REVERSE LOGISTICS: THE IMPACT OF TIMING AND RESOURCES

REVERSE LOGISTICS:THE IMPACT OF TIMING AND RESOURCES

by

R. Glenn RicheyThe University of Alabama

Patricia J. Daugherty

Stefan E. GenchevThe University of Oklahoma

and

Chad W. AutryBradley University

Customer responsiveness. Speed to market. Automatic replenishment. These are buzzwords com-monly applied to today’s supply chain logistics strategies. Primary emphasis is on quick, accurate,efficient fulfillment of demand. However, there’s another consideration that needs to be factored intothe strategic equation – reverse logistics. Traditional delivery-oriented logistics systems often don’tprovide comparable quality on returns. There’s good reason for this: reverse logistics flow is verydifferent from forward flow of product. As Tibben-Lembke and Rogers (2002) note:

…a reverse logistics flow is much more reactive, with much less visibility. Firms generally do not initiate reverse logistics activity as a result of planning and decisionmaking on the part of the firm, but in response to actions by consumers or downstreamchannel members. (p. 272)

While some companies have developed effective means of coping with the reactive nature ofreverse logistics, the logistics systems at many other companies are ill equipped to handle reverseproduct flow (Knemeyer, Ponzurick, and Logar 2002; Stock and Lambert 2001). In fact, “…manycompanies are just beginning to understand the importance of reverse logistics” (Tibben-Lembke2002, p. 223). The fact that many firms have been slow to develop reverse logistics capabilities raisessome interesting issues. Researchers in marketing and management have long debated the advan-

JOURNAL OF BUSINESS LOGISTICS, Vol. 25, No. 2, 2004 229

Note: This manuscript was handled by the Systems Editor and was subject to the standard blind-review process. The authors would like to thank the Systems Editor and the reviewers for theirconstructive comments and suggestions.

tages of industry pioneers or first movers compared to subsequent entrants into the industry (Golderand Tellis 1993; Lambkin 1988; Lieberman and Montgomery 1998). What about the logistics area?Are firms that develop reverse logistics programs relatively late compared to their competitors ata disadvantage? How does the relative timing (early, etc.) of a formal reverse logistics program influ-ence the program’s performance? In such instances, the focus would be on the development of a formal program as contrasted to a non-standard case-by-case approach to handling returns.

Previous research is somewhat mixed regarding the impact of timing of new programs (Lieberman and Montgomery 1998). However, some researchers have found that timing and program performance are influenced by resource allocations. Deployment of differential resourcesresults in differences in performance for new programs (of any type) (Cho, Kim, and Rhee 1998).This is based on the premise that resources can be used to develop capabilities. If this is indeed true,firms should be able to leverage reverse logistics resource commitment to yield positive resultsregardless of the timing (early/late) of reverse program involvement (relative to competitors). Evenlate entrants may be able to catch up or surpass industry participants provided they allocate suffi-cient resources (Cho, Kim, and Rhee 1998; D’Aveni 1994). Results of a survey of reverse logisticspractices in the automobile aftermarket industry helped to gain a better understanding of these issues.

In the following sections, a brief overview of relevant literature is presented and formalhypotheses are developed. Then the research methodology is discussed followed by the results andmanagerial implications.

REVERSE LOGISTICS

Reverse logistics, the process of moving goods from their typical final destination for the pur-pose of capturing value or for proper disposal (Rogers and Tibben-Lembke 1998), should be gain-ing more attention within business operations because of the volumes of returns involved and thepotential for reclaiming value that would otherwise be lost or significantly diminished. However,returns management may be the most neglected part of supply chain management (Norek 2002). AsStock, Speh, and Shear (2002) note, reverse logistics “shouldn’t be viewed as a costly side-show tonormal operations” (p. 16). Instead they point out that reverse logistics should be seen as an oppor-tunity to build competitive advantage, cut costs, and improve customer satisfaction. With a good returnshandling system, reverse logistics can even evolve into a profit center (Andel 1997).

