (Dyer & Singh, 1998). Whereas the RBV focuses on internal strategic resources, the RV contends that a competitive advantage also originates from inter-firm resources that cannot be captured or owned by one firm in isolation (Dyer & Singh, 1998; Lavie, 2006; Lorenzoni & Lipparini, 1999). Inter-firm integration can often result in win–win situations where the total supply chain benefits are increased due to the use of hard to imitate specialized assets, skills and information. Mesquita, Anand, and Brush (2008) argue that the RBV and RV can be seen as complementary rather than competitive theories. They present empirical evidence showing that joint knowledge acquisition, suppliers' investment in dyad-specific assets and capabilities, and buyer-supplier alliance relational governance are partnership-specific resources that cannot be explained by the RBV alone.

Secondary Theoretical Lenses

In addition to the primary theories, four secondary theories were identified among the articles in the sample. With roots in both the RBV and RV, the knowledge-based view (KBV; Argote, 1999; Grant, 1996; Kogut & Zander, 1992) states that SCI can help firms coordinate and deploy knowledge resources by exchanging valuable information across the organizational boundary with key suppliers and customers. Social exchange theory (SET), with origins in sociology (Blau, 1964; Emerson, 1962) and relational marketing (Dwyer, Schur, & Oh, 1987; Morgan & Hunt, 1994), has been used to explain the need for closer interaction between organizations, which posits that the basic motivation for integration is the seeking of rewards and avoidance of punishments (Emerson, 1962). Transaction cost economics (TCE) also highlighted some of the integration benefits (Williamson, 1975). Transaction costs are the expenses generated by identifying fair market prices, negotiating and carrying out economic exchange. With respect to SCI, TCE predicts that firms should fare better if they appropriately adjust their governance mechanisms to the underlying transactions (Williamson, 1975, 1991, 2008). The information processing theory (IPT) posits that coping with information is an organization's main task and that more information has a positive link with performance (Galbraith, 1973). This, however, is not a constant effect as an inflection point can be reached at which more information does not lead to better performance.

Hypothesis Development

The theoretical bases provide a lens for examining the 80 independent samples included in this meta-analysis. Researchers in our sample have used different definitions, dimensions and operationalizations to examine SCI (for a more thorough review see: Fabbe-Costes & Jahre, 2008; Van der Vaart & van Donk, 2008). While there was divergence among researchers, an aggregate view of SCI is important and is used as a starting point for evidence that SCI indeed has an effect on firm performance. Generally, a meta-analysis can be utilized effectively not only to examine narrow, well-defined constructs, but also to assess relationships involving more broadly defined constructs (Crook, Ketchen, Combs, & Todd, 2008; Nair, 2006). As mentioned earlier, integration among companies within the supply chain can be complex and requires unique capabilities that may be difficult or costly to imitate (Barney, 2012; Chen et al., 2009a,2009b). Being able to manage these integrative relationships better than the firm's competitors is a valuable internal strategic resource. As such, we predict that SCI enables management to achieve a sustainable competitive advantage which can be viewed by improved firm performance (Barney, 2012). Therefore, the first hypothesis for this research is the following.

H1: Supply Chain Integration is positively related to firm performance.

Dimensions of SCI

After the evaluation of the overall effect of SCI on firm performance, we have the opportunity to directly evaluate whether diverging construct measurement types alter the nature, or magnitude, of the broader relationship. Researchers made the distinction between different dimensions of integration. Frohlich and Westbrook (2001, p. 187) explicitly focus on the operational aspect in their definition: “The development of shared operational activities with customers and/or suppliers.” Flynn et al. (2010, p. 59) define SCI as “the degree to which a manufacturer strategically collaborates with its supply chain partners and collaboratively manages intra- and inter-organization processes,” emphasizing the strategic nature of SCI. Because of such divergent definitions, a more comprehensive classification of constructs was necessary. This classification was developed based on a synthesis of the classifications shown in Table 2, with the goal of succinctly classifying all retained articles.

Table 2. Supply Chain Integration Dimensions
AuthorsSCI Types (Dimensions)Description
Lee (2000)Information IntegrationThe sharing of information and knowledge among and members of the supply chain (demand information and inventory status, capacity plans, production schedules, and promotion plans, demand forecasts and shipment schedules).
Coordination and resource sharingRefers to the redeployment of decision rights, work and resources to the best-positioned supply chain member (e.g., VMI, CRP programs, shared warehousing, inventory pooling).
Organizational and relationship linkageTight organizational relationships between companies (integrated communication channels, performance measures and incentives).
Ireland and Webb (2007)StrategicIntention of the firms within the supply chain to integrate their actions and interactively adjust their behaviors while pursuing opportunities over time. Includes both short-term (e.g., supplier scheduling, visibility) and long-term goals and efforts (e.g., joint flexibility, adaptation).
OperationalProduct and process integration across firms within strategic supply chains (e.g., allowing suppliers to assume responsibility for product engineering activities and product development; including suppliers to understand the complexity and scope of coordinated processes).
TechnologicalSharing of knowledge and capabilities within the strategic supply chain.
Van der Vaart and and van Donk (2008)PracticesTangible activities or technologies that play an important role in the collaboration of a focal firm with its suppliers and/or customers (e.g., use of EDI, VMI, integrated production planning, delivery synchronization).
AttitudesMeasures attitude of buyers and suppliers towards each other (intangible), e.g., long-term orientation, joint problem sharing and planning, trust.
PatternsInclude activities like frequent visits, face-to-face, meetings/communication, formal periodic evaluations of suppliers/customers.
Kim and Lee (2010)StrategicThe extent to which supply chain partners actually forecast demand and plan business activities jointly while taking into account each other's long-term success.
SystemsThe extent to which supply chain partners strive to make and keep their communication systems compatible with each other to be ready for inter-firm forecasting and planning in addition to routine electronic transactions and information exchange within the supply chain.
Saeed, Malhotra, and and Grover (2005)StrategicThe extent to which members of the supply chain have developed joint knowledge sharing routines that facilitate use of innovative practices, sharing of new ideas, and working together in identifying and implementing improvement initiatives.
OperationalThe extent to which supply chain members link decisions at different stages of the supply chain by routinely coordinating various operational processes and activities through information sharing.
FinancialThe extent to which supply chain members jointly invest in projects of mutual interest.

