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Exploring the Potential Impact of Relationship Characteristics and Customer Attributes on the Outcomes of Third-Party Logistics Arrangements

Posted on: Friday, 18 March 2005, 03:00 CST

Abstract

This article uses a relationship marketing perspective as the basis for evaluating third-party logistics arrangements. In particular, the study investigates whether 3PL relationship outcomes (e.g., customer retention, service recovery) are influenced by select relationship characteristics (e.g., communication, reputation) and/or select customer attributes (e.g., firm size, number of outsourced logistics functions). The article reports the findings from a study of 388 users of third-party logistics services that investigated these potential linkages. Regression analyses were applied to the constructs of interest in order to learn about the degree of influence. The findings suggest that "substance trumps style" in the sense that relationship characteristics, rather than customer attributes, have the more significant impacts on relationship outcomes.

The worldwide usage and importance of third-party logistics (3PL) services (also referred to as logistics outsourcing) has grown dramatically since 1990. For example, Sheffi (1990) argued that a combination of economic, regulatory, and technological changes would cause 3PL services in the United States "to experience explosive growth" during the 1990s. Indeed, annual expenditures for 3PL services in the United States did not exceed $10 billion until 1993 (Barks 1994); today, by contrast, annual U.S. 3PL expenditures are approaching $80 billion (Gecker 2004). In a similar fashion, usage rates of 3PL services have increased from approximately 40 percent of Fortune 500 companies in the early 1990s (lieb 1992) to approximately 65 percent of these companies in the early part of the 21st century (Lieb and Kendrick 2002).

While third-party logistics has been suggested to be one of the key issues in contemporary logistics management (Murphy and Poist 2000), one of the challenges in trying to evaluate the growing body of empirical studies is that researchers often employ different definitions of third-party logistics (Skjoett-Larsen 2000). For instance, Coyle, Bardi, and Langley (2003, p. 425) suggest that third-party logistics involves an external organization "that performs all or part of a company's logistics functions." This rather "broad" definition appears to suggest that any logistics activity (function), such as transportation, warehousing, or inventory management, that is not provided "in house" can qualify as third-party logistics.

An alternative, more "narrow," definition suggests that third- party logistics involves "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" (Murphy and Poist 1998, p. 26). This definition is applied in the present article, in part because it suggests that 3PL incorporates strategic, and not just tactical, dimensions (Skjoett- Larsen 2000).

Another reason for preferring this more "narrow" definition of third-party logistics is that the view of logistics outsourcing arrangements as a "longer-term" exchange suggests possible benefits from incorporating relationship marketing theory into the study of third-party logistics, as suggested by Stock (1997; 2002). More specifically, relationship marketing is the opposite of transactional marketing in the sense that transactional marketing is focused on a single, short-term exchange between buyer and seller (Gundlach and Murphy 1993). Relationship marketing, by contrast, focuses on the interaction between buyers and sellers and is concerned with multiple exchanges over time between buyer and seller (Christopher, Payne, and Ballantyne 1991).

Figure 1. Product Types and Marketing Strategy

Figure 1, which provides a continuum with respect to product types and marketing strategy, indicates industrial (business-to- business) services to be the preferred product category that should use relationship marketing. As such, an integration of relationship marketing theory into the study of third-party logistics arrangements would appear to be justified on the basis that third- party logistics represents one type of industrial service.

THE PRESENT ARTICLE

Relationship marketing is both an important and popular topic in the contemporary business literature, with suggestions that it may become (or has already become) a new paradigm in the marketing discipline (Eriksson and Mattsson 2002). While relationship marketing first emerged during the 1970s, it did not achieve critical literature mass until the 1980s and 1990s (Rao and Perry 2002). Moreover, the relative newness of the relationship marketing concept means that numerous definitions have appeared in the literature (Harker 1999; Wong and Sohal 2002). For purposes of the present manuscript, we will use Morgan and Hunt's (1994, p. 22) frequently cited definition of relationship marketing, namely, "marketing activities directed at establishing, developing, and maintaining successful relational exchanges."