Practically all businesses must deal with returns. This may be the result of damaged or defec-tive shipments, incorrect shipments, overstocks, and what are commonly referred to as “marketing-related returns.” Marketing-related returns involve products customers have returned because theychanged their minds, didn’t like the product, etc. There is yet another kind of return that some companies must routinely handle. For some companies, the reverse supply chain is an integral partof their business; they must regularly bring products back for refurbishing or remanufacturing. Forexample, Bosch sells power hand tools that have been remanufactured (Guide and Van Wassenhove2002). By doing so, they reclaim value that would otherwise have been lost.

230 RICHEY, DAUGHERTY, GENCHEV, AND AUTRY

Remanufacturing operations are common in certain industries; they are especially critical inthe automobile industry for handling of returned parts. Thus, the focus of the current research is onthe automobile aftermarket industry. Automobile aftermarket industry participants routinely bringback core products such as starters to remanufacture them and sell the newly reworked products. Theindustry must also deal with the other types of returns previously mentioned such as incorrect ship-ments and overstocks.

RESEARCH HYPOTHESES

The following research hypotheses were developed in order to test proposed relationshipsinvolving the timing of reverse logistics program implementation and performance. The issue ofresource commitment (to reverse logistics programs) was also examined.

Timing of Reverse Logistics Program Introduction

Conventional business wisdom has been that early entrants into a market for a specific prod-uct or category of products enjoy an enduring competitive advantage over later entrants (Lambkin1988). Those early entrants – sometimes referred to as pioneers – have been shown to have a long-lived market share advantage (Parry and Bass 1990; Robinson and Fornell 1985). Industrial orga-nization economics concepts of barriers to entry and consumer information acquisition have beenused to explain the performance advantage (Bain 1956; Stigler 1961).

Some researchers have come to believe that the assumed advantage of early entry is not nearlyas automatic as has been portrayed. The advantage may be negated by technological and economicuncertainties inherent in new markets and also because of firms’ differential capabilities to exploitopportunities (Aaker and Day 1986; Lambkin 1988; Wensley 1982). “Fast followers” may be ableto successfully take on first movers (Levitt 2001). The fast followers copy (and perhaps improve upon)what has been done by others. Those with early involvement can be at a disadvantage if laterentrants free ride on their investments or leverage a change in technology or consumer needs (Gurumurthy, Robinson, and Urban 1995; Lieberman and Montgomery 1998).

Apparently, in some instances, later entrants (those not acting first or early) have been able toleapfrog over the early competitors. The impact of order of entry may also be industry specific. Theextent and nature of early (or late) advantages need to be more fully understood (Golder and Tellis1993). The current research attempts to provide more insight into the topic by examining a specificindustry, i.e., the automobile aftermarket industry. Other industries would need to be examined todetermine the extent of generalizability of the hypothesized relationships.

As Lieberman and Montgomery noted after more than a decade of examining first-mover/timing issues in their own research, many “fundamental conceptual problems…remain unresolved(p. 1111).” Additional research is needed to clarify the basic issues involved. Therefore, the first hypoth-esis was developed for testing within a reverse logistics program context.

H1: Timing of reverse logistics program introduction is positively related to program respon-siveness, quality, and economic performance.


Resource Commitment to Reverse Logistics Programs

The Resource-Based View (RBV) of the firm suggests that much of the differences in perfor-mance between or among firms result from resource heterogeneity (Christmann 2000). Simplystated, greater (or different) resource bases result in different/better performance. However, how afirm leverages the resources is critical (Cho, Kim, and Rhee 1998).

Later entrants may be able to overcome earlier entrants or latecomer disadvantages. This maybe because early entrants pioneer the market and their resources have been developed to meet earlymarket requirements which have changed over time (Cho, Kim, and Rhee 1998). Other explanationsinclude the fact that later entrants may have differentiated experience which is advantageous or mayhave “deeper pockets” than competitors. Later entrants may prevail based upon superior resources(D’Aveni 1994). Han, Kim, and Kim (2001) noted a trend in the 1990s of “an ever increasing num-ber of late entrants leapfrogging into market-leader positions (p. 1).” Frequently the late moveradvantage is attributed to innovative practices (Shankar, Carpenter, and Krishnamurthi 1998).