The linkage between integration efforts and firm performance is a central tenant of this research. Because SCI requires investment, the objective of management is to see a return on that investment. All articles in our sample tested this linkage. In line with our theoretical lenses, we view SCI as a resource, which enables the firm to achieve a competitive advantage and thus leads to comparably better performance.

After reviewing these diverging, though related, views and analyzing the vast sample of empirical studies collected for the meta-analysis, three dimensions were developed to compare and contrast the specific effects of SCI on firm performance. This classification encompasses a wide range of prior conceptualizations which is necessary in a comprehensive summary of the literature. When management in two firms first engages in SCI, they share data and information (Kim & Lee, 2010; Lee, 2000; Olorunniwo, & Li, 2011; Saeed, Malhotra, & Grover, 2005). Thus, (1) information integration refers to the coordination of information transfer, collaborative communication and supporting technology among firms in the supply chain. The next dimension in the progression is when management integrates activities in addition to the sharing of information (Ireland & Webb, 2007; Kim & Lee, 2010; Kulp, Lee, & Ofek, 2004; Lee, 2000; Saeed et al., 2005; Van der Vaart & van Donk, 2008): (2) Operational integration refers to the collaborative joint activity development, work processes and coordinated decision making among firms in the supply chain. The last dimension builds on the previous two and goes beyond activities focusing on attitudes (Ireland & Webb, 2007; Lee, 2000; Saeed et al., 2005; Van der Vaart & van Donk, 2008): (3) Relational integration refers to the adoption of a strategic connection between firms in the supply chain characterized by trust, commitment and long-term orientation (Chen, Paulraj, & Lado, 2004; Dyer & Hatch, 2006; Hult, Ketchen, & Slater, 2004; Johnson, 1999).

H2a: Information Integration is positively related to firm performance.

H2b: Operational Integration is positively related to firm performance.

H2c: Relational Integration is positively related to firm performance.

Dimensions of Firm Performance

As the focus of this research, the linkage between SCI and firm performance is evaluated in more detail. While most empirical studies find a significant positive association between SCI and firm performance, some also reveal significant negative effects, and the magnitude of the association varies considerably. To better understand this relationship, performance effects collected in the meta-analysis were summarized and evaluated across three categories. Financial firm performance was measured using either revenue minus cost-based measures, such as profitability and return on assets, or purely revenue-based measures, like sales and market share. Customer-oriented performance consists of measures related to an improvement in customer satisfaction and customer loyalty, or closely related constructs. Finally, operational performance consists of improvements in key competitive capabilities including cost, quality, delivery, flexibility and innovation (Hill, 1994; Ward, McCreery, Rizman, & Sharma, 1998). Analyses were conducted both on aggregate firm performance and each separate dimension. Several studies found a significant relationship between SCI and firm performance. Thus, we hypothesize that SCI is positively correlated with different measures of firm performance.

H3a: Supply Chain Integration is positively related to business performance.

H3b: Supply Chain Integration is positively related to relational performance.

H3c: Supply Chain Integration is positively related to operational performance.

Moderator Analysis

One of the advantages of a meta-analysis is that it enables the researcher to examine theoretically relevant measurement characteristics that may explain the variability in effect sizes (Hunter & Schmidt, 1990). These moderators enable examination of a more detailed and specific view of SCI and of firm performance. The moderators were evaluated by constructing specific subgroups that can then be compared against main effects to determine the impact of that specific moderator.

In addition to the previously described hypotheses, we examined four scopes of SCI: supplier integration, customer integration, external integration and internal integration. These were mutually exclusive constructs that appeared in the sample articles. While some researchers focused on specific aspects, like customer and supplier integration (Cousins & Menguc, 2006; Homburg & Stock, 2004; Koufteros, Cheng, & Lai, 2007), others used more expansive constructs to illustrate the scope of integration efforts (Frohlich & Westbrook, 2001; Thun, 2010). Supplier integration (Chen, Tian, Ellinger, & Daugherty, 2010a; Corsten & Felde, 2004; Flynn et al., 2010; Lee, Kwon, & Severance, 2007) moves beyond just buying and selling activities and involves close relationships that involve suppliers in activities like product development and manufacturing support (Croxton, García-Dastugue, Lambert, & Rogers, 2001). Customer integration (Germain & Iyer, 2006; Sanders, 2008) is the mirror image of supplier integration and it depends on proactively determining the requirements of the customer and ensuring to meet those requirements (Powell, 1995). External integration is integration with customers and suppliers simultaneously (Jayaram, Kannan, & Tan, 2004; Stank et al., 2001). While not a core SCI construct for this study, several articles included internal integration into their research models, some see internal integration between the four walls of the company as an implicit component of SCI (Flynn et al., 2010; Rosenzweig, Roth, & Dean, 2003), while others operationalize it as an antecedent or complement to external integration (Narasimhan, Swink, & Viswanathan, 2010; Sanders, 2007). We refer to it as the integration between functions or departments within a single firm (Braunscheidel & Suresh, 2009; Closs & Savitskie, 2003; Koufteros, Rawski, & Rupak, 2010).

