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Third-party logistics: a meta-analytic review and investigation of its impact on performance.


Since its inception in the 1980s, the concept of third-party logistics (3PL)--defined as the outsourcing of multiple logistics activities (Sink, Langley & Gibson, 1996)--has become a dominant reality for the movement and storage of goods and information in today's supply chains. An earlier synthesis of the vast, descriptive data from the "Lieb and Langley" annual studies, conducted by Ashenbaum, Maltz and Rabinovich (2005), suggests annual growth rates of between five and ten percent for the 3PL industry. And the most recent of these annual studies, comprised of survey responses from more than 2,000 industry executives, found that over 54% of shippers' transportation spend and 39% of their warehouse operations spend were outsourced (Langley, 2012).

As noted by Maloni and Carter (2006), the three primary reasons for outsourcing logistics services are (1) service improvements, (2) cost reduction, and (3) a desire by the organizations that purchase these logistics services to focus on their own, nonlogistics core competencies. Thus, an important research question to be able to answer is whether 3PLs can improve customer service and firm performance. In addition, an understanding of the type of customer-3PL relationship that is needed to improve customer service and, ultimately, firm performance is also desirable to provide a more granular answer to this research question. Maloni and Carter (2006) identified 45 survey-based papers in the 3PL domain as of 2004, (1) and their results suggest a trend of increasing research interest surrounding 3PLs. Given the 1.0 years that have passed since Maloni and Carter's data collection efforts, it is likely that a "critical mass" of empirical research exists, which would allow a more formal, meta-analytic examination of key constructs surrounding 3PLs, including the buyer-3PL relational orientation, customer service, and firm performance relationships.

Our meta-analytic research method allowed us to aggregate and summarize the existing research in the domain of 3PLs. The major benefit of this approach is that we were able to obtain results that are valid beyond the limited scope of primary studies and move toward making empirical generalizations. Individual studies, because of their relatively small sample sizes, usually lack the statistical power needed to explain the magnitude of a statistical relationship in the population (Hunter, 2001; Lipsey & Wilson, 2001). Another methodological advantage of a meta-analytic study is that sampling or measurement errors can be corrected (Hunter & Schmidt, 1990).

This paper has two major research objectives. The first research objective is to delve more deeply into the extant 3PL literature, by investigating the impact of 3PL customer service on firm performance, with an examination of the 3PL--customer relational governance structure as a potential antecedent to customer service. We apply both transaction cost economics and the resource-based view to develop hypotheses that propose this set of relationships. The second research objective is to identify other key constructs surrounding the 3PL--customer relationship that have been studied in the extant literature and to investigate the patterns of potential relationships between these constructs. The value of doing so will be a better understanding among the research community concerning relationships that have not yet been examined, as well as relationships that have been sufficiently studied that might not warrant future investigation, at least in a linear, direct fashion.

The remainder of our article is organized as follows. In the next section, we provide a review of the 3PL literature, and we describe the evolution of the 3PL industry across three eras since its appearance in the scholarly literature in the late 1980s. We then introduce the study's hypotheses. Afterward, we describe the study's methodology and present the results of our analyses. Finally, we discuss our results and provide suggested avenues for future, meaningful 3PL research.


Logistics Outsourcing and the 3PL Literature

A review of the literature reveals that third-party logistics and logistics outsourcing have meant different things to different people since the subjects first appeared in the academic literature in the late 1980s. Definitions are critical in the conduct of meta-analyses for they help to ensure consistency in conceptual domain and enhance our understanding of focal constructs (Hunter & Schmidt, 2004). Early conceptualizations of third-party logistics characterize logistics outsourcing quite broadly, as employing an outside company to perform all or part of another company's materials management and product distribution functions (Simchi-Levi, Simchi-Levi (Sz Kaminsky, 1999). As an example, Ellram and Cooper (1990, p. 1) define third-party logistics providers as "outside parties who provide [shippers with] functions not performed by the firm. These can include transportation carriers, warehousers, bankers, brokers, and suppliers to these functions, such as stevedores."

Over time, however, the concept of third-party logistics evolved toward service offerings of greater complexity, usually encompassing a combination of services ( 0 j ala, Andersson & N aula, 2006). Further, the arrangements between customers and providers of outsourced logistics service reflect the growing complexity in relationship with emphasis placed on high levels of formality and longer-term commitments, as opposed to arm's-length transactional arrangements. This orientation is captured in the definition of "modern third-party logistics" offered by Leahy, Murphy and Poist (1995, p. 5) as: "a relationship between a shipper and third-party which, compared with basic services, has more customized offerings, encompasses a broader number of service functions, and is characterized by a longer term, more mutually beneficial relationship." Several successive works (e.g., Knemeyer, Corsi & Murphy, 2003; Mello, Stank & Esper, 2008; Murphy & Poist, 1998) embrace this more comprehensive form of logistics outsourcing to a point where, today, many regard third-party logistics providers as asset- or non-asset-based external parties that may be consulted for any and all matters related to logistics service (Yeung, Selen, Sum & Huo, 2006) and that are often engaged in the strategic coordination of their customers' supply chain activities (Zacharia, Sanders & Nix, 2011).

"Third-party logistics" began to appear in the titles of conference papers and journal articles in the late 1980s. A comprehensive review of the 3PL literature conducted by Marasco (2008) chronicles the development of the topic, finding that 11 academic articles focused on 3PLs during the 6-year period from 1989 to 1994. In the successive 6 years (1995-2000), the topic was studied in 55 artides, and in the next 6 years (2001-2006), it was studied in 86 articles. Literature reviews by Maloni and Carter (2006) and Selviaridis and Spring (2007) point to similar growth in the works dedicated to logistics outsourcing.

Succinctly, 3PL research can be characterized as consisting of three broad eras. The first era consists of descriptive works that capture the burgeoning phenomenon of logistics outsourcing. Prevalent among these works are those by Lieb and colleagues dating back to 1992 featuring both shipper (e.g., Lieb, 1992; Lieb & Bentz, 2005a) and provider (e.g., Lieb & Bentz, 2005b; Lieb & Randall, 1996) perspectives. Langley and colleagues have conducted annual surveys of 3PL customers since 1996 (Sink et al., 1996). These works primarily examine the motives for outsourcing, the functions outsourced, and challenges and opportunities for improved logistics outsourcing. Also prominent among these foundational works is the Who's Who in Logistics directory generated by Armstrong and Associates. Dating back to 1995, this annual report estimates the size of the 3PL industry in the United States each year and serves as an industry directory and evaluative reference for shippers. Annual surveys conducted by these authors reinforced the credibility and growth in the 3PL market in the United States.

The second era of research ushers the refinement of key concepts, the establishment of hypothesis testing, and a stronger orientation toward explanation and normative prescription. This work begins to appear in the latter half of the 1990s and continues to focus largely on logistics outsourcing arrangements in the United States. Beyond describing market developments, research in this era begins to explore the characteristics of successful outsourcing arrangements (e.g., Boyson, Corsi, Dresner & Rabinovith, 1999; Daugherty, Stank & Rogers, 1996; Knemeyer & Murphy, 2004; Murphy & Poist, 1998; Sink & Langley, 1997) and the outcomes associated with logistics outsourcing (e.g., Sinkovics & Roath, 2004; Stank, Daugherty & Ellinger, 1996; Stank, Coldsby, Vickery & Savitskie,

2003). In testing hypotheses, these works begin to refine key constructs and introduce theories to explain proposed antecedent and consequent relationships to outsourcing. Maloni and Carter (2006) find that more advanced analytical approaches (e.g., regression, factor analysis, and structural modeling) had become commonplace in examining complex structures of 3PL relationships.

