Reverse Logistics

Reverse logistics capabilities:antecedents and cost savings

Eric P. Jack, Thomas L. Powers and Lauren SkinnerUniversity of Alabama Birmingham, Birmingham, Alabama, USA

Abstract

Purpose – The use of reverse logistics has received increased attention in the literature, although therole that reverse logistics capabilities plays in enabling firms to achieve cost savings has not beenempirically examined. Reverse logistics capabilities can enable retailers to enhance their returnpolicies and improve their overall cost position. This paper aims to address these issues.

Design/methodology/approach – Based on a survey of 295 retailers, this paper evaluates theinfluence of customer and retailer related antecedents of reverse logistics capabilities and theirsubsequent impact on cost savings.

Findings – The results indicate that resource commitments and contractual obligations positivelyinfluence reverse logistics capabilities and that these capabilities result in cost savings. Customeropportunism is found to be negatively related to reverse logistics capabilities. It is also reported thatreverse logistics capabilities partially mediates the relationship between resource commitments,contractual arrangements, and reverse logistics cost savings.

Originality/value – This work builds on the recent research in reverse logistics; however, unlikeother contributions in this research stream, the role of retailers who perform a critical role in this areais addressed.

Keywords Reverse scheduling, Contracts, Cost reduction, Retailers

Paper type Research paper

IntroductionReverse logistics has received increased attention in the marketing and supply chainliterature as it reflects the ability of a firm within a channel of distribution to positivelyinfluence the relationship that it has with its customers (Horvath et al., 2005). Reverselogistics also has major cost implications for both the firm and its supplier (Daughertyet al., 2005). For many retail organizations without adequate capabilities to implementa reverse logistics strategies, returns may improve customer service but are a financialburden with the costs exceeding the benefits. The use of reverse logistics isincreasingly becoming a competitive necessity in an overall supply chain strategy(Daugherty et al., 2001). Despite the importance of reverse logistics to both researchersand practitioners, there are relatively few studies that have used empirical data toinvestigate this key product flow that exists in many supply chains (Srivastava andSrivastava, 2006).

Within the reserve logistics domain, the product returns process has emerged as akey element that can influence the customers’ purchase decisions and thus, an effectiveproduct returns process is viewed as a competitive advantage (Stock et al., 2006).Today’s cash strapped customer is extremely risk adverse and in response to thisreality, some retailers view relatively liberal returns policies as critical to retainingconsumers. Furthermore, with the increase of online purchases, many customers areconcerned with how an online purchase will translate into a store return, if necessary.

The current issue and full text archive of this journal is available at

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Received June 2009Revised October 2009Accepted December 2009

International Journal of PhysicalDistribution & Logistics ManagementVol. 40 No. 3, 2010pp. 228-246q Emerald Group Publishing Limited0960-0035DOI 10.1108/09600031011035100

It must be kept in mind, however, that because of the increase in returns and theirassociated costs many retailers have recently become more restrictive in their returnpolicies. The results of a survey conducted by Harris interactive reported that:

[. . .] 92% of customers are somewhat or very likely to shop again [. . .] if the returns process ifconvenient [. . .] on the other hand, 82% are not likely or not very likely at all to shop again[. . .] if the returns process is inconvenient (Mermelstein, 2006, p. 15).

This business practice is now prevalent across all retail formats from brick and mortarto e-tailing, making research in the area timely and managerially relevant.

This work builds on the recent research on reverse logistics in the following areas:antecedents and consequences of product returns (Petersen and Kumar, 2009); productreturns processing (Stock and Mulki, 2009); managing product returns (Stock et al.,2006); and developing effective logistics programs (Richey et al., 2005). However, unlikeother contributions in this research stream, we specifically focus on the perspectives ofretailers who perform a critical role in reverse logistics. Since the process of consumerreturns begins with the retailer, this research focuses on the reverse logisticscapabilities that retailers develop in order to implement their returns policies and movereturned products back upstream. Reverse logistics capabilities include the accuracyand the availability of information that is used, and the process and timeliness ofreverse logistics information. Reverse logistics capabilities also include the internaland external connectivity and usefulness of that information. These capabilitiesrepresent a bundle of information-related processes that enable a firm to better manageits reverse logistics activities that may in turn relate to cost savings.

The purpose of this research is to determine how reverse logistics capabilitiesimpact the relationship between antecedents (customer and firm related) and the costsavings achieved from reverse logistics strategies. The specific research questionsexamined in this research are:

RQ1. What are the relationships between customer orientation, customeropportunism, resource commitments, contractual arrangements, and reverselogistics cost savings?

RQ2. How do reverse logistics capabilities mediate these relationships?

In order to address these research questions, path analysis and multiple regressionmodels are developed and tested to analyze these relationships based on a survey of295 retailers. This research contributes to the literature by empirically testing andvalidating the impact of the factors that influence reverse logistics cost savings forretailers. In the following section, a literature review is presented that examines theissue of reverse logistics and identifies the antecedents that contribute to cost savings.Following the literature review, the methodology is presented including the pathanalysis and multiple regression models that were used to test the hypothesizedrelationships. The hypotheses are tested and managerial implications and directionsfor future research are provided.

