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REVERSE LOGISTICS IN THE AUTOMOBILE AFTERMARKET INDUSTRY

By

Patricia J. DaughertySiegfried Chair in Marketing and

Supply Chain ManagementThe University of Oklahoma

R. Glenn RicheyAssistant Professor of Marketing and

Supply Chain ManagementThe University of Alabama

Bryan J. HudgensDoctoral Candidate

The University of Oklahoma

and

Chad W. Autry

Assistant Professor of MarketingBradley University

Submitted to The International Journal of Logistics Management

Contact Information:

Patricia J. DaughertySiegfried Chair in Marketing and

Supply Chain ManagementRoom 2, Adams Hall

Price College of BusinessThe University of Oklahoma

Norman, OK 73019T: 405-325-5899F: 405-325-7688

E: [email protected]


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Patricia J. Daugherty is Division Director and Siegfried Chair in Marketing and SupplyChain Management at The University of Oklahoma. She received a Ph.D. in Marketingand Logistics from Michigan State University. She has published extensively in logisticsjournals and has co-authored two books. Her current research interests include supplychain relationships and reverse logistics.

R. Glenn Richey (Ph.D., The University of Oklahoma) is an Assistant Professor ofMarketing and Supply Chain Management at The University of Alabama. He haspublished in journals including: American Business Review, International Journal ofPhysical Distribution and Logistics Management, International Journal of LogisticsManagement, Journal of Business Logistics, Journal of International Management, andhas co-authored a channel relationship strategy chapter in a text. His current researchinterests include partner-technology fit, supply chain relationships, and reverse logistics.

Bryan J. Hudgens is a Doctoral Candidate in Marketing and Supply Chain Managementat The University of Oklahoma. His current research interests include strategicpurchasing, supply chain relationships, and reverse logistics.

Chad W. Autry is an Assistant Professor of Marketing at Bradley University. His currentresearch interests include supply chain staffing issues and reverse logistics programs. Dr.Autry received his Ph.D. from The University of Oklahoma in 2001.

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REVERSE LOGISTICS IN THE AUTOMOBILE AFTERMARKET INDUSTRY

ABSTRACT

Reverse logistics is one of the toughest supply chain challenges. One approach toachieving more effective reverse logistics is to adopt a relationship-oriented perspective.Two aspects of a relationship-orientation trust and relationship commitment wereexamined by surveying senior marketing and logistics personnel from the automotiveaftermarket industry. Relationship commitment was found to mediate the relationshipbetween trust and reverse logistics performance. Reverse logistics program performancewas found to be more effective and efficient when relationship commitment was present.

INTRODUCTION

Reverse logistics represents big bucks for many firms. U.S. reverse logistics costs

are estimated to exceed $35 billion per year [1]. Product returns average about 6% of

sales, but can vary widely depending on the industry and type of product. For example,

much higher returns are common for books, greeting cards, and merchandise from mail

order and on-line catalogs. One-quarter of all on-line purchases were returned during the

1999 holiday season [2]. Thus, because of the sheer volumes involved and the potential

for damaging customer relations, reverse logistics is or should be a top business

priority [3]. Its also a significant challenge for business.

Reverse logistics has been described as going the wrong way [4]; movement is

against the normal flow in the distribution channel. In contrast to typical distribution

practices, goods flow from a consumer or from a location near the point of consumption

[5]. Rogers and Tibben-Lembke provide a concise definition of reverse logistics: the

process of moving goods from their typical final destination for the purpose of capturing

value or (for) proper disposal [6]. Reverse movements may be prompted by

environmental concerns or green logistics initiatives associated with recycling and

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recovery [7]. Reverse logistics can also be prompted by value reclamation goals. The

latter (value reclamation) is the primary focus of the current research.

Often reverse logistics means theres a problem. Product may be returned for a

number of reasons including but not limited to defects or damage, customer

dissatisfaction, and lower than projected sales [8]. Also, firms are dealing with more

returns due to more liberal returns policies, increasing use of consignment inventory,

shorter product lifecycles, and more demanding customers [9]. Firms that dont recognize

the importance of an effective reverse logistics program run the risk of seriously harming

their organizations reputation and alienating customers [10]. In some industries, reverse

logistics doesnt signify a problem situation. Instead, its a normal part of business. For

example, in industries such as the automobile aftermarket, remanufacturing used products

or components is the norm. Products slated for remanufacturing or repair must be

retrieved as efficiently as possible.

Regardless of the reason for returns, customers expect the selling firm to be willing

and able to handle returns. Developing a relationship orientation involving trading partner

trust and commitment is one way to facilitate better reverse logistics program performance

and help firms respond to customer returns-related demands. The current research

explores those issues within the automobile aftermarket industry. The following narrative

provides background on the industry, the research design and data collection procedures,

and results of a survey. Managerial implications are presented providing insight into

developing effective reverse logistics programs.

BACKGROUND: THE AUTOMOBILE AFTERMARKET

The automobile market can be segmented into two distinct customer bases: OEM

(original equipment manufacturers) and the aftermarket. The manufacturers and dealers

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that make up the OEM market focus primarily on vehicle assembly and marketing

(although most dealers do some repair). In contrast, the aftermarket gets its name from its

primary focus on repair services provided after the primary sale of a vehicle. It includes

the entire traditional supply chain. This can involve independents such as gas stations or

chains such as Autozone or Pep Boys as well as jobbers, remanufacturers, manufacturers,

wholesalers, and retailers [11]. The intent of the automobile aftermarket is to make repair

parts readily available in a wide range of businesses and locations. Service is the primary

competitive weapon in the automobile aftermarket industry.

The automobile aftermarket industry not only must deal with multiple locations

and distribution of a large number of SKUs, it must also contend with a high rate of

returns. Skip Potter, Vice President of Membership for the Automobile Aftermarket

Industry Association, estimates that industry returns range from 15 to 20% of sales.