Although there is a growing body of literature associated with relationship marketing, there are still a myriad of unanswered questions with respect to it (Rao and Perry 2002; Wong and Sohal 2002). These questions tend to involve demographic attributes and include the potential influences of firm size and the size and nature of the relevant customer base, among others (Stewart and Durkin, 1999). For example, Coviello, Brodie, and Munro (2000) indicate that existing marketing models and theories, to include relationship marketing, have generally been based on the practices and behaviors of larger firms.

A review of the literature suggests that relatively few articles have approached third-party logistics from a relationship marketing perspective. For instance, Moore (1998) found trust to be a positive influence on third-party relationship effectiveness and that trust can be facilitated by sharing the benefits, burdens, and risks associated with a particular arrangement. Moreover, Moore and Cunningham (1999) found more effective logistics outsourcing relationships to be characterized by higher levels of trust, equity, and commitment than are less effective outsourcing relationships. More recently, Knemeyer and colleagues (2003) explored the characteristics of partnership development within logistics outsourcing relationships.

Although third-party logistics arrangements appear to be an appropriate venue for studying relationship marketing concepts, much remains to be learned. In an effort to address part of this literature void, this article investigates third-party logistics arrangements from a relationship marketing perspective. More specifically, the manuscript investigates whether 3PL relationship outcomes (e.g., customer retention, service recovery) are influenced by select relationship characteristics (e.g., communication, reputation) and / or select customer attributes (e.g., firm size, number of outsourced logistics functions).

We believe that the article addresses Stock's challenge (2002) to broaden the scope of the logistics literature by incorporating concepts from other disciplines-in this case, relationship marketing. In addition, by incorporating relationship marketing theory into the study of third-party logistics, another of Stock's challenges (1997) is met. Moreover, the manuscript also adds to the literature in the sense that it specifically investigates the potential impacts of customer attributes upon relationship marketing outcomes (Stewart and Durkin 1999).

The remainder of the article is organized as follows: The next section provides a discussion of the relevant relationship marketing characteristics and outcomes, along with a discussion of the relevant customer attributes. This is followed by a methodology section, to include a discussion of the data collection, an overview of the tests of construct validity and a profile of the responding organizations. Next comes a section that presents the relevant results and this is followed by a final section that discusses the study's academic and practitioner implications along with suggestions for future research.

RELATIONSHIP MARKETING CHARACTERISTICS AND OUTCOMES

Relationship Characteristics

Although there are a number of characteristics associated with relationship marketing, the following are generally considered to be some of the more important: investment, reputation, satisfaction, communication, and opportunistic behavior (Anderson and Weitz 1992; Morgan and Hunt 1994). A review of practitioner literature concerning logistics outsourcing relationships provides additional anecdotal support for the importance of these characteristics. To this end, this article uses five relationship characteristics in the context of a third-party logistics arrangement: (1) specific investments by a 3PL provider; (2) reputation of the 3PL provider; (3) user satisfaction with previous outcomes of the 3PL relationship; (4) user communication with the 3PL provider; and (5) instances of opportunistic behavior by the provider. Each of the five is more fully elaborated in the paragraphs that follow.

Provider's specific investments. As a general rule, the perception of a provider's specific investments in a 3PL arrangement should signal \that the provider can be trusted. Quite simply, a provider's specific investments in people, lasting assets, and procedures should raise the stakes-and, hopefully, the trust-in the arrangement (Ganesan 1994). Examples of such investments in a 3PL context include the training of warehousing personnel, dedicated electronic hookups for inventory control, and purchasing relationship-specific equipment. Based on previous work by Ganesan (1994), a provider's specific investments are measured with a three- item (listed in Appendix A), sevenpoint Likert scale (where 1 = strongly disagree and 7 = strongly agree).