There were also several types of firm performance that were examined individually. Within business performance, we specifically examined financial performance and customer-oriented performance. Financial performance is an important measure of firm performance and has been used in several studies within our sample (Germain, Davis-Sramek, Lonial, & Raju, 2011; Vickery, Jayaram, Droge, & Calantone, 2003). Customer-oriented performance is a more perception-based measure that includes attitudes like satisfaction and loyalty (Johnston, McCutcheon, Stuart, & Kerwood, 2004; Narasimhan, Jayaram, & Carter, 2001). Within operational performance, there were enough studies to evaluate the specific effects of cost, quality, delivery, innovation and flexibility. The importance of evaluating these relationships is to enable us to gain a deeper understanding of where exactly the performance benefits from better SCI arise. The conceptual framework for this research is depicted in Figure 2.

Figure 2.

Research Framework


In this section, we first describe sample selection. Next, the coding of the studies is explained. Then, we detail the meta-analytic procedures that were used to test the hypotheses.

Sample Selection

To test our research model, we gathered correlations between relevant constructs and followed the random coefficient meta-analysis approach suggested by Hunter and Schmidt (2004). Relevant articles for the meta-analysis were identified via a literature search using the EBSCO Business Source Complete database including appropriate keywords. An overview of the search terms and search results is shown in Table 3. The search was restricted to academic peer-reviewed journals, and it was ensured that the search results were indeed research articles and not editorials or book reviews. No other limitations were placed on the search. This procedure yielded 552 articles that were further inspected. Individual items of the constructs were assessed to ensure that the authors used measures of the constructs of interest that had face validity and that inter-construct zero-order correlations were obtainable. To be considered usable, the articles had to employ an empirical research methodology and include at least one measure of SCI and one measure of firm performance, as shown in Table 3. Forty-eight articles were retained. In addition to the keyword search, a “snowballing” approach was also used to retrieve additional studies and it required us to inspect articles that were cited by our retained articles or that cited our retained articles. This procedure yielded another 38 usable articles. In total, 86 articles using 80 independent samples (k) and representing 17,467 observations (N) were successfully identified, coded and used for further analysis. They are shown in Appendix A.

Table 3. Search Terms and Results
Search TermsResultsEmpiricalUsableRetained
“supply chain integration”256 863530
“supply chain collaboration”123 271412
“supplier integration” 64 8 2 1
“customer integration” 58 14 3 2
“supplier collaboration” 21 2 1 0
“customer collaboration” 15 8 4 3
Snowballing     38
Total Articles5521455986


A formal coding framework, based on the theoretical framework and potential moderators, was established and employed independently by two of the authors. All articles were double-coded and any discrepancies were resolved via discussion. The inspection of articles required a thorough assessment of the scale items to identify several characteristics of the scale: (1) we determined whether the scale was consistent with any of our definitions of SCI; (2) we assessed whether the construct was consistent with any of the SCI dimensions in Figure 2; (3) we examined whether the construct was consistent with any of types of firm performance; and (4) we identified whether there were any moderators (Figure 2). To ensure that the items of each construct should reflect the respective subgroup, 75% of the items should closely match our definition for that construct (Hunter & Schmidt, 2004). Each article was evaluated in the above manner, and we obtained correlations and reliabilities for all constructs of interest. The independent coding exposed fourteen differences, yielding an inter–rater reliability of 95.93 percent (14 differences/(86 studies * 4 codings per study)). Multiple publications based on the same sample were treated as a single sample to maintain the assumption of independence among correlations (Hunter & Schmidt, 2004). In the case of multiple correlations for one relationship, the composite of the correlation coefficients was computed using aggregation methods described in the meta-analytic procedures section. If zero-order inter-construct correlations or reliabilities were not reported in the article, we solicited the required information via e-mail. If we were not successful, the tracing rule was used to reproduce the correlations of interest (Kenny, 1979). In the case that only item-level correlations were reported, a confirmatory factor analysis was used to derive the inter-construct correlations (Droge, Jayaram, & Vickery, 2004). Each article was evaluated and the constructs were classified into a-priori categories (Figure 2), which were then used to test the hypotheses, by splitting the studies into different sub-groups depending on the operationalization of the constructs and the use of the previously mentioned moderators.

Table 4. Results for Specific Supply Chain Integration Subgroups
Relationship (Impact on Firm Performance) k N r o r c RangeCredibility Interval Q Fail Safe
  1. *p-value < 0.05; **p-value < 0.01.