BackgroundReverse logistics involves all activities associated with a product or serviceafter the point of sale with the ultimate goal being to optimize or make more

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efficient aftermarket activities, thus saving money for the firm involved (Rogers andTibben-Lembke, 1999). Reverse logistics has been defined as:

[. . .] the process of planning, implementing, and controlling the efficient, cost effective flow ofraw materials, in-process inventory, finished goods, and related information from the point ofconsumption to the point of origin for the purpose of recapturing or creating value or properdisposal (Rogers and Tibben-Lembke, 1999, p. 2).

Reverse logistics has become a competitive necessity for several reasons including:increasing trend in customer returns, the increasing use of consignment inventory,shorter product lifecycles, and more demanding customers (Daugherty et al., 2001).Reverse logistics is increasingly being considered as a strategic process that capturesvalue through customer satisfaction and cost control (Richey et al., 2005; Rogers andTibben-Lembke, 2001). As the volumes of returns increase world wide, firms are nolonger able to ignore the reverse flow of products (Stock et al., 2002). In a retail context,reverse logistics involves the process of handling and the eventual disposition of goodsreturned from customers (Horvath et al., 2005). As retail margins become narrower,reverse logistics has become a major concern for retail managers due to the costs ofstorage, loss of current sales, potential recoverable product value, and the importanceof both customer and channel partner relations (Daugherty et al., 2005). When an endcustomer begins the reverse logistics process, it is usually with a retailer. How well theretailer manages the reverse logistics process may determine its cost savings as well asthe customer’s satisfaction with the retail encounter (Horvath et al., 2005).

Antecedents of reverse logistics capabilitiesIn their research, Petersen and Kumar (2009) focused on customer buying behavior andthey defined the antecedents and consequence of product returns from that perspective.In this research, we are interested in the factors that derive influences reverse logisticscapabilities and resulting cost savings from the perspective of the retailer. Reverselogistics cost savings are the savings that the retailer incurs from implementingreverse logistics processes to support their returns policies. In order to understandwhat factors may influence reverse logistics capabilities and resulting cost savings, weconsider specific antecedents. These include elements that are related to therelationship between the retailer and its customers, elements that are internally relatedto the firm’s reverse logistics resources and its contractual policies with suppliers.Customer orientation and customer opportunism reflect the retailer’s customer-focusedintentions and process-driven relationships with its customers, while, resourcecommitments reflect the managerial, technical, and financial resources that are appliedto reverse logistics processes. Contractual arrangements represent the back-endprocesses with other channel members that involve the development of commongoals. Finally, reverse logistics capabilities represent the internal capabilities andprocesses that the firm deploys to effectively implement its reverse logistics activities.The conceptual framework that guides this research is shown in Figure 1. It ishypothesized that reverse logistics capabilities are related to four antecedents(the firm’s customer orientation, customer opportunism, resource commitments, andcontractual commitments), and that these capabilities, in turn, are related to reverselogistics cost savings.

Customer orientation. A customer orientation represents the retailer’s attitudes andactivities that work toward satisfying customer needs (Deshpande et al., 1993).

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Customer orientation involves several aspects that are reflected in the measure used inthis research. These include that product and service development is based oncustomer-focused information, the retailer has a good sense of how its customers valueits products and services, the retailer is customer-focused, the retailer competes basedon customer driven differentiation, and that the retailer believes that the businessexists to serve customers.

A customer orientation implies that an organization can develop a sustainablecompetitive advantage by understanding and meeting the needs of their customers(Deshpande et al., 1993). A customer-oriented firm understands and meets the realneeds of its customers, therefore becoming more likely to have satisfied customers whocome back and tell their friends (Brady and Cronin, 2001). This process can entailfostering long-term relationships with customers to create a sustainable competitiveadvantage (Brady and Cronin, 2001). Customer orientation involves a firm’swillingness to put its customers first to create customer value. One of the ways thatretailers can create customer value is by working with suppliers who will minimizecosts. It is because of these supplier benefits that retailers strive to develop strongrelationships with their supply chain partners. One of the main ways that a retailer canwork with suppliers to create value for the final consumer is by working towardsdeveloping reverse logistics capabilities that foster a customer-driven returns policyfor the customer. A higher level of customer orientation is hypothesized to encouragethe retailer to develop the capabilities to improve return processes for the consumer.Therefore:

H1. Customer orientation is positively related to reverse logistics capabilities.