The third era of 3PL research is characterized by greater internationalization in the study of logistics outsourcing phenomena. It is believed that logistics outsourcing as an industry practice is spreading, with arrangements in Western Europe, select Asian locations, and Australia demonstrating advanced applications. Author affiliations seem to underscore this trend, with research commonly examining outsourcing in the largest markets in Europe (e.g., Germany and the United Kingdom), Asia (e.g., China, Japan, and Korea), and Australia. More recently, 3PL research from smaller or less developed markets from around the world is making appearances.

Research in this third era often offers contributions through replication of studies conducted in North America, studying similar phenomena and employing consistent definitions and operationalizations of constructs. In other instances, cross-national studies are employed to improve understanding of different settings to refine the boundary conditions of theoretical explanations. Examples of this approach include Bookbinder and Tan (2003), Wang, Chu, Zhou and Lai (2008), Wang, Lai and Zhao (2008), and Wallenburg, Cahill, Goldsby and Knemeyer (2010). Replications and cross-national comparisons are instrumental toward the conduct of meta-analysis. For one, it underscores the growing interest in a phenomenon. For another, successive studies of a phenomenon support the compilation of samples for greater precision in meta-analysis. Finally, compiling a collection of interconnected constructs across multiple studies allows for testing hypotheses that are not testable within the individual studies (Eden, 2002; Goldsby & Autry, 2011). The three eras of 3PL research provide sufficient basis upon which to conduct meta-analytic investigation of critical relationships.

Hypothesis Development

Transaction cost economics (TCE) is a particularly relevant lens by which to study the outsourcing of logistics services to 3P1s, as it considers, in part, the type of governance structure that firms can use when they make the decision to outsource (Williamson, 1985). Given that 3PLs involve relationships where the market has already been chosen over vertical integration, this facet of TCE is particularly relevant to studies of existing 3PL operations and relationships. From a TCE perspective, firms can employ three distinct types of structures to govern the hybrid, interor-ganizational relationships that fall between simple markets and hierarchy--muscular, benign, and credible (Williamson, 2008). Firms that employ a muscular approach often provide suppliers with rigid specifications and chose the supplier that offers the lowest price based on these specifications. There is little if any collaboration under a muscular approach, and "Muscular buyers not only use their suppliers, but they often 'use up' their suppliers and discard them" (Williamson, 2008, p. 10).

Firms that employ a benign governance structure implicitly rely on cooperation as a means of mitigating unforeseen contingencies in the relationship. These relationships are characterized by a long time horizon and the seeking of mutual gain, with "trust supplant(ing) power as the key concept" (Williamson, 2008, p. 10). Williamson also considers a governance structure in which a long time horizon and mutual gain exist, but where trust is tempered by a recognition that either party might be tempted to act opportunistically, particularly if "there is a lot at stake." This credible contracting approach is thus one where both parties consider potential hazards and develop credible commitments to mitigate those potential hazards. Such credible commitments can include, for example, shared technology.

In reality, most relational governance structures between industrial buyers and suppliers incorporate facets of both benign and credible structures: a long time horizon, the sharing of mutual benefits and burdens, loyalty, commitment, and trust--although that trust is tempered by a lack of naivete and a realization that defection is always possible (El'ram, 1991; Ellram & Cooper, 1990; Kaufmann & Carter, 2006). In line with the benign and credible contracting approaches of TCE, relationship marketing (e.g., Morgan & Hunt, 1994), and the buyer-supplier partnership literature (e.g., Anderson & Nanis, 1990), we conceptualize a relational governance structure between 3PLs and their customers as being characterized by "norms of sharing [burdens and benefits] and commitment based on trust" (Morgan & Hunt, 1994, p. 20), although trust might be mitigated by credible commitments or other forms of safeguarding. Commitment and trust, in particular, are key variables in interorganizational relationships, because they encourage cooperation between organizations and a focus on long-term and mutual benefits and discourage opportunistic behavior (Morgan & Hunt, 1994, p. 22). Given the pivotal role that commitment and trust have been shown to play in the relationship marketing literature, and their prominence within ICE, we posit that, within 3PL relationships:

Hl: Relational governance structures are characterized by a) trust and b) commitment.

For the purposes of our study, we define trust as one member of a buyer-supplier dyad having confidence in the abilities of the other member and thus being willing to rely on the other member of the dyad; this definition thus encompasses both credibility and benevolence (Anderson & Narus, 1990; Moorman, Deshpande & Zaltman, 1993). We define commitment as "an enduring desire to maintain a valued (interorganizational) relationship" (Moorman, Zaltman & Deshpande, 1992, p. 316).

Transaction cost economics can also help inform the potential relationship between a relational governance structure and a key construct examined in many extant 3PL studies: customer service. Procured services are less tangible than products (Sampson & Spring, 2012), and service supply chains can be more challenging to manage (Barney, 2012; Maul!, Geraldi & Johnston, 2012). It can thus be more difficult to consider contingencies and contracting hazards (Will i a mson, 1975) surrounding specifying service outcomes. Suppliers of 3PL services that are in more adversarial, muscular relationships with their customers may perceive their customers as having taken advantage of them through the use of the customers' power to force providers to offer extremely low prices. These suppliers may then be more likely to act opportunistically and extract their "pound of flesh" by defecting from the spirit of the contract through offering lower levels of customer service facets that are not prescribed in the contract. These actions can include, for example, withholding information and dedicating resources to other customers with which the supplier has a more relational governance structure. Together, this suggests the following:

H2: There is a positive relationship between a relational governance structure and logistics customer service.

Finally, there is an interest among researchers concerning the possible performance outcomes--such as shipper and receiver operational performance and ultimately improved marketing and financial perfor-mance--that might result from enhanced logistics customer service (Green, Whitten & Inman, 2008; Wallenburg, 2009). The resource-based view, with its focus on the potential for resources to allow a firm to achieve a sustained competitive advantage and thus improve performance, is a particularly relevant lens by which to consider the potential relationship between logistics customer service and performance.

Barney (1991) defines resources as the attributes of physical, human, and organizational capital that can allow a firm to employ strategies that improve efficiency and effectiveness to yield a competitive advantage. A sustained competitive advantage can be achieved when a firm strategy uses resources in a manner that is not "simultaneously being implemented by any current or potential competitors and when these other firms are unable to duplicate the benefits of the strategy" (Barney, 1991, p. 102). Further, these resources should be (1) valuable, (2) rare to both a firm's current and potential competitors, (3) imperfectly imitable, and (4) nonsubstitutable.