1. H1: Supply Chain Integration8017,2480.32*0.36*−0.180.840.060.66526.02**61994.80
2. H2a: Information Integration336,7230.33*0.38*0.090.780.070.69201.00**17995.80
3. H2b: Operational Integration336,7000.300.340.090.90−0.010.70260.78**5236.25
4. H2c: Relational Integration142,6510.36*0.41*0.150.790.060.75103.69**3282.30
5. Supplier Integration4810,6010.29*0.33−0.050.790.030.63312.92**3600.88
6. Customer Integration317,0030.250.29−0.180.73−0.010.59207.22**10086.52
7. External Integration153,9490.35*0.42*0.160.690.100.73121.14**8159.23
8. Internal Integration224,6270.30*0.34*0.090.640.070.60111.69**1235.56

Meta-Analytic Procedures

The widely employed meta-analytic procedures described by Hunter and Schmidt (1990, 2004) were followed for the hypothesis testing via three stages: (1) the main effect testing; (2) the moderator existence testing; and (3) the moderating effects testing. In the first stage, the overall relationship correlation between SCI and firm performance was assessed. The correlation (r) was used to assess the relationships between the constructs of interest (see Geyskens, Krishnan, Steenkamp, & Cunha, 2009; and Shadish & Haddock, 1994 for discussion on different effect sizes). Corrections were applied for measurement error (Hedges & Olkin, 1985; Hunter & Schmidt, 2004; Rosenthal, 1991). If no scale reliability was reported or if a single-item scale was used, the common practice of substituting the mean reliability was followed (Chen, Damanpour, & Reilly, 2010b; Crook et al., 2008; Mackelprang & Nair, 2010; Nair 2006). Each sample was weighed by its compound attenuation factor, which consists of the reliability of the scale and the sample size. Several articles had more than one correlation of interest and those were combined into a composite (Arthur, Bennett, & Huffcutt, 2001) following Hunter and Schmidt (1990, pp. 457–460). Publication bias, also referred to as the “file drawer problem,” may occur because studies that produce statistically nonsignificant findings are less likely to be submitted to journals or be accepted for publication (Rosenthal, 1979). Therefore, the “fail safe number” was assessed for each group and sub-group, which indicates how many additional studies would have to be found to obtain a nonsignificant result (Rosenberg, 2005).


To test our study's hypotheses, the correlation between our constructs of interest was evaluated. The results are shown in Tables 4 and 5. To determine the strength and significance of the relationship, several measures were calculated. For each relationship, the number of independent samples (k) and the overall sample size (N) are provided. The observed correlation (ro) and the corrected correlation (rc) were computed (Hunter & Schmidt, 2004). These measures provide a point estimate of the sample correlation that is then assessed as to whether it is significantly different from zero. The range of uncorrected correlations and the 90% credibility interval are also reported (Hunter & Schmidt, 2004, p. 83). The Q Statistic is a measure of heterogeneity and a large significant value points to unexplained variance being present in the sample or subsample (Hunter & Schmidt, 1990, p. 111). The fail safe numbers for each subgroup range from 107 to 61,995, and thus, we conclude there is little risk of additional studies changing the results we obtained.

Table 5. Results for Specific Firm Performance Subgroups
Relationship (Impact of Supply Chain Integration) k N r o r c RangeCredibility Interval Q Fail Safe
  1. *p-value < 0.05; **p-value < 0.01.

9. H3a: Business Performance337,7680.29*0.330.060.720.030.63226.66**29399.73
10. Financial Performance184,4980.**1794.81
11. Customer-oriented Performance58320.31**0.37**0.250.370.260.481.56106.97
12. H3b: Relational Performance61,3780.64**0.72**0.370.860.441.0133.70**2825.34
13. H3c: Operational Performance6012,0720.31*0.35*−0.180.840.090.60274.38**8870.43
14. Cost244,0700.170.21−0.180.58−0.070.4898.74**967.58
15. Quality112,4830.23***365.19
16. Delivery224,6710.25*0.30*0.100.420.140.4650.55**2623.70
17. Innovation112,1860.22*0.26*0.090.430.110.4122.60*655.58
18. Flexibility163,2740.190.22−0.090.440.040.4042.33**353.14

The relationships between SCI and firm performance were evaluated, and the results provide evidence that the link is positive and significant (Table 4). To examine the more specific types of firm performance, an overall view of SCI is necessary, and thus, we examined the aggregate effect of SCI on overall firm performance first. The other relationships can then be compared subsequently. The corrected correlation between SCI and firm performance was 0.36, which is significant at the 0.05 level, and thus, we conclude there is support for H1. In addition to the main effects testing, several moderators, based on the operationalization of the SCI construct, were tested and are shown in Table 3. Three types of integration were evaluated specifically: information (H2a), operational (H2b) and relational (H2c). While information integration and relational integration show significant correlation with firm performance, operational integration does not have a significant correlation with firm performance. Drawing on the RBV, it can be argued that these types of integration are more difficult to imitate and as such can lead to better firm performance. The nominal correlation does not differ widely from the others, but the variance and the credibility interval explain why it cannot be concluded that it is significantly different from zero. Four additional scopes of SCI were evaluated: supplier, customer, external and internal integration. There is weak support for a significant correlation between supplier integration because the corrected correlation has a larger standard deviation that prevents us from concluding it is larger than zero. There is no support to conclude that a significant correlation between customer integration and firm performance exists in this sample. External and internal integration show a positive and significant correlation with firm performance.

