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Examining_the_impact_of_revers.pdf
SPECIAL ISSUE PAPER
Examining the impact of reverse logistics disposition strategies
Lauren R. Skinner Marketing and Industrial Distribution Department, UAB School of Business,
University of Alabama, Birmingham, Alabama, USA, and
Paul T. Bryant and R. Glenn Richey Management and Marketing Department,
Culverhouse College of Business Administration, The University of Alabama, Tuscaloosa, Alabama, USA
Abstract
Purpose – The objective of this paper is to empirically examine the impact that different disposition strategies have on strategic performance in the reverse logistics process. This research also includes the role of the returns policy in the customer decision-making process as a foundation for determining the appropriate disposition strategy.
Design/methodology/approach – A general review of the literature and depth interviews with logistics professionals following commonly employed investigative techniques provided the foundation for the study. A survey was developed and mailed to the senior supply chain operations professional at 400 companies in the auto parts industry resulting in 118 usable responses.
Findings – The current research shows that under instances of active resource commitment to reverse logistics programs, operations and supply chain managers may expect superior performance by choosing destroying, recycling, refurbishing, and/or remanufacturing of product.
Practical implications – If firms focus on reverse logistics activities as a must do, a strategic approach that examines outcomes rather than day-to-day operations is suggested. If managers do not have adequate resource support for reverse logistics, they should destroy the product. The other disposition options all require significant resources in order to reclaim value from returns.
Originality/value – Traditional strategy research has focused on the importance of a strategic fit between a firm’s internal strengths and weaknesses and the external environment. In contrast, a resource approach stresses internal aspects of the firm. This study combines the two views along with examining the effects of resource commitment.
Keywords Distribution management, Competitive advantage, Returns
Paper type Research paper
1. Introduction Reverse logistics involves the handling and disposition of goods returned from the customer. As John Corrigan, Vice President and CIO at Estee Lauder noted, reverse logistics:
[. . .] used to be something that happened in the back room and wasn’t considered strategic [. . .] (but now it’s) being brought to senior management’s attention and viewed as an addressable and important part of business (Caldwell, 1999, p. 48).
Reverse logistics has become a managerial priority because of the assets/value involved and the potential impact on customer relations (Daugherty et al., 2005).
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0960-0035.htm
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International Journal of Physical Distribution & Logistics Management Vol. 38 No. 7, 2008 pp. 518-539 q Emerald Group Publishing Limited 0960-0035 DOI 10.1108/09600030810900932
Customers expect their vendors to be willing and able to handle returns (Daugherty et al., 2003). In today’s competitive retail environment, patrons often choose one retailer over another based on the retailer’s returns policy. However, this can represent a formidable challenge. As Norek (2003) noted, in many instances, a “staggeringly high percentage of merchandise sold is destined to be returned” (p. 54). Rogers and Tiben-Lembke (1999) reported that return rates are very much industry-specific and cited rates of returns that range from 3 percent to as high as 50 percent. Return rates that high warrant top priority status.
Products are returned for a wide range of reasons including defects or damage, customer dissatisfaction, and, especially in the business-to-business context, lower than projected sales (Barsky and Ellinger, 2001). In February of 2006, Marketing Management noted the importance of convenient or what industry calls “liberal” retail return policies in retaining customers. The results of this survey conducted by Harris Interactive reported that 92 percent of customers are somewhat or very likely to shop again if the returns process if convenient. On the other hand, 82 percent are not likely or not very likely at all to shop again if the returns process is inconvenient (p. 5). This business practice is now prevalent across all retail formats from brick and mortar to e-tailing. This process of managing returns remains as an unaddressed problem for managers and retailers alike. Many firms have found they must grant liberal returns allowances (in effect, anything can be sent back in many instances) to keep key customers happy (Reda, 1998). Others are focusing more on returns due to mandated environmental regulations requiring retrieval and/or recycling (Guide and Wassenhove, 2002). Finally, some firms develop returns programs to handle product brought back for remanufacturing, refurbishing, and/or subsequent re-sale. When worn out or obsolete products are remanufactured, “it’s not uncommon for companies to realize higher margins on these remanufactured products than they do on new items” (Stock et al., 2002, p. 16).
All of this indicates that there is great potential for improving value, solidifying customer relations, positively influencing company ROI, and even generating competitive advantage (Malone, 2004). Thus, in order to survive today’s increasingly fickle customer, many firms are “grappling” with how to best strategically manage their reverse logistics processes (Tibben-Lembke, 2002). As retailers embrace more convenient or liberal return policies they create the need for complex inventory management practices impacting cost, space, and forecasting at all levels of the marketing channels and supply chains. This complexity magnifies the importance of managing reverse logistics (RL) and returns policy at the retail level.
The reverse logistics processes generally are considered to include: authorization of returns, transportation, auditing, product disposition, and creating information about the kinds of products being returned and where they are coming from (Trebilcock, 2001). One of those processes – product disposition – is the focus of the current research. For too many firms, what to do with returns is almost an afterthought. Many options are available: products can be abandoned, salvaged for scrap, re-sold elsewhere, etc. Additionally, the current research was interested in understanding the role of the returns policies from a customer service perspective. How important is the returns’ policy in the customer’s purchase decision. The objective of the current research was to empirically examine the impact that different disposition strategies have on performance and to examine the role of the returns policy in the consumer decision making process. The influence of resource commitment was also addressed.
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In the following sections, theoretical foundations are presented and used to develop a conceptual model of the relationship between disposition strategies and performance and the role that resource commitment plays and the importance of the returns policy from the customer perspective. A two-part study is implemented to study these factors. Study one is a qualitative study concentrated on the customer’s perspective of the retail return policy. Study two measures the hypotheses tested in the conceptual model; this includes the methodology, statistical analysis, and results. The final sections cover discussion and conclusions followed by implications for future research.
2. Theoretical foundations 2.1 Resourced based view (RBV) The resource based view (RBV) of the firm emphasizes the idea that resources owned or controlled by the firm have the potential for providing enduring competitive advantage when they are inimitable and not readily substitutable (Peteraf, 1993). Resources are considered central to understanding firm performance (Amit and Shoemaker, 1993). Resources include all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness (Barney, 1991; Daft, 1983). Resources can include both tangible and intangible assets (Sampler, 1998). For example, knowledge (Winter, 1987), core competencies (Prahalad and Hamel, 1990), and learning (Senge, 1990) are all considered to be types of (intangible) resources.
Traditional strategy research has focused on the importance of a strategic fit between a firm’s internal strengths and weaknesses and the external environment (opportunities and threats; Das and Teng, 2000). Therefore, competitive environment and competitive position are emphasized. In contrast, a resource approach stresses internal aspects of the firm. As such, competitive strategy should be more influenced by accumulated resources than by the environment; what a firm possesses would determine what it accomplishes. Resources become fundamental drivers of firm performance (Conner, 1991). Logistics-related research has provided confirmation. Closs and Xu (2000) identified differences in firms’ logistics competency because of differences in resource allocation. Daugherty et al. (2001) directly addressed resource commitment in a reverse logistics context. Based upon a survey of catalog retailers, commitment of management resources was found to significantly influence achievement of reverse logistics program goals. Hult et al. (2002) used the resource-based view as the overarching theory base for their study of cultural competitiveness within supply chains and its effects on order fulfillment cycle time.
2.2 Business strategy and performance Strategy has been defined as:
[. . .] the fundamental characteristics of the match that an organization achieves among its skills and resources and the opportunities and threats in its external environment that enables it to achieve its goals and objectives (Hofer and Schendel, 1978).
The basic premise of the strategy implementation literature is “that different business strategies require different configurations of organizational practices to achieve optimal performance” (Slater and Olson, 2000, p. 813).
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To be successful, a firm must be able to efficiently and effectively execute competitive strategy in such a manner as to achieve and sustain positional advantages (Morgan et al., 2004). Competitive strategy decisions – in the current research context, selection of reverse logistics disposition strategies – can differentially impact performance. If there’s an appropriate match, i.e. the best disposition strategy is selected, this should contribute to enhanced effectiveness and superior performance (Slater and Olson, 2000).
The management literature provides numerous illustrations of the strategy-performance relationship (Hoskisson et al., 1999). The strategy-performance relationship has also been examined within the supply chain context (Olavarrieta and Ellinger, 1997). Wisner (2004) utilized structural equation modeling and developed a model illustrating supply chain strategies-firm performance relationships. Vickery et al. (2003) examined the supply chain strategy-financial performance relationship within the auto parts industry. No direct relationship was found between supply chain integration (a supply chain strategy) and financial performance. However, an indirect relationship was indicated. The relationship of supply chain integration to financial performance was found to be indirect through customer service, i.e. “customer service was found to fully mediate the relationship between supply chain integration and firm performance” (p. 523).
2.3 A grounded view of reverse logistics disposition An organization’s returns policy is a component of its customer service offering. The disposition strategy that the organization adopts is going to be correlated with its returns policy. For example, an organization that adopts a refurbishing strategy might have more stringent requirements for the condition of the product upon customer return. This research suggests that an organization’s returns policy is an opportunity for competitive advantage. In order to evaluate the importance of the returns policy in the customer decision making process, exploratory interviews were conducted to determine to what extent the role of the returns policy plays in the customer’s shopping decision.
The purpose of these interviews was to get a broad understanding of reverse logistics competencies from the consumer perspective. In addition, these interviews attempted to ascertain how customer might manipulate returns policies in order to serve their own self-interests. These interviews were conducted asking customers about their returns policy preferences from a general retail perspective. This context was chosen for its broadness and its applicability at the customer level. In addition, the research hopes to connect the importance of the returns policy and disposition strategy across multiple industries.
Qualitative interviews were performed using a semi-structured interview guide. Students in a Master’s level marketing class at a major southeastern university were told to interview three different people of varying demographic backgrounds. These students were briefed about the general purpose of the study but were given limited instructions in order to ensure that the interviews remained open and free flowing to the respondents.
The interviews were then coded independently by two PhD students. The researchers then compared codes and developed a comprehensive coding strategy. On items where the coding differed, an independent third party assigned the appropriate code.
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Based on the exploratory interviews, an organization’s returns policy does play a role in a customer decision to shop there. This is especially true in multi-channel retailing. Customers who purchase products online are very concerned about the flexibility of their product returns. The following are a few examples of customer responses:
If I buy a gift for someone, I make SURE that gift is returnable. I always end up buying the wrong thing for that person (Female, 25 years old).
Return policies are definitely important. I make sure I know the terms, like if I need a receipt or if it is within a certain time frame (Female, 50 years old).
I purchased shoes [online] last week, the return policy was very flexible, that is why I chose that website (Female, 37 years old).
Additionally, the interviews also found that customers tended to “abuse” return policies. For example, customers go to electronics’ retailers like Best Buy to purchase big screen TV’s to host a super bowl party, and then return the TV within several days. Young women go to fashion retailers like Nordstrom’s to buy the “perfect” prom dress, only to stuff the tags in and return it days after prom. An athlete wants a certain pair of shoes for the big game, so he purchases them, plays, and then returns them to the store (National Retail Federation, www.nrf.com, December, 2004). With increased access to product and retailer information, return opportunism is on the rise (Rogers and Tiben-Lembke, 1999). Customers are finding new and creative ways to take advantage of retailers regarding returns, warranties, and service policies. The following excerpts from our interviews illustrate these phenomena:
Some people are chronic returners because they are perfectionists; they have to have the right color, or the product has to fit a certain way (Female, 35 years old).
When I worked in retail, I had a friend who returned shoes after wearing them (Female, 37 years old).
I think that there are two types of people. The people who like to shop and buy a lot and the people who scam (Female, 25 years old).
The qualitative interviews outlined the importance that the returns policy plays in the customer’s decision-making process. Additionally, the interviews uncovered the “dark side” of returns and how customers will knowingly take advantage or a retailer’s returns policy. The exploratory interviews were done in the retailing context because that is the easiest context to discuss with consumers.
An organization will develop its returns policy based on the needs of its customers. Additionally, the returns policy should be structured around the customers’ willingness to adopt the returns policy. An organization will structure its returns policy based on these criteria. One of the foundational elements, from a logistics standpoint, of a returns policy is the product disposition. Customers are no longer concerned with the product once it is returned, but for organizations, this is a critical component of the returns policy as it is a major expense. The disposition strategy will be directly related to the returns strategy. The next section discusses the disposition strategy and the role of that strategy in the reverse logistics and returns policy offering related to and moving beyond the customer qualitative analysis to a quantitative estimation.
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3. Conceptual model and hypothesis development 3.1 Disposition strategies The current research focused on one specific reverse logistics-related strategic decision – disposition. When the decision is made to retrieve product within the supply chain, a number of options are available as to disposition, i.e. what is done with the returned product. Norek (2003) noted that companies have “at least” five recovery options:
(1) sell as new;
(2) repair or repackage and resell as new;
(3) repair or repackage and resell as used;
(4) resell at a lower value to a salvage house; and
(5) sell by the pound to a salvage house.
Norek’s items are listed in descending order of revenue return. Different cost structures and revenue recovery are associated with the different disposition strategies.
Disposition options are often industry or product-specific and depend upon characteristics of the product such as price/value, cost to transport, shelf life of the product, and market demand patterns. When products are of sufficiently high value and can be remanufactured for re-sale, efficient reverse logistics can even function as a profit center (Stock et al., 2002). Companies such as Canon and Xerox routinely remanufacture products. The automobile parts market (the focus of the current research) provides another example of an industry that recovers product for remanufacture. However, the automobile parts industry does not rely entirely upon remanufacturing as their only reverse logistics product disposition strategy. Based upon interviews with reverse logistics managers in the industry, five disposition strategies emerged as the most used:
(1) destroying;
(2) recycling;
(3) refurbishing;
(4) remanufacturing; and
(5) repackaging of returned products.
Products are destroyed when they cannot be sold/used at the current location and return is not feasible (perhaps because of prohibitively high transportation costs, too low of volume to warrant additional handling, etc.). For the destroyed disposition category, only two performance indicators – economic performance and operational responsiveness – were examined. Operational service quality is not applicable in this category. For the other four disposition strategies, all three-performance indicators (discussed in the next section) were examined.
Recycling – taking product back for re-work or disposal – is often mandated by regulation. Recycling is also chosen when materials in the original product can be used for another product or subassembly.
Refurbishing and remanufacturing differ with respect to the degree of improvement, i.e. the amount of effort needed to up-grade the product (Rogers and Tiben-Lembke, 1999). Remanufacturing involves the greater effort of the two. In both instances, the re-worked product is subsequently re-sold.
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Repackaging is self-explanatory. No re-work or remanufacturing is required, but the product is repackaged to prepare it for reshipment and re-sale.
3.2 Resource commitment A primary challenge for businesses today is to direct the focus and level of resource commitment (Amaldos et al., 2000). Previous empirical research has confirmed the importance of resource commitment. As more resources – financial, human, and physical resources – are committed, the program or process is more likely to show superior performance (Isobe et al., 2000). Thus, the current research examined commitment of technological, managerial, and financial resources.
3.3 Performance Both economic and operational performance were examined in the research. Logistics and supply chain managers work to achieve the best balance between level of service provided and the cost to provide the service. The intent is to maximize economic performance. Therefore, an evaluation of economic performance is an important indicator of a firm’s reverse logistics performance. Two types of operational performance were measured – operational responsiveness and operational service quality. Operational responsiveness deals with promptness in the returns handling process – ease of obtaining return authorization, length of time for credit processing, and handling of reconciliation of charge-back. Operational service quality measures condition and timeliness of re-work or repairs related to returns.
3.4 Disposition strategy-performance hypotheses The earlier discussion provided support for a strategy – performance relationship. Selection of a good strategy, one with a good “fit” for the organization, should contribute to enhanced effectiveness and superior performance (Slater and Olson, 2000). The current research tests this proposal by examining the relationship between selected type of disposition strategy and firm performance in terms of economic performance, operational responsiveness, and operational service quality. The hypotheses (H1 through H5) for the five disposition strategies follow:
H1. The strategic decision to destroy returned products is positively related to reverse logistics: H1a. economic performance; and H1b. operational responsiveness.
H2. The strategic decision to recycle returned products is positively related to reverse logistics: H2a. economic performance; H2b. operational responsiveness; and H2c. operational service quality.
H3. The strategic decision to refurbish returned products is positively related to reverse logistics: H3a. economic performance; H3b. operational responsiveness; and H3c. operational service quality.
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H4. The strategic decision to remanufacture returned products is positively related to reverse logistics: H4a. economic performance; H4b. operational responsiveness; and H4c. operational service quality.
H5. The strategic decision to repackage returned products is positively related to reverse logistics: H5a. economic performance; H5b. operational responsiveness; and H5c. operational service quality.
3.5 Resource commitment-performance hypotheses RBV serves as the theoretical foundation for the final hypothesis. If an organization has the appropriate internal resources, performance can be positively influenced (Conner, 1991). By focusing resources, strategies can be implemented more effectively (Barney, 1991).
H6. Regardless of the disposition strategy selected, the commitment of resources specifically to reverse logistics programs is positively related to reverse logistics: H6a economic performance; H6b operational responsiveness;and H6c operational service quality.
Figure 1 presents the conceptual model with hypotheses.
4. Methodology-empirical examination of conceptual model The following section illustrates the methodology of the research including the field based interviews used to develop the survey instrument. Psychometric concerns regarding scale reliability and validity are also addressed.
4.1 Survey instrument A general review of the literature and depth interviews with logistics professionals following commonly employed investigative techniques provided the foundation for the study. Interviews were conducted with eight people – six operations managers actively involved in and familiar with reverse logistics and two academics with reverse logistics-related operations research backgrounds. The interviews lasted between 45 and 60 min. Using the interviews as a guide, previously published works were canvassed and a questionnaire was developed. The questionnaire was pre-tested by an additional eight people – three business executives, two consultants, and three academics – for clarity and completeness. Feedback received during this subsequent review of the questionnaire was incorporated into the final version of the survey.
4.2 Data collection The sampling frame was chosen from companies who specialize in auto parts. This group is a large trade association representing companies involved in all aspects of the auto parts industry. A random sample of the membership provided 400 selected companies and a total of 118 companies responded to the survey (32 percent)[1].
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The surveys were mailed to the senior supply chain operations professional in the company under the assumption that this person would have knowledge of company-implemented reverse logistics programs. If the recipient did not feel qualified to provide the necessary information, he or she was asked to forward the survey to the appropriate manager. The initial mailing was sent to 150 companies. An incentive of $1 was included with the survey. Subsequently, packets were mailed to the remaining companies including a $2 incentive in order to increase the response rate. Three weeks later, a follow-up mailing was sent to the nonrespondents from each wave. Follow-up phone calls were also made after each mailing. To test for nonresponse bias, a MANOVA wave analysis was performed to compare late and early respondents across key variables (Armstrong and Overton, 1977). Variables examined include firm size, industry, and age of relationship for respondents of each of the four waves. No significant differences were discovered (a ¼ 0.05).
4.3 Operational measures and psychometric concerns A number of previous supply chain and operations studies were helpful in the operationalization of constructs; scales were modified as necessary to fit the auto parts context. The complete scales for each construct including descriptive statistics, exploratory factor analysis, and a coefficients are provided in the Appendix.
Resource commitment was measured on a three-item scale (Das and Teng, 2000). Utilizing a seven-point range, respondents were asked to indicated level of commitment (1 ¼ little and 7 ¼ substantial) of technological, managerial, and financial resources.
Figure 1. Conceptual model
Destroyed
Recycled
Refurbished
Remanufactured
Repackaged
RL Disposition Strategy Type
Economic Performance
Operational Responsiveness
Operational Service Quality
Resource Commitment
H1– H5
H6
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Mean scores for the three items ranged from 3.06 to 3.31 indicating low levels of commitment of resources to reverse logistics programs at the respondent firms.
Performance was measured in three areas: economic performance and two types of operational performance, i.e. responsiveness and service quality (Richey et al., 2005). The differentiation between Operational responsiveness and Operational service quality can be defined by the underlying mechanisms that they are measuring. Operational responsiveness is concerned with the effectiveness of the processes and procedures that are required during the returns process and have direct impact on partners and customers. Operational service quality is measuring the parameters of efficiency and timeliness regarding the activities directly impacting the physical product and is mainly seen as an internal cost for the firm. A five-item measure was used to gauge economic performance in terms of asset recovery, cost containment, profitability, labor productivity, and reduced inventory investment on a seven-point scale (1 ¼ not at all effective and 7 ¼ extremely effective). Mean scores range from 4.18 to 4.65 indicating moderate levels of success or effectiveness. Operational responsiveness was measured on a 7-point scale (1 ¼ not at all capable and 7 ¼ extremely capable). Three items were used to measure operational responsiveness. Mean scores for the three items ranged from 5.14 to 6.10. The respondent firms have developed the capability to process “paperwork” requirements in a prompt manner. Two operational service quality items were measured; mean scores were 4.64 for timeliness and 5.42 for quality. The respondent firms appear to be fairly adept with respect to the operational-related performance dimensions examined.
Table I displays zero-order product moment correlations of the variables. Each of the reliability estimates exceeds the suggested minimum a of 0.70 (coefficient a ¼ 0.78-0.89) (Nunnally, 1978). Discriminant validity was also assessed using a procedure advocated by Gaski and Nevin (1985). A correlation between two scales that is lower than the reliability of each of those scales is indicative of good discriminant validity. All scales had reliability estimates in excess of the between-scale correlations. Factor analysis using maximum likelihood estimation and varimax rotation was conducted to further define measurement quality (Anderson and Gerbing, 1988; Gerbing and Anderson, 1988). Analysis of the scale items produced uni-dimensional constructs, each with an eigenvalue .1. As mentioned earlier – the suggested minimum coefficient a of 0.70 (Netemeyer et al., 1995; Nunnally and Bernstein, 1994) was observed for all constructs.
Variable No. of items Mean SD 1 2 3 4
1. Resource commitment 3 9.45 5.56 (0.87) 2. Economic performance 5 21.77 7.76 0.122 (0.89) 3. Operational responsiveness 3 5.14 4.07 0.170 0.374 * * (0.81) 4. Operational service quality 2 10.06 3.08 0.251 * 0.377 * * 0.374 * * (0.78)
Notes: Coefficient a on diagonal. * p , 0.05; * *p , 0.01
Table I. Descriptive statistics and
zero-order product moment correlations
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5. Statistical analysis and results The hypotheses were tested by using multiple regression analysis in a two-step sequence. First, each of the H1 through H5 concerning the influence of the disposition strategy on the reverse logistics performance outcomes was tested using multiple regression analysis. Step two tested the relationships between disposition strategy selection and reverse logistics performance and the resource commitment that moderates these effects (H6). Moderated multiple regression was used as suggested by Stone and Hollenbeck (1989) and subsequently used by Morgan and Piercy (1998).
Each of the antecedent variables was first regressed on to each performance outcome in a standard linear expression ðY ¼ b0 þ b1XÞ. Then, each equation was re-estimated including:
. each of the moderator variables ðY ¼ b0 þ b1X þ b2ZÞ; and
. adding the cross product of the independent and moderator variables ðY ¼ b0 þ b1X þ b2Z þ bXZÞ.
Changes in R 2(DR 2) were assessed to determine if there were significant increases when the cross product (XZ) entered into the regression equation. This provided an indication of the influence of the moderator variable (Stone and Hollenbeck, 1989).
Table II shows the results of the hypotheses testing. The left-hand portion of the table illustrates the direct effects of reverse logistics disposition method on the performance variables. The results indicate that only the destroyed and recycled method choices have a direct effect on one reverse logistics performance outcome. Both disposition methods impact only the operational responsiveness outcome, but quite differently. The destroyed method has a positive impact on operational responsiveness (b ¼ 0.612; p , 0.1), while the recycled method has a serious negative impact (b ¼ 20.959; p , 0.05). Figure 2 details the two direct relationships supporting H1b and H2b.
The right-hand portion of Table II provides the results of the moderated regression when entering resource commitment into the model.
5.1 Model 1 With the destroyed disposition strategy as the independent variable in Model 1, resource commitment is found to have important interaction effects between product destruction and economic performance (b ¼ 0.181; DR
2 ¼ 0.030; p , 0.05) and
product destruction and operational service quality (b ¼ 0.303; DR 2 ¼ 0.086; p , 0.10), indicating that resource commitment is a significant moderator in this model. Both economic performance and operational service quality are positively affected. With operational responsiveness as the dependent variable, no significant interaction can be discussed due to the significant direct effect found in step one above. H6a and H6c are supported.
5.2 Model 2 With the recycled strategy as the independent variable in Model 2, resource commitment is found to have important interaction effects between product recycling and economic performance (b ¼ 0.294; DR 2 ¼ 0.081; p , 0.10), indicating that
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1 4
0 .1
2 6
0 .0
1 3
0 .2
7 9
0 .0
1 1
0 .0
8 2
0 .0
0 3
0 .3
1 6
2 0 .0
6 0
0 .0
9 9
0 .0
0 4
R ef
u rb
is h
ed ,
R es
o u
rc e
C o m
m it
m en
t, R
ef u
rb is
h ed
* R
es o u
rc e
C o m
m it
m en
t
0 .3
8 9
2 0 .1
3 3
0 .0
8 0
0 .1
3 1
0 .0
0 5
0 .1 1 8
0 .0 9 7
0 .3 7 6
0 .1 9 2
0 .1 1 3 *
0 .2
8 4
2 0 .0
4 1
2 0 .0
7 7
0 .1
0 3
0 .0
0 5
R em
a n
u fa
ct u
re d
0 .0
8 3
0 .1
8 6
0 .9
4 9
R em
a n u fa ct u re d :
M o d el 4
0 .4
0 4
0 .1
6 4
– 0 .4
9 3
0 .2
4 3
– 0 .3
3 4
0 .1
1 2
–
R em
a n
u fa
ct u
re d
& R
es o u
rc e
C o m
m it
m en
t 0 .4
5 0
2 0 .0
8 9
0 .1
6 9
0 .0
0 6
0 .3
1 2
0 .3
5 0
0 .3
3 7
0 .0
8 2
0 .3
4 2
2 0 .0
2 1
0 .1
1 2
0 .0
0 0
(c o n ti n u ed
)
Table II. Results of multiple
regression and moderated regression
Reverse logistics disposition
strategies
529
E co
n o m
ic p
er fo
rm a n
ce O
p er
a ti
o n
a l
re sp
o n
si v
en es
s
O p
er a ti
o n
a l
se rv
ic e
q u
a li
ty E
co n
o m
ic p
er fo
rm a n
ce O
p er
a ti
o n
a l
re sp
o n
si v
en es
s O
p er
a ti
o n
a l
se rv
ic e
q u
a li
ty D
ir ec
t ef
fe ct
s b
b b
M o d
er a te
d re
g re
ss io
n fi
n d
in g
s (e
n te
r) B
R 2
D R
2 b
R 2
D R
2 b
R 2
D R
2
R em
a n
u fa
ct u
re d
, R
es o u
rc e
C o m
m it
m en
t, R
em a n
u fa
ct u
re d
* R
es o u
rc e
C o m
m it
m en
t
0 .3
6 6
2 0 .1
0 5
2 0 .2
2 3
0 .2
1 1
0 .0
4 1
0 .1 9 4
0 .3 0 4
0 .3 9 7
0 .4 7 0
0 .1 3 3 *
0 .3
3 8
2 0 .0
2 3
2 0 .0
1 1
0 .1
1 2
0 .0
0 0
R ep
a ck
a g
ed 0 .1
1 0
0 .7
0 7
2 0 .2
7 6
R ep a ck a g ed : M o d el 5
N A
N A
– N
A N
A –
N A
N A
– R
ep a ck
a g
ed &
R es
o u
rc e
C o m
m it
m en
t N
A N
A N
A N
A N
A N
A N
A N
A N
A
R ep
a ck
a g
ed ,
R es
o u
rc e
C o m
m it
m en
t, R
ep a ck
a g
ed * R
es o u
rc e
C o m
m it
m en
t
N A
N A
N A
N A
N A
N A
N A
N A
N A
F 2 .2
3 3
* 1 2 .0
0 3
* *
1 .6
6 0
*
R 2
0 .8
1 7
0 .9
7 3
0 .6
6 4
N o te s :
* p ,
0 .1
0 ;
* * p ,
0 .0
5
Table II.
IJPDLM 38,7
530
resource commitment is a significant moderator in this model for recycling of product and economic performance. Economic performance is positively affected. With operational responsiveness as the dependent variable, no significant interaction can be discussed due to the significant direct effect found in step one above. With operational service quality as the dependent variable, no significant interaction effects are evident (DR 2 ¼ 0.000; p . 0.1). H6a is again supported.
5.3 Model 3 With the refurbished strategy as the independent variable in Model 3, resource commitment is found to have important interaction effects between product refurbishing and operational responsiveness (b ¼ 0.181; DR 2 ¼ 0.030; p , 0.05), indicating that resource commitment is a significant moderator in this model for refurbishing product and operational responsiveness. Operational responsiveness is positively affected. With economic performance as the dependent variable, no significant interaction effects are evident (DR 2 ¼ 0.005; p . 0.1). The same is true for operational service quality (DR 2 ¼ 0.005; p . 0.1). H6b is supported.
5.4 Model 4 With the remanufactured strategy as the independent variable in Model 4, resource commitment is found to have important interaction effects between product remanufacturing and operational responsiveness (b ¼ 0.397; DR 2 ¼ 0.133; p , 0.10), indicating that resource commitment is a significant moderator in this model for remanufacturing of product and operational responsiveness. Operational responsiveness is positively affected. With economic performance as the dependent variable, no significant interaction effects are evident (DR 2 ¼ 0.041; p . 0.1). The
Figure 2. Examination of main
effects
Destroyed
Recycled
Refurbished
Remanufactured
Repackaged
Notes: *p < 0.01; **p < 0.05
Disposition Strategy Type
Economic Performance
Operational Responsiveness
Strategic Performance
Operational Service Quality
b = 0.612*
b = – 0.959**
Reverse logistics disposition
strategies
531
same is again true for operational service quality (DR 2 ¼ 0.000; p . 0.1). H6b is again supported.
5.5 Model 5 Model 5, using the repackaged strategy as the independent variable, cannot be tested. Due to the small number of firms that responded to this question only the direct effects could be examined. Nevertheless, models 1 through 4 provide significant evidence supporting all the important relationships proposed in H6. Figure 3 details the significant moderating impact of resource commitment.
6. Discussion and conclusion Today firms are increasingly confronted with the need to manage the reverse flow of products across the supply chain. Operations and supply chain managers recognize the importance of implementing reverse logistics programs and selecting the most appropriate reverse disposition method. Still, managers complain that the rewards from such programs are yet to be fully realized. As with many new initiatives in supply chain management (e.g. see technology adoption and implementation), performance outcomes related to reverse logistics programs may remain sub-par without appropriate resource commitment.
Figure 3. Examination of moderating effects
Destroyed
Recycled
Refurbished
Remanufactured
Notes: * p < 0.01; ** p < 0.05
Repackaged
Economic Performance
Operational Responsiveness
Operational Service Quality
β =
0. 18
1* *
β =
0. 29
4* *
β =
0. 30
3*
β =
0. 37
6*
β = 0.
39 7*
Resource Commitment
IJPDLM 38,7
532
Retailers struggle with the cost versus customer service tradeoff involved in developing their returns’ policies. Return policies are a signal to customer of convenience and an assurance of quality. The more confident the retailer is with their returns policy, the more the consumer trusts the quality of the retailer’s product. In addition, a retailer’s returns policy is a major component of its customer service offering bundle.
Employing limited resources seems to only work for firms choosing to destroy returned product. In such instances, the selling firms take the “full hit” immediately. Trading partners may be impressed that the returns problem has gone away. However, there are likely to be detrimental effects on cash flow for the firm destroying the product. Recycling programs are even less effective in the absence of resource commitment. When limited resources are applied to recycling programs, sub-optimal results are likely and operational responsiveness suffers. The additional work required and the potential for delays are likely to negatively impact partner satisfaction.
Committing significant resources to a reverse logistics program may very well be the key to realizing superior performance. Although customer service ratings are unlikely to be enhanced (due to the fact that supply chain partners are disappointed in the original condition of the product or the “problem” involved), resource commitment can improve a firm’s economic and operational performance. The current research shows that under instances of active resource commitment to reverse logistics programs, operations and supply chain managers may expect superior performance by choosing destroying, recycling, refurbishing, and/or remanufacturing of product.
6.1 Limitations This study has several limitations that should be noted before generalizing the results to all firms. First, this study is cross sectional looking at essentially one industry (auto parts) and one period of time. Additionally, only firms in North America (US) are examined limiting the studies applicability to an international context. Finally, there are some potential environmental issues (e.g. governmental policy or subsidies) that could be modeled in future studies to better understand strategic intent and performance.
6.2 Managerial decision making Since many firms seem to focus on reverse logistics activities as a must do, we suggest a strategic approach that examines outcomes rather than day-to-day operations. If managers do not have adequate resource support for reverse logistics, they should choose to destroy the product. The other disposition options – recycling, refurbishing, remanufacturing, and repackaging – all require significant resources in order to reclaim value from returns. At the very least, destroying the product will result in improved responsiveness to their partners and, hopefully, result in gains in other areas. The decision choices become more complex and potentially much more beneficial if operations and supply chain managers can get the resource support they need for reverse logistics handling.
Logistics and distribution operations traditionally have dealt with cost/service trade-offs. Keeping this in mind, reverse logistics managers should first be guided by overall corporate or organizational goals. Is the organization’s primary objective financial? Is the organization more service/operational focused? What do key
Reverse logistics disposition
strategies
533
customers want? The answers to these questions can provide direction to the implementation process.
If the primary thrust is cost containment and/or revenue/profit enhancement, then destroying or recycling represent potentially viable disposition strategies. Products should be destroyed when it’s too costly to return the product, the product is very low value or is “perishable,” there’s no alternative market/buyer readily available, etc. Recycling represents an easier decision area. Products are either appropriate for recycling or they are not. Also, in many instances, recycling is mandated by environmental law or strongly encouraged by environmental policy groups.
Alternately, if operational service or responsiveness is top priority, firms may succeed by implementing disposition strategies of destroying, refurbishing, or remanufacturing product. Destroying the product has the result of making the trading partner’s problem “go away.” Sufficient resources can ensure that happens – and that everything is handled in a timely manner. With sufficient resources, processes can be put in place to provide immediate returns authorization, credit processing, and charge-back handling. Customer satisfaction is protected even though it is costly for the selling firm. However, for key customers, such a sacrifice is not only warranted, it may be unavoidable to preserve long-term relations. If products can be refurbished or remanufactured and sufficient resources are committed to the returns handling, similar positive operational responsiveness can result.
Table III provides a summary and guide for making reverse logistics disposition strategy decisions.
7. Implications for future research The limitations of this study open the door for extended research in reverse logistics disposition choices. First, supply chain and operations researchers should extend this study to examine multiple industries. For example, one would expect the food and soft-line supply chains to experience different outcomes in terms of a specific disposition method choice. The same could be true for international markets and for supply chains that cross national borders and experience the impacts of culture, tariffs, quotas, etc. Multiple industry and country analysis should be employed to develop a better theoretical grounding of reverse logistics disposition choice.
Furthermore, additional research may examine the role that the disposition strategies have directly on end customer satisfaction. Today’s consumers are becoming increasingly aware of sustainability and environmental issues. The role of the returns policy is critical to the success of a retailer. An extension of that should examine the importance the product disposal plays in the consumer’s decision making process as well.
There are also opportunities to examine the impact of disposition choice over time. Potentially, firms that practice resource supported reverse logistics will experience learning effects improving performance, or conversely, open the door for abuses by their business partners (returning perfectly good products that are unwanted due to partner error). A longitudinal study could improve understanding of the impact and management of reverse logistics. Another area for potential inquiry is the examination of handling reverse logistics in-house versus outsourcing. Do disposition strategy results vary significantly when external specialists are used? Finally, operations researchers have made only limited attempts to model the impacts of reverse logistics
IJPDLM 38,7
534
S el
ec ti
n g
a re
v er
se lo
g is
ti cs
d is
p o si
ti o n
st ra
te g
y M
a y
: F
o rm
a ll
y co
m m
it ti
n g
re so
u rc
es to
a re
v er
se lo
g is
ti cs
d is
p o si
ti o n
st ra
te g
y M
a y
: E
co n
o m
ic p
er fo
rm a n
ce O
p er
a ti
o n
a l
re sp
o n
si v
en es
s O
p e ra
ti o
n a
l s er
v ic
e q
u a li
ty E
c o
n o
m ic
p er
fo rm
a n
ce O
p er
a ti
o n
a l
re sp
o n
si v
en es
s O
p e ra
ti o
n a
l s er
v ic
e q
u a li
ty
D es
tr o y
ed Im
p ro
v e
Im p
ro v
e Im
p ro
v e
R ec
y cl
ed W
o rs
en Im
p ro
v e
R ef
u rb
is h
ed Im
p ro
v e
R em
a n
u fa
ct u
re d
Im p
ro v
e R
ep a ck
a g
ed
N o te :
B la
n k
sp a ce
s re
p re
se n
t n
o n
-s ig
n ifi
ca n
t fi
n d
in g
s
Table III. Managerial summary of
research results
Reverse logistics disposition
strategies
535
on firm performance (Fleischmann et al., 1997; Linton and Johnston, 2000). The current study offers a solid empirical grounding for detailed modeling of reverse logistics distribution strategy choice and performance.
Note
1. Twenty-eight respondents were dropped from the sample due to bad addresses or having left the company in question (i.e. return to sender).
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Appendix. Scale items (The Appendix, Table AI, follows overleaf.)
Corresponding author Lauren R. Skinner can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints
IJPDLM 38,7
538
C o n
st ru
ct (a
n ch
o rs
) F
a ct
o r2
M ea
n S
D
F a ct
o r
lo a d
in g
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. es
ti m
a te
(t -v
a lu
e)
R es
o u
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co m
m it
m en
ta (L
it tl
e ¼
1 ,
S u
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a n
ti a l ¼
7 ,
a ¼
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P le
a se
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ic a te
th e
le v
el o f
te ch
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E co
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(N o t
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a ll
ef fe
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e ¼
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tr em
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e ¼
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1 :
H o w
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4 .6
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7 0 .7
7 (9
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2 :
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ef fe
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ee n
in h
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co st
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m en
t re
la te
d to
re v
er se
lo g
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cs 4 .5
3 1 .5
3 0 .8
0 (9
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3 :
H o w
ef fe
ct iv
e h
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y o u
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fi ta
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re la
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to re
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g is
ti cs
4 .2
2 1 .5
7 0 .8
9 (1
1 .2
0 )
4 :
H o w
ef fe
ct iv
e h
a s
y o u
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m p
a n
y b
ee n
in h
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d li
n g
la b
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p ro
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it y
re la
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to re
v er
se lo
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ti cs
4 .1
9 1 .4
5 0 .7
8 (9
.3 3 )
5 :
H o w
ef fe
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a s
y o u
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m p
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v en
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in v
es tm
en t
re la
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se lo
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4 .1
8 1 .6
4 0 .6
9 (7
.9 4 )
O p
er a ti
o n
a l
re sp
o n
si v
en es
sc (N
o t
a t
a ll
ca p
a b
le ¼
1 ,
1 :
E a se
o f
o b
ta in
in g
re tu
rn a u
th o ri
za ti
o n
6 .1
0 1 .1
6 0 .6
5 (6
.9 4 )
E x
tr em
el y
ca p
a b
le ¼
7 , a ¼
0 .8
1 5 6 )
2 :
L en
g th
o f
ti m
e fo
r cr
ed it
p ro
ce ss
in g
5 .3
1 1 .4
4 0 .8
9 (1
0 .0
1 )
3 :
H a n
d li
n g
o f
re co
n ci
li a ti
o n
fo r
ch a rg
e- b
a ck
s 5 .1
4 1 .4
7 0 .7
6 (8
.3 8 )
O p
er a ti
o n
a l
se rv
ic e
q u
a li
ty c
(N o t
a t
a ll
ca p
a b
le ¼
1 ,
1 :
Q u
a li
ty o f
re -w
o rk
o r
re p
a ir
5 .4
2 1 .5
4 0 .8
2 (7
.9 1 )
ex tr
em el
y ca
p a b
le ¼
7 , a ¼
0 .7
8 0 8
d )
2 :
T im
el in
es s
o f
re -w
o rk
o r
re p
a ir
4 .6
4 1 .5
1 0 .7
8 (7
.6 1 )
N o te s :
* p ,
0 .1
0 ;
* * p ,
0 .0
5 ;
* *
* p ,
0 .0
1 .
a T
h e
p ro
g ra
m ti
m in
g it
em s
w er
e n
o t
a p
p ro
p ri
a te
fo r
fa ct
o r
a n
a ly
si s,
a s
ea ch
re p
re se
n ts
a si
n g
le m
o m
en t
in a
fi rm
’s li
fe .
T h
u s,
th e
it em
s w
er e
tr ea
te d
a s
(c ro
ss -s
ec ti
o n
a l)
in d
iv id
u a l
in d
ic a to
rs o f
a re
v er
se lo
g is
ti cs
p ro
g ra
m in
it ia
ti o n
S o u r c e s :
b D
a s
a n
d T
en g
(2 0 0 0 );
c R
ic h
ey et
a l.
(2 0 0 5 );
d P
ea rs
o n
C o rr
el a ti
o n ¼
0 .3
7 4
* *
*
Table AI.
Reverse logistics disposition
strategies
539
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