Consistent with the RBV perspective, the current research recognizes the importance ofresource commitment to developing reverse logistics-related capabilities. Capabilities are “complexbundles of skills and collective learning…that ensure superior coordination of functional activities”(Day 1994, p. 2). The resource commitment construct was included as a moderator of the relation-ship between reverse logistics program timing and performance outcomes (Figure 1) through itsdeployment in reverse logistics capability building. In other words, the relationship between program timing and performance outcomes may differ depending upon resources committed toreverse logistics programs. Whether involvement in reverse logistics programs is first, early, orlate, resources may be able to positively influence performance.

The following hypotheses address the issue.

H2a: Resource commitment positively moderates a relationship between timing of reverselogistics program introduction and reverse logistics program responsiveness.

H2b: Resource commitment positively moderates a relationship between timing of reverselogistics program introduction and reverse logistics program quality.

H2c: Resource commitment positively moderates a relationship between timing of reverselogistics program introduction and reverse logistics program economic performance.

Literature in supply chain management and logistics suggests that service oriented outcomes,such as responsiveness and quality, may contribute to a firm’s overall economic performance(Mentzer, Flint, and Hult 2001; Wisner 2003). More specifically, reverse logistics responsivenessand reverse logistics quality have been discussed as having the potential to directly impact a firm’sbottom line (Rogers and Tibben-Lembke 2001). More responsive firms may develop the ability tofix problems before they happen. Firms that focus on improved quality may reduce the amount ofrework as well as the number of forward and reverse movements that a product(s) must make.These are only a few of the examples that logistics managers have encountered relating reverse logis-tics efficiencies to better performance (Tan, Yu, and Arun 2003). Thus, the following hypothesis isproposed.


H3a: Reverse Logistics responsiveness will have a positive effect on firm economic performance.

H3b: Reverse Logistics quality will have a positive effect on firm economic performance.

FIGURE 1

CONCEPTUAL MODEL

SAMPLE AND DATA COLLECTION

The following section describes the methodology of the study including the pilot interviewsused to develop the survey instrument. Basic psychometric concerns regarding scale reliability andoverall validity are also addressed.

The Survey Instrument

In-depth telephone interviews were conducted with executives from six companies activelyinvolved with reverse logistics. These interviews followed commonly employed exploratory tech-niques. The main emphasis was placed on reasons for returns, the firm’s resources dedicated toreverse logistics, and the specifics of the reverse logistics process as well as an assessment of theoverall effectiveness of their reverse logistics efforts. Parallel with an extensive literature review,the interview information provided the basis for developing a draft survey questionnaire. Feed-back received during a subsequent review of the questionnaire was incorporated into the final ver-sion of the survey. Eight people provided input during the review – two academics familiar with reverse

First

Early

Late

Responsiveness

Quality

Economic

Resource Commitment Performance Outcomes

Reverse Logistics Program Timing


logistics, two consultants, two business executives, and two representatives of the AutomobileAftermarket Industry Association.

Data Collection

Data were collected from the Automotive Aftermarket Industry Association (AAIA) membercompanies. The AAIA is a large trade association representing companies involved in all aspects ofthe automotive aftermarket industry. AAIA provided a list of 900 member companies; a random sam-ple of 400 companies was selected for this study. A total of 118 companies responded to the survey.Demographic information about the respondent companies is provided in Table 1.

TABLE 1

DEMOGRAPHIC DATA1

Minimum Maximum Mean Standard Deviation

Number full-time employees 11 25,000 525 2,358

Number full-time employees 0 300 10 38assigned to reverse logistics

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

1Firm size did not result in significant differences when tested versus the constructs in this study.

The surveys were mailed to the senior marketing or logistics person in the company under theassumption that this person would have knowledge of the company’s reverse logistics program. How-ever, if the recipient did not feel qualified to provide the necessary information, he or she wasasked to forward the survey to the most appropriate person.