Such resources may, paradoxically, be easier for firms to achieve externally (e.g., at the supply chain level) than internally (Barney, 2012; Hunt Sa Davis, 2012). Barney (2012, p. 4) notes that "resource-based theory ... points to the importance of heterogeneous purchasing and supply chain management capabilities in creating the imperfectly competitive strategic factor markets that makes competitive advantage in product markets possible" and that the RBV predicts that supply chain management capabilities and processes have the potential to be valuable, rare, imperfectly imitable, and nonsubstitutable resources, although "Of course, this is ultimately an empirical question." Priem and Swink (2012, p. 9) also assert that "Particularly with regard to relationship resources, many firms excel in value creation and capture by virtue of unique relationships with ... (supply chain) partners."

Achieving high levels of logistics customer service should require a close and collaborative relationship between a buyer and service supplier--or in other words a relational governance structure, as defined earlier--and these high levels of logistics customer service are likely not achievable in all 3PL-customer relationships. Thus, customer service can be thought of as a heterogeneous resource that, under the resource-based view, is:

* at least somewhat rare--mastery of logistics excellence remains elusive; despite more than three decades of outsourcing practice, service arrangements do not always achieve their goals; the frequency of rebids perhaps alludes to this proposition, as does the number of lawsuits filed for performance failures or contract violations;

* difficult to imitate-3PL-client relationships are rooted in interpersonal relationships between the two organizations; high-performing interpersonal relations are regarded as difficult for competitors to dislodge;

* certainly nonsubstitutable, because all firms track and attempt to manage logistics customer service, although to varying degrees of success; and

* likely valuable, because a minimum level of logistics customer service is generally considered to be an order qualifier, while higher levels of logistics customer service beyond the order-qualifying threshold can allow a firm to differentiate itself vis-a-vis its competitors.

Placing logistics customer service within the resource-based view in this manner, we put forth the study's final hypothesis:

H3: There is a positive relationship between customer service and firm performance.


In this section, we describe the sample selection, explain the coding scheme for the final 69 articles that were meta-analyzed, and then detail the meta-analytic procedures that were used to generate the data for testing the hypotheses and conducting additional, post-hoc analyses.

Sample Selection

The focus of our data collection was to obtain correlations between constructs of interest from articles that describe research in the 3PL industry. The random coefficient meta-analysis approach suggested by Hunter and Schmidt (2004) was employed in this study. The sample of primary sources was gathered in two steps. First, we conducted a manual review of four well-known logistics/supply chain management journals that focus on empirical research (International Journal of Logistics Management, International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, and Journal of Supply Chain Management). Empirical studies of the 3PL industry were gathered and inspected for appropriate keywords to be used for the comprehensive keyword search. Five keywords were selected: "3PL," "third-party logistics," "logistics outsourcing," "logistics service," and "logistics service provider." We verified that the keywords returned all relevant studies identified via our manual search. Then, we conducted full-scale searches in the EBSCO and ABI-Inform databases, using the previously identified keywords.

Our database of articles contained 864 papers, and of these, 241 at-tides were selected for further close examination as they were articles that used an empirical 3PL sample and reported quantitative results. The researchers thoroughly evaluated the individual items used to operationalize the constructs to ensure that the original authors used measures of the constructs to properly classify them. It was also ensured that zero-order correlations between constructs were obtainable. We initially found 81 articles that were suitable; however, the required information was not available, and thus, our sample of usable articles had to be reduced. Overall, 69 articles using 54 independent samples (k) and representing 9,386 observations (N) were successfully identified, coded, and used for further analysis. These articles are shown in Appendix A.

APPENDIX A List of Samples and Articles

Article  Sample                           Sample
No.         No.  Article        Journal    Size   Country

1          1     Anderson,      JSCM        309   Asia Pacific
                 Devinney and

2          2     Briggs,        JBR         110   North
                 Landry and                       America

3          2     Briggs,        IMM         110   North
                 Landry and                       America

4          3     Cahill,        JBL         248   USA
                 Knemeyer and

5          4     Chen, Tian,    JBL         124   China
                 Ellinger and

6          5     Cho, Ozment    IJPDLM      117   USA
                 and Sink

7          6     Davis,         IMM         142   USA
                 Golicic and

8          7     Deepen et al.  JBL         549   Germany

9          8     Ellinger,      IMM         123   USA
                 Hult, Elmadag
                 and Richey

10         9     Ellinger,      JBL         161   USA
                 Keller and
                 Bas (2010)

11        10     Grawe et al.   TRE         312   USA

12        11     Hartmann and   JSCM        155   Germany
                 De Grahl

13        12     Hartmann and   IJPDLM      172   Germany
                 De Grahl

14        13     Hofenk,        JSPM         77   Netherlands
                 Semejin and

15        14     Huo, Selen,    IJOPM       159   Hong Kong
                 Yeung, Hoi
                 and Zhao

16        15     Jayaram and    IJPE        411   USA
                 Tan (2010)

17        16     Jeffers        IJOPM        64   USA

18        16     Jeffers,       DS           64   USA
                 Muhanna and
                 Nault (2008)

19        17     Kersten and    IJQRM       229   Germany
                 Koch (2010)

20        18     Klein (2007)   JOM          91   USA

21        19     Knemeyer et    JBL         388   USA
                 al. (2003)

22        19     Knemeyer and   JSCM        388   USA

23        19     Knemeyer and   TJ          388   USA

24        20     Krizman        EBR          58   Slovenia

25        21     Lai, Li, Wang  JSCM        105   China
                 and Zhao

26        22     Lai, Ngai and  TRE         134   Hong Kong
                 Cheng (2002)

27        23     Lai, Tian and  IJ PR       119   China
                 Hou (2012)

28        21     Lai, Zhao and  IMDS        105   China
                 Wang (2006)

29        24     Lao, Choy,     MBE         184   Hong Kong
                 Ho, Chung and
                 Chung (2011)

30        25     Large, Kramer  IJPDLM       42   Germany
                 and Hartmann

31        26     Li (2011)      IJPE        176   USA

32        27     Lin (2006)     JAAB        114   Taiwan

33        28     Lin (2007)     JTMC        557   China

34        28     Lin (2008a)    IJTMSD      557   China

35        29     Lin (2008b)    IJM         142   Taiwan

36        28     Lin and Ho     SCMIJ       557   China

37        30     Liu, Grant,    IJPDLM      114   China
                 McKinnon and
                 Feng (2010)

38        31     Liu and Lyons  TRE         204   Taiwan

39        32     Menon,         JBL          41   USA
                 McGinnis and

40        33     Moore (1998)   IJPMM       339   USA

41        33     Moore and      IJPDLM      339   USA

42        34     Murphy and     JBL          37   USA
                 Poist (2000)

43        35     Panayides      EJIM        251   Hong Kong

44        35     Panayides      IMM         251   Hong Kong

45        35     Panayides and  TRE         251   Hong Kong
                 So (2005a)

46        35     Panayides and  MEL         251   Hong Kong
                 So (2005b)

47        36     Potocan        TBRC        287   Slovenia

48        37     Rabinovich et  JOM         196   USA
                 al. (2007)

49        38     Rafiq and      JBL         183   UK

50        39     Rajesh,        TRE          30   Singapore
                 and Ganesh