In addition to the different operationalizations of SCI, we also evaluated whether the correlation between SCI and firm performance is impacted by measurement differences in the dependent variables. The results are shown in Table 5. We found weak support for H3a, but strong support for H3b and H3c. While business performance, which is conceptualized as the top line benefits of SCI, has weak support, additional subgroups were evaluated, such as financial performance and customer-oriented performance. The correlation between SCI and financial performance was not significant. The evaluation of the link between SCI and customer-oriented performance was highly significant and did not show any heterogeneity, so that we can conclude that there is no evidence for significant moderators being present in the sample. This was the only subgroup where it was possible to resolve all the heterogeneity. Additional subgroups of operational performance were evaluated: cost, quality, delivery, innovation and flexibility. The relationships between SCI and quality, delivery, and innovation were significant. No significant relationship between SCI and cost and SCI and flexibility could be found. The implications of these results are described in the next section in addition to limitations and suggestions for future research.


In this study, we accumulated and integrated the results of empirical research on the relationship between SCI and firm performance that may lead to generalizable evidence for advancement of theory and practice on SCI. Inconsistencies in original research results may be due to artifacts such as sample sizes and measurement errors in the original studies. Subgroup analysis of moderators showed that the majority of samples have a significant relationship between different operationalizations of SCI and operationalizations of firm performance along theoretical expectations. A necessary implication of this meta-analysis for future research is that when results are contradicting or nonsignificant, it may be due to the study's heterogeneous factors, for example, the industry, the type of companies that were considered, or even the time period in which the research was conducted. In that case, a detailed assessment of significant differences in correlation coefficients for various subgroups may explain deviating results.

Implications for Theory

In initiating this study, we encountered an expansive literature base that appeared to use an array of perspectives and different theories in investigating SCI. We examine SCI under the lens of the RBV of the firm and the extensions of the RBV that were R-A theory and the RV. Our primary objective for this meta-analysis was to investigate whether SCI as a firm resource is related to better firm overall performance. This can help determine whether SCI should be viewed as a source of competitive advantage. In this article, we have presented a theoretical framework to aid in providing parsimony and to distinguish between three dimensions of SCI (information, operational and relational). Our classification of SCI can be used to interpret past research on SCI and clarifies the concept for future research. Setting a baseline via this meta-analysis synthesizes existing knowledge and aids scholars in scoping new research.

The overall positive and significant relationship between SCI and firm performance (H1) is a significant, but not surprising result (Christopher, 2005). Closer integration is significantly correlated with better firm performance. This finding fits with the theoretical bases that we highlighted in this research. While this result does not specifically point to causality, it should be expected that firms engaging in integration efforts should experience higher firm performance as a result. It is also apparent that this general relationship is characterized by considerable heterogeneity (= 526.02**), which was then reduced in subsequent subgroups.

Prior research provides support for the notion that firms have the opportunity to leverage integration mechanisms with customers and suppliers to achieve organizational performance benefits (Cooper, Lambert & Pagh, 1937; Lambert, Cooper, & Pagh, 1998; Stevens, 1989; Lee, 2000). At the same time, researchers have noted that firms working toward higher levels of SCI are likely to face a number of challenges (Fabbe-Costes & Jahre, 2007; Fawcett & Magnan, 2002). In the next step, three separate operationalizations of SCI were analyzed. Information integration is significantly correlated with firm performance (H2a). This result can be explained by arguing that sharing of information will enable both companies to operate more efficiently (Zhou & Benton, 2007). It can also be explained with IPT, where better information for the right parts of a firm can lead to a competitive advantage. In addition, internal integration also has a significant correlation with firm performance. These two types of integration are generally considered as requiring the least involvement and effort.

For the relationship between operational integration and firm performance, we did not find a significant correlation (H2b). A higher level of integration likely causes temporarily higher costs, and it is possible that the resulting increase in performance is not large enough to recoup those higher costs. Firms that were surveyed in the original studies may also not have been able to yet recognize the positive results of operational integration, as these benefits may take longer to realize. It is not possible to obtain a firm-specific perspective with our research, but we find it likely that the risk of failure at this stage of SCI is significant and therefore the results we obtain have such a large variance. This interpretation is additionally supported by the two moderators, supplier integration and customer integration, having a nonsignificant correlation with firm performance. This is not to say that an improvement in firm performance does not occur, but our results seem to indicate that the variance, and therefore the associated risk, is higher regarding the return on investment.

Relational integration, which mainly draws on the RV as its theoretical base (Dyer & Singh, 1998), does have a significant correlation with firm performance (H2c). It is understood that only firms that have a long-term relationship will be able to attain such a level of integration. Therefore, it is not surprising that the payoff from closer integration is higher and there is less risk involved with that type of integration. It is also reasonable to assume that firms that are seeking this level of integration do have more experience with integration efforts and the chance for success is increased even more. As such we believe that it should not be uniformly assumed that tighter integration is better in all situations. It comes with a higher level of management effort, and there must be a business reason for investing resources into the relationship. This view is supported by Lambert, Emmelhainz, and Gardner (1996) who present a model that describes how firm relationships should be structured at the appropriate level. Both companies should take into account the drivers, facilitators and management resources available for managing the relationship.

The evaluation of different measures of firm performance revealed several noteworthy findings. The impact of SCI on business performance, which is generally associated with revenue generation and profitability, is not significant (H3a). The weak link to revenue generation and the bottom line is not surprising because most of the benefits from SCI are expected to be in the form of cost savings (Madhok, & Tallman, 1998). Such efficiencies often do not have the profitability impact to warrant a significant change in the bottom line as the savings do not have the same profit leverage as revenue increases (Marn & Rosiello, 1992). More specifically, financial performance also had a nonsignificant effect, which is additional evidence. The impact of SCI on customer-oriented performance, which specifically is related to relational outcomes such as satisfaction, trust and commitment, has a high and significant correlation. This result points to the fact that closer integration with customers and to a lesser extent with suppliers can have intangible benefits that can improve the relationship. These benefits may not be immediately measurable in financial or business terms, but marketing researchers argue that lagged financial benefits occur as a result of customer-oriented performance (Guo, Kumar, & Jiraporn, 2004).