The mail packet contained: 1) the survey questionnaire; 2) an AAIA-endorsed cover letterexplaining the study; and 3) a nominal monetary incentive. During the initial mailing, the survey packetwas sent to 150 companies from the identified sample of 400 AAIA members. A $1 incentive wasincluded. Subsequently, packets were mailed to the remaining 250 companies. The only differencebetween the two mailings was that a $2 incentive was provided for the second group (250). Theincreased incentive in the second mailing was used in an attempt to increase the response rate. Several weeks after the initial “incentive based” mailings, a follow-up mailing was sent to the non-respondents from each wave. Follow-up phone calls were made after each mailing. An overall31.72% response rate was achieved (Table 2).


TABLE 2

BREAKDOWN OF RESPONSES

MailingSurveys Surveys Returned Response Running TotalMailed Received Undeliverable Rate Response Rate

First mailing 150 18 10 12.86% 12.86%$1 Incentive

Follow-up/Reminder 122 21 17.21% 27.86%for Mailing One

Second mailing 250 54 18 23.28% 26.27%$2 Incentive

Follow-up/Reminder 232 25 10.78% 31.72%for Mailing Two

Totals 400 118 28 31.72%

ANOVA was used to perform wave analysis in order to check for non-response bias (Armstrongand Overton 1977). Each of the four mailings (two primary mailing waves with a follow-up mailing for each) was analyzed covering the relevant variables. No significant differences were found(� = .05). Therefore, non-response bias was not considered to be an issue.

Scale Items and Constructs

This study primarily utilized existing scales. Scales that were previously used in non-logisticscontexts were adapted, as necessary, to a reverse logistics setting. Descriptive statistics, exploratoryfactor analysis, and alpha coefficients for each scale item and construct are described in detail in Table 3.

Exploratory Factor Analysis was used to assess and validate the basic operational constructs(Anderson and Gerbing 1991; Gerbing and Anderson 1988). Maximum Likelihood estimation andVarimax rotation were used to define overall measurement quality – suggested minimum coefficientalpha of .70 (Netemeyer, Burton, and Lichtenstein 1995; Nunnally and Bernstein 1994) – with theobserved coefficient alphas ranging from .7808 to .8898. Analysis of the items comprising theexpected multi-item constructs produced single dimension loadings for each construct, all witheigenvalues greater than one. Each of the constructs loaded as expected, i.e., was confirmed. Noneof the scale items had a cross loading exceeding .200. The actual scale items, standardized factorestimates, t-values, and Cronbach’s Alpha for each construct are shown in Table 3. In addition, a testof discriminant validity is reported in Table 4. Here the shared variance between multi-item scalesis compared to the reliability for each scale (Gaski and Nevin 1985). In all cases, the variances forthe unidimensional constructs were greater than the shared variances of the pairs.


236 RICHEY, DAUGHERTY, GENCHEV, AND AUTRYTA

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JOURNAL OF BUSINESS LOGISTICS, Vol. 25, No. 2, 2004 237TA

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TABLE 4

TESTS FOR CONSTRUCT DISCRIMINANT VALIDITY

Resource EconomicCommitment Responsiveness Quality Performance

Reliability .870 .816 .781 .890

Construct Correlation Coefficients

Resource 1.000Commitment

Responsiveness .170 1.000

Quality .251* .374** 1.000

Economic .122 .374** .377** 1.000Performance

* p < .05** p < .01

Program timing

Three scale items were used to assess relative entry (early/late) into a formal reverse logisticsprogram. Using a 7-point scale (1 = strongly disagree and 7 = strongly agree), respondents were askedto indicate level of agreement with the statements that: 1) they developed programs before their com-petition; 2) they were among the first in their industry to address reverse logistics; and 3) they wereamong the last to develop formal reverse programs. Means were 3.68, 3.55, and 3.04 respectivelyfor the three items.