Provider's reputation. The literature suggests that one individual is more willing to commit to another if the other person has a reputation for cooperative behavior (Pruitt 1981). A similar mechanism occurs between organizations and serves to check misbehavior, thus building trust, particularly in long-term relationships (Macauley 1963). To this end, providers of 3PL services signal their future actions through their performance and behavior in other relationships. By demonstrating their abilities to help users improve their logistics performance, 3PL providers can develop a reputation for having the ability to deliver the logistics improvements that companies are looking for when outsourcing logistics activities. The three items (presented in Appendix A) used to measure provider's reputation are adapted from Anderson and Weitz (1992), and are evaluated on a seven point Likert scale (where 1 = strongly disagree and 7 = strongly agree).

Satisfaction with previous outcomes. This construct focuses on a user's perception of equity in the exchange with a 3PL provider. In a long-term relationship, satisfaction with past outcomes tends to indicate equity in the exchange, which should provide confidence that parties are not being taken advantage of and that both parties are concerned about each other's welfare (Ganesan 1994). LaLonde and Cooper (1989) suggest that successful past dealings with a third party are usually essential before establishing longer-term relationships with them. This may be done by using an incremental approach in the sense of providing limited amounts of business to 3PL providers. If performance is satisfactory, the arrangement can transition to a longer-term partnership-style relationship. Seven items (presented in Appendix A), adapted from Ganesan (1994), are used to measure a 3PL user's satisfaction with previous outcomes; the items are evaluated using a seven-point semantic differential scale.

Communication with the provider. In terms of exchange relationships, communication can be described as the formal as well as informal sharing of meaningful and timely information between firms (Anderson and Narus 1990). Meaningful and timely communication facilitates relationship building by assisting in resolving disputes and aligning perceptions and expectations (Etgar 1979). Communication is without question an important component of successful 3PL arrangements (Bowersox et al. 1989; Ellram and Cooper 1990; Gardner and Cooper 1988; LaLonde and Cooper 1989). Four communication items (listed in Appendix A) measure a user's perception of the formal and informal sharing of meaningful and timely information with their 3PL provider. These items, adapted from Anderson, Lodish, and Weitz (1987), are evaluated on a seven- point Likert scale where 1 = strongly disagree and 7 = strongly agree.

Opportunistic behavior by the provider. According to John (1984, p. 279), "The essence of opportunistic behavior is deceit-oriented violation of implicit or explicit promises about one's appropriate or required role behavior." In the 3PL context, opportunism reflects provider behavior(s), such as broken or unfulfilled promises, that reduces a user's belief in the provider's trustworthiness. In this article, opportunistic behavior is investigated using a nine-item (listed in Appendix A), seven-point Likert scale (where 1 = strongly disagree and 7 = strongly agree), with the nine items being adapted from John (1984).

Outcomes Associated with Relationship Marketing

Barnes (2001), along with Boles, Barksdale, and Johnson (1997), suggests a set of outcomes-customer retention; customer referrals; service recovery-of relationship marketing activities. Additionally, consistent with several recent studies (Whipple, Frankel, and Anselmi 1999; Janda, Murray, and Burton 2002), performance outcomes of relationships have become a focus of inquiry. Each of these outcomes should directly impact a company's ability to manage long- term success and shareholder value. To this end, this article investigates four outcomes associated with relationship marketing in the context of a third-party logistics arrangement: (1) retention, (2) referrals, (3) service recovery, and (4) operational performance improvements. Each of the four is more fully described in the paragraphs that follow.

Retention. Retention involves keeping customers by meeting and exceeding their needs. Customer retention is generally recognized to be much less expensive than customer acquisition; a common rule of thumb is that it costs five times as much to acquire a new customer as to retain an existing customer. Importantly, supplier/customer relationships have been both theoretically and empirically linked to customer retention (Boles, Barksdale, and Johnson 1997). Retention is measured using an eight-item (presented in Appendix B), seven- point Eikert scale (where 1 = strongly disagree and 7 = strongly agree) adapted from Rusbult, Farrell, Rogers, and Mainous (1988).