A few studies used relational performance as an outcome measure, and the correlation was the highest we obtained in this study (H3b). Most of the studies (four of six) used relational SCI as the antecedent. While this result is impressive, we must also caution that we cannot exclude cognitive bias as one of the explanations for this result. It would be more impressive if such high correlations would be obtained with financially based performance measures. We generally advise researchers against the use of fully perceptual measures because this often overlooked concern of cognitive bias can inflate the relationship.

Most studies (= 60) in our sample used operational performance as one of the outcome variables and a positive and significant aggregate correlation was found (H3c). While this aggregate view of performance is important, the specific subgroups of operational performance should be evaluated further. With three of five subgroups not having significant correlations, several conclusions can be drawn. Cost improvements are a not significant outcome of SCI, and this result, while surprising to some, highlights the fact that substantial resource commitment is necessary when undertaking integrative activities between customers and suppliers. The impact of SCI on quality was also not significant, and we attribute this to quality being an imprecise measure that can be interpreted in several ways. As such, it may not be possible to accurately trace the improvements to quality gains. In addition, quality is always a moving target, and the overall customer perception of quality will be influenced by the competitors in the marketplace. The correlation between SCI and delivery performance was positive and significant. One of the main areas where firms integrate is how products are delivered, thus it is not surprising that delivery performance is significantly affected by SCI. Another significant effect is the link between SCI and innovation. We draw on SET to explain that managers from customers and suppliers working together might create opportunities for innovation, purely based on the fact that they are interacting with each other. Therefore, SCI has the effect that people from different companies can likely encounter opportunities for improvement in their daily interactions as part of a larger integration initiative. The last subgroup that was evaluated was the impact of SCI on flexibility, and we attribute this result to the fact that different companies may have different constraints (Christopher & Holweg, 2011). While the constraints become clearer through integration, it may not be possible to overcome them.

Implications for Managers

A meta-analysis, such as the one described in this article, can help managers understand the level and significance of the relationship between SCI and firm performance. More specifically, this study can help answer questions such as what type of SCI has the chance to lead to the highest benefits in terms of firm performance. There is evidence that SCI leads to higher firm performance, in general; however, more importantly managers should understand what will be most beneficial to their organization. On the basis of our results, all levels of integration can be beneficial for firm performance; however, operational integration can have varying results. The operational benefits of SCI are only found in the areas of delivery performance and innovation. This result provides support to the notion that managers should not expect quick payoffs from their integration initiatives, like cost savings and quality improvement, but it is more likely that longer term, more durable performance gains can be obtained.

Limitations of the Research

As with any research, there are several limitations that must be pointed out. By definition, a meta-analysis relies on available studies. While we performed a thorough literature search and “snowballing” to identify all suitable articles, there still is the possibility that some studies were missed. However, due to the number of samples that we were able to obtain (80 independent samples) and the high fail-safe numbers (Tables 4 and 5), we are confident that any additional studies would be unlikely to change the results. While every effort was made to obtain all the information necessary for each suitable study, we did encounter some difficulties in retrieving correlations and reliabilities for some studies. If that information was not available from the authors and we could not impute it otherwise, we had to drop those studies from this meta-analysis. We were able to obtain a significantly larger number of samples than some other recently published articles using this methodology (Mackelprang & Nair, 2010; Nair, 2006). However, due to restrictions in the sample, we were not able to examine more sample-specific moderators.

Suggestions for Future Research

It is of critical importance for our discipline to assess a phenomenon over an extended period of time. Once there are sufficient empirical studies, a meta-analysis can be performed to aggregate the results. This methodology should be employed to study other phenomena in the supply chain management domain. It would be interesting to see how SCI is viewed over an extended period of time. While such longitudinal studies are difficult to operationalize, they would add significantly to our understanding of this important phenomenon.

Due to the small sample size of the SCI customer-oriented subgroup, it is clear that additional studies are necessary in this area. The nonsignificant correlations between SCI and cost performance, and SCI and quality performance might be be due to the focus of operational-level management primarily on delivery issues. As such, we suspect that in the future we may find different results and as such it is worth investigating this effect more.

At this point, we would like to encourage authors, referees and editors to agree to a consistent standard of reporting for empirical survey-based research, which can only improve methodological rigor. At a minimum, the correlations between the latent variables and the reliabilities of the constructs should be reported. In addition, detailed information on how the scales were developed should be required to trace the origin of a scale and to enable other researchers in the field to assess the quality of the constructs. Such a standard would not just make it easier to aggregate studies in a meta-analysis, but also enable readers to evaluate studies more quickly and objectively. As we have seen in some of the studies we evaluated, some constructs that were based on previously developed measures were modified without explanation. Therefore, we call on our colleagues to adhere to this standard in order to increase the methodological rigor of our field.

Supply chain integration has been a highly researched topic in the past 20 years. In this meta-analysis, we examined this significant body of literature to quantitatively summarize the results. The main benefit of our analysis is that we were able to estimate the overall population effect of SCI on firm performance and within the relevant subgroups. The positive association reinforces the importance of this construct, but the significant amount of heterogeneity in most subgroups is evidence that additional research is necessary before we can make generalizable statements. As such we call for more research on the relationship between SCI and firm performance.