To explore the issue of program entry timing further, it was necessary to divide the total respon-dent group into three categories based upon the responses to the three items. Three groups were formedusing a hierarchical cluster analysis – first (41 respondents), early (37 respondents), and late (40 respon-dents). This analysis was run both with three groups pre-defined and grouping set to free as well ashierarchical and non-hierarchical approaches to examine predictive validity and enhance verifica-tion. In all instances, the results were three groups and cluster sizes and results were similar. Bothseed points and means were found to be significantly different. Based on this result, the free group-hierarchical results were considered to be less artificial and thus retained. In examining a dendogram,it was further determined that no problematic outliers existed in the study. Final group assignmentwas developed using squared Euclidean distance and Wards method (Hair et al. 1998). To verify thecluster analysis, a random split was performed on the data set that also resulted in three significantlydifferent groups. Results of the cluster analysis are presented in Table 5.


TABLE 5

FINAL RESULTS OF CLUSTER ANALYSIS

Final Cluster Item Center

Survey Items First Early LateStrongly Disagree = 1, Strongly Agree = 7 Cluster Cluster Cluster

We were one of the first firms in our industry to address 7 5 1reverse logistics issues.

We had developed returns handling programs before our 7 5 1competition did.

Relative to other firms in our industry, we were one of the 7 7 1first firms to come up with formal returns handling programs.(Reverse Coding Removed)

Membership (n) 41 37 40

Resource commitment

To better understand how resource commitment affects the reverse logistics process, respondents were asked to assess levels of resource commitment in three areas: 1) Technological,2) Managerial, and 3) Financial. Managers report relatively low levels of resource commitment toreverse logistics. This is consistent with the low awareness of the importance of reverse logistics pro-grams in many businesses today. Respondents either do not view providing additional financialresources to reverse logistics programs as a high priority or they have not been successful in secur-ing adequate funding. This fact is reflected in the lowest mean score of 3.06 (7-point scale: 1= little and 7= substantial) for financial resource commitment. Apparently, securing managerialresource commitment has been just as difficult (3.08). However, the respondents report slightlyhigher resource commitment (3.31) in the area of technology. Reverse logistics is informationintensive. Thus, it is not surprising that the most effort/support was found in the area of technolog-ical resource commitment.

Performance-related items

The next two constructs in Table 3 measure reverse logistics program performance. The scaleitems for Responsiveness and Quality were developed specifically for this project, rating performanceon a 7-point scale where 1 = not at all capable and 7 = extremely capable.

Responsiveness:

In terms of responsiveness related to reverse logistics programs, managers feel that their firmsare doing a pretty good job in streamlining the process of return-handling services provided to


customers. The mean scores of 6.10 for ease of obtaining return authorization, 5.31 for length oftime for credit processing, and 5.14 for handling reconciliation for charge-backs show that, in fact,companies have a workable reverse logistics system in place.

Quality:

Additionally, the managers report a relatively high level of performance in the actual reverselogistics handling process, rating quality of rework or repair at 5.42 on the 7-point scale. This resultindicates that firms not only have workable reverse logistics systems in place, but that these systemsprovide high-quality rework or repair performance activities. In contrast, managers reported aslightly lower performance score on the timeliness of rework or repair (4.64). Although companieshave the systems in place, they are still somewhat slow in reverse logistics handling. They need toput more emphasis on speeding up returns handling.

Economic performance

The final construct of interest involved survey items pertaining to economic performance.Respondents were asked how effective their companies have been in achieving reverse logistics objec-tives (7-point scale with 1= not at all effective and 7 = extremely effective). Among the mean scoreson recovery of assets (4.65), cost containment (4.53), profitability (4.22), labor productivity (4.19),and reduced inventory (4.18), no significant statistical differences were recorded (alpha = .05). Ingeneral, reverse logistics program performance was rated as more than somewhat effective onachieving economic-based performance level. These results can provide support for the develop-ment of formal reverse logistics programs. The programs have the potential for enhancing service(as reflected in the Responsiveness and Quality areas). Based upon the economic analysis, they canalso make a positive impact – perhaps even to the point they can be considered profit centers.