These returns fall into two categories -- expected (or planned) and unexpected returns.

For example, engine starters represent a category of product with expected returns. Used

starters are returned for remanufacturing and subsequent re-sale. Returns must be made

to allow remanufacturing. In addition to the expected returns associated with

remanufacturing, there are also unexpected returns associated with poor sales, incorrect

shipments, etc.

The perspective of the buyers in the industry has traditionally been very short-term.

However, many industry participants have adopted a longer-term orientation in recent

years with an emphasis on long-term cooperation and even collaboration, because of the

importance of inventory availability and the volume of returns.

LITERATURE REVIEW AND RESEARCH HYPOTHESES

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Reverse logistics can be prompted by any of several reasons including cost

reduction, regulatory motivations (for example, packaging and disposal legislation),

customer satisfaction, value reclamation, and/or corporate citizenship [12]. Regardless of

the motivation, a solid reverse logistics program offers the potential to create a

competitive advantage [13]. Establishing and maintaining a good working relationship

with customers should facilitate the reverse logistics process. Thus, a relationship-

orientation or relationship marketing approach aimed at attracting, developing, and

retaining customer relationships [14] provides a good platform for developing successful

reverse logistics programs. Two aspects of a relationship-orientation are of particular

interest trust and commitment. Trust and commitment have been shown to lead to

cooperative behaviors conducive to marketing program efficiency, productivity, and

effectiveness [15]. Relationships that include trust and commitment can facilitate the

management of reverse logistics. Potential benefits include improved partner retention

and satisfaction through the liberalization of returns policies, increased flexibility and

agility through management by exception, and streamlined credit processing through the

implementation of damage and defective percentage off-invoice initiatives.

Trust

Trust exists when one party has confidence in an exchange partners reliability and

integrity [16]. Trust involves an expectation held by an individual that another can be

relied on [17]. The existence of trust is particularly important with respect to buyer-seller

exchange relationships. Buyer-seller relationships are almost always unequal; one party

has more power, better positioning, and/or more resources. Because of the unevenness of

power, the other party is likely to feel vulnerable unless trust is present. As such, trust is

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the mutual confidence that no party to an exchange will exploit anothers vulnerabilities

[18].

Generally, trust is considered to be comprised of two dimensions: objective

credibility and benevolence [19]. Objective credibility refers to an expectancy that the

partners word or written statement can be relied on. Benevolence is the extent to which

one partner is genuinely interested in the other partners welfare and is motivated to seek

joint gain.

Distribution-related research has shown that downstream channel partners that

trust suppliers exhibit higher levels of cooperation and exert more effort on the part of the

supplier. Channel partners that trust suppliers also tend to be more committed to and

intend to stay in the relationship [20]. Trust is viewed as a highly effective means of

fostering cooperation across all types of interorganizational relationships [21]. Thus, trust

in their customers appears important for suppliers who want to reap maximum benefits

from the exchange relationship.

In a recent review of the literature on trust, Atuahene-Gima and Li found that both

the academic literature and the popular press have a strong normative bias toward the

inherent value of trustthat is, trust is good for performance. However, they continue,

there is little empirical evidence to support the validity of this viewpoint [22]. One

study by Smith and Barclay, however, did find a positive relationship between trust and a

firms ability to achieve superior performance [23]. The first hypothesis is offered to

further explore the issue.

H1: Higher levels of trust are related to better reverse logistics programperformance.

Relationship Commitment

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Relationship commitment an enduring desire to maintain a valued relationship

[24] is central to relationship marketing. Parties to a relationship commit because of the

potential for achieving valuable outcomes [25]. Expected benefits exceed the costs

associated with maintaining the relationship. As Day notes, Before mutual benefits can

be realized, the partners must demonstrate to each other that they are fully committed . . .

[26]. However, commitment does not necessarily reflect the potential for achieving

immediate benefits. Commitment may, in fact, imply a willingness to make short-term

sacrifices to realize longer-term benefits [27].

Within the current research context of reverse logistics programs, commitment

may certainly involve short-term sacrifices. Reverse logistics is resource intensive.

Considerable time, effort, and physical resources are needed to effectively handle reverse

logistics; however, such a commitment can result in long-term benefits over time. Thus,

commitment can provide both benefits and liabilities [28]. Anderson and Weitz

characterize it as stability and sacrifice [29].

Empirical evidence of relationship commitments role in enhancing performance

has been related to reductions in partner opportunism [30]; synergistic improvements in

risk sharing and learning [31]; and increasing levels of firm/partner flexibility [32].

However, a question remains as to whether a selling firms relationship commitment to the

customer actually impacts performance directly. Specifically, does the selling firms level

of relationship commitment to customers positively influence reverse logistics

performance? Kalwani and Narayandas research supports the premise that firms investing

in long-term relationships with customers (relationship commitments) gain cost and

performance advantages (profits) over transaction-oriented firms [33]. The current

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research attempts to make a contribution by testing this relationship empirically within a

reverse logistics context.

H2: The higher the level of relationship commitment, the greater theprobability a firm will achieve better reverse logistics programperformance.

A Mediating Effect

Some researchers have indicated that the trust/commitment/performance

relationship may not be so straightforward. Morgan and Hunt define a more complex

relationship in which relationship commitment is suggested to mediate the relationship

between trust and performance [34]. The sequencing becomes important. For example,

when firm managers develop trust in a customer, they may become more open to making

changes in returns policies/processes. However, they may not actually make changes

unless they believe it is worthwhile to establish a long-term relationship, that is, make a

long-term commitment. Such an indirect relationship may help explain why some studies

on the role of trust in determining performance reported non-significant findings [35].

For these reasons, relationship commitment is viewed as a key mediator of trust

and reverse logistics performance in the current research. If relationship commitment is

present, it will enable the buyer-seller dyad to perform with greater efficiency and

effectiveness. Therefore:

H3: Relationship commitment mediates the relationship between trust andreverse logistics performance.