Customer opportunism. Opportunism occurs when an individual or organization actsin its own self-interest (Williamson, 1975, p. 6). Opportunistic behavior may be evidentin buyer-seller relationships in which one party uses the relationship to improvehis/her own position at the expense of the other. Opportunistic behavior is

Figure 1.Hypothesized model

Customerorientation

Resourcecommitments

Reverselogistics

capabilities

Reverselogistics

cost savings

H1

Customeropportunism

Contractualarrangements

H5

H3

H2

H4


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counteractive to collaborative behavior, which involves a trusting relationship(Williamson, 1975, p. 26). When opportunism exists it represents a source of potentialharm and undermines perceptions of trustworthiness (Williams, 2007). Returns policiesare often seen by retailers as a zero-sum game; retailers consider return policies as thecost of doing business (Padmanabhan and Png, 1997). Empirical research indicatesthat accepting retailer returns in periods of minimal uncertainty actually benefit themanufacturer (Padmanabhan and Png, 1997), ignoring the impact on the retail channelpartner. Customer opportunism may increase returns and consequently reverselogistics costs. When deciding where to shop, and what to buy, the retailer’s returnpolicy is important to customers, thus causing opportunistic customers to takeadvantage of the retailers reverse logistics policies. As an example, customers maypurchase products for a one-time use and then return the product for a number ofreasons, some of which are unethical (Seiders and Berry, 1998). Despite liberalizationof return policies across industries, some firms have tightened the restrictions on the“no questions asked” return policies. Some retailers continue to offer liberal returnpolicies in the hopes of developing a service-based competitive advantage which putspressure on competitors to do the same. However, high levels of customer opportunismmay force the retailer to lower its reverse logistics capabilities that may also reduce theavailability of these capabilities for other customers. Therefore, it is hypothesized that:

H2. Customer opportunism is negatively related to reverse logistics capabilities.

Resource commitment consists of the financial, technical, and managerial resourcesthat are committed to reverse logistics capabilities. Each of these resources arenecessary for reverse logistics capabilities to be realized and implemented. Financialresources by definition are necessary to fund a strategic process such as reverselogistics. Given the role of information identified as the source of reverse logisticcapability, investment in this area becomes necessary. Investing in informationtechnology can help retailers develop a sustainable competitive advantage bydeveloping technological capabilities that are difficult for other retailers to imitate(Day, 1994; Srinivasan et al., 2002). Technical resources of the organization must alsobe put into place in order to carry out the creation of these capabilities. Retailers thatencourage a technological orientation are likely to have improvements in their overallfirm and service performance (Zhou et al., 2005). Investing in technology can improvereverse logistics capabilities allowing for more efficiency between the retailer and thesupplier. Reverse logistics capabilities are not solely based on the funding oftechnology. In order for meaningful capabilities to be created, management mustexpend time and effort to determine the capabilities required and the means necessaryto create them. Managerial resources include the skills, experience, knowledge, andintelligence of the employees in a firm (Richey and Wheeler, 2004). It is hypothesizedthat there is a positive relationship between the deployment of resources and enhancedreverse logistics capabilities. Therefore:

H3. Increased levels of resource commitments are positively related to reverselogistics capabilities.

Contractual arrangements. Contractual arrangements are part of a socializationprocess with other channel members that involves arranging relationships thatdeliberately promote goal congruence (Wathne and Heide, 2000). Socialization implies

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that the retailer develops contractual relationships in order to promote partnershipsthat are oriented towards effective reverse logistics. Governance mechanisms must bein place in order to strengthen the relationships between the retailer and its keysupplier. In order for firms to minimize the risk of opportunism from their supply chainpartners, they can implement governance mechanisms to reduce the amount ofopportunistic behavior. One of the methods for reducing the risk is to implementformal contracts. Formal contracts outline the goals, responsibilities and benefits of thesupply chain partner relationship. Contracts can act as a building base forimplementing future governance mechanisms in times of uncertainty. Contracts cancontribute to improved performance in partner relationships with minimal uncertainty(Cannon et al., 2000). Based on the positive outcomes related to capabilities that mayaccrue due to beneficial supplier contracts, it is hypothesized that:

H4. Contractual arrangements are positively related to reverse logistics capabilities.

Reverse logistics capabilities represent the internal processes that the firm uses toeffectively implement its reverse logistics activities. These capabilities address severaldifferent areas as seen in Table I, however, they are all aspects the use of information tobetter manage the reverse logistics process. The specific reverse logistics capabilitiesthat are considered include the accuracy and the availability of information that isused, and the process and timeliness of reverse logistics information. Reverse logisticscapabilities also include the internal and external connectivity and usefulness of thatinformation. These capabilities represent a bundle of information-related processesthat enable a firm to better manage its reverse logistics activities and are hypothesizedto be positively related to reverse logistics cost savings. Therefore, it is hypothesizedthat:

H5. Reverse logistics capabilities are positively related to reverse logistics costsavings.

These hypothesized relationships between the antecedents and consequences ofreverse logistics capabilities are seen in Figure 1. This conceptual model reflects therelationships developed from the literature review and the method used to test theserelationships.