51        40     Reeves,        TRE          55   USA
                 Caliskan and
                 Ozcan (2010)

52        41     Rollins,       IJPDLM      235   Finland
                 and Mehtala

53        42     Sahay and      IJPDLM      130   India
                 Mohan (2006)

54        43     Schmoltzi and  JSCM        226   Germany

55        44     Shah and       APJML       100   India

56        45     Sinkovics and  JBL         142   UK
                 Roath (2004)

57        46     Stank and      TRE         143   USA

58        47     Stank et al.   JBL         111   USA

59         4     Tian,          IJPDLM      124   China
                 Ellinger and
                 Chen (2010)

60        48     Tian, Lai and  IJPDLM      115   China

61        49     Wallenburg     JSCM        298   Germany

62        49     Wallenburg     EJM         298   Germany
                 and Lukassen

63        50     Wallenburg     IJPDLM      212   Germany
                 and Raue

64        21     Wang, Huo,     IMDS        105   China
                 Lai and Chu

65        21     Wang, Chu, et  SCM         105   China
                 al. (2008),
                 Wang, Lai and
                 Zhao (2008)

66        51     Wanke,         IJOPM        93   Brazil
                 Arkader and

67        52     Wilding and    IJPDLM       50   Europe

68        53     Yeung (2006)   MEL          72   Hong Kong

69        54     Yeung, Zhou,   IJPE        150   China
                 Yeung and
                 Cheng (2012)

Coding and Correlation Retrieval

A coding system was set up to classify the constructs in the original studies per Lipsey and Wilson (2001). First, a coding protocol was prepared for each coder. An a priori construct categorization was combined with an exploratory approach to identify other constructs employed in the primary studies. The definitions for each construct category, including representative examples of studies using that construct, are shown in Appendix B. This categorization offers good coverage of the most common constructs in the literature. Then, the constructs were individually recorded in a spreadsheet and dassified into broader categories of similar constructs. We ensured that the items of each construct reflect the respective subgroup and 75% of the items had to closely match the other constructs (Hunter & Schmidt, 2004). To maintain independence among samples, multiple publications based on the same sample were treated as a single sample (Hunter & Schmidt, 2004). All articles were coded by the authors and then verified to maintain objectivity and consistency across the different coders. The intercoder agreement rate was 93.12% (19 disagreements over four codings and 69 articles), and disagreements were resolved via discussion. In addition to the specific information on the variables, such as correlation coefficients and reliability estimates, we collected information on the study itself, such as sample size, industry, and focal firm.


Construct Definitions

Construct                Definition                    Examples

Asset           Refers to (idiosyncratic) assets     Rabinovich
specificity     that are "specialized to a           et al. (2007),
                specific transaction" and are        Stank and
                not easily transferable to other     Daugherty
                purposes or uses (Williamson,        (1997)
                1991, p. 555).

Uncertainty     It generally consists of             Reeves et al.
                environmental uncertainty            (2010),
                (the "circumstances                  Wallenburg
                surrounding an exchange              and Lukassen
                cannot be specified ex ante")        (2011)
                and behavioral uncertainty
                ("performance cannot be
                easily verified
                expost"). Uncertainty has
                also been conceptualized
                as consisting
                of both complexity and
                an environment that is complex
                and/or changing rapidly is more
                uncertain than an
                environment that is
                simple and more static
                (Duncan 1972).

Opportunism     Williamson (1975, p. 6) as,          Knemeyer
                "self-interest seeking with guile"   and Murphy
                and "guile" is defined as "lying,    (2005), Lai et
                stealing, cheating, and              al. (2012)
                calculated efforts to mislead,
                distort,disguise, obfuscate, or
                otherwise confuse" (Williamson,
                1985, p. 47), In addition,
                of relationship norms can
                also be constituted as
                opportunistic behavior
                (Wathne and Heide 2000).

Information     We define it as collaborative        Klein (2007),
sharing         communication and coordinating       Jeffers et al.
                the information transfer             (2008)
                between firms. There are four
                classes of information that can be
                shared: (1) order, (2) operational,
                (3) strategic, and (4)
                strategic/competitive (Seidmann
                and Sundararajan, 1997).

Operational     It is defined as coordinated         Chen, Tian, et
cooperation     actions to achieve mutually          al (2010),.
                beneficent outcomes with expected    Chen, Damanpou
                reciprocation over time (Anderson    and Reilly
                & Narus, 1990), Specifically,        (2010),
                we refer to "collaborative joint     Sinkovics and
                activity development, work           Roath (2004)
                processes, and coordinated
                decision-making (Leuschner et al,
                2013b)" between customers
                and the 3PL.

Relational      Firms can employ three               Jayaram and
governance      distinct types of structures to      Tan (2010),
structure       govern the hybrid,                   Knemeyer et
                interorganizational relationships    al. (2003),
                that fall between simple markets     Moore and
                and hierarchy--muscular, benign,     Cunningham
                and credible (Williamson,            (1999)
                2008). In the 3PL-customer
                relationship, both benign
                and credible dimensions
                are included and
                are characterized by a long
                time horizon, the sharing of
                mutual benefits and burdens,
                commitment, and trust
                (Ellram, 1991; Ellram
                & Cooper, 1990; Kaufmann
                & Carter, 2006).

Information     We define it as the                  Jeffers et
technology      adoption and use of                  al. (2008),
                information technology (IT)          Lai et al.
                by the 3PL to                        (2008)
                facilitate better visibility
                and increase efficiency. The
                constructs in
                this category focus on
                the types and
                use of IT at the 3PL

Operational     We define it as the adoption         Lin (2008a,
technology      and use of operational               b); Lin
                technology in transportation         and Ho
                and warehousing to                   (2009)
                improve efficiency.
                A good example is the use
                of radio frequency
                identification (RFID) for
                efficiently tracking and
                tracing cargo.

Logistics       It is the output of a firm's         Huo et al.
                logistics system. It is              (2008),
customer        measured by the "Seven Rs"           Rafiq and
service         (Bienstock, Mentzer, and Bird        Jaafar
                1996; Mentzer, Flint, and            (2007);
                Hult 2001). The                      Stank et
                "Seven Rs" describe a                al. (2003)
                company's ability
                to deliver the right product
                in the right amount at the
                right place at the right time
                for the right customer in
                the right condition
                at the right price (Coyle,
                Bardi and Langley 2002;
                Stock and Lambert 2001).

Cost reduction  It is the ability of the             Wallenburg
                3PL to continuously                  (2009),
                reduce costs and as such to          Wang et
                offer the shipper the best           al. (2010)

Innovation      It is "an idea, practice,            Anderson et
                or object that                       al. (2011),
                is perceived as new by               Panayides (2006,
                an individual or other unit          2007)
                of adoption" (Rogers 2003,
                p. 12).
Operational     It "refers to perceived              Liu and Lyons
performance     performance improvements             (2011),
                that the logistic outsourcing        Tian et al.
                relationship has provided the        (2010)
                user" (Knemeyer & Murphy,
                2004,p. 39). More specifically,
                it consists of improvements
                in competitive capabilities
                such as service and delivery
                quality,and flexibility (Hill
                1994; Ward et at. 1998).

