diffusion of green supply chain management

Diffusion of greensupply chain managementExamining perceived quality of green

reverse logistics

Benjamin T. Hazen and Casey CegielskiDepartment of Management, College of Business, Auburn University, Auburn,

Alabama, USA, and

Joe B. HannaDepartment of Aviation and Supply Chain Management, College of Business,

Auburn University, Auburn, Alabama, USA

Abstract

Purpose – Extant research has yielded conflicting results regarding the relationship betweenadoption of green supply chain management (GSCM) practices and competitive advantage. Thepurpose of this paper is to further investigate this relationship by examining the case of green reverselogistics (GRL).

Design/methodology/approach – Through the lens of diffusion of innovation andresource-advantage theory, the authors examine whether or not consumers perceive products madevia GRL practices to be equivalent to brand-new products in terms of quality. A survey method is used togather data from a diverse sample of 533 participants. Data are analyzed via ANOVA to test thehypotheses.

Findings – The findings suggest that consumers perceive products made via some GRL practices tobe inferior to brand-new products in terms of quality. However, participants indicated no perceivedquality difference between products made with recycled materials and brand-new products.

Research limitations/implications – The findings suggest that adoption of some GSCM practicesmay not necessarily lead to competitive advantage, which may inhibit diffusion of GSCM. This studyis limited by its focus on just one aspect of competitive advantage. Future studies should examine therelationship between GSCM adoption and other measures of competitive advantage.

Practical implications – Understanding that consumers may perceive products made via someGRL activities as being inferior in quality to brand-new products, firms wishing to employ GRL maywish to compete on other dimensions, such as low price or service.

Originality/value – This research corroborates previous research findings that suggest adoption ofGSCM may not lead directly to competitive advantage. Future research is suggested to continue tobuild this body of literature.

Keywords Supply chain management, Logistics management, Reverse logistics, Green logistics,Sustainability, Diffusion of innovation, Resource-advantage, Quality

Paper type Research paper

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

www.emeraldinsight.com/0957-4093.htm

The views expressed in this article are those of the authors and do not reflect the official policy orposition of the United States Air Force, Department of Defense, or the US Government.

Greensupply chainmanagement

373

The International Journal of LogisticsManagement

Vol. 22 No. 3, 2011pp. 373-389

q Emerald Group Publishing Limited0957-4093

DOI 10.1108/09574091111181372

1. IntroductionSustainability is an emerging business megatrend that is causing a fundamental shift inthe competitive landscape (Lubin and Esty, 2010) and quickly becoming a key driver ofinnovation (Nidumolu et al., 2009). As such, businesses in all areas of the supply chainare considering the adoption of a variety of sustainability initiatives in order to achievecompetitive advantage, or at least maintain a competitive parity. Environmentalsustainability practices in the supply chain are often referred to as green supply chainmanagement (GSCM), which has become a topic of interest for both business leaders andacademic researchers alike (Nikbakhsh, 2009; Sarkis, 2003). However, the literature inthis area is not broadly developed and the implications regarding adoption and diffusionof various GSCM practices are not well understood (Srivastava, 2007). As such,Sarkis et al. (2011) posit that diffusion of innovation may provide an appropriatetheoretical basis for additional GSCM research.

Regarding diffusion of innovation in the supply chain, Grawe (2009) proposed aconceptual model of logistics innovation, where competitive advantage mediates therelationship between a logistics innovation and the diffusion of that innovation. Basedon resource-advantage (R-A) theory (Hunt and Morgan, 1995, 1996) and his review ofprevious diffusion of logistics innovation research (Persson, 1991), Grawe (2009)proposed that logistics innovation is positively related to a firm’s competitiveadvantage. In turn, when other firms observe the competitive advantage realized viaadoption of the innovation, Grawe (2009) proposed that additional firms willsubsequently seek to adopt the innovation, which then perpetrates the diffusion of theinnovation within the industry.

Unfortunately, empirical research investigating the relationship between competitiveadvantage and GSCM adoption is scarce. Although conceptual literature supports theproposition that GSCM leads to competitive advantage (Markley and Davis, 2007), fewstudies have tested this relationship. In addition, the limited quantity of empirical workin this area has yielded some conflicting results. Some research suggests thatimplementation of GSCM is not directly linked to measures of competitive advantage(Kim, 2011); other studies have found such a relationship to be significant (Rao and Holt,2005; Zhu and Sarkis, 2004).

In this study, we seek to contribute to the literature by further investigating therelationship between GSCM adoption and competitive advantage. To do so, theremainder of this manuscript is organized as follows. First, we review extant GSCMliterature and introduce a common operationalization of GSCM, green reverse logistics(GRL). This study uses the case of GRL to investigate diffusion of GSCM; thus, the idea ofGRL is developed through discussion of the overlap between GSCM and RL. We thenreview literature regarding perceived quality, where we describe why we use perceivedquality as a proxy for competitive advantage. Next, we review literature on diffusion ofinnovation theory, which leads to a discussion of Grawe’s (2009) logistics innovationmodel and R-A theory. This discussion builds the case for our study’s hypotheses. Wefollow with an explanation of our research methodology, where we describe a surveymethod to measure relationships between GRL adoption and consumers’ perceivedquality. The findings of the research are then reported and our hypotheses are tested.Finally, conclusions are discussed, to include practical and theoretical implicationsalong with recommendations for future research.

IJLM22,3

374

2. Literature review2.1 GSCM and GRLSrivastava (2007, pp. 54-5) defines GSCM as:

[. . .] integrating environmental thinking into supply-chain management, including productdesign, material sourcing and selection, manufacturing processes, delivery of the finalproduct to the consumers as well as end-of-life management of the product after its useful life.

Indeed, there exists a wide spectrum of opportunity to implement GSCM initiativesthroughout the supply chain. However, end-of-life product management has emergedas one of the primary strategies for operationalizing GSCM (Zhu et al., 2008). RL is theterm commonly used to describe end-of-life product management and is defined as:“[. . .] the role of logistics in product returns, source reduction, recycling, materialssubstitution, reuse of materials, waste disposal, and refurbishing, repair, andremanufacturing” (Stock, 1998, p. 20).

Extant literature investigates the overlapping features of GSCM and RL (Murphy andPoist, 2000; Rogers and Tibben-Lembke, 2001; Van Hoek, 1999). Recycling, reusing, andremanufacturing are considered to be RL functions that also serve to green the supplychain (Rogers and Tibben-Lembke, 2001). These functions embody what we will refer toas GRL (Van Loon, 2010). Although these RL functions have been previously employedfor uses other than going green, the concept of employing RL for the purpose ofimplementing GSCM may be thought of as an innovation because it has the ability toprovide new business opportunities (Afuah, 2003). Thus, our study examines theimplementation of GSCM through the lens of diffusion of innovation, where weoperationalize GSCM via investigation of GRL. Before we further discuss our study’sdiffusion of innovation underpinnings, we must first introduce some additional keyconcepts; we now briefly define the GRL concepts of reuse, remanufacture, and recycleand then introduce the concept of perceived quality.

2.1.1 Reuse. The option for direct reuse presents itself when a customer returns anunused product back to the place of purchase (usually the retailer), thus injecting theproduct back into the supply chain. This process includes products that are completelyunused and products that are returned after such light use that upgrade is not requiredin order to return the product to new status. Reuse also refers to the utilization ofreusable packaging or shipping materials. At the retailer level, once the product is nolonger serviceable or requires some sort of remanufacturing activity (i.e. cleaning,replacing accessories, repackaging, etc.) direct reuse is no longer an option. At thisjuncture, the product must be sent further down the RL chain for remanufacturing inorder to return it to new condition. For purpose of general definition, a product can onlybe available for reuse if the location in which it resides in the supply chain possessesthe capability to return the product to retail condition.

2.1.2 Remanufacture. Remanufacturing encompasses repairing, refurbishing, oroverhauling an item in order to extend the life of and derive value from the originalcore unit. The decision to remanufacture occurs when the possibility of direct reuse iseither no longer available (the product is in used condition) or not economical (there isno longer a market requirement for the product). If managed properly, this option cancreate lucrative business opportunities through recapturing otherwise lost value(Clendenin, 1997; Giuntini and Andel, 1995). Remanufacturing denotes improving theproduct from its current condition (e.g. end-of-life) to that of a condition


375

acceptable for reuse. The upgraded condition can vary greatly, depending on theremanufacturing technique chosen and the strategic purpose of the upgrade.

2.1.3 Recycle. Recycling is the process of recovering any piece of a returned productthat may contain value. This process encompasses a wide variety of activities, fromcannibalizing entire subassemblies to extracting materials to resell as a commodity.Early literature in RL focused on recycling (Guiltinan and Nwokoye, 1975; Pohlen andFarris, 1992). In fact, some assert that RL has been most closely associated withrecycling and environmental matters (Daugherty et al., 2002). Extracting value from thereturned product, ensuring regulatory compliance, and investigating green implicationsare popular topics in recycling research. For example, a variety of case studies catalogfirms that recover value from used products, such as tires, paper, paint, and beveragecontainers (Roy et al., 2006).

2.2 Perceived quality as a measure of competitive advantagePorter (1980) describes three generic strategies for competing in the marketplace:low-cost leadership, differentiation, and focus. One avenue for creating an advantagewith differentiation is via building brand reputation (Grant, 1991). In fact, in hisdiscussion of differentiation, Porter (1980) refers to caterpillar tractor as an example,which he argues has built a strong brand reputation for offering high-quality products.Because brand reputation is a function of consumer perception of exclusivity, firmswith a reputation for high-quality differentiate themselves from other firms (Porter,1980). When consumers perceive products offered by a given firm to be of higherquality than the firm’s competitors, the firm has achieved a competitive advantage viaachieving superior customer value (Woodruff, 1997). To this end, a variety of studieshave investigated various aspects of quality in terms of its contribution to competitiveadvantage (Kroll et al., 1999). However, we found that investigation of perceivedquality of products made via RL is markedly absent in the literature. Thus, the effect ofadopting GRL products on consumer perception of the quality of the organization’sproducts, and subsequently the competitiveness of the organization, is unknown.

Understanding the differences between quality of new products as opposed to reused,remanufactured, or recycled products has been noted as an area in need of furtherinvestigation (Prahinski and Kocabasoglu, 2006). This lack of understanding isevident in extant operations management literature, where divergent assumptions ofconsumer-perceived quality are used in the creation of optimization models. Forexample, in developing a mathematical model to determine optimal order quantities withremanufacturing across new product generations, Bhattacharya et al. (2006) assume thatnew and remanufactured products are perfect substitutes, implying no perceived oractual differences in quality. Indeed, many research studies have treated items made viaGRL practices and brand-new products as interchangeable (Atasu and Cetinkaya, 2006;Bayindir et al., 2003; Souza et al., 2002; Teunter, 2001; Toktay et al., 2000).

Other studies assume products made via GRL to be of lower perceived quality(Arunkundram and Sundararajan, 1998; Debo et al., 2005; Ferguson and Toktay, 2006;Tan and Kumar, 2006; Vorasayan and Ryan, 2006). For example, Ferguson andToktay’s (2006) research regarding the effect of competition on used product recoverystrategies assumes that new and remanufactured products are of equal quality,but consumers are not willing to pay as much for a remanufactured product asthey are for a new product because of consumers’ perception of lower quality.

IJLM22,3

376

Regardless of the quality assumption employed, many of the studies cited aboveaddress unknown consumer perception of GRL product quality as a limitation. Ourstudy seeks to offer insight into how consumers may perceive these products.

2.3 Diffusion of logistics innovationDiffusion is, “the process in which an innovation is communicated through certainchannels over time among the members of a social system” (Rogers, 2003, p. 5).Innovation is defined as, “an idea, practice, or object that is perceived as new by anindividual or other unit of adoption” (Rogers, 2003, p. 12). Rogers (2003) offers a summaryof how innovations are diffused and presents a generalized diffusion of innovation model.However, researchers in a variety of fields have tailored this general model to fit theirspecific field. For example, authors in the management information systems fielddeveloped an information technology implementation model (Kwon and Zmud, 1987;Zmud and Apple, 1992). Building upon classical diffusion theory, this model has beenused by a variety of authors to investigate diffusion of information technology for use inlogistics (Cooper and Zmud, 1990; Premkumar et al., 1994). Although much of thediffusion literature in logistics has been focused on information technology innovations(Chen et al., 2009; Germain et al., 1994; Patterson et al., 2003, 2004; Williams, 1994),additional innovations peculiar to logistics have been studied. These innovations includeideas such as contingency planning, containerization, and cross-docking (Grawe, 2009;Skipper et al., 2009). Thus, although innovation diffusion models borrowed from otherdisciplines may be helpful for investigating some logistics innovations, the logistics fieldrequires its own innovation model in order to address the specific nuances of the logisticsfield and the innovations used for logistics. In this regard, Grawe (2009) presented alogistics innovation model, which is based upon a critical survey of diffusion ofinnovation and logistics literature and grounded in R-A theory.

As Grawe’s (2009) research suggests, past logistics innovation studies haveuncovered a wide variety of antecedents to innovation adoption. In addition, Grawe’s(2009) model suggests that competitive advantage is a direct outcome of innovationadoption. The model further suggests that widespread diffusion of the innovation is afunction of additional firms noting the competitive advantage gained by the firms thathave already adopted the innovation, thus motivating these additional firms to adoptthe innovation. This proposed model is grounded in R-A theory, which provides thetheoretical justification for the proposed relationships discussed above.

As described by Hunt and Morgan (1995, 1996), the R-A theory of competition positsthat organizations seek competitive advantage in the marketplace via obtaining acomparative advantage in resources. This competitive advantage then translates intosuperior financial performance. Conversely, a comparative disadvantage leads to acompetitive disadvantage in the marketplace and, subsequently, inferior financialperformance. Finally, parity in resources leads to a parity in market position, whichleads to parity in financial performance. In sum, if R-A theory can be extended toexplain competitive factors in logistics innovation adoption, then the adoption ofGSCM should invariably lead to outcomes that provide competitive advantage.However, limited investigation and conflicting research findings (Kim, 2011; Rao andHolt, 2005; Zhu and Sarkis, 2004) have garnered inconclusive evidence regarding therelationship between GSCM adoption and competitive advantage.


377

Although the efficacy of the R-A theory of competition has been demonstrated in themarketing literature (Hunt and Madhavaram, 2006) and even in some facets of thesupply chain literature (Hunt and Davis, 2008), the theory is still largely untested andhas not been thoroughly extended to other disciplines (Griffith and Yalcinkaya, 2010).Thus, in an attempt to extend R-A theory to logistics innovation and further validatethe logistics innovation model presented by Grawe (2009), our study tests whether ornot adoption of GSCM (operationalized in our study as GRL) is positively relatedto competitive advantage (operationalized in our study as consumer perception ofquality). As such, we developed the following hypotheses:

H1. Participants will perceive that a reused product is lower in quality than a newproduct.

H2. Participants will perceive that a remanufactured product is lower in qualitythan a new product.

H3. Participants will perceive that a product made with recycled materials islower in quality than a new product.

Figure 1 shows how the above hypotheses integrate into Grawe’s (2009) logisticsinnovation model. Grawe’s (2009) model proposes that logistics innovation is directlyrelated to competitive advantage. We test this proposition by examining how adoptionof GRL (logistics innovation) affects consumers’ perceived quality (competitiveadvantage).

3. Research design and methodologyThe purpose of this study is to investigate the relationship between GSCM adoptionand competitive advantage. To explore this relationship, we use GRL as a proxy forGSCM and perceived quality as a proxy for competitive advantage. Because testing thehypotheses presented requires measuring consumer perceptions of quality, a surveymethodology was employed. The remainder of this section describes our method forbuilding the study’s instrument and collecting data.

3.1 Instrument developmentGarvin (1987) theorizes that product quality is comprised of eight basic dimensions, whichare: performance, features, reliability, conformance, durability, serviceability, aesthetics,and perceived quality. This last dimension, perceived quality, is defined as individuals’subjective judgment of the degree of conformance to requirements (Larson, 1994).

Figure 1.Research model inreference to Grawe’s(2009, p. 364) logisticsinnovation model

Environmental factorsOrganization of labor (-)CompetitionCapital scarcity

Organizational factorsKnowledgeTechnologyRelationship network factorsFinancial resourcesManagement resources

Logistics innovation(Green reverse logistics)

Logistics innovation diffusion

Competitive advantage(Consumers’ perceived quality)

H1-H3

IJLM22,3

378

Garvin’s conceptualization of quality is used often in the literature as a basis formeasuring product quality to an industry or artifact of interest (Curkovic et al., 2000;Larson, 1994; Safizadeh and Ritzman, 1996; Vickery et al., 1997). Using Garvin’s (1987)dimensions of quality as the basis for item development, Larson (1994) developed ameasure of perceived quality. Principal components analysis conducted by Larson (1994)indicated the items load on one factor. Cronbach’s a was 0.82.

We adapted Larson’s (1994) perceived quality measure to construct a measure ofperceived quality. Six items each were used to measure perceived quality of reusedproducts, remanufactured products, and products made with recycled materials. Ourquestionnaire asked participants to compare reused items, remanufactured items, anditems using recycled materials with brand-new items. A five-point Likert-type scale wasemployed and ranged from “new is much higher” to “new is much lower.” Example itemsare: “degree to which remanufactured items perform as intended” and “lifespan of itemsmade with recycled materials.”

The instrument was adapted into a web-based format for ease of distribution. Asidefrom the survey items, standard definitions of reuse, remanufacturing, and recyclingwere provided at the top of the survey so as to facilitate consistent understanding of theterms. In order to enhance construct validity, a pilot test and pre-test were conducted.Two logistics professionals and three university professors who have publishedextensively in the logistics field reviewed the instrument for content. Interviews wereconducted with each reviewer and feedback was garnered regarding the wording ofquestions, technical functions of the web-based survey, and general concerns regardingthe validity of the survey.

Upon minor revisions suggested via the pilot test, the survey was administered to aclass of 40 graduate students in the college of business at a large Southeastern university.Pre-test results from the sample of 29 respondents indicated consistent responses withineach item and no apparent issues with the instrument or technical services. No furtherchanges were made to the instrument after the pre-test was completed.

3.2 Data collectionThe target population for this study encompasses individuals who have experienceusing products made via GRL. As such, the pool of prospective participants is quitelarge and a variety of samples may be selected that will adequately representthe population. Three sampling frames were solicited and combined into one compositesample. Participants for this study are derived from the following:

. a cross-section of executives, technical architects, and sales managers at awest-coast branch of a Fortune 500 information technology services firm(referred to as the “corporate” sample);

. active duty military aircraft maintenance managers and technicians located inthe Northeastern USA (referred to as the “maintenance” sample); and

. students from a large Southeastern university, a small Southeastern university,and a large Northern university (referred to as the “student” sample).

These diverse samples were chosen in order to represent a wide variety ofdemographics, geographic locations, and experience levels with GRL activities suchthat the authors could explore any significant differences in perception. Data collectionprocedures for each of these samples are outlined below.


379

Student participants for this study were solicited via their course instructors atthree separate institutions. Instructors teaching various business courses e-mailed alink to the web-based survey to their students on behalf of the researchers. Eachstudent in each targeted class was afforded the opportunity to take the survey. Sincedata collection was anonymous, it was not possible to identify responders andnon-responders, so no reminders were sent to specific individuals, yet class-widereminders were distributed after two weeks. A total of 539 student participants weresolicited and 352 surveys were completed for a response rate of 65.3 percent.

The corporate sample was solicited via a senior executive at the company who agreedto e-mail a link to the web-based survey to peers and subordinates on behalf of theresearchers. Participants were asked to forward the survey to others in the organizationto maximize participation. A reminder was sent by our executive contact after two weeks.Although this procedure does not allow for calculation of response rate, past research(Rogelberg et al., 2002; Stanton, 1998) recommends this practice when research is notconcerned with the particular organization. In sum, this process yielded 95 responses.

The maintenance sample was solicited via a senior-level maintenance managerwithin a military aircraft maintenance unit. An e-mail with a link to the web-basedsurvey was distributed to every member of the unit comprised of approximately450 individuals. A reminder was sent to the same distribution list after two weeks.In sum, 106 responses were generated from the maintenance sample for a response rateof 23.6 percent. Participant demographics are illustrated in Table I.

3.3 Addressing validity threatsWe assessed non-response bias using wave analysis as suggested by Rogelberg andStanton (2007). Participants who provided responses after the initial two weeks of datacollection (when reminders were sent) were identified as late responders. Surveyresponses from these late responders were then compared with those given by thosewho responded in the first two weeks of data collection. T-tests were conducted on arandom selection of six survey items. We found no significant differences in responses,thus suggesting that non-response bias was not of concern in this study.

We analyzed our data for missing values using PASW 18. We conducted Little’smissing completely at random test. Results of this test suggest that missing data arenot dependent upon either the observed data or other missing data. Missing values

Student samplen ¼ 352

Maintenancesample n ¼ 106

Corporatesample n ¼ 95

Aggregatesample n ¼ 553

Counta % Count % Count % Counta %

GenderMale 215 61.3 80 75.5 57 60.0 352 63.7Female 136 38.7 26 24.5 38 40.0 200 36.3Age18-22 228 64.7 15 14.2 0 0.0 243 43.823-25 82 23.3 33 31.1 13 13.7 127 23.026-35 32 9.1 43 40.6 46 48.4 122 22.136-45 9 2.6 14 13.2 21 22.1 44 8.046 þ 1 0.3 1 0.9 15 15.8 17 3.1

Note: aOne participant did not indicate genderTable I.Participant demographics

IJLM22,3

380

represented only 0.8 percent of the total survey item responses and the estimation,maximization algorithm was used to estimate and impute missing values.

As recommended by Podsakoff and Organ (1986), we used Harmon’s one factor testto determine if common method bias was a threat. Analysis of the unrotated factorsolution revealed that no general factor accounted for more than 50 percent of thevariance, thus common method bias does not appear to be a problem.

4. ResultsTo begin our analysis, ANOVA was used to investigate potential mean differences acrosssamples and demographics. Both within- and between-sample Tukey tests were conductedto determine if any sample or demographic significantly differed from the aggregatesample means. Less than 5 percent of all possible age/gender/sample comparisons differedsignificantly. This suggests that demographic traits examined in this study (age, gender,geographic location, vocation) do not significantly affect consumer perception of quality.As such, we deemed it appropriate to analyze the results in aggregate.

One-way ANOVA was used to determine significant differences between perceivedquality of brand-new products and products made via GRL. A mean of three indicatesthat participants feel that the GRL product is the same as new in terms of quality.Results of the analysis are illustrated in Table II.

Using the results in Table II, we used a mean score range to test each of ourhypotheses. If the mean score is between 2.76 and 3.25, we conclude that participantsfeel that the GRL product is similar to brand-new products in terms of quality. If themean score is between 1.00 and 2.75, we conclude that participants feel that the GRLproduct is lower in quality than brand-new. If the mean score is between 3.26 and 5.00,we conclude that participants feel that the GRL product is higher in quality thanbrand-new. Table III summarizes the results of the hypothesis tests.

Hypothesis Result

H1. Participants will perceive that a reused productis lower in quality than a new product

Accept

H2. Participants will perceive that a remanufacturedproduct is lower in quality than a new product

Accept

H3. Participants will perceive that a product madewith recycled materials is lower in quality than anew product

RejectTable III.

Hypothesis results

Cronbach’s a Meana SD Sig.b

Reuse quality 0.84 2.63 0.41 ,0.01Remanufacture quality 0.82 2.57 0.44 ,0.01Recycle quality 0.79 2.89 0.42 ns

Notes: aFive-point scale: 1 – new is much higher, 3 – new is similar, 5 – new is much lower;bsignificantly differs from brand-new product

Table II.Results


381

5. DiscussionThe results suggest that participants perceive reused and remanufactured products asbeing of lower quality than commensurate new products. In addition, the resultssuggest that participants view products made with recycled materials to be of similarquality to that of commensurate new products. These findings render both practicaland theoretical implications in regard to the diffusion of GRL and GSCM.

5.1 Theoretical implicationsIn regard to diffusion of GRL, the findings suggest that consumer perception of quality mayhinder diffusion of some of these practices. According to Grawe’s (2009) diffusion model, afirm chooses to adopt a logistics innovation only if it yields a competitive advantage. Thisstudy suggests that firms may hesitate to adopt reuse or remanufacturing becauseconsumers may perceive that quality of these products are inferior to brand-new products,which may reduce the firm’s competitiveness. However, the results suggest no perceiveddifference in quality between products made with recycled materials and brand-newproducts. Thus, the findings suggest that not all GRL activities are perceived as equal.

As demonstrated in Table II, the mean perception of quality scores associated withrecycling are greater than those associated with remanufacturing and reusing,indicating that consumers may have a more favorable view of products made withrecycled materials than with remanufactured or reused products. Although theseresults suggest that firms that wish to compete in terms of quality should feel morecomfortable using recycled materials in the manufacture of their products than if theywere to practice remanufacturing or reuse, these findings contradict the currentliterature in regard to which process may be most desirable to adopt.

Based on the work of Stock (1992) and Kopicki et al. (1993), Carter and Ellram (1998)conceptualized the idea of a RL hierarchy. This hierarchy posits that RL activities thatrequire the least amount of resources in order to extract the greatest amount of valueshould be pursued first. Others (Prahinski and Kocabasoglu, 2006; Rogers et al., 2002;Staikos and Rahimifard, 2007) have since refined the RL hierarchy. In order from mostdesirable to least desirable, these RL activities are:

. reuse;

. product upgrade;

. materials recovery; and

. waste management.

Once the most ideal option is no longer feasible, the firm should move down thehierarchy to the next most value-producing and least resource-demanding option.

Following this logic, the activities of reuse, remanufacturing, and recycling may beranked ordinally from most to least green; when fewer resources are required by a processto return a product to market, the less impact the process has on the environment.Furthermore, we assume that the less work required to release a product back into themarket, the closer it should be to new in terms of quality. Thus, the GRL options shouldlogically be ordered from most desirable to least desirable as follows:

. reuse;

. remanufacture; and

. recycle.

IJLM22,3

382

This proposed hierarchy is shown in the left side of Figure 2.In contrast, the results of this study suggest that consumers do not perceive the same

hierarchy, as shown in the right side of Figure 2. This may be because consumersassociate “recycled” with being desirable whereas there is more ambiguity in regards toexactly what happens when a product is remanufactured or reused; consumers may lacka general understanding of the green implications of these processes. More research isrecommended in this area to determine why recycling is held in higher regard byconsumers when compared to the other GRL activities. Not only should researchinvestigate the actual quality of these products to determine if our proposed hierarchy inFigure 2 holds, but should also investigate why perceptions differ from reality.

In regard to these perceptual differences, we suggest that recycling may beperceived as better than reuse or remanufacturing because of extensive marketing andmedia campaigns that espouse the benefits of recycling. Consumers may be morefamiliar with recycling than with reusing or remanufacturing, which may lead to morepositive perceptions. For example, many cities collect recyclable materials separatelyfrom other household refuse. Subsequently, the Environmental Protection Agency andlocal governments often advertise the importance and significance of recycling(Environmental Protection Agency, 2011). However, there are many other activitiesthat are just as green (if not more so) than recycling that do not receive such attention.Research aimed at specifically comparing consumer perception of recycling to othergreen activities may yield interesting results. We suspect that perceptions may bebiased toward the more ubiquitous green activities and that perception is notnecessarily aligned with reality.

5.2 Practical implications and limitationsResults indicate that consumers view products made via remanufacturing or reusingactivities as being lower in quality than new products. This is consistent with previousliterature that assumes consumers perceive these products to be of lower quality(Arunkundram and Sundararajan, 1998; Debo et al., 2005; Tan and Kumar, 2006;Vorasayan and Ryan, 2006). In accordance with Porter’s (1980) three genericcompetition strategies, this implies that firms that choose to employ reuse andremanufacturing should adopt a competitive strategy to compete on price, focus,

Figure 2.GRL hierarchy

Reuse

-----------------

Remanufacture

-------------------------------

Recycle

Recycle

-----------------

Remanufactureand

Reuse

Most Desirable

Least Desirable

PerceptionReality


383

or another facet of differentiation. For example, firms who use remanufacturing orreuse may expect to offer consumers lower pricing or greater levels of service if theywish to compete with new products.

In contrast, our findings suggest that firms that employ recycling and wish tocompete on quality may not be negatively affected. This presents a win-win situationfor those adopting recycling – firms may be able to save money on materials, present a“green” image, and still compete with brand-new products.

This study is limited by its focus on just one aspect of competitive advantage.Future studies should examine the relationship between GRL adoption and othermeasures of competitive advantage. In addition, other GSCM activities should beexamined in the same manner.

6. ConclusionGreen practices and environmental stewardship are beginning to shape our economyand drive the way in which firms compete (Lubin and Esty, 2010). Thus, furtherunderstanding of the implications of this megatrend is needed. Our study offers someinitial insight into this area via utilizing R-A and diffusion of innovation theories alongwith a recently published logistics innovation diffusion model as a framework toassess how consumer perception of quality may affect the diffusion of GSCM. Wefound that participants view remanufactured products and reused products to be oflesser quality than brand-new products. Products made with recycled materials werefound to be perceived by participants as being equal to brand-new products in terms ofquality. Industry professionals can use the results of this study to better understandthe perceptions of their consumers when deciding whether or not to adopt GRL.Researchers can use this study as a starting point to further investigate therelationship between GSCM and competitive advantage.

References

Afuah, A. (2003), Innovation Management: Strategies, Implementation and Profits, Oxford,New York, NY.

Arunkundram, R. and Sundararajan, A. (1998), “An economic analysis of electronic secondarymarkets: installed base, technology, durability and firm profitability”, Decision SupportSystems, Vol. 24 No. 1, pp. 3-16.

Atasu, A. and Cetinkaya, S. (2006), “Lot sizing for optimal collection and use of remanufacturablereturns over a finite life-cycle”, Production and Operations Management, Vol. 15 No. 4,pp. 473-87.

Bayindir, Z.P., Erkip, N. and Gullu, R. (2003), “A model to evaluate inventory costs in aremanufacturing environment”, International Journal of Production Economics, Vols 81/82,pp. 597-607.

Bhattacharya, S., Guide, J., Daniel, R., V. and Van Wassenhove, L.N. (2006), “Optimal orderquantities with remanufacturing across new product generations”, Production andOperations Management, Vol. 15 No. 3, pp. 421-31.

Carter, C.R. and Ellram, L.M. (1998), “Reverse logistics: a review of the literatureand framework for future investigation”, Journal of Business Logistics, Vol. 19 No. 1,pp. 85-102.

IJLM22,3

384

Chen, J.V., Yen, D.C. and Chen, K. (2009), “The acceptance and diffusion of the innovative smartphone use: a case study of a delivery service company in logistics”, Information& Management, Vol. 46 No. 4, pp. 241-8.

Clendenin, J.A. (1997), “Closing the supply chain loop: reengineering thereturns channel process”, International Journal of Logistics Management, Vol. 8 No. 1,pp. 85-102.

Cooper, R.B. and Zmud, R.W. (1990), “Information technology implementationresearch: a technological diffusion approach”, Management Science, Vol. 36 No. 2,pp. 123-39.

Curkovic, S., Vickery, S.K. and Droge, C. (2000), “An empirical analysis of the competitivedimensions of quality performance in the automotive supply industry”, InternationalJournal of Operations & Production Management, Vol. 20 No. 3, pp. 386-403.

Daugherty, P.J., Myers, M.B. and Richey, R.G. (2002), “Information support for reverse logistics:the influence of relationship commitment”, Journal of Business Logistics, Vol. 23 No. 1,pp. 85-106.

Debo, L.G., Toktay, L.B. and Van Wassenhove, L.N. (2005), “Market segmentation andproduction technology selection for remanufacturable products”, Management Science,Vol. 51 No. 8, pp. 1193-205.

Environmental Protection Agency (2011), “Recycle city”, available at: www.epa.gov/recyclecity/(accessed 30 May 2011).

Ferguson, M.E. and Toktay, L.B. (2006), “The effect of competition on recovery strategies”,Production and Operations Management, Vol. 15 No. 3, pp. 351-68.

Garvin, D.A. (1987), “Competing on the eight dimensions of quality”, Harvard Business Review,Vol. 65 No. 6, pp. 101-9.

Germain, R., Droge, C. and Daugherty, P.J. (1994), “A cost and impact typology of logisticstechnology and the effect of its adoption on organizational practice”, Journal of BusinessLogistics, Vol. 15 No. 2, pp. 227-48.

Giuntini, R. and Andel, T.J. (1995), “Advance with reverse logistics: part 1”, Transportation& Distribution, Vol. 36 No. 2, pp. 73-5.

Grant, R.M. (1991), “The resource-based theory of competitive advantage”, CaliforniaManagement Review, Vol. 33 No. 3, pp. 114-