MethodologyMeasurement scalesThe scales used in this research were adopted from previous research or weredeveloped specifically for this study. Each construct was measured on a seven pointLikert scale. Customer orientation was measured using five items adapted fromDeshpande et al. (1993). Of the original nine-item scale, five items were retained. Thesefive items focus specifically on the service differentiation of the retailer. Customeropportunism was developed for this research and was evaluated from the perspectiveof the retailer based on a five-item scale in which retailers were asked to rate how likelytheir customers were to alter or falsify information in the returns process. This scalewas based on previous research in the business to business context of opportunism(Joshi and Arnold, 1997). Resource commitment was measured by using a three-itemscale based on the financial, technical, and managerial resources that are committedto reverse logistics activities developed for this research. Respondents were asked to


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Factors (reliabilities) Scaled itemsFactor

loadings

Customer orientation (0.863) 1. Our product and service development is basedon customer-focused information 0.740

2. We have a good sense of how our customersvalue our products and services 0.840

3. We are more customer-focused than ourcompetitors 0.787

4. We compete primarily based on customerdriven product and service differentiation 0.847

5. We believe that this business exists to servecustomers 0.838

Customer opportunism (0.931) 1. Sometimes our customers will use any meansnecessary to further their own interests at theexpense of this organization’s profitability 0.791

2. In our negotiations, sometimes our customersexaggerate the extent of product damage, ordamage caused by us, in order to getconcessions from us 0.911

3. Sometimes our customers make adjustments intheir story to cope with changes in ourcustomer service policy 0.901

4. Sometimes our customers alter the factsslightly in order to get what they want 0.921

5. Sometimes our customers promise to do thingswithout actually doing them later 0.840

Resource commitments (0.950) Please indicate the levels of resource commitmentto reverse logistics/returns handling within yourcompany (using one to seven Likert scale)1. Financial resources 0.8992. Technological resources 0.8973. Managerial resources 0.900

Contractual arrangements (0.950) 1. When discussing sensitive issues with our keysupplier, they sometimes ask us to refer to ourcontract 0.831

2. Our key supplier follows the “letter of the law”when it comes to our contract with them 0.770

3. When we need a concession from our keysupplier it is not uncommon for them to referback to our past written agreements 0.890

4. When issues arise, our key supplier is morelikely to follow a formal written agreement thatdevelop a new gentleman’s agreement 0.860

Reverse logistics capabilities(0.946)

Please assess your firm’s information systemcapabilities in the following areas related toreverse logistics1. Accuracy of information 0.8302. Availability of information 0.8513. Daily download of information 0.8104. Formatted on exception basis 0.8405. Formatted to facilitate usage 0.858

(continued )Table I.CFA results

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indicate the degree to which their firm devoted managerial, technical, and financialresources towards developing reverse logistics strategies. Contractual arrangementswere measured using a four-item measure developed by the authors to measure theextent to which retailers and suppliers engage in a contractual relationship that wasrelevant to this research. For example, respondents were asked to rate the extent towhich they developed formal contracts with their suppliers, used these contracts, orreferred back to the contract in times of uncertainty.

Reverse logistics capabilities were measured by nine items developed also as part ofthis research. In order to develop a comprehensive list of reverse logistics capabilities,a content analysis of the returns’ policies of the top 100 retailers as rated by Fortunemagazine was conducted. Based on this, the authors developed the comprehensive listof what processes retailers were using to implement their returns’ policies with theircustomers. These capabilities were found to directly related to the use of informationuse and were found to consist of one dimension when validity was examined andtogether produced a high reliability score. Cost savings were measured using fouritems designed specifically for this research. Respondents were asked to indicate theircost savings due to reverse logistics activities, and if these activities reduced costs dueto lowering environmental compliance costs and if they reduced the firm’s cost positionrelative to competitors. It should be noted that each of the cost savings items reflect adifferent perspective on realizing cost savings from engaging in reverse logisticsactivities, however, as explained below, each of these item loaded on the samedimension for cost savings, and contributed to an acceptable reliability score.

Data collectionThe survey was administered using an online survey instrument. While there is somedebate about online surveys, research suggests that internet panels do not have asignificant negative impact on the data (Dennis, 2001; Pollard, 2002) and in manyinstances allow researchers to conduct high-quality research (Braunsberger et al.,2007). For this survey, the customer panel chosen was retail business owners and top

Factors (reliabilities) Scaled itemsFactor

loadings

6. Real-time information 0.8937. Timeliness of information 0.8898. Internal connectivity/compatibility 0.8749. External connectivity/compatibility 0.878

Cost savings (0.893) 1. We are realizing cost savings because of ourreverse logistics activities 0.762

2. We incur lower compliance costs withenvironmental regulations due to our returnshandling method 0.725

3. Our strategy for dealing with returnedmerchandise improves our cost positionrelative to our closest competitors 0.771

4. Our reverse logistics program is saving usmoney 0.790

Note: Factors, standardized reliabilities, and factor loadings Table I.


235

management retail executives who had frequent interactions with their key suppliers,as they are usually the ones who are most likely to engage in leadership behaviors.