ANALYSIS AND RESULTS

The hypotheses were tested using multiple regressions in a two-step design. First, Hypothesis1 concerning the influence of the timing of reverse logistics program introduction on programresponsiveness, quality, and economic performance was tested using multiple regression. Step twotested the relationships between expected moderating interaction effects (Hypothesis 2a, b, c) ofresource commitment and timing on program responsiveness, service, and economic performanceusing moderated multiple regression as recommended by Stone and Hollenbeck (1989) and updatedby Morgan and Piercy (1998).

Table 6 presents results of step one testing Hypothesis 1. The column on the left side of the tableshows the three timing decisions (first, early, and late entrants) while the corresponding rightcolumns display effect size (standard ß) and significance (t-value) of each relationship. As shownin row one, being the First in the industry to implement a formal reverse logistics program has nosignificant impact on firm Responsiveness (ß = -.142), Quality (ß = -.335), or Economic outcomes


(ß = .257). The results in row two indicate that Early implementers of reverse logistics programs hasa significant positive impact on all three of the before mentioned constructs: Responsiveness (ß =.449; p < .05), Quality (ß = .520, p < .05), and Economic (ß = .389; p < .05) providing support forH1. Finally, row three shows mixed results. As expected, Late implementers of reverse logistics pro-grams will experience a negative impact on firm Responsiveness (ß = -.262; p < .05) and Economic(ß = -.231; p < .05) outcomes, while there is no significant impact on Quality (ß = .095). Figure 2shows the significant effects found in the multiple regression and the effect sizes of each effect.

TABLE 6

EXAMINATION OF EFFECTS

Responsiveness Quality Economic

Timing of Standard Standard StandardReverseLogistics Beta Beta BetaProgram Timing ß R2 t-value ß R2 t-value ß R2 t-value

1. First -.142 .040 -1.422 -.335 .015 -1.539 -.257 .038 .262

2. Early .449** .233 2.612 .520** .246 2.391 .389** .202 1.735

3. Late -.262** .132 -1.992 .095 .010 .825 -.231** .112 -1.996

* p < .10** p < .05

*** p < .01

FIGURE 2

EXAMINATION OF SIGNIFICANT EFFECTS

First

Early

Late

Responsiveness

Quality

Economic

- .262

- .231

+ .449

+ .520

+ .389


The analysis discussed in the previous section tests all possible relationships in the conceptualmodel (Y = b0 + b1X). Using the methodology of Stone and Hollenbeck (1989), each equation isretested integrating moderator variables (Y = b0 + b1X+ b2Z), and then adding the cross product (inter-action) of the independent and moderating variables (Y = b0 + b1X+ b2Z+ bXZ). Thus, changes inR2 and significant interaction correlations were assessed to determine significant increase in the inter-action indicating influence of the moderating variable.

Table 7 provides the results of the moderated regression when Resource Commitment is enteredinto the model. It is important to note that moderation is not examined for Early implementers inthe areas of Responsiveness, Quality, and Economic outcomes or late implementers in the areas ofResponsiveness and Economic outcomes due to the previously reported significant (direct) effectsbetween timing and outcomes (see Baron and Kenny 1986). Model 1 provides support for Hypoth-esis 2a and 2c for firms that are first to develop reverse logistics programs. With Responsiveness,Quality, and Economic Outcomes coded as the dependent variables, Resource Commitment is foundto have an important moderating interaction with both Responsiveness (∆R2 = .013; p < .05) and Economic Outcomes (∆R2 = .071; p < .10) when firms are the first to initiate reverse logistics programs. Quality is not significantly impacted for Early developers (∆R2 = .045). However, support is found for Hypothesis 2b for Late developers. Resource Commitment is found to have animportant moderating interaction with Quality (∆R2 = .020; p < .05) when firms wait until Late toimplement their reverse logistics programs. Figure 3 shows the significant moderating effects ofResource Commitment found in the moderated regression and the effect sizes of each effect.