Financial       We included measures                 Grawe et
performance     of financial strength                al. (2012),
                and performance that affect          Kersten and
                the income statement and             Koch (2010)
                balance sheet of a company,
                such as profitability, sales
                growth, and asset reduction

Market          We include intangible                Sinkovics and
performance     performance outcomes                 Roath (2004),
                that enhance the image of            Stank et al.
                the company in the                   (2003)
                marketplace, such
                as brand awareness and
                reputation.In addition, it is
                improved performance with
                specific customers as a result
                of the ongoing business

Satisfaction    It is "a positive affective          Briggs et al.
                state resulting from the             (2010),
                appraisal of all aspects of          Li (2011)
                a firm's working relationship
                with another firm" (Anderson
                & Narus, 1984).

Loyalty         It is a long-term commitment         Large et al.
                to repurchase that includes a        (2011),
                favorable cognitive attitude         Stank et al.
                toward the selling firm              (2003)
                (Morgan & Hunt, 1994).
                In addition,it includes "the
                intention of a buyer of
                logistics services to
                purchase the same services
                and additional service from
                the current provider in
                the future, as well a
                s the buyer's activities
                in recommending
                this provider to others" (Cahill
                et al., 2010; p. 255).

Trust           It is the ability of the             Hofenk et
                customer to rely on the              al. (2011),
                3PL to meet promises and             Klein (2007)
                expectations and vice versa
                (Zucker 1986; Anderson &
                Narus,1990; Moorman
                et al., 1993; Ganesan 1994;
                Morgan & Hunt, 1994),

Commitment      It is the belief "that an            Knemeyer et
                ongoing relationship                 al. (2003),
                with another is so important         Schmoltzi
                as to warrant maximum                and Wallenburg
                efforts at maintaining               (2012)
                it; that is, the committed party
                believes the relationship
                is worth working on to
                ensure that it endures
                indefinitely" (Morgan &
                Hunt, 1994, p. 23).

In cases where interconstruct correlations or reliabil-ities were not reported in the article, we made multiple attempts to solicit the required information from the authors. If this was not successful, other methods, such as the tracing rule and other conversions, were 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 interconstruct correlations (e.g., Rabinovich, Knemeyer & Mayer, 2007).

Meta-Analytic Procedures

We followed Hunter and Schmidt (1990, 2004) for calculating the correlations between constructs of interest. For each available pair of constructs, the correlation (r) was calculated, which is an estimate of the population correlation. We also corrected for artifacts such as measurement error (Hunter & Schmidt, 2004; Rosenthal, 1991). In the few cases that no measures of scale reliability were reported in the original articles or if a single-item scale was used, we substituted the mean reliability (Chen, Damanpour & Reilly, 2010; Leuschner, Charvet & Rogers, 2013; Leuschner, Rogers & Charvet, 2013). In addition, when averaging the correlations, each sample was weighted by its compound attenuation factor (scale reliability and sample size). If a study had more than one correlation of interest, we combined them and treated them as a single correlation. It has been shown that studies with nonsignificant results have a lower chance of being published (Rosenthal, 1979). Therefore, we assessed the "fail safe number," which indicates how many additional results would have to be found to obtain an overall nonsignificant result (Rosenberg, 2005). For statistically significant correlations (p < 0.05), the fail safe numbers ranged from 99.02 to 5,107.60. An overview of the results is shown in Table 1, which contains the number of independent samples (h) and the overall sample size (N).


Number of Samples (k) and Aggregate Sample Size (N)

                  1   2    3      4    5       6    7      8

1   Asset         -  339    -    258    -     430    -      -

2   Uncertainty   2    -  429    143    -    1182  699    128

3   Opportunism   -    3    -      -    -     918    -      -

4   Information   2    1    -      -  213    1712  155     30

5   Operational   -    -    -      2    -     337    -      -

6   Relational    2    7    5      6    3       -  793    635

7   Information   -    2    -      2    -       3         557

8   Operational   -    2    -      1    -       4    1      -

9   Logistics     1    8    1      6    3      21    -      1

10  Cost          -    3    1      -    -       4    -      -

11  Innovation    -    3    -      1    2       3    3      2

12  Operational   1    3    3      4    2      11    3      7

13  Financial     -    3    -      2    -       8    3      5

14  Market        5    6    4      2    3      18    5      7

15  Satisfaction  2    1    -      4    2       8    2      2

16  Loyalty       3   11    2      1    1       8    2      3

17  Trust         3    2    5      2    -       7    1      4

18  Commitment    2    -    2      1    1       3    1      4

      9   10    11    12    13    14    15    16    17   18

1    143    -     -   388     -   884   157   545   699  311

2   1806  562  1014   757   821  1186   110  2162   315    -

3    248  248     -  1066     -   851     -   721   831  128

4    761    -   549   727   475   526   963   115   206  115

5    421    -   607   266     -   355   607   155     -  226

6   3629  623  1024  1876  1686  3455  1034  1565  1246  516

7      -    -   726   260   726  1131   263   327    91  105

8    557    -   417  1393  1121  1602   236   715   607  545

9      -  962  1492  1326  1208  2834   962   796   235    -

10     6    -   457   231   305  1054   111   657     -    -

11     4    2     -   474  1092  1238   847   453     -    -

12     9    2     3     -  1293  2044   768   523   818  105

13     6    3     4     8     -   851    64     -    --  321

14    17    6     5    11     4     -   336  1235   699  215

15     6    1     4     3     1     3     -  1114   503  115

16     5    3     2     2     -     7     7     -   454  115

17     1    -     -     3     -     3     2     2     -  243

18     -    -     -     1     1     2     1     1     3    -

The cells above the diagonal contain the aggregate sample sizes
(N) of the relationship, and the cells below the diagonal contain
the number of samples (K) for the relationship.

Meta-Analytic Structural Equation Modeling

To test the study's hypotheses, a path analysis was performed. First, we constructed the meta-analytic correlation matrix shown in Table 2. Then, we performed a structural equation modeling analysis in SPSS's AMOS 21, using the meta-analytic correlation matrix data. In line with other research, we used the harmonic mean sample size to compute standard errors (Viswesvaran & Ones, 1995). We estimated two models. Model 1 conceptualizes firm performance as a second-order construct, made up of operational performance, financial performance, and market performance. Model 2 estimates the direct links to the three types of performance measures. In both cases, the analysis was performed using the maximum likelihood algorithm, and a non-positive-definite correlation matrix was allowed as an input (Cheung, 2008; Cheung & Chan, 2005).