These relationships are shown in Figure 1.

Figure 1Theoretical Model

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Reverse LogisticsPerformance Outcomes


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SAMPLE AND DATA COLLECTION

This section describes the methodology for a broad-based survey and details the

procedures used to develop the survey instrument and to collect the data. Additionally,

basic psychometric issues, such as scale reliability and validity, are discussed.

Data Collection

A mail survey was used to collect data. The survey was developed following an

extensive review of the literature and in-depth telephone interviews with personnel from

six companies actively involved in reverse logistics. These interviews were exploratory in

Reverse Logistics Performance Outcomes

1.Cost Containment2.Environmental Regulatory Compliance

3.Improved Customer Relations

4.Improved Labor Productivity

5.Improved Profitability

6.Recovery of Assets

7.Reduced Inventory Investment

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Trust

Relationship

Commitment

H 1

H 2H 3

1

2

3

4

5

6

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nature, and lasted between thirty and sixty minutes each. Modifications were made based

on feedback received during the pretest. The final survey instrument incorporated both

existing scales and scales adapted from previous studies.

The survey was sent to Automotive Aftermarket Industry Association (AAIA)

member companies. The AAIA is a large trade association representing companies

involved in all aspects of the automotive aftermarket industry. The AAIA provided a list

of 900 member companies from which 400 companies were randomly sampled.

Respondent companies ranged in size from very small (one employee) to giant

corporations (25,000 employees), and the number of employees assigned full-time to

reverse logistics ranged from zero to 300. Sales volume ranged from $300,000 to $7

billion. Table 1 summarizes demographic information on the companies. The surveys

were addressed to the senior marketing or logistics person in the company (assumed to be

most familiar with reverse logistics operations for his or her company). If the identified

recipient did not feel qualified to complete the survey, he or she was asked to forward it to

the most appropriate person. The mail packet included a copy of the survey, an AAIA-

endorsed cover letter explaining the study, and a nominal monetary incentive.

Table 1

Demographic Data

Minimum Maximum Mean Standard DeviationNumber full-time employees 1 25,000 525 2,358Number full-time employeesassigned to reverse logistics

0 300 10 38

Sales volume (dollars) $300K $7B $159M $662M

Two mailings were conducted. In the first, 150 AAIA member firms were sent a

survey packet and a $1.00 incentive. The second mailing, to 250 additional AAIA firms,

included a survey packet and a $2.00 incentive. The second mailing served the dual

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purposes of increasing the number of respondents and enabling tests of non-response bias.

The increased incentive for the second mailing was not intended as a directly testable

manipulation; rather, it was an attempt to increase response rate. Follow-up phone calls

were made after each mailing and an additional mailing was sent to all non-respondents.

Table 2 presents a breakdown of responses.

Table 2Breakdown of Responses

Surveys Mailed Surveys Received Response RateFirst mailing 150 39 26.0%

Second mailing 250 79 31.6%

Totals 400 118 29.5% (*)* 28 surveys returned/non-deliverable; effective response rate of 31.7% (118/372)

Wave analysis was used to check for non-response bias [36]. Each of the four

mailings (two primary mailings with a follow-up mailing for each) was counted as a

separate wave, for a total of four waves. Wave analysis, in the form of MANOVA

performed covering relevant variables, found no significant differences ( = .01) that

would indicate non-response bias.

Psychometric Concerns

The scales used in this study are either existing scales or scales adapted from

previous studies; scales that were used previously in non-logistics contexts were adapted

as needed to measure the specific constructs in this study. The complete scales for each

construct are included in the Appendix.

Table 3 displays descriptive statistics and correlations. Each of the reliability

estimates exceeds the suggested minimum coefficient alpha of .70 [37], with coefficient

alphas ranging from .73 to .83. Discriminant validity was assessed following the approach

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used by Gaski and Nevin, in which a correlation between two scales that is lower than the

reliability of each scale is considered to be a reasonable indicator of discriminant validity

[38]. All scales met this criteria; each scale had a reliability measure (coefficient alpha)

exceeding the correlations between that scale and all other scales.

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Table 3Descriptive Statistics and Correlations of Scales

Scale No. of items Mean(*) s.d. 1 2 3 4

Overall reverselogistics effectiveness

1 4.82 1.54 1 Item

Trust 4 4.30 1.94 .132 (.76)Commitment 5 6.07 .985 -.051 .252** (.73)Reverse logisticsperformance

7 4.72 1.81 .733** .270** .077 (.82)

Coefficient alphas on diagonal, in bold print

* All 7-point scales: 1 = lowest, 7 = highest; **P-value significant at .05

RESULTS AND DISCUSSION

Data analysis employed a three-step approach. First, descriptive statistics of

individual items were estimated to assess the overall nature of the market. Next, both

multivariate and univariate regression analyses of the trust commitment performance

relationship were performed to test the hypothesized model. Finally, the summated items

were broken out into a between subjects profile to estimate which performance measures

are significantly impacted by the trust-commitment relationship.

Descriptive Statistics

The Appendix presents means and standard deviations of all items used to estimate

the relevant research constructs. Follow-up analysis using the Games-Howell multiple

comparison procedure identified specific means that differed significantly ( = .05) within

each scale. Games-Howell was chosen because each scale violated the assumption of

homogeneity of variance [39]. Significantly different means within the respective scales

for each construct are shown in the far right column of the Appendix, and are discussed

below.

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Program Effectiveness

It is important to gauge the general level of reverse logistics program

effectiveness. The manufacturer respondents were asked to rate the overall effectiveness

of their reverse logistics program (7-point scale from 1 = not at all effective to 7 =

extremely effective). As shown, the mean response on the self-assessment of overall

effectiveness was 4.82. This can be characterized as somewhat effective. These managers

believe their reverse logistics programs are good, but certainly not great.