The survey instrument invited 1,429 respondents who visited the online survey.However, respondents who did not answer yes to the key question, “do you interactdirectly with your key suppliers?” were not allowed to continue through the survey.Respondents were asked this question in order to ensure their awareness of theircontractual agreements with their suppliers. Of those that answered “yes” to theaforementioned question, 318 completed the survey, a response rate of 23 percent.Twenty-three of these completed surveys were dismissed due to all neutral answeringor to incompletion, leaving 295 total, representing a final response rate of 20 percent.Respondents were asked to specify their industry (textiles, automotive, etc.), but mostresponded “retail” so it was impossible to categorize most respondents by NorthAmerican Industry Classification System codes. An analysis was conducted toexamine whether any non-response biases were present in the data. Tests wereconducted to rule out any potential non-response bias by comparing early to lateresponders on all study variables and demographics. No significant differences werefound between early and late respondents across the study variables.

Reliability and validityIn order to test for the statistical conclusion validity of the survey and to addresspossible instances of covariation, several statistical tests were performed on the data.To assess the responses for normality, tests for skewness and kurtosis were performedon each of the indicators for each construct. None of the items in the survey showedinappropriate skewness or kurtosis. The data appeared to be normally distributed.The next step in evaluating the constructs presented in the conceptual model was toperform a confirmatory factor analysis (CFA) to evaluate the dimensionality andreliability of each of the scales. Researchers normally use the correlation matrix of theobserved variables in order to identify the underlying factors that can potentiallyexplain the variation in the data. Thus, a CFA was performed on all of the responses inorder to validate these constructs. Principal component analysis was used along with avarimax orthogonal rotation in order to effectively detect the factors. This CFAdemonstrated that six distinct factors explained 77.5 percent of the variability in thedata. These six factors are displayed in Table I along with their standardizedreliabilities and the factor loadings of the individual scaled items.

Results and findingsAs a first step towards evaluating the strength of the relationships between the constructs,a bi-variate correlation analysis was performed using the Pearson correlation analysis andthese results are shown in Table II. This analysis showed that all the variables in the modelexcept for customer orientation were positively related to reverse logistics cost savings.It was also found that the moderating variable (reverse logistics capabilities) waspositively related to the outcome variable (cost savings) and also positively related to twoindependent variables (contracts and resources). While this correlation analysis of thedirect relationships was interesting, it did not provide further insights about the indirectlinkages between the independent variables that drive these cost savings, particularlywith reverse logistics capabilities as the mediator in this model. The next step in theanalysis was to use path analysis to test the hypothesized relationships.

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Path analysisPath analysis was used to establish model fit and to identify the direct and indirecteffects of the hypothesized cause variables on effect variables (Ahmad et al., 2004).A beta coefficient is calculated for each path and the coefficient’s sign indicatesthe direction of the relationship (positive or negative) and its magnitude represents thestrength of the interrelationship between the variables. The x 2 statistic relative to thedegrees of freedom, the goodness of fit index (GFI), the adjusted goodness of fit index(AGFI), the comparative fit index (CFI), the normed fit index (NFI), and the root meansquare error of approximation (RMSEA) were used to gauge the adequacy of themodels. The criteria used to evaluate path models include:

. a non-significant x 2 statistic indicating that the measured variances andcovariances of the underlying variables are accurately reflected by the linkageshypothesized in the model;

. x 2 statistic per degree of freedom where a desirable ratio is less than 3;

. the GFI, AGFI, CFI and NFI where values above 0.85 are desirable; and

. the RMSEA should be less than 0.10 (Bentler and Bonett 1992).

The model was first tested using the full sample and then by sub-groupings of responsesbased on the length of relationship that the retailer had with its key suppliers (short,medium and long). As shown in Table III, using the full sample, while the majority of the fitindices were supportive of a good fit (x 2/df ¼ 4.13, GFI ¼ 0.99, AGFI ¼ 0.90, CFI ¼ 0.98,NFI ¼ 0.97, and RMSEA ¼ 0.106) the x 2 statistic was significant, indicating a lack of fit.However, there is some evidence that the x 2 statistic tends to be significant for largesamples (Streiner, 2006). Therefore, we considered the model using sub-groupings ofresponses based on the length of relationship between the respondent and their keysuppliers. Using this approach, we found statistically valid results where all the fit indiceswere acceptable and the non-significantx 2 statistic indicated that the measured variancesand covariances of the underlying variables are accurately reflected by the linkageshypothesized in the model. For example, for the long-term relationships sub-sample, all thefit indices were supportive of a good model fit (x 2/df ¼ 1.49, GFI ¼ 0.98, AGFI ¼ 0.90,CFI ¼ 0.98, NFI ¼ 0.96, and RMSEA ¼ 0.07).

Customerorientation Contracts


Costsavings Resources

Customeropportunism

Customerorientation 1Contracts 0.024 1Reverse logisticscapabilities 20.041 0.263 * * 1Cost savings 0.046 0.259 * * 0.590 * * 1Resources 20.047 0.340 * * 0.269 * * 0.455 * * 1Customeropportunism 0.067 0.263 * * 20.021 0.125 * 0.249 * * 1

Note: Correlation is significant at *0.05 and * *0.01 levels (two-tailed)Table II.

Correlation analysis


237

So, although it was demonstrated that using path analysis, the hypothesized modelmeets the requirements for a good statistical fit, the authors acknowledge the technicalshortcomings associated with these path analysis results. Therefore, the decision wasmade to evaluate the research hypotheses using both path analysis and multipleregression analysis. While regression analysis does not explicitly account formeasurement error, the readers should note that the results for the hypotheses fromboth the path analysis and the multiple regression analysis were consistent.