TABLE 7

EXAMINATION OF MODERATING EFFECTS

ReverseLogistics Moderated RegressionProgram (Variable EnteredTiming in Model) Responsiveness Quality Economic

ß R2 ∆R2 ß R2 ∆R2 ß R2 ∆R2

First Model One -.142 .040 .587 -.335 .015 .235 -.257 .038 .909First in our industry

First in our industry, .109 .078 .012Resource Commitment .217 .098 .175 .162 .040 .133 .005 .039 .968

First in our industry, .182 .088 .044Resource Commitment, -.077 .023 -.468First in our industry * .427** .232 .013** .150 .043 .045 .513** .120 .071**Resource Commitment


TABLE 7 (CONT.)

EXAMINATION OF MODERATING EFFECTS

ReverseLogistics Moderated RegressionProgram (Variable EnteredTiming in Model) Responsiveness Quality Economic

ß R2 ∆R2 ß R2 ∆R2 ß R2 ∆R2

Late Model Two .095 .010 .587Late in our industry

Late in our industry, .133Resource Commitment .269 .045 .128

Late in our industry, .188Resource Commitment, -.272Late in our industry * .618* .105 .020*Resource Commitment

p < .10; ** p < .05; *** p < .01

Finally, to examine Hypothesis 3a and 3b, firm reverse logistics Responsiveness and firmreverse logistics Quality were simultaneously regressed on to firm Economic Performance. Asexpected, firm Responsiveness (ß = .306; p < .05) and Quality (ß = .266; p < .05) have significantpositive relationships to Economic Performance. Table 8 shows the results of this test and Figure32 shows the significant effects found in the multiple regression and the effect sizes of each effect.

TABLE 8

EXAMINATION OF RELATIONSHIP BETWEEN OUTCOME VARIABLES

Economic

StandardBetaß t-value

Responsiveness .306** 2.665

Quality .266** 2.315

* p < .10; ** p < .05; *** p < .01


2Figure 3 should not be read as a path model or structural equation model. It is a simple summary of theregression analysis devised to assist the reader.

FIGURE 3

EXAMINATION OF SIGNIFICANT MODERATING EFFECT OFRESOURCE COMMITMENT

DISCUSSION AND MANAGERIAL IMPLICATIONS

This research explored the issue of timing of introduction of a formal reverse logistics program.Previous research results regarding new programs or product introductions are mixed. In someinstances, being the first is believed to give a firm a competitive edge; beating the competition canplace a firm in an advantageous situation. Alternately, some researchers say it’s better to enter lateand “catch up” fast perhaps by exploiting or copying what others have done. The current researchdidn’t support either of these perspectives. Table 9 presents a summary detailing results relating toprogram timing and the associated performance/managerial implications.

TABLE 9

SUMMARY OF FINDINGS: THE INFLUENCE OF TIMING

Program Timing Performance Implications

First Entrant No positive impact. Pioneers face many challenges.

Early Entrant Positive impact. It’s easier to get up to speed by copying others and/or learning from their mistakes.

Late Entrant Negative impact. It’s hard to catch up. The earlier entrants have had extensive time to perfect their programs.

Resource Commitment

+ .427

+ .513

+ .618

Quality

Economic

Responsiveness First

Late

+ .306

+ .266


Table 10 details implications related to resource commitment. Maybe timing isn’t the issue. Ifenough resources are committed to a project, it certainly should influence performance. For reverselogistics among the automobile aftermarket industry participants examined, differences emerged whentiming and resources were examined.

TABLE 10

SUMMARY OF FINDINGS: THE INFLUENCE OF RESOURCE COMMITMENT

Program Timing If Resources are Committed:

First Entrant Resources can help overcome early challenges and immediately impact performance positively (Responsiveness and Economic Performance). Why? Resources can betargeted to achieve improvements. For example, information systems can be tailored to specific customers or processing can be automated to save time and money.

Late Entrant Resources can be used to achieve better quality. Later entrants can learn from others and immediately adopt approaches/technology in use.

The current research identifies a strategic direction that is within the control of individualfirms. Timing is a given, i.e., either other firms have a formal reverse logistics program or they don’t.Thus, an individual firm’s “order of entry” is already determined to a degree. In contrast, a firm canact independently to decide to allocate resources to build capabilities. Such capabilities can posi-tively influence reverse logistics performance.