Trust

To understand how the relationship construct Trust affects reverse logistics

processes, respondents were asked to assess several items (7-point scale from 1 = strongly

disagree to 7 = strongly agree) related to both the objective credibility and the

benevolence dimensions ofTrust. Overall, the respondents report relatively low levels of

trust in their customers. This is evident in that all of the items Customer Truthfulness

(4.75), Customer Sharing of Best Judgment (4.75), and Customer Empathy (4.11), and

Customer Consideration (3.60) fall near the neutral condition (4.0). At best, this is

indicative of a weak form of trust. One item, Customer Consideration (3.60), shows that

managers indicated a modest level of distrust when asked about their customers decision

making and how it affects the selling firm. The two scale items relating to objective

credibility, Customer Truthfulness and Customer Sharing of Best Judgment, showed

overall higher levels (both 4.75) than did the two scale items relating to benevolence,

Customer Empathy (4.11) and Customer Consideration (3.60). Apparently, the

respondents felt they could trust their customers to keep their word, but that their

customers generally did not have benevolent motivesfor example, a desire to seek joint

gains or to go the extra mile for the focal firm.

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Follow-up analysis was again conducted using the Games-Howell multiple

comparison procedure to examine whether the differences in the mean scores reported for

the four scale items relating to trust were significant (= .05). The results of this follow-

up analysis, reported in the far right column of the Appendix, confirm the intuitive results

above. The two scale items relating to objective credibility, Customer Truthfulness and

Customer Sharing of Best Judgment, are statistically indistinguishable from each other.

Additionally, these two items are statistically significantly higher than the two scale items

related to benevolence, Customer Empathy and Customer Consideration. Finally, the two

scale items for benevolence are statistically indistinguishable from each other. In

summary, the manufacturer respondents believe their customers are more reliable

(objective credibility) than they are interested in the manufacturers welfare (benevolence).

Relationship Commitment

To better understand the influence of the second construct, Relationship

Commitment,onreverse logistics performance, respondents were asked to assess several

scale items (7-point scale from 1 = strongly disagree to 7 = strongly agree). The resultant

ratings were consistently high. Respondents expect to be Supplying Their Customers for

Some Time (6.38), believe their relationship with the customerDeserves Their Maximum

Effort to Maintain (6.26), Intend to Maintain Their Relationship Indefinitely (6.24), and

Are Very Committed to the Relationship (6.13). These four items, all relating to the long-

term expectations toward the relationship, were rated very high. The fifth item, which

measured Willingness to Dedicate Resources to the Customers Program, was solidly

positive as well (5.37).

The follow-up analysis of the Relationship Commitment scale again used the

Games-Howell multiple comparison procedure to examine whether significant differences

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existed ( = .05) among the five items comprising the scale. Results are reported in the

right hand column of the Appendix. This analysis showed the first four items Supplying

Their Customers for Some Time, the belief that their relationship with the customer

Deserves Their Maximum Effort to Maintain, that they Intend to Maintain Their

Relationship Indefinitely, and that they Are Very Committed to the Relationship were

statistically indistinguishable from each other. The fifth item, Willingness to Dedicate

Resources to the Customers Program, was statistically lower than the first four items.

While the managers indicate high levels of commitment to their customers, they are

significantly less willing to dedicate resources to their customers programs.

Reverse Logistics Performance

The final construct of interest involved survey items pertaining to reverse logistics

Performance. Respondents were asked how effective their firms are in achieving seven

reverse logistics objectives (7-point scale from 1 = not at all effective to 7 = extremely

effective). The respondents seem to believe their firms are doing the best at meeting

mandatory requirements relating to Environmental Regulatory Compliance (5.75). Their

reverse logistics programs have also been effective at Improving Customer Relations

(5.48). However, the managers indicated their companies have been less successful when

it comes to the specific financial/efficiency outcomes ofRecovery of Assets (4.66), Cost

Containment(4.54), Improved Profitability (4.24), Improved Labor Productivity (4.20),

and Reduced Inventory Investment (4.18). Apparently, the manufacturers reverse

logistics programs have focused on mandated compliance with environmental regulations

and keeping customers happy relating to returns. Such a prioritization is understandable.

However, reverse logistics programs rate lower on achieving economic-based operating

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level objectives. This most likely reflects traditional cost-service trade-offs as well as the

fact that reverse logistics doesnt offer great opportunities for economies of scale.

Follow-up analysis of the scale items for Reverse Logistics Performance examined

whether significant differences exist ( = .05) among the managers assessments of their

firms success in meeting performance objectives. The results, reported in the far right

column of the Appendix, showed that the responses for the first two items

Environmental Regulatory Compliance and Improving Customer Relations were

statistically indistinguishable from each other and significantly higher than the responses

for the remaining five items. Responses for the remaining five scale items Recovery of

Assets, Cost Containment, Improved Profitability, Improved Labor Productivity, and

Reduced Inventory Investment were also statistically indistinguishable from each other.

The follow-up analysis confirms the initial assessment that the managers are doing better

at assuring regulatory compliance and improving customer relations than they are at

achieving financial and efficiency outcomes.

Analysis of the Model

A basic elimination technique, which uses a reverse order approach to test for

direct and mediation effects, was used to test the hypotheses [40]. First, each scale was

summated to allow for construct/path regression estimation. Next, Trustwas regressed

onto Relationship Commitment and was found to have a significant impact ( = .252; p < .

01) providing support for Hypothesis 3. Finally, using multiple regression (Multivariate

GLM), both the Trust and Relationship Commitment constructs were estimated

simultaneously to predict Performance. As proposed by Hypothesis 2, Relationship

Commitment ( = .000; = .222; p < .05) was found to be a significant predictor of

Performance. However, Trust ( = .081; = .196) was not found to be a significant

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predictor of Performance, rejecting Hypothesis 1 and supporting Relationship

Commitment as a mediator in the relationship between Trust and Performance [41].

Composite regression results are reported in Table 4.