Multiple regression analysisIn order to evaluate H1-H4 multiple regression was used to test the influence ofcustomer orientation, customer opportunism, resource commitments, and contractualagreements on reverse logistics capabilities. Then, H5 was evaluated by regressingreverse logistics capabilities on reverse logistics cost savings. As seen in Table IV,H2-H4 were supported. Customer opportunism (H2; b ¼ 20.113, p ¼ 0.021), Resourcecommitments (H3; b ¼ 0.179, p ¼ 0.000) and contractual arrangements (H4; b ¼ 0.194,p ¼ 0.000) were found to be significantly related to reverse logistics capabilities.As hypothesized, customer opportunism was negatively related, and resourcecommitments and contractual arrangements were positively related. As seen inTable V, H5 was also supported with reverse logistics capabilities found to besignificantly related to reverse logistics cost saving (b ¼ 0.385, p ¼ 0.000). Customerorientation (H1) was not significantly related to reverse logistics capabilities.The reader should note that the results for these hypotheses were consistent underboth path analysis and regression analysis methods.

Next, a post hoc analysis was performed to evaluate how reverse logisticscapabilities mediate the relationship between the dependent variable (reverse logisticscost savings) and the four antecedents (customer orientation, customer opportunism,resource commitments, and contractual arrangements). Baron and Kenny (1986)recommended that mediation tests be done using three equations, where Y is theoutcome variable, X is the independent variable and M is the mediator:

Y ¼ b10 þ b11X þ error ð1Þ

M ¼ b20 þ b21X þ error ð2Þ

Y ¼ b30 þ b31X þ b32M þ error ð3Þ

Path analysis results Regression analysis resultsx 2/df 4.31 Model ( p-value) 0.00GFI 0.99AGFI 0.90CFI 0.98NFI 0.97RMSEA 0.10Hypotheses Beta values SE p-value Beta values SE p-valueH1 20.04 0.07 0.63 20.04 0.07 0.63H2 20.11 0.05 0.02 20.11 0.05 0.02H3 0.19 0.05 0.00 0.18 0.05 0.00H4 0.18 0.05 0.00 0.19 0.05 0.00H5 0.39 0.03 0.00 0.39 0.03 0.00

Table III.Summary of path andregression analysisresults

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Table IV.H1-H4 multiple

regression results


239

In the first equation the direct path is tested between the independent variable and theoutcome (Y ¼ reverse logistics cost savings; X ¼ each antecedent separately:customer orientation, customer opportunism, resource commitments, and contractualagreements). In the second equation, each of the independent variables are regressed onthe mediator (M ¼ reverse logistics capabilities; X ¼ each antecedent: customerorientation, customer opportunism, resource commitments, and contractualagreements). In the third equation both the independent variable and the mediatorare regressed on the outcome variable (Y ¼ reverse logistics cost savings; X ¼ eachindependent variables and M ¼ reverse logistics capabilities). For mediation to occureach equation must be significant and there should be an improvement in R 2 betweenequations (2) and (3). For full mediation to occur, the beta for X in equation (3) should benon-significant. For partial mediation, the beta for X in equation (3) must be less thanthe beta for X in equation (1).

As seen in Table VI, equation (1) was only significant for resource commitment andcontractual arrangements. Thus, only these two antecedents could be considered asmediated by reverse logistics capabilities. For both resource commitment andcontractual arrangements equations (2) and (3) were also significant. In addition, therewere improvements in R 2 from equations (1)-(3) for both variables when the Mediatorwas included in the model in equation (3). For example, for resource commitments,R 2 changed from 0.207 to 0.442 and for contractual arrangements, R 2 changed from0.067 to 0.359. Finally, in both instances there was evidence of partial mediationbecause the correlation between X and Y was reduced when the mediator wasaccounted for in the model (resource commitments changed from b ¼ 0.235 to 0.165and for contractual arrangements it changed from b ¼ 0.148 to 0.061). Therefore, therelationships between both resource commitments and contractual arrangements,and cost savings were partially mediated by reverse logistics capabilities. However,reverse logistics capabilities did not serve as a mediator for customer orientation norfor customer opportunism.

DiscussionUsing responses from 295 retail managers, this research has empirically evaluated therelationships between three entities: antecedents (customer orientation, customeropportunism, resource commitments, and contractual arrangements), the mediator(reverse logistics capabilities) and the outcome variable (logistics cost savings).Overall, these results tell a very interesting story. When the direct linkages between theantecedents and the moderating variable (reverse logistics capabilities) are evaluatedusing regression analysis we found significant relationships as hypothesized for H3and H4, indicating positive linkages between resource commitments and reverselogistics capabilities, and between contractual arrangements and reverse logisticscapabilities. For H2, (customer opportunism) the result was negative and significantindicating a negative relationship as hypothesized. We also found a positive andsignificant relationship for H5, indicating a direct and positive relationship between

Variable (X) Dependent variable (Y) Beta R 2 Significance ( p-value)

H5 RL capabilities (X) Cost savings 0.385 0.348 0.000Table V.H5 regression result

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reverse logistics capabilities and cost savings. However, the result for H1 (customerorientation) was not significantly related to reverse logistics capabilities.

The results from our hypotheses testing can be explained from two perspectives:front-end customer-related activities and back-end processes that are both required toeffectively handle customer returns. The results for H1-H4 indicate that there is a cleardelineation between the impact of front-end customer-related antecedents versusback-end process-related related antecedents. For the customer-related antecedents(customer orientation and customer opportunism) we found no positive relationshipswith reverse logistics capabilities. However, the antecedents that represent the back-endprocesses (resource commitments and contractual arrangements) were positively andsignificantly related to these reverse logistics capabilities. Since we found no significantrelationship between customer orientation and reverse logistics capabilities, we believethat more research is needed to test this important relationship. On the other hand, thenegative and significant results for customer opportunism underscore the negativerelationship between opportunistic customer behavior and the level of return logisticscapabilities that a retailer may deploy. For example, in the presence of high customeropportunism, a retailer may have a more stringent return policy and therefore havelower capabilities. The result for H5 (that reverse logistics capabilities do improve

Variable (X)

Equation (1):Antecedents ¼ XCost savings ¼ Y

Equation (2):Antecedents ¼ XRL capabilities ¼ Y

Equation (3):Antecedents ¼ XCost savings ¼ Y

Customerorientation (X) NS NS NSRL capabilities (M) N/A N/A 0.387R 2 0.002 0.002 0.353Significance level ofF 0.428 0.483 0.000Customeropportunism (X) 0.069 NS 0.075RL capabilities (M) N/A N/A 0.389R 2 0.016 0.001 0.360Significance level ofF 0.032 0.715 0.000Resourcescommitments (X) 0.235 0.214 0.165RL capabilities (M) N/A N/A 0.329R 2 0.207 0.073 0.442Significance level ofF 0.000 0.000 0.000

Equation (1) for Y asdependent variable

Equation (2) for M asdependent variable

Equation (3) for Y asdependent variable

Contractualarrangements (X) 0.148 0.230 0.061RL capabilities (M) N/A N/A 0.366R 2 0.067 0.069 0.359Significance level ofF 0.000 0.000 0.000

Table VI.Results for mediation

using regression analysis


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cost savings) is also very important to note as it indicates that cost savings wereachieved by these increased reverse logistics capabilities.

Another contribution of this study was demonstrated by the mediating role thatreverse logistics capabilities perform in deriving reverse logistics cost savings. TheBaron and Kenny (1986) procedure was used to test the mediating effect of reverselogistics capabilities in this mode. Here, we found that these capabilities partiallymediated the relationship between resources and the outcome variable (reverse logisticscost savings) and also between contractual arrangements and the outcome variable(reverse logistics cost savings). These results suggested that retailers should focus noton the independent relationships between the antecedents and the outcome (reverselogistics cost savings), but rather on the mediating role that reverse logistics capabilitiesplay within their returns system. Retailers should focus on their internal capabilities andback-end processes as represented by their reverse logistics capabilities, resourcecommitments and contractual arrangements. While financial, technical and managerialresources are used to implement the reverse logistics capabilities to handle these returns,they also facilitate reduce cost when these returns are handled in an efficient manner.Similarly, having contractual arrangements in place with key suppliers and vendorswere also found to be significant contributors from the back-end processes that arerequired to handle these returns. One explanation for this finding is that many retailersare electing to outsource their returns processing through third-party logisticsarrangements in order to benefit from more efficient returns processes that help toreduce their costs. Here, our research findings are consistent with those of otherresearchers. For example, Stock and Mulki (2009) found that improvements in productreturns processing can improve profitability through cost reductions and higherrecovery rates for returned products. They also reported that many large retailers (e.g.Wal-Mart and Target) rely on outsourcing arrangements to derive these cost savings.

Our findings are based on the perspectives of retailers who form the initial link inthe reverse flow of product. Unlike traditional down-stream marketing channel flowsthat are initiated by manufacturers, the reverse logistics flow of product mostfrequently begins with a return exchange made from a customer to a retailer. As moreretailers embrace more convenient or liberal return policies they create the opportunityfor more complex inventory management practices impacting cost, space, andforecasting at all levels of the marketing channels and supply chains. This complexitymagnifies the importance of managing reverse logistics and returns policy at the retaillevel. In today’s competitive environment, many retailers look for ways to streamlinefunctions to realize cost savings, while minimizing the impact on their customer serviceoffering. While the returns policy is a critical component of an organization’s customerservice strategy, it should not become a financial burden for a retailer. If a retailercarefully cultivates this value-added service by developing their reverse logisticscapabilities, improved performance and lower costs are realizable.