Referrals. The ultimate test of a customer's relationship with a service provider may be whether a customer is willing to become an advocate for a service provider, promoting the service provider to others, and even defending the service provider from detractors (Cross and Smith 1995). Indeed, word-of-mouth referrals appear to be critical for companies identifying potential third-party logistics providers (Boyson, Corsi, Dresner, and Rabinovich 1999). A three- item (presented in Appendix B), sevenpoint Likert scale (where 1 - strongly disagree and 7 = strongly agree), adapted from Boles, Barksdale, and Johnson (1997), is used to measure and evaluate referrals.

Service recovery. Although mistakes are likely to occur in nearly every arrangement, recovery from these mistakes appears to be important when involved in a relational exchange. More specifically, Priluck (2003) found that satisfactory service recovery was associated with higher levels of satisfaction and lower intentions to exit among participants in relational exchanges than for participants in transactional exchanges. The four items (see Appendix B) used to measure service recovery are adapted from Morgan and Hunt (1994), and are evaluated on a seven-point Likert scale anchored by 1 - strongly disagree and 7 = strongly agree.

Operational performance improvements. This construct measures the perceived logistics operational performance enhancements that the outsourcing arrangement has supplied to the user. Wicks (1999) argues that without trust firms will be unable to enable certain organizational processes that may be crucial to firm performance. In a 3PL context, Spira (1999) suggests that logistics outsourcing arrangements are likely to be unsuccessful if one party fails to do what is expected by the other party. The operational performance improvements are adapted from Newton, Langley, and Alien (1997) and involve an eleven-item (presented in Appendix B), seven-point Likert scale again anchored by 1 = strongly disagree and 7 = strongly agree.

Building upon the information on relationship characteristics and associated outcomes presented in this section, this article proposes the following:

Proposition 1: The relationship characteristics of provider's specific investment, provider's reputation, satisfaction with previous outcomes, communication with the provider, and opportunistic behavior by the provider will influence (a) customer retention; (b) a customer's willingness to provide a referral for their provider; (c) a provider's ability to recover from a service issue; and (d) a customer's perception of the operational performance improvements provided by their 3PL.

Customer Attributes of Interest

As discussed previously, there is a growing body of literature associated with relationship marketing; however, there are still a myriad of unanswered questions with respect to it (Rao and Perry 2002; Wong and Sohal 2002). To this end, the current research will focus on selected demographic attributes of customers of third- party logistics services. The customer attributes to be examined include customer size, the length of the relationship between the customer and the 3PL, the number of 3PL relationships in place, the number of logistics functions outsourced, and the type of functions outsourced by the customer.

Consistent with Connell (2001), this study uses the number of employees as the measure of customer size. The length of the relationship between the customer and the 3PL is measured by the self-reported number of months that the customer reported having a relationship with the focal 3PL. Moreover, the number of 3PL relationships in place was measured by a selfreported number of 3PLs currently being used by the customer.

The number of logistics functions outsourced and the type of functions outsourced by the customer (respondent) were measured as follows: Each respondent was provided a list of thirty possible logistics functions (drawn from previous 3PL research) that might be outsourced. Respondents were asked to indicate which functions were currently outsourced; as such, the number of logistics functions outsourced is a simple summation of the number of functions checked by each participant.

With respect to the type of functions outsourced, respondents were grouped into two categories: those whose outsourcing a\ctivities were "transportation focused" and those whose outsourcing activities were "logistics focused." For purposes of this study, transportation-focused customers include those who outsource only transportation-related functions such as inbound traffic control, outbound traffic control, and freight bill payment, among others. While logistics-focused customers could outsource transportation-related functions, they would also outsource additional functions such as inventory management, warehousing, and product assembly, among others.