Table 4: Regression ResultsTrust to Relationship Commitment to Performance

Hypothesis 1 Wilks df R 2

Adj R2Standardized Beta Test-statistic

(Multivariate F-test)

Trust toPerformance1

.081 61 .100.115

.196 1.016

Hypothesis 2 Standardized Beta Test-statistic

(Multivariate F-test)

RelationshipCommitment toPerformance1

.000 61 .153.138

.222 1.446*

Hypothesis 3 Standardized Beta Test-statistic(Univariate T-test)

Trust toRelationshipCommitment

na 104 .830.822

.252 2.789**

*P-value significant at .05; **P-value significant at .01

Post Hoc Analysis of Performance

Following estimation of the hypothesized relationships, a more detailed analysis of

the between item characteristics of the Performancescale was performed by breaking the

multiple regression (GLM) down into the individual scale items. This technique was used

to protect against error inflation caused by performing multiple univariate tests. Results

are presented in Table 5.

The Trust-Relationship Commitment relationship is shown to have a positive

significant impact onImproved Customer Relations (F = 2.265, p < .05), which confirms

1 It should be noted that Performance is treated as a multivariate outcome variable thus allowing for bothenhanced control of error and detailed post hoc analysis. The antecedents in the model, Trust andCommitment, are treated as summated unidimensional constructs to defend against integer overflow dueto the restricted sample size. (See James Gill, Generalized Linear Models: A Unified Approach.Thousand Oaks, CA: Sage Publications, 2001.)

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the respondents general assessment of how well their firms reverse logistics programs

help achieve this goal. Interestingly, the Trust-Relationship Commitment relationship also

has a positive significant impact onImproved Labor Productivity (F = 3.281, p < .05). In

the section on reverse logistics performance outcomes, the managers reported that on

average reverse logistics programs had essentially no affect on Improved Labor

Productivity (mean rating of 4.2 out of 7.0). This post hoc analysis suggests those reverse

logistics programs characterized by higher levels of trust and relationship commitment

haveImproved Labor Productivity as well.

The Trust-Relationship Commitment relationship also has a positive significant

impact on Cost Containment(F = 3.612, p < .10) andRecovery of Assets (F = 1.719, p < .

10). The managers reported that on average reverse logistics programs again had

little affect on Cost Containment (mean 4.54 out of 7.0) and Recovery of Assets (mean

4.66 out of 7.0). Here again the post hoc analysis reveals that those reverse logistics

programs characterized by higher levels of trust and relationship commitment have greater

levels ofCost ContainmentandRecovery of Assets.

Finally, Trust-Relationship Commitment has non-significant impacts on

Environmental Regulatory Compliance (F = 1.513; P > .10), Improved Profitability (F =

1.240; P > .10), and Reduced Inventory Investment (F=0.869; P > .10). Managers

reported their reverse logistics programs were most effective (mean 5.75 out of 7.0) at

achieving Environmental Regulatory Compliance (see Appendix). This post hoc

evaluation makes sense, however, because an outcome heavily controlled by external

governing mechanisms (laws) probably does not need high levels of trust and relationship

commitment to motivate better performance. The results for bothImproved Profitability

and Reduced Inventory Investment show no discrimination between those relationships

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characterized by high levels of trust and relationship commitment and those relationships

with lower levels.

The Trust-Relationship Commitment relationship appears to have several positive

affects on reverse logistics programs. First, it leads to improved customer relations; this

finding adds weight to the side of the relatively indecisive literature that suggests trust

does matter in business-to-business relationships. Second, firms in higher-trust, higher-

commitment relationships experience greater labor productivity; their employees

responsible for reverse logistics seem to believe that the people returning products are

doing so for a valid reason, and they respond accordingly. Finally, higher-trust, higher-

commitment relationships lead to lower costs and greater recovery of assets than would be

the case in lower-trust, lower-commitment relationships. By building trust and

commitment, firms can extract more bang for their buck by minimizing the costs incurred

in operating a reverse logistics program.

Table 5

Between Item Analysis of Significant OutcomesTest Statistic

Improved Customer Relations 2.265**Improved Labor Productivity 3.281**Cost Containment 3.612*

Recovery of Assets 1.719*Environmental Regulatory Compliance 1.513Improved Profitability 1.240Reduced Inventory Investment .869

*P-value significant at .10; **P-value significant at .05

Figure 2

Final Model

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Reverse Logistics

PerformanceOutcomes


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LIMITATIONS AND FUTURE RESEARCH

This research project suggests that trust does positively influence the success of

reverse logistics programs, but only when relationship commitment is present as well.

While it offers interesting and meaningful results, this study, like all others, has its

limitations and offers opportunities for future research to clarify the body of knowledge on

reverse logistics.

The study survey used a cross-sectional examination of companies related to the

automotive aftermarket industry. Future research could expand the results to other

industries to confirm the generalizability of the results. Additionally, relationships are

inherently dyadic. This study focused on the perspective of the upstream member of the

Reverse Logistics Performance Outcomes

1.Cost Containment

3.Improved Customer Relations

4.Improved Labor Productivity

6.Recovery of Assets

22

Trust RelationshipCommitment

1

3

4

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dyad, that is, on the company receiving the returned product. Future research should

expand to include the downstream perspective, that is, the company returning the product.

Reverse logistics programs are not one size fits all. Future research in reverse

logistics should explore this diversity. Many companies still have not implemented formal

reverse logistics programs. What does that mean in terms of catching up? Does an

advantage accrue to first mover firms? Or can later entrants gain an advantage by

learning from those who go first? Do innovative firms outperform other firms, or does an

advantage result from awaiting the establishment of best practices? Do firms with

formalized procedures outperform those with less formalized procedures?

CONCLUSIONS AND MANAGERIAL IMPORTANCE

In closing, it is important to note that the study assesses the level of trust and

commitment that the selling (manufacturing) firms have in their customers. Many studies

have addressed the opposite relationship, that is, customer trust in sellers [42]. This is one

of the first studies to look at the influence of trust in and commitment to customers.