Appendix A: List of Samples and Articles

SampleArticleAuthor(s)JournalYearSample SizeMin rMax rMean rNo. of r's
11Vickery, S. K., Jayaram, J., Droge, C., Calantone, R.JOM2003 570.010.580.3112
2Droge, C., Jayaram, J., Vickery, S. K.JOM2004
3Scannell, T. V., Vickery, S. K., Droge, C. L.JBL 2000
24Johnston, D. A., McCutcheon, D. A., Stuart, F. I., Kerwood, H.JOM20041640.300.430.36 6
35Bagchi, P. K., Skjoett-Larsen, T.IJLM 2005 1490.080.190.1316
46Benton, W. C., Maloni, M.JOM 2005 1800.340.860.59 3
7Maloni, M., Benton, W. C.JBL 2000
58Carr, A. S., Pearson, J. N.JOM 1999 7390.400.400.40 1
69Chen, I. J., Paulraj, A. Lado, A. A.JOM20042210.040.270.16 6
710Corsten, D., Felde, J.IJPDLM2005135−0.020.400.22 3
811Cousins, P. D., Handfield, R. B., Lawson, B., Petersen, K. J.JOM 2006 1110.350.620.4611
12Lawson, B., Tyler, B. B., Cousins, P. D.JOM 2008
913Cousins, P. D., Lawson, B.BJM 2007 1420.140.650.4210
14Cousins, P. D. Menguc, B.JOM2006
1015Dong, Y., Carter, C. R., Dresner, M. E.JOM 2001 1240.500.500.50 1
1115Dong, Y., Carter, C. R., Dresner, M. E.JOM2001131−0.18−0.18−0.18 1
1216Flynn, B. B., Huo, B., Zhao, X.JOM20106170.220.460.33 6
1317Fynes, B., de Burca, S., Voss, C.IJPR 2005 2000.280.280.28 1
1418Germain, R., Iyer, K. N.JBL20061520.000.550.24 7
19Iyer, K. N, Germain, R., Claycomb, V. A.IM 2009
1520Golicic, S. L., Mentzer, J. T.JBL20063220.750.820.79 2
1621Jayaram, J., Kannan, V. R., Tan, K. C.IJPR20045270.080.230.16 2
1722Johnson, J. L.JAMS19991600.210.270.24 3
1823Krause, D. R., Handfield, R. B., Tyler, B. B.JOM 2007 3700.090.370.28 8
1924Moberg, C. R., Whipple, T. W., Cutler, B. D., Speh, T. W.IJLM 2004 2490.110.480.2414
2025Narasimhan, R., Kim, S. W.JOM 2002 3790.080.170.11 9
2125Narasimhan, R., Kim, S. W.JOM20022440.030.160.09 9
2226Narasimhan, R., Jayaram, J., Carter, J. R.POM20011790.280.280.28 1
2327Narasimhan, R., Nair, A.IJPE 2005 2280.290.710.50 2
2428Prahinski, C., Benton, W. C.JOM 2004 1390.230.250.24 2
2529Sanders, N. R., Premus, R.JBL 2005 2450.240.370.31 2
2630Shin, H., Collier, D. A., Wilson, D. D.JOM 2000 1760.200.530.33 4
2731Stank, T. P., Keller, S. B., Daugherty, P. J.JBL20013060.320.380.35 2
2832Swink, M., Narasimhan, R., Wang, C.JOM 2007 224−
2933Hult, G. T., Ketchen, D. J., Slater, S. F.AMJ2004 58−0.360.550.23 8
3034Rosenzweig, E. D., Roth, A. V., Dean, J. W.JOM20032380.170.320.27 7
3135Paulraj, A., Lado, A. A., Chen, I. J.JOM 2008 2210.210.280.23 4
3236Deveraj, S., Krajewski, L., Wei, J. C.JOM2007120−0.050.400.17 2
3337Li, G., Yang, H., Sun, L., Sohal, A. S.IJPE 2009 1820.780.900.84 2
3438Carr, A. S., Kaynak, H.IJOPM 2007 2230.070.310.1910
39Carr, A. S., Kaynak, H, Muthusamy, S.IJMTM2008
3540Quesada, G., Rachamadugu, R., Gonzalez, M., Martinez, J. L.SCM 2008 646−
3641Sezen, B.SCM 2008 1250.120.350.27 6
3742Wong, C. Y., Boon-itt, S., Wong, C.JOM20111510.230.460.3812
3843Frohlich, M. T., Westbrook, R.,JOM20024850.440.450.45 2
3944Boon-Itt, S., Paul, H.MRN 2006 28−0.270.500.2415
4045Braunscheidel, M. J., Suresh, N. C.JOM20092180.080.520.3112
46Braunscheidel, M. J., Suresh, N. C., Boisnier, A. D.HRM 2010
4147Lau, A. K., Yam, R. C., Tang, E. P.IMDS 2007 2510.070.240.1711
48Lau, A. K., Tang, E. P., Yam, R. C.JPIM 2010
4249Kim, D., Cavusgil, E.JBIM 2009 1840.310.420.37 2
4350Villena, V. H., Gomez-Mejia, L. R., Revilla, E.DS 2009 1330.160.220.19 2
4451Mollenkopf, D., Dapiran, G. P.IJLRA 2005 1940.160.250.2110
4552Squire, B., Cousins, P. D., Lawson, B., Brown, S.IJOPM 2009 1040.040.040.04 1
4653Vlachos, I., Bourlakis, M.SCFIJ 2006 970.180.430.31 2
4754Wang, E. T., Tai, J. C., Wei, H. L.JMIS 2006 1490.190.320.26 2
4855Sanders, N. R.JOM20082410.210.340.28 8
4956Narayanan, S., Jayaraman, V., Luo, Y., Swaminathan, J. M.JOM 2011 2050.600.640.62 2
5057Olorunniwo, F. O., Li, X.SCM2010 650.300.660.47 4
5158Wiengarten, F., Humphreys, P., Cao, G. Fynes, B., McKittrick, A.SCM 2010 1530.150.530.33 3
5259Gimenez, C., Ventura, E.IJOPM 2005 640.290.730.50 3
5360Rai, A., Patnayakuni, R., Seth, N.MISQ2006110−0.040.400.2412
5461Lee, C. H., Huang, S. Y., Barnes, F. B., Kao, L.TQM 2010 1320.340.390.37 2
5562Jayaram, J., Tan, K. C.IJPE 2010 4110.280.280.28 1
5663Lee, G. J.JBBM 2010 1700.370.370.37 1
5764Tai, Y. M., Ho, C. F., Wu, W. H.IJPR 2010 1370.420.420.42 1
5865Cao, M., Vonderembse, M. A., Zhang, Q., Ragu-Nathan, T. S.IJPR 2010 2110.690.690.69 1
5966Zacharia, Z. G., Nix, N. W., Lusch, R. F.JOM 2011 4730.540.620.58 2
6067Ha, B., Park, Y., Cho, S.IJOPM 2011 265−0.150.730.37 3
6168Nakano, M.IJPDLM 2009 650.530.700.62 3
6269Vereecke, A., Muylle, S.IJOPM 2006 3740.060.310.1524
6370Closs, D. J., Savitskie, K.IJLM20033060.080.450.24 3
6471Dabhilkar, M., Bengtsson, L., von Haartman, R., Ahlstro, P.JPSM 2009 136−0.060.310.09 9
6572Eltantawy, R. A., Giunipero, L., Fox, G. L.IMM 2009 1520.720.720.72 1
6673Koufteros, X., Rawski, G. E., Rupak, R.DS20101910.110.160.14 3
6774Koufteros, X., Vonderembse, M., Jayaram, J.DS 2005 2440.030.380.19 9
6875Chen, H., Daugherty, P. J., Roath, A. S.JBL20091240.580.690.64 2
6976Chen, H., Tian, Y., Ellinger, A. E., Daugherty, P. J.JBL20101240.430.430.43 1
7077Lee, C. W., Kwon, I. W., Severance, D.SCM20071220.540.610.58 6
7178Nyaga, G. N., Whipple, J. M., Lynch, D. F.JOM 2010 3700.300.350.33 2
7278Nyaga, G. N., Whipple, J. M., Lynch, D. F.JOM20102550.430.490.46 2
7379Griffith, D. A., Harvey, M. G., Lusch, R. F.JOM20062900.170.230.20 2
7480Gulati, R., Sytch, M.ASQ20071510.180.600.40 3
7581Handfield, R. B., Petersen, K., Cousins, P., Lawson, B.IJOPM 2009 1510.130.510.31 4
7682Germain, R. Davis-Sramek, B., Lonial, S. C., Raju, P. S.JBL 2011 1750.100.100.10 1
7783Zhou, H., Benton, W. C.JOM20061250.280.360.32 2
7884Rodrigues, A. M., Stank, T. P., Lynch, D. F.JBL 2004 2840.340.480.42 3
7985Mesquita, L. F., Anand, J., Brush, T. H.SMJ20082530.310.310.31 1
8086Forza, C.IJPDLM 1996 430.170.280.22 4