When the back-end resources and contractual arrangements along with themediating role of reverse logistics capabilities are considered systematically andholistically, they provide the foundation for competitive advantage because they arekey to the returns process that retail end-customers value and use. When these threeelements (resources, contracts, and capabilities) are in place, retailers can realize highercost savings through such elements as lower compliance cost with environmentalregulations, improved competitiveness, and efficiencies in meeting customer needs.

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While other possible beneficial outcomes were not tested in this research, there isempirical evidence that ultimately, satisfied customers can lead to repeat purchases,improved customer loyalty, increased market share, and greater financial returns.

Limitations of the researchThere are several limitations of the research that should be noted. One possible limitationof this study is the use of an online survey as a data collection tool. Internet surveys yieldusable results, however, the survey instrument for this study employed a system intowhich respondents self-selected. This might create a varied type of non-response bias.In addition, the caliber of individuals who self-select into this program might notnecessarily be completely representative of the retail population. Internet and papersurveys should be used to validate the findings in this research. An additional limitation isthat this research was done at a singular and specific point in time that could bias theresults. For example, this study was done during the early months of summer. For manyretailers, this is the slowest time of year. At this time, only their most loyal customersmight come in. In order to effectively capture a better gestalt of a retail serviceenvironment, a longitudinal study might need to be done to capture service perceptionsduring different operating seasons and/or years. In this research cost savings weremeasured based on the perception of the respondent. It would be desirable to include actualcost savings in the research design, however, this information could be difficult for therespondent to report and could adversely affect the response rate. In addition, financiallyrelated perceptual measures are often used in the literature (Narver and Slater, 1990).

Another limitation of this study is that respondents were asked to rate themselves incomparison with their main competitor. Also, respondents were also asked to ratecustomer opportunism from the perspective of the customer. This creates anopportunity for managerial hubris as the managerial respondents cannot speak directlyfor the customer. Some of the constructs are better measured from customer responses.Future research should attempt to triangulate results directly with customer responsesin order to minimize this limitation. Respondents were also asked to evaluate theirrelationships with their key suppliers. In these instances, the retailer is actually thecustomer, which could skew their perceptions of these relationships. In order to get amore complete picture of relationship quality, a dyadic perspective should be employed.Although it would have been desirable to conduct this study from the perspective of adyadic relationship, the research design did not allow for this possibility. Finally, thisstudy was limited to domestic respondents within the United States. As today’s retailenvironment competes on a more global basis, it is imperative that we expand ourresearch beyond traditional boundaries. Customers and retailers in other countries mayhave different service expectations; suppliers in other countries may well have differentvalues. These should be examined in order to understand service requirements forretailers on a more international scale. It is also important to note that the variablesexamined in this study are limited by what is taking place today in firm reverse logisticsactivities. As the importance of reverse logistics increases there will be changes in theprocesses use, such as the use of collection points for online returns (Weltevreden, 2008).

Summary and directions for future researchThere are several areas for future research that must be considered in light of thepresent work. The focus of the present research was on reverse logistics capabilities


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and their influence on cost savings. The development of the capabilities measure thatwas done specifically for this investigation resulted in items that were related to theuse of information and consisted of one dimension. Future research is suggested tofurther explore this construct for additional dimensions and to measure it accordingly.This research was evaluated using the retailer as the primary channel memberresponsible for reverse logistics and its resultant cost savings. There are many otherindustries where the manufacturer plays a more important role in reverse logisticsactivities and future research can evaluate this from the manufacturer’s perspective,both on retailer as well as manufacturer cost savings. Reverse logistics cost savingsand its antecedents may be generalizeable across industries and business settings, butthis remains to be seen. Of particular interest is the role of reverse logistics in ane-commerce setting, where the e-retailer may serve in a similar capacity as the retailersexamined in this study, but with significantly different reverse logistics processes andprocedures that may result cost savings derived from variables outside the domain ofthis research.

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Further reading

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About the authorsEric P. Jack received his PhD in Operations Management from the University of Cincinnati. He isan Associate Professor of Management and is the Associate Dean of the University of Alabamaat Birmingham School of Business. His research has been published in several journals includingthe Journal of Operations Management, Production and Operations Management, and QualityManagement Journal. His work experience includes 21 years as a US Air Force officer where hisresponsibilities at various international locations involved facility planning, design,construction, and maintenance.

Thomas L. Powers received his PhD in Marketing and Transportation Administration fromMichigan State University. He is a Professor of Marketing at the Graduate School ofManagement at the University of Alabama at Birmingham and also holds a joint appointment inthe School of Health Professions. His research interests include reverse logistics, volumeflexibility, and various aspects of marketing strategy. His research has been published in severaljournals including the Journal of Business Research, Production and Operations Management,and Health Care Management Review. Thomas L. Powers is the corresponding author and can becontacted at: [email protected]

Lauren Skinner received her PhD in Marketing from the University of Alabama. She is anAssistant Professor at The University of Alabama at Birmingham. Her research interests focuson supply chain management, retail supply chains, services within the retail supply chain, andretailing pedagogy. Her research has been published in International Journal of PhysicalDistribution & Logistics Management, Journal of Business Logistics, and Journal of BusinessResearch.

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