Corrected Meta-Analytic Correlations ([c.sub.c]) Between
Study Variables

                     1      2     3     4      5     6     7

1   Asset

2   Uncertainty      -

3   Opportunism      -      -

4   Information      -      -      -

5   Operational      -      -      -     -

6   Relational       -   0.11   0.08  0.60     -

7   Information      -      -      -     -     -  0.32

8   Operational      -      -      -     -     -  0.71       -

9   Logistics        -   0.15      -  0.54        0.54       -

10  Cost             -   0.34      -     -     -  0.55       -

11  Innovation       -   0.59      -     -     -  0.75    0.59

12  Operational      -   0.12   0.16  0.35     -  0.45       -

13  Financial        -   0.09      -     -     -  0.37    0.26

14  Market        0.15  -0.06   0.18     -  0.60  0.35    0.38

15  Satisfaction     -      -      -  0.60     -  0.49       -

16  Loyalty       0.19   0.42      -     -     -  0.57       -

17  Trust         0.09      -  -0.07     -     -  0.28

18  Commitment       -      -      -     -     -  0.31       -

      8       9      10    11    12    13    14    15   16  17









9      -

10     -  0.61

11     -  0.69     -

12  0.31  0.41     -     -

13  0.31  0.48     -  0.53  0.42

14  0.36  0.52  0.25  0.53  0.42  0.40

15     -  0.61     -  0.55  0.38     -     -

16  0.31  0.71  0.60     -     -     -  0.39  0.51

17  0.25     -     -     -  0.40     -  0.08     -   -

18  0.48     -     -     -     -     -     -     -   -   -

All cells in italics are based on a small number of sample
(k [greater than or equal to] 3 and <5) and small sample sizes
(N [greater than or equal to] 500 and <1,000) and should therefore
only be regarded as rough estimates.

All correlation coefficients inbold are statistically different from
zero at p<0.05


To test our hypotheses, we evaluated the aggregated correlations found in our original studies. The corrected correlations ([r.sub.c]) are shown in Table 2. For some pairs of constructs, no primary studies reported results or the aggregate sample sizes, and the number of studies was not large enough to calculate a reliable estimate of the population correlation. For other pairs, the number of samples that reported the correlation and the aggregate sample size was small (N [greater than or eqaul to] 500 but <1,000 and k [greater than or equal to] 3 but <5). Nevertheless, we calculated those correlations, and they are shown in italics; however, we stress that the correlations displayed in italics are initial estimates that should receive additional attention in future research, due to their comparatively small sample sizes and low number of studies. In the correlation matrix, the values in bold are different from zero at the 0.05 level of significance.

To test our first two hypotheses, we assess the correlations between Relational Governance Structure and Trust (Hla) and Commitment (H1 b). Both correlations are not significantly different from zero, and therefore, we reject the hypotheses. For Ella, the uncorrected correlations range from 0.02 to 0.65, and the 95% credibility interval ranges from -0.02 to 0.59. There is a significant heterogeneity factor (Q = 30.36**), which leads us to believe that additional moderators might be present in the sample. The estimate for 1-11b is based on a rather small sample (k = 3 and N = 516), and we believe that additional research will bring clarity to the magnitude of this relationship.

The correlation data shown in Table 2 were then used to estimate a meta-analytic structural equation model. The path diagram for Model 1 is shown in Figure 1. The path loadings displayed in boxes represent the fully mediated model, and the path loadings not inside of boxes represent the partially mediated model. The overall fit of the fully mediated model was adequate (CFI = 0.957; .RMR = 0.067). There is support for H2 and H3 in this model as evidenced by the positive and significant path coefficients; these standardized path coefficients are 0.54 (p < 0.001) and 0.74 (p < 0.001), respectively. These specific relationships are consistently positive and significant throughout the other models we estimated. It is notable that a significant amount of the variance of the logistics customer service construct and the second-order performance constructs was explained in the model (29.2% and 54.4%, respectively). Also included in Figure 1 are the results of a partially mediated model. The overall fit of the partially mediated model was better (GFI = 0.982; RMR = 0.029). A formal test for mediation (Baron & Kenny, 1986) revealed partial mediation and a significant indirect effect (z = 5.76, p < 0.001) using the Sobel (1982) test. In addition, the proportion of mediation is 44.13% (lacobucci, Saldanha & Deng, 2007). This suggests partial mediation, with relational governance structure improving firm performance through its positive effect on logistics customer service, as well as its direct, albeit weaker positive impact on firm performance.

In addition to Model 1, we also estimated a second model, individually assessing each of the three performance dimensions. The path diagram of Model 2 is shown in Figure 2 and includes the fully and partially mediated models. The overall fit was adequate for the sequential (GFI = 0.938; RMR = 0.080) as well as the mediated model (GFI = 0.964; RMR = 0.051). We conclude that the impact of logistics customer service on operational and market performance is stronger than on financial performance, which is not significant.


While we were not entirely surprised to find that the correlation between trust and a relational governance structure was nonsignificant (Hla), we were surprised to discover that the correlation between commitment and a relational governance structure was nonsignificant (Hlb). One explanation for the lack of support for H la is that firms that utilize a relational, rather than an arm's-length (i.e., muscular) governance structure might employ credible as opposed to benign governance structures. With such credible structures, trust might be moderated by credible commitments/ safeguarding. A post hoc examination of Table 2 finds that relational governance structure is positively and significantly correlated with information technology ([r.sub.c]= 0.32) and operational technology (ye, = ([r.sub.c] = 0.71), although as a caveat these correlations are based on a relatively small number of studies (k = 3 and k = 4, respectively). The aggregate correlation between commitment and relational governance (H1b) shown in Table 2 is positive and certainly not small (0.31). Given the relatively few studies and small aggregate sample size, there may simply not be enough data to show the significance of this relationship. Future research might explore in more depth the roles that commitment and trust play in the governance structures between 3PLs and their customers. Finally, some of the other characteristics of relational governance structures, such as information sharing (([r.sub.c] = 0.60) and loyalty (([r.sub.c] = 0.57), are also positively and significantly related to the relational governance structure construct.

Our findings show strong support for Hypotheses 2 and 3. A relational governance structure can allow 3PLs and their customers to work closely together to improve logistics customer service, which can in turn enhance overall firm performance. In addition, there is a significant, positive, and direct relationship between a relational governance structure and overall firm performance (Figure 1), suggesting that more collaborative 3PL--shipper relationships can enhance firm performance in other ways, perhaps via innovation, which is significantly and positively correlated with relational governance and all three types of performance (Table 2).

The analyses displayed in Figure 2 provide a more granular investigation of the relationships between relational governance structure, logistics customer service, and the three performance dimensions. Here, we found that relational governance structure has a very significant and positive mediating effect on operational, financial, and market performance through its impact on logistics customer service. Interestingly, there is also a significant and strong direct effect of relational governance structure on operational performance, which reduces its mediated effect through logistics customer service. Thus, while a relational governance structure enhances logistics customer service and ultimately better operational performance, a relational governance structure also directly improves operational performance through mechanisms other than enhanced logistics customer service. One possibility is that relational governance structures include high levels of information and operational technology, as noted above, which may help to improve operational performance.

One concern regarding the path analyses displayed in Figures 1 and 2

is that the results are based on aggregated samples of both shippers and 3PL suppliers. There has been controversy in the industry as to whether benefits in 3PL-shipper relationships accrue to the 3PL supplier or the buyer (Richards, 2006). This same question is often raised in the trade literature concerning broader buyer-supplier relationships across spend categories (Yuva, 2005). To investigate the potential differences across shippers and 3PLs, we modeled the relationships posited by H2 and H3 as two separate path analyses. The results of these analyses are displayed in Figure 3.