  • Rudolf Leuschner (Ph.D., The Ohio State University) is an assistant professor in the Department of Supply Chain Management and Marketing Sciences at Rutgers University in Newark, New Jersey. His research interests include logistics customer service, supply chain management, and sustainability. Dr. Leuschner also is pursuing studies of the generalizability of research and replication, with a specific focus on meta-analysis. His work has appeared in many outlets, including the Journal of Business Logistics and the Journal of Supply Chain Management.

  • Dale S. Rogers (Ph.D., Michigan State University) is a professor of logistics and supply chain management, and Co-Director of the Center for Supply Chain Management, at Rutgers University in Newark, New Jersey. He also serves as the Leader in Sustainability and Reverse Logistics Practices for ILOS (Instituto de Logistics e Supply Chain) in Rio de Janeiro, Brazil. In 2012, Dr. Rogers became the first academic recipient of the International Warehouse and Logistics Association Distinguished Service Award in its 120-year history.

  • François F. Charvet (Ph.D., The Ohio State University) is the Logistics Network Strategist for Staples, Inc. In this role, he is responsible for setting the strategic direction governing Staples' network of fulfillment centers, distribution centers and delivery organizations. Dr. Charvet currently is leading the introduction of network optimization tools and promoting analytics-driven methods to support strategic supply chain design initiatives at Staples. Prior to joining the private sector, Dr. Charvet was an assistant professor of supply chain management at Northeastern University. He has published the results of his research into supply chain analytics, supply chain integration and collaboration, and logistics customer service in outlets that include the Journal of Business Logistics, the Journal of Supply Chain Management, and Supply Chain Forum.