Returns/reverse logistics handling is an area that provides an opportunity for abuse

customers may take advantage through the level and/or types of returns. The respondents

report marginal levels of trust in their customers; yet they are strongly committed to the

relationship. They may not deeply trust their customers, but they need them.

One specific way firms can develop closer relationships is to signal their

benevolence toward their partners. This study found that manufacturers are committed to

their customers and believe their customers generally treat them fairly. However, the

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manufacturers in this study dont feel their customers have the manufacturers best

interests in mind. This likely causes the manufacturers to be more wary about working to

improve the levels of trust in the relationships. The question becomes how much to

invest in a relationship with someone you dont feel is out to meet your best interests?

But the benefits of a strong, committed relationship are worth some risk. Manufacturers

that signal their good intentions (benevolence) could encourage their customers to

reciprocate. Trust would increase, commitment would grow, and reverse logistics

performance would improve.

The current research shows that when relationship commitment is high, important

benefits accrue in terms of customer relations and economics. By working to develop

both trust and relationship commitment, firms could reap big rewards. For example, if

both trust and relationship commitment are present, it could be that the manufacturing

firms would have to do less monitoring. Less monitoring of returns means fewer

resources would need to be committed. Significant resources in the form of time and

money could be saved. An equally important consideration is reverse logistics cycle time

for customers. With close, positive relationships founded on trust and mutual

commitment, it might be possible to streamline or reduce steps and time involved in

returns authorizations. Customers could immediately get returns back into the system.

The reverse logistics goal of value reclamation could be accomplishedfaster.

Working to develop trust and longer-term commitment is likely to impact

customer satisfaction for two reasons. First, a positive climate is created. Second, as

stated previously, overall reverse logistics performance improves. The right collaborative

environment could help to develop a reverse logistics process that improves service and

helps to cut costs.

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REFERENCES

[1] Pogorelec, John, Reverse Logistics is Do-able, Important, FrontlineSolutions, Vol. 1, No. 10 (2000), pp. 68-69.

[2] Stock, James R., The 7 Deadly Sins of Reverse Logistics, MaterialHandling Management, Vol. 56, No. 3 (2001), pp. 5-11.

[3] Rogers, Dale S. and Ronald Tibben-Lembke, An Examination of ReverseLogistics Practices,Journal of Business Logistics, Vol. 22, No. 2 (2001), pp. 129-148.

[4] Stock, James R. and Douglas M. Lambert, Strategic Logistics Management.New York, NY: McGraw-Hill, 2001, p. 24.

[5] Murphy, Paul R. and Richard P. Poist, Management of LogisticalRetromovements: An Empirical Analysis of Literature Suggestions, Transportation

Research Forum, Vol. 29, No. 1 (1989), pp. 177-184.

[6] Rogers, Dale S. and Ronald S. Tibben-Lembke, Going Backwards: ReverseLogistics Trends and Practices, Pittsburgh, PA: RLEC Press, 1999.

[7] Byrne, Patrick M. and Alison Deeb, Logistics Must Meet the GreenChallenge, Transportation and Distribution, Vol. 34, No. 2 (1993), pp. 33-37; Carter,Craig R. and Lisa M. Ellram, Reverse Logistics: A Review of the Literature andFramework for Future Investigation, Journal of Business Logistics, Vol. 19, No. 1(1998), pp. 85-102; Ferguson, Neil and Jim Browne, Issues in End-of-Life ProductRecovery and Reverse Logistics, Production Planning & Control, Vol. 12, No. 5

(2001)pp. 534-547; Klassen, Robert D. and Curtis P. McLaughlin, The Impact ofEnvironmental Management on Firm Performance,Management Science, Vol. 42, No. 8(August 1996), pp. 1199-1214; Marien, Edward J., Reverse Logistics as CompetitiveStrategy, Supply Chain Management Review, Vol. 2, No. 1 (1998), pp. 43-52; Pohlen,Terrence L. and Martin T. Farris II, Reverse Logistics in Plastics Recycling,International Journal of Physical Distribution and Logistics Management, Vol. 22, No. 2(1992), pp. 35-47; and Wu, Haw-Jan and Steven C. Dunn, Environmentally ResponsibleLogistics Systems, International Journal of Physical Distribution & LogisticsManagement, Vol. 25, No. 2 (1995), pp. 20-39.

[8] Barsky, Noah P. and Alexander E. Ellinger, Unleashing the Value in theSupply Chain, Strategic Finance, Vol. 82, No. 7 (2001), pp. 32-37.

[9] Giuntini, Ron and Tom Andel, Master the Six Rs of Reverse Logistics,Integrated Warehouse and Distribution, No. 36, No. 3 (1995), pp. 93-98; Reda, Susan,Getting a Handle on Returns, Stores, Vol. 80, No. 12 (1998), pp. 22-26; and, Smith,Craig N., Robert J. Thomas, and John A. Quelch, A Strategic Approach to ManagingProducts Recalls,Harvard Business Review, Vol. 74, No. 5 (1996), pp. 102-112.

25


26/31

[10] Daugherty, Patricia J., Chad W. Autry, and Alexander E. Ellinger, ReverseLogistics: The Relationship between Resource Commitment and Program Performance,Journal of Business Logistics, Vol. 22, No. 1 (2001), pp. 107-123.

[11] Potter, Skip, Vice President of Membership, Automotive AftermarketIndustry Association (AAIA), Personal Interview.

[12] Rogers, Dale S. and Ronald S. Tibben-Lembke, Going Backwards: ReverseLogistics Trends and Practices, Pittsburgh, PA: RLEC Press, 1999; Stock, James R.,Development and Implementation of Reverse Logistics Programs, Oak Brook, IL:Council of Logistics Management, 1998; Stock, James, Thomas Speh, and Herbert Shear,Many Happy (Product) Returns,Harvard Business Review, Vol. 80, No. 7 (2002), pp.16-17; Thomas, James, Shift Your Supply Chain into Reverse, Supply Chain e-Business, Dec. (2001), pp. 30-31.