What does this mean for managers dealing with returns? The primary lessons to be learned fromthe current research are:

1) If you haven’t already started a formal reverse logistics program, now is the time to start.

2) If you are involved in reverse logistics, be sure you make enough of a resource commitmentto be able to gain the potential benefits.

3) And – although this goes beyond the scope of the research – if you aren’t willing or able tocommit sufficient resources to reverse logistics, perhaps it’s time to consider outsourcing.

LIMITATIONS AND FUTURE RESEARCH

Most empirical research studies investigate a segment of a market or industry and thus createlimitations. The current research focuses on the automobile aftermarket industry where returns aredramatically important. Despite the fact that researchers have found reverse logistics to be animportant topic in other industries and contexts (Autry, Daugherty, and Richey 2001) continuedresearch is required for a better understanding of the industry specific and global importance of reverselogistics. Thus, the narrow scope of the current research is a limitation, but allows for a jumping offpoint for other studies leading to a greater understanding of the norms of reverse logistics management.


Methodologically, the study is limited by the use of single respondent methodology and the factthat the analysis is based quite heavily on perceptual data. This was unavoidable due to the complexitiesof collecting data and the confidentiality assurance given to the respondents. Future research shouldseek out performance data that are not subject to the limitations of the current data set. Specifically,this study uses perceptual performance measures to gauge the influence of the timing on outcomes.Hard financial performance measures are certainly important and may have exhibited differentpatterns of performance. Future research should include these measures. Additionally, a basic analy-sis of the relationship between the outcome variables uncovers an instance of possible partial medi-ation that, in a different study, may be better estimated using structural equation modeling. Finally,the survey is a static design and thus examines businesses at a point in time rather than over a firm’sevolution. Future research could examine the evolution of reverse logistics strategy in longitudinalform. Overall, researchers should not limit their studies to cross sectional analysis. The future studyof reverse logistics will require longitudinal temporal analysis and comparison across multipleindustries. Additionally, researchers should consider testing the partial mediation of the outcome vari-ables in a more dynamic model using structural equation modeling.

Other avenues for expansion of reverse logistics research certainly exist. Specifically, researchersmay consider examining the role of process in reverse logistics by looking into the impact of formalization and manufacturer/retailer policy on efficiency and effectiveness. Relationship and collaborative studies should look at key account management issues, flexibility/adaptability, and theuse of technology to manage reverse logistics. Finally, more sophisticated models should be exam-ined to tie in additional sources of variance/causality.

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ABOUT THE AUTHORS

R. Glenn Richey (Ph.D. – The University of Oklahoma) is an Assistant Professor of Market-ing and Supply Chain Management in the Culverhouse College of Commerce and Business Administration at The University of Alabama. He has published in academic journals including American Business Review, International Journal of Logistics Management, International Journalof Physical Distribution & Logistics Management, Journal of Business Logistics, Journal of International Management, and Journal of Marketing Channels.

Patricia J. Daugherty (Ph.D. – Michigan State University) is Division Director and SiegfriedChair in Marketing and Supply Chain Management at The University of Oklahoma. Dr. Daughertyhas published in a number of academic journals including International Journal of Logistics Management, International Journal of Physical Distribution & Logistics Management, Journalof Business Logistics, and Journal of Retailing, and has co-authored two books.

Stefan E. Genchev (MBA – The University of Oklahoma) is a Ph.D. student in Marketing andSupply Chain Management at The University of Oklahoma. His current research interests are logis-tics operations and reverse logistics. Before joining the Ph.D. program, he worked for DHLInternational for four years in Bulgaria.

Chad W. Autry (Ph.D. – The University of Oklahoma) is an Assistant Professor of Marketing at Bradley University. His current research interests are reverse logistics, returned prod-uct disposition, and warehouse management systems. Dr. Autry’s previous publications haveappeared in the Journal of Business Logistics, the Journal of Retailing, and other leading journalsin the areas of logistics and supply chain management.


REVERSE LOGISTICS: THE IMPACT OF TIMING AND RESOURCES

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