Thus, based on the information on potential customer attributes and associated outcomes discussed in this section, this article proposes the following:

Proposition 2: The customer attributes described above will influence (a) customer retention; (b) a customer's willingness to provide a referral for their provider; (c) a provider's ability to recover from a service issue; and (d) a customer's perception of the operational performance improvements provided by their 3PL.

METHODOLOGY

Data Collection

The data come from a mail survey sent to 5,000 U.S. professionals who were asked to supply information concerning various aspects of the arrangement between their company and a self-selected current third-party provider of logistics services (hereafter referred to as the focal relationship). These 5,000 professionals, each representing a distinct company, were randomly selected from the names on an outsourcing list of a major logistics trade magazine. Each professional received a postcard prenotification and an initial mailing of the survey. Approximately one month after the initial mailing, a random sample of 2,000 nonrespondents received a follow- up mailing (Diamantopoulos, Schlegelmilch, and Webb 1991).

Fifty-five surveys indicated that their organization was not currently engaged in a logistics outsourcing arrangement (as defined in this study), while another 72 were returned because of bad addresses or because the contact person was no longer employed by a particular company. A total of 388 usable responses were received, representing an effective response rate of approximately 8 percent (388 divided by 4873). Although an 8 percent response rate is relatively low, the 388 responses would appear to offer a plentiful database in the sense that previous 3PL studies have involved no more than 250 responses (Boyson, Corsi, Dresner, and Rabinovich 1999).

Moreover, there are suggestions that lack of response bias may be a more important consideration than a high response rate (Babbie 1990; Salant and Dillman 1994). To this end, three tests for nonresponse bias were performed; one method, consistent with Boyson, Corsi, Dresner, and Rabinovich (1999), compared the Standard Industrial Classification (SIC) codes of the responding organizations to the SIC codes of the trade magazine's total subscription list (approximately 12,000 subscribers). The results of this analysis are presented in Table 1. A second method compared early and late respondents (Armstrong and Overton 1977) in terms of key non-demographic questions. Finally, a third test for nonresponse bias involved contacting a randomly selected group of thirty nonrespondents who were asked to answer the same questions used to compare early and late respondents. Each of the three tests suggests that nonresponse bias is not an issue in the present study.

Construct Validity

The scale means and the coefficient alphas for the individual relationship characteristics and relationship marketing outcomes are presented in Tables 2a and 2b. The scale items for the individual relationship characteristics and relationship marketing outcomes were analyzed separately using principle components analysis, in order to check for evidence of convergent validity. The scale items of each measure exhibited high and significant item intercorrelations and factored into a single item, thus establishing the unidimensionality of each measure. Discriminant validity between the constructs was assessed using structural equation modeling as suggested by Fornell and Larcker (1981) and Garver and Mentzer (1999). In every comparison, the chi-square difference test was found to be statistically significant. In addition, the correlation confidence interval between each pair of constructs did not contain 1. Thus, the results suggest the existence of discriminate validity.

Respondent Demographics

With respect to firm size, 26.2 percent of responding organizations employ between one and 200 workers, and another 20.3 percent employ between 201 and 500 workers; the remaining 53.5 percent employ more than 500 workers. Moreover, approximately 20 percent of the respondents indicated that they were either a vice president or director of logistics (with logistics encompassing "distribution,""logistics,""supply chain," or "transportation"), while another 60 percent could be classified as some type of logistics manager. The remaining 20 percent of respondents held a number of different titles, to include "distribution supervisor,""logistics analyst,""operations manager," and "transportationplanner." Thus, in terms of job titles, it would appear that respondents should have familiarity with various issues associated with the outsourcing of logistics services.