[13] Doran, Anthony, Solving the Product Takeback Problem, UnpublishedResearch Paper, (2003); Stock, James, Thomas Speh, and Herbert Shear, Many Happy

(Product) Returns,Harvard Business Review, Vol. 80, No. 7 (2002), pp. 16-17.

[14] Berry, Leonard L. and A. Parasuraman,Marketing Services, New York: TheFree Press, 1991.

[15] Morgan, Robert M. and Shelby D. Hunt, The Commitment-Trust Theory ofRelationship Marketing,Journal of Marketing, Vol. 58, No. 3 (1994), pp. 20-38.

[16] Moorman, Christine, Rohit Deshpande, and Gerald Zaltman, FactorsAffecting Trust in Market Research Relationships, Journal of Marketing, Vol. 57, No. 1(1993), pp. 81-101; and, Morgan, Robert M. and Shelby D. Hunt, The Commitment-

Trust Theory of Relationship Marketing, Journal of Marketing, Vol. 58, No. 3 (1994),pp. 20-38.

[17] Rotter, Julian B., A New Scale for the Measurement of Inpersonal Trust,Journal of Personality, Vol. 35, No. 4 (1967), pp. 651-665.

[18] Barney, Jay B. and Mark H. Hansen, Trustworthiness as a Source ofCompetitive Advantage, Strategic Management Journal, Vol. 15 (1994), pp. 175-190;and, Sabel, Charles F., Studied Trust: Building New Forms of Cooperation in a VolatileEconomy,Human Relations, Vol. 46, No. 9 (1993), pp. 1133-1170.

[19] Doney, Patricia J. and Joseph P. Cannon, An Examination of the Nature ofTrust in Buyer-Seller Relationships, Journal of Marketing, Vol. 61, No. 2 (1997), pp.35-51.

[20] Anderson, Erin, Leonard M. Lodish, and Barton A. Weitz, ResourceAllocation Behavior in Conventional Channels,Journal of Marketing Research, Vol. 23,No. 1 (1987), pp. 254-262; Doney, Patricia J. and Joseph P. Cannon, An Examination ofthe Nature of Trust in Buyer-Seller Relationships, Journal of Marketing, Vol. 61, No. 2(1997), pp. 35-51; and, Morgan, Robert M. and Shelby D. Hunt, The Commitment-Trust

26


27/31

Theory of Relationship Marketing,Journal of Marketing, Vol. 58, No. 3 (1994), pp. 20-38.

[21] Rindfleisch, Aric, Organizational Trust and Interfirm Cooperation: AnExamination of Horizontal versus Vertical Alliances,Marketing Letters, Vol. 11, No. 1(2000), pp. 81-95.

[22] Atuahene-Gima, Kwaku and Haiyang Li, When Does Trust Matter?Antecedents and Contingent Effects of Supervisor Trust on Performance in Selling NewProducts in China and the United States, Journal of Marketing, Vol 66., No. 3 (2002),pp. 61-81.

[23] Smith, J. Brock and Donald W. Barclay, The Effects of OrganizationalDifferences in Trust on the Effectiveness of Selling Partnerships, Journal of Marketing,Vol. 61, No. 1 (1997), pp 3-21.

[24] Moorman, Christine, Gerald Zaltman, and Rohit Deshpande, Relationships

Between Providers and Users of Marketing Research: The Dynamics of Trust Within andBetween Organizations,Journal of Marketing Research, Vol. 29, No. 3 (1992), pp. 314-329.


[26] Day, George S., Advantageous Alliances, Journal of the Academy ofMarketing Science, Vol. 23, No. 4 (1995), pp. 297-300.

[27] Dwyer, Robert F., Paul H. Schurr, and Sejo Oh, Developing Buyer-Seller

Relationships,Journal of Marketing, Vol. 51, No. 2 (1987), pp. 11-27.

[28] Gundlach, Gregory T., Ravi S. Achrol, and John T. Mentzer, The Structureof Commitment in Exchange,Journal of Marketing, Vol. 59, No. 1 (1995), pp. 78-92.

[29] Anderson, Erin and Barton A. Weitz, The Use of Pledges to Build andSustain Commitment in Distribution Channels, Journal of Marketing Research, Vol. 29,No. 1 (1992), pp. 18-34.

[30] Gundlach, Gregory T., Ravi S. Achrol, and John T. Mentzer, The Structureof Commitment in Exchange,Journal of Marketing, Vol. 59, No. 1 (1995), pp. 78-92.

[31] Bartholemew, S., National Systems of Biotechnology Innovation: ComplexInterdependence in the Global System, Journal of International Business Studies, Vol.26, No. 3 (1997), pp. 367-403; and, Hamel, Gary, Yves L. Doz, and C. K. Prahalad,Collaborate With Your Competitors and Win,Harvard Business Review, Vol. 89, No. 1(1989), pp. 133-139.

[32] Doz, Yves L. and Gary Hamel, Alliance Advantage, Boston: HarvardBusiness School Press, 1998; and Hamel, Gary, Yves L. Doz, and C. K. Prahalad,

27


28/31

Collaborate With Your Competitors and Win,Harvard Business Review, Vol. 89, No. 1(1989), pp. 133-139.

[33] Kalwani, Manohar U. and Narakesari Narayandas, Long-TermManufacturer-Supplier Relationships: Do They Pay Off for Supplier Firms, Journal ofMarketing, Vol. 59, No. 1 (1995), pp. 1-16.


[35] Grayson, K. and Tim Ambler, The Dark Side of Long-Term Relationships inMarketing Services,Journal of Marketing Research, Vol 36, No. 1 (1999), pp. 132-141;and Moorman, Christine, Gerald Zaltman, and Rohit Deshpande, Relationships BetweenProviders and Users of Marketing Research: The Dynamics of Trust Within and BetweenOrganizations,Journal of Marketing Research, Vol. 29, No. 3 (1992), pp. 314-329.