The results displayed in Figure 3 provide overall support for the study's hypotheses for both the shipper and 3PL samples. The paths between relational governance structure and logistics customer service are significant for both the shipper ([r.sub.c] = 0.61, p<0.0001) and 3PL([r.sub.c] = 0.46, p<0.0001) samples. There are also positive and significant relationships between logistics customer service and the three performance dimensions for the shipper sample and between logistics customer service and two of the three performance dimensions--operational performance and market performance--for the 3PL sample. In addition, there are significant and direct relationships between relational governance structure and operational and financial performance for the shipper sample and between relational governance structure and all three performance dimensions for the 3PL sample.

It is not surprising that there is not a direct relationship between the governance structure that a shipper has with a 3PL and the shipper's market performance, because we would expect that an improvement in market performance, particularly on the outbound, distribution side, would accrue due to the enhanced logistics customer service experienced by the shipper's customers, rather than being directly impacted by the shipper's governance structure with the 3PL supplier. Further, while there is not a relationship between logistics customer service and financial performance for shippers, the enhanced operational and market performance experienced by shippers might ultimately lead to better financial performance. For 3Pl.s, engaging in relational governance structures with their customers can improve operational, financial, and market performance, both directly and through improved logistics customer service.

Overall, our results suggest that shippers and their 3PL suppliers can improve logistics customer service and multiple dimensions of performance by establishing more collaborative, relational governance structures. Importantly, our meta-analytic findings suggest that this is not a win-lose proposition. Instead, both sides can improve firm performance by creating relationships that, if not benign from a TCE perspective, are at least credible. The aggregate correlations reported in Table 2 suggest that these relationships are characterized by loyalty and the sharing of information. Information technology and operational technology also appear to be a part of these relationships and may further enhance performance. In addition, the tenets of TCE and a credible form of relational governance structure suggest that technology might act as a safeguard that augments trust in these relationships.

Managerial Implications

Our meta-analysis, which is based on 9,386 observations across 54 samples, allows us to present key takeaways that should be generalizable across a wide variety of 3PLs and their customers. The analysis reports a preponderance of support for beneficial performance outcomes associated with logistics outsourcing arrangements--for both suppliers and customers of the services. This is promising in itself, as it reinforces the growth in third-party provisions around the world. In particular, those arrangements characterized by relational governance structures that encompass mutuality and the sharing of information yield heightened logistics customer service that, in turn, contributes to the operational and market performance of firms. The 3PL providers that create such relational governance structures with their customers can improve their operational, financial, and market performance, both directly and via improved customer service outcomes. Individual studies conducted in a variety of settings point to these relationships, finding support in most analyses. The meta-analysis provides a level of confirmation unattainable in single studies. It suggests that outsourcing arrangements selectively entered into by shippers and providers can justify the three primary motives for outsourcing: 1) service improvements, 2) cost reduction, and 3) focus on nonlogistics core competencies (Maloni & Carter, 2006). In turn, where customers of these services find value, opportunities are present for providers.

Although our analysis proved inconclusive concerning the influence of trust and commitment on relational governance structure, providers and customers are encouraged to pursue relational governance approaches in order to achieve the desired outcomes of the involved parties. Trust should be augmented with safeguarding mechanisms to avoid opportunistic behavior of the other party in the relationship. Where trust is instilled, however, it seems that loyalty and the benefits of retention and business growth can take root. Aside from learning the business and capabilities of the counterpart, trust-based relationships are also likely to produce innovation in service arrangements. Proactively innovating how products are delivered to their customers seems to have a stronger effect on firm performance, compared with, for example, logistics customer service and cost reduction. In light of the highly competitive third-party logistics market, it becomes critical for providers to differentiate themselves. While such differentiation could potentially take place by offering the best service at a commensurate price, our analysis points to an ability to find solutions that are either unattainable for the individual customer or, perhaps, not even conceived by the customer. This will continue to be the evolution in the third-party logistics market, as providers are sought for their brains as well as the brawn they demonstrate through conventional operational activities.

Limitations of the Research

As with all meta-analyses, this methodology relies on available studies, and as such, the quality of the results depends on the results obtained from the primary research. An additional challenge is to obtain an accurate estimate of the population effect. As these two goals must be balanced, we decided to restrict our search to published academic articles. We believe that this limitation does not diminish the validity of our results as we performed a rigorous literature search and found very high fail safe numbers. Therefore, we are confident that the presence of additional studies that might not have been included in the meta-analysis would not significantly change our results. As previously explained, we followed rigorous procedures to obtain all the information for each primary study we retained. Nevertheless, we were not able to obtain all correlations and reliabilities.

A common limitation of meta-analysis research is the "apples to oranges" aggregation of articles. We must point out that we cannot control for specific sample effects and the only way to account for this is to have a large enough number of articles. While we were able to obtain a reasonably large number of articles overall, certain relationships can only be assessed based on a small number of studies (e.g., the italicized correlations reported in Table 2); therefore, the reader should interpret those correlations with caution. Another, albeit minor, limitation is the inability to evaluate constructs of interest over time. While this is not a limitation of the research method per se, we can only evaluate what is available in primary research. However, it is possible that the lack of support for Hypothesis 1 might be explained by analyzing trust and commitment over time.

Suggestions for Future Research

The conduct of a meta-analysis implies an abundance of research on a topic of contemporary interest and importance. The current analysis explored relationships in the broad phenomenon of logistics outsourcing. Despite a growing body of empirical hypothesis testing on the subject, opportunities for additional investigation remain abundant. The limitation of small aggregate samples and studies for certain aggregate correlations, for instance, provides opportunities for future research. Information sharing is certainly one construct that is in need of further investigation. As suggested earlier, technology might act as a safeguard that moderates the relationship between trust and a relational governance structure. Researchers could also investigate the potential moderating role of other safeguards, such as qualification procedures and pledges (Rindfleisch & Heide, 1997).

We found relatively few articles that explored the relationships between key interorganizational relationship constructs, such as satisfaction and trust. In fact, for satisfaction, only two interconstruct correlations, relational governance satisfaction and satisfaction loyalty, were examined with sufficient frequency to meet our sample threshold. For trust, we were only able to examine the relational governance--trust relationship, per lila. Additional research is necessary to ascertain the influence of these key variables on customer service and firm performance. Further, the studies examining the constructs of satisfaction and trust, to date, have done so from the perspective of customers. We recommend that future research also examines the prospects for these constructs from the perspectives of 3PL providers. That is, what is the influence of supplier satisfaction and supplier trust in customers toward relational outcomes and performance? As the services of 3PLs become increasingly differentiated and the providers become more distinct in the market, they will employ greater selectivity in their customer relationships (Lieb, 2008). It will, therefore, be essential for customers to market themselves effectively to "choice" providers. Instilling trust and minimizing fears of opportunism could prove instrumental in gaining the confidence and commitment of these preferred 3PLs.