Table 1. Distribution of Survey Respondents among Industrial Segments Compared Against the Distribution of the Population

Table 2a. Construct Mean Scores and Reliability Coefficients- Relationship Characteristics

As further support for the appropriateness of the sample, the respondents were asked to indicate their level of responsibility for the focal third-party relationship using a seven-point semantic differential scale, anchored by "no responsibility" (1) and "primary responsibility" (7). The mean score for this item, 5.9016, suggests that the respondents tend to have a great deal of responsibility for the focal relationship. Moreover, on average, respondents had more than five years of involvement with the focal relationship, which again suggests that respondents are familiar with the subject matter.

Table 2b. Construct Mean Scores and Reliability Coefficients- Outcomes of Relationship Marketing

Table 3a. Number of Outsourced Logistics Activities

Table 3b. Most Frequently Outsourced Activities (mentioned by at least 20 percent of organizations)

According to the data in Tables 3a and 3b, approximately 50 percent of the responding organizations outsource three or fewer logistics activities, with "outbound traffic control" being the most frequently outsourced activity (47.2 percent of respondents). Only two other activities, "inbound traffic control" (outsourced by 37.9 percent of respondents) and "carrier negotiation & contracting" (outsourced by 30.2 percent of respondents), are outsourced by more than 30 percent of the responding organizations. Although the most popular outsourcing activities in this study are different from those in previous 3PL studies (Boyson, Corsi, Dresner, and Rabinovich 1999; Lieb and Kendrick 2002; Murphy and Poist 2000), these previous studies have evaluated a less comprehensive group of activities than the thirty listed in this study.

RESULTS

In order to provide a more parsimonious discussion of the results, a single score was computed by calculating an average of the individual items for each construct, a procedure consistent with Gibson, Rutner, and Keller (2002). As mentioned previously, the mean scores for each construct, and their respective reliability coefficients, are presented in Tables 2a and 2b. Note that all of the reliability coefficients in Tables 2a and 2b exceed .70, a figure generally regarded as the lowest acceptable reliability coefficient (Nunnaly 1978).

Regression analyses were applied to these construct mean scores in order to learn about the degree to which the four relationship marketing outcomes were influenced by the various relationship characteristics and customer attributes. Results from the regression analyses, presented in Table 4, indicate that the r-square values range from a low of .311 for service recovery to a high of .577 for referrals. (These r-square values compare favorably with the regression values reported in a recent Transportation Journal article by Crum and Morrow [2002]). In addition, based on the F- statistic, each of the four regression analyses is statistically significant at the .01 level. The regression equations associated with each of the four relationship outcomes are discussed in greater detail in the paragraphs that follow.

Table 4. Regression Results-Outcomes of Relationship Marketing Dependent Variables

Retention. Table 4 presents the regression results for the ten constructs that could impact a provider's ability to keep customers by meeting and exceeding their needs (i.e., customer retention). Three of the ten constructs, satisfaction with previous outcomes, communication with the provider, and length of relationship, exhibit statistical significance. Moreover, the positive coefficients for each construct suggest a positive relationship between the constructs and the likelihood of a customer staying with its current 3PL. As such, these findings suggest that increases in the length of the relationship, satisfaction with prior outcomes, and communication between a 3PL and its customer all serve to strengthen customer retention.

Referrals. The regression results for referrals, presented in Table 4, indicate four of the ten constructs exhibit statistically significant influence on whether a customer is willing to become an advocate for a service provider, promoting the service provider to others, and even defending the service provider from detractors. Three of the significant constructs, length of the relationship, communication with the provider, and satisfaction with previous outcomes, have positive coefficients. In other words, increases in the length of the relationship, communication with the prov\ider, and satisfaction with previous outcomes should lead to an increase in customer referrals.

On the other hand, opportunistic behavior by the provider has a negative influence on the willingness of customers to provide referrals for their 3PLs. This suggests that as the amount of perceived opportunistic behavior by a 3PL provider increases, a customer's willingness to provide referrals for that 3PL decreases. The findings associated with customer referrals reinforce the positive contributions of building a history of satisfactory performance and high levels of communication as well as demonstrate the potential negative impact of a 3PL acting in its own self interest. In particular, 3PLs should realize that opportunistic actions may have negative consequences, such as a refusal to provide referrals.

Service recovery. The regression results for service recovery, presented in Table 4, indicate three of the ten constructs exhibit statistically significant influence on a customer's willingness to forgive mistakes by the provider and continue the relationship. Two of the constructs, communication with the provider and opportunistic behavior by the provider, are statistically significant at the .01 level, while provider's specific investments is statistically significant at the .05 level. Communication with the provider and the provider's specific investments are positively related to service recovery, suggesting that service recovery can be enhanced with stronger communications between customers and their 3PLs as well as with greater provider investments in the outsourcing arrangement.

Although opportunistic behavior by the provider also has a positive coefficient, this may be a somewhat unexpected finding because it suggests a positive relationship between opportunistic behavior and service recovery-in other words, as opportunistic behavior increases, so does the willingness of a customer to forgive a 3PL provider's mistakes. While further inquiry into this finding is necessary, one possible explanation is that even if 3PLs engage in opportunistic behavior, customers may be hesitant to end the arrangement, perhaps because of the amount of time and resources already committed to the arrangement.

These findings reinforce the positive contributions that a provider's investment in relationship-specific assets and communication efforts has on the relationship with its customers. That is, service recovery can be increased by investing in relationship-specific assets and focusing on customer communication. Moreover, 3PLs should realize that while they may be forgiven for engaging in opportunistic behavior, such behavior is not without consequence; indeed, as pointed out above, customers are not likely to provide referrals in the face of opportunistic behavior by 3PL providers.

Operational performance improvements. Table 4 presents the regression results for the ten constructs that might impact a provider's ability to deliver operational performance improvements to their customers. Three of the ten constructs, satisfaction with previous outcomes, communication with the provider, and the number of functions outsourced, exhibit statistical significance. The data in Table 4 indicate a positive relationship between each of these three constructs and a customer's perception of the performance improvements that its current 3PL is able to provide.

More specifically, the findings suggest that satisfactory performance and high levels of communication with the customer should be positive influences on the 3PL's ability to deliver operational performance improvements to the customer. Table 4's findings also suggest that improvements in operational performance can be positively impacted by increasing the number of outsourced functions. This might indicate that 3PLs that offer more functional expertise have an advantage over other 3PLs in delivering improvements in operational performance.

Evaluating the Propositions

The results appearing in Table 4 can be used to evaluate the two propositions that were presented earlier in this article. According to Proposition 1, various relationship characteristics will influence (a) customer retention; (b) a customer's willingness to provide a referral for their provider; (c) a provider's ability to recover from a service issue; and (d) a customer's perception of the operational performance improvements provided by their 3PL. The data in Table 4 offer support for Proposition 1 in the sense that each of the four possible relationship outcomes was influenced by at least two of the relationship characteristics. Furthermore, one relationship characteristic, communication with the provider, emerged as a statistically significant construct across all four outcomes, while another characteristic, satisfaction with previous outcomes, was statistically significant for three of the four outcomes. Provider's reputation was the only relationship characteristic that failed to be statistically significant across any of the four outcomes.

Proposition 2 suggested that selected customer attributes will influence (a) customer retention; (b) a customer's willingness to provide a referral for their provider; (c) a provider's ability to recover from a service issue; and (d) a customer's perception of the operational performance improvements provided by their 3PL. The results in Table 4 tend not to support Proposition 2. None of the four relationship outcomes was influenced by more than one customer attribute, and service recovery was not influenced by any of the customer attributes. In addition, length of the relationship is the only customer attribute that influences more than one relationship outcome. Furthermore, three of the five customer attributes, customer size, type of functions outsourced, and the number of 3PL relationships, did not influence any of the four outcomes.

SUMMARY AND IMPLICATIONS

This article reports the findings from a study of 388 users of third-party logistics services that investigated whether certain 3PL relationship outcomes are influenced by relationship characteristics and /or customer attributes. In general, the findings suggest that relationship characteristics have a more profound influence than customer attributes on relationship outcomes. In fact, one relationship characteristic, communication with the provider, exhibited statistically significant influences on all four of the relationship outcomes investigated in this study. Alternatively, three customer attributes, firm size, type of functions outsourced, and the number of 3PL relationships, did not significantly influence any of the relationship outcomes.

These and other findings appear to have several implications for the practitioner community. For one, the findings suggest that "substance trumps style" in the sense that relationship characteristics, rather than customer attributes, have the more significant impacts on relationship outcomes. Importantly, the lack of statistical significance for firm size (a customer attribute) suggests that operational improvements, such as logistics responsiveness and logistics cost reduction, can be achieved by many 3PL users-regardless of their size. Furthermore, the findings reinforce the importance of executing the basics with respect to 3PL arrangements. That is, the literature regularly extols the necessity of good communication for a successful 3PL relationship-and, indeed, good communication emerges as the most important overall characteristic in terms of influencing relationship outcomes.

The results also provide interesting guidance for 3PL providers in the sense that 3PL customers can identify, and will act upon, opportunistic behavior by 3PLs. More specifically, although opportunistic behavior by a provider may not result in the immediate termination of an existing 3PL arrangement, such behavior is likely to result in user unwillingness to provide referrals on the 3PL provider's behalf for other potential outsourcing arrangements.

The findings also have implications for the academic community. For instance, except for the length of the relationship construct, the results generally do not support the proposition that customer attributes have an influence on relationship outcomes. This is potentially surprising given that an identified gap in the relationship marketing literature involves the potential impacts of customer attributes upon relationship marketing outcomes (Stewart and Durkin 1999). As such, additional research should be conducted to learn more about whether relationship outcomes are influenced by other types of customer attributes. One such attribute, the relational orientation of 3PL customers, could be measured by using Coviello, Brodie, and Munro's (2000) framework of marketing practice (i.e., transactional marketing, database marketing, interaction marketing, network marketing).

Furthermore, academicians should find plentiful research opportunities associated with relationship marketing aspects of thirdparty logistics arrangements. For example, while the present study collected data associated with a self-selected 3PL provider, respondents did not have to specifically identify the 3PL provider. Such information could be useful for identifying whether companies that use larger, more well-known 3PL providers might view their relationships differently from those using "niche" 3PLs that specialize in providing a more limited number of logistics functions. In light of literature suggestions that large suppliers sometimes struggle to have relationships with their customers (Rao and Perry 2002), perhaps "niche" 3PLs offer added value by fostering relationships with their customers.

Moreover, while the present study employed previously validated relationship marketing-related constructs, it is possible that other surrogates exist for relationship quality. For example, Palmer (1997) suggests that product branding, by offering assura\nces of quality and consistency, acts as a substitute for personal relationships in situations where direct relationships with product providers are difficult to achieve. If logistics providers indeed struggle to have relationships with customers, are there specific practices, activities, and/or processes-distinct from previously validated relationship marketing-related constructs-that signal their intentions towards relational exchanges?

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Mr. Knemeyer, CTL, is assistant professor of business logistics, Fisher Col\lege of Business, The Ohio State University, Columbus, Ohio 43210; e-mail knemeyer.4@osu.edu. Mr. Murphy, CTL, is professor of business logistics, Holer School of Business, John Carroll University, University Heights, Ohio 44118; e-mail drmurphy@jcu.edu.

Appendix A. Constructs and Items-Relationship Characteristics

Appendix B. Constructs and Items-Outcomes Associated with Relationship Marketing

Copyright American Society of Transportation and Logistics Winter 2005

Source: Transportation Journal

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