[36] Armstrong, J. Scott and Terry S. Overton, "Estimating Non-Response Bias in

Mail Surveys," Journal of Marketing Research, Vol.14, No.3 (1977), pp. 396-402; andLambert, Douglas M. and Thomas C. Harrington, Measuring Non-Response Bias inCustomer Service Mail Surveys, Journal of Business Logistics, Vol. 11, No. 2 (1990),pp. 5-25.

[37] Nunnally, Jum C. and Ira H. Bernstein,Psychometric Theory. 3rd Ed., NewYork, NY: McGraw-Hill, Inc, 1994.

[38] Gaski, John F. and John R. Nevin, The Differential Effects of Exercised andUnexercised Power Sources in a Marketing Channel, Journal of Marketing Research,Vol. 22, No. 2 (1985), pp. 130-142.

[39] Toothaker, Larry E., Multiple Comparison Procedures. Newbury Park, CA:Sage Publications, Inc, 1993.

[40] Barron, Reuben M. and David A. Kenny, The Moderator-Mediator VariableDistinction in Social Psychological Research: Conceptual, Strategic, and StatisticalConsiderations,Journal of Personality and Social Psychology, Vol. 51, No. 6 (1986),pp. 1173-1182; Goodman, Lisa A. On the exact variance of products,Journal of theAmerican Statistical Association, Vol. 55, No. 1 (1960), pp. 708-713; Hoyle, Rich. H., &David. A. Kenny , Statistical power and tests of mediation, in R. H. Hoyle (Ed.),Statistical strategies for small sample research. Newbury Park: Sage, 1999; Judd,Charles. M., & David A. Kenny, Process analysis: Estimating mediation in treatmentevaluations,Evaluation Review, Vol. 5, No. 5 (1981), pp. 602-619; and, Kenny, David.A., Deborah. A. Kashy, and Nial Bolger, Data analysis in social psychology, in D.Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology Vol. 1, 4thed., Boston, MA: McGraw-Hill, 1998, pp. 233-265.

[41] Barron, Reuben M. and David A. Kenny, The Moderator-Mediator VariableDistinction in Social Psychological Research: Conceptual, Strategic, and Statistical

28


29/31

Considerations,Journal of Personality and Social Psychology, Vol. 51, No. 6 (1986),pp. 1173-1182.

[42] Garbarino, Ellen and Mark S. Johnson, The Different Roles of Satisfaction,Trust, and Commitment in Customer Relationships,Journal of Marketing, Vol 63, No. 2(1999), pp. 70-87; Kennedy, Mary Susan, Linda K. Ferrell, and Debbie Thorne LeClair,Consumers Trust of Salesperson and Manufacturer: An Empirical Study, Journal ofBusiness Research, Vol 51, No. 1 (2001), pp. 73-86; and, Sirdeshmukh, Deepak, JagdipSingh, and Barry Sabol, Consumer Trust, Value, and Loyalty in Relational Exchanges,Journal of Marketing, Vol. 66, No. 1 (2002), pp. 15-37.

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APPENDIXMEASUREMENT OF VARIABLES

Mean s.d.Statistically Significant

Differences (.05 level) (*)

Program Effectiveness:How would you rate the overall effectiveness of your currentReverse Logistics/Returns Handling program? (1 = Not at alleffective; 7 = extremely effective)

4.82 1.54 N/A

Trust: (Kumar, Scheer, and Steenkamp, 1995b) ( = .76)Please indicate the extent to which you agree or disagree with thefollowing statements, relative to your primary customer. (1 =Strongly Disagree; 4 = Neutral; 7 = Strongly Agree)Objective Credibility

a. We are confident that the customer tells the truth.(Customer Truthfulness)

b. Whenever the customer gives us advice, we know they aresharing their best judgment. (Customer Sharing of BestJudgment)

Benevolence

c. When we share our problems with the customer, we areconfident that they will be understanding. (Customer Empathy)

d. We can count on the customer to consider how theirdecisions and actions will affect us. (Customer Consideration)

4.75

4.75

4.11

3.60

1.41

1.17

1.43

1.54

a, b > c, d

Relationship Commitment: (Morgan and Hunt, 1994; Andersonand Weitz, 1989) ( = .73)Please indicate the extent to which you agree or disagree with thefollowing statements, relative to your primary customer. (1 =Strongly Disagree; 4 = Neutral; 7 = Strongly Agree) a. We expect to be supplying this customer with products forsome time.

b. The relationship my firm has with the customer deservesour maximum effort to maintain.

c. My firm intends to maintain the relationship we have withthis customer indefinitely.

d. The relationship my firm has with the customer issomething we are very committed to. e. We are willing to dedicate people and resources to thiscustomersreverse logistics program.

6.38

6.26

6.24

6.13

5.37

.72

.78

.75

.80

1.62

a, b, c, d, > e

Reverse Logistics Performance: (Autry, Daugherty, andRichey, 2001) ( = .83)Please indicate how effective your company has been in achievingthe following objectives related to Reverse Logistics and handlingof returned merchandise.(1 = Not at all effective; 4 = Somewhat effective; 7 = Extremely

effective) a. Environmental regulatory complianceb. Improved customer relationsc. Recovery of assets (products)d. Cost containmente. Improved profitabilityf. Improved labor productivityg. Reduced inventory investment

5.755.484.664.544.244.204.18

1.081.101.441.471.471.311.47

a, b > c, d, e, f, g

(*) For example, in the scale for reverse logistics capabilities, the mean response for ease ofobtaining return authorization was significantly higher than the responses for all other items; the

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mean responses for quality of rework, length of time for credit processing and handlingreconciliation of charge-backs were statistically indistinguishable, but were higher than theresponses for timeliness and use of internet; finally, the mean response for timeliness was higherthan the mean response for use of internet

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