While affective constructs such as interorganizational trust and opportunism may help to explain the performance outcomes of outsourcing arrangements, recognizing that provider and customer organizations are composed of individuals reminds us that interpersonal relationships factor into outsourcing success and failure as well (Gligor & Autry, 2012). Investigation of the roles of key individuals on both sides of the outsourcing arrangement could prove valuable. The findings of Grawe, Daugherty and McElroy (2012) yield worthwhile insights concerning the interorganization-al commitment demonstrated by provider implants at client locations, yielding relational capital and reciprocal forms of commitment. In other words, positioning one or more key relationship managers at client sites impacts the quality of the relationship. Are there key individuals in a relationship that exhibit a greater influence on the outcomes of outsourcing arrangements? What happens should these key individuals leave the arrangement or be assigned elsewhere? Are there ways to effectively manage the loss of such individuals? Further, what are the optimal team dynamics on each side of the relationship to foster improved performance? These questions underscore that interor-ganizational phenomena call for sociological examination on multiple levels that, to date, remain underexplored in logistics outsourcing arrangements.

A construct that demonstrates great promise, yet remains understudied, is innovation (Arlbjorn & Paul-raj, 2013). Innovation registered correlation values with seven other constructs ranging from 0.53 to 0.75 (Table 2). However, it was only examined in five or more samples with one construct--market performance ([r.sub.c] = 0.53). Wallenburg et al. (2010) define the related concept of proactive improvement as, "ex-post adaptations, as they would be termed in TCE-made by an LSP within an outsourcing relationship (that) are an act of innovation as they are 'an idea, practice, or object that is perceived as new by an individual or other unit of adoption" (Rogers, 1995, p. 7). Further, proactive improvement is found to yield higher levels of perceived performance in logistics outsourcing arrangements, per Deepen, Coldsby, Kne-meyer and Wallenburg (2008), who illustrate that proactive improvement contributes not only to goal achievement, but also to goal exceedance in a demonstrable fashion. Busse and Wallenburg (2011) echo the call for further examination of innovation processes and systems among logistics service providers.

On a related note, research is necessary to explore effective mechanisms for risk and reward sharing in outsourcing relationships. Much has been said anecdotally about gain-sharing arrangements, but norms for sharing burdens and risks can vary considerably. Are gain-sharing provisions, like those espoused in performance-based logistics (Randall, Pohlen Hanna, 2010) effective as a means of inciting innovation, finding service improvements and uncovering incremental cost reductions? If so, what forms of gain sharing prove most effective under different relational arrangements?

Abundant research opportunities remain, as well, for deeper analysis of situational variables and their influence on the governance structure--performance linkage. The current analysis examined a wide collection of outsourced services. However, arrangements can vary considerably in terms of the specific services procured, the duration of contracts, relationship history among the parties and the degree of control exerted by the provider in performing the contracted service (s) (i.e., an asset-based provider performing the services as opposed to a non-asset-based provider subcontracting the work). Deeper analysis of these arrangements would likely demonstrate that relational governance structure requires adaptation to the buying situation. Of course, such an analysis requires capturing these situational variables and reporting their prospective influence.

One situational construct that found sample sufficiency for examination with four other variables, yet was found to be significant with only one, is uncertainty. Uncertainty was found to have a positive relationship with loyalty in outsourcing arrangements. This relationship is likely to be an intermediated one, with a relational form serving as a coping mechanism to reduce uncertainty. It is also possible, however, that uncertainty represents a meaningful control or moderating variable to incorporate in analysis of 3PL governance and performance, as recommended by Maloni and Carter (2006). Additional situational factors that one might consider incorporating in such analyses include relational norms, complexity, power asymmetry, conflict, mutual dependence, and reciprocity.

Particularly valuable would be the collection and analysis of these situational factors in logistics outsourcing relationships occurring outside North America. Deeper analysis of these factors in international settings would enrich our understanding of how outsourcing relationships that are increasingly conducted across national boundaries should adapt to cultural differences among contracting parties.

Lastly, while a priori relationships were not posited for the three specific dimensions of performance, it is worth noting that we found significant, positive aggregate correlations between operational performance and financial performance ([r.sub.c] = 0.42) as well as financial performance and market performance ([r.sub.c] = 0.40) (Table 2). Although the correlation between operational performance and market performance is high in absolute terms ([r.sub.c] = 0.42), it lacks statistical significance attributed to a wide range of findings among the samples. Such an outcome might be explained by missing artifacts in the analysis. That is, one or more moderators are likely influencing the relationship between operational performance and market performance. The competitiveness of the market, for instance, might mute such a relationship such that excellence in operational performance is not always rewarded by gains in market performance. Further examination of the interrelationships between the dimensions of firm performance is merited, with due consideration afforded to prospective moderating factors. And, additional analyses should be conducted to examine possible moderators to the other hypothesized relationships that were investigated in this paper.


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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 supply chain finance. In addition to these topics, Dr. Leuschner also is interested in the generalizability of research and its replication, with a particular emphasis on meta-analysis. Among the outlets that have published his earlier articles are the Journal of Business Logistics and the Journal of Supply Chain Management.

Craig R. Carter (Ph.D., Arizona State University) is a professor in the Supply Chain Management Department of the W.P. Carey School of Business at Arizona State University in Tempe, Arizona. His primary research activities focus on sustainable supply chain management and encompass ethical issues in buyer-supplier relationships, environmental supply management, diversity sourcing, perceptions of opportunism surrounding electronic reverse auctions, and the broader, integrative concepts of social responsibility and sustainability. Prior to his academic career, Dr. Carter spent five years working in the areas of transportation and logistics with Ryder Systems, Hechinger Company, and the U.S. Department of Transportation. He also has conducted field-based supply management research with over 100 Fortune 1000-size firms in the United States and Germany, which includes work with CAPS Research and McKinsey 81 Company. Dr. Carter's research has been published in the Journal of Supply Chain Management, Journal of Operations Management, Journal of Business Logistics, Decision Sciences, Journal of Business Ethics, Trans porta-don Research Part E, Transportation Journal, and International Journal of Physical Distribution and Logistics Management.

Thomas J. Goldsby (Ph.D., Michigan State University) is a professor of logistics in the Fisher College of Business and the Associate Director of the Center for Operational Excellence at The Ohio State University in Columbus, Ohio. His research interests include the strategic implications of logistics and supply chain management; he currently is a research associate of the Global Supply Chain Forum and a Research Fellow of the National Center for the Middle Market, both of which are based at The Ohio State University. Dr. Goldsby is the co-author of Lean Six Sigma Logistics: Strategic Development to Operations Success and of Global Macrotrends and Their Impact on Supply Chain Management.

Zachary S. Rogers (MBA, University of Nevada) is a doctoral candidate in the supply chain management program in the W.P. Carey School of Business at Arizona State University in Tempe, Arizona. His primary research interests are in sustainable supply chain development and behavioral judgment and decision-making. He also is interested in closed-loop supply chains and cradle-to-grave product stewardship. Currently, Mr. Rogers is studying the interaction between sustainability and human behavior.

(1.) However, a large percentage of these papers reported only descriptive statistics, rank orderings, and/or means tests using single-scale items and could not be included in a meta-analysis of common constructs surrounding third-party logistics.


Rutgers University


Arizona State University


The Ohio State University


Arizona State University
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Author:Leuschner, Rudolf; Carter, Craig R.; Goldsby, Thomas J.; Rogers, Zachary S.
Publication:Journal of Supply Chain Management
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Date:Jan 1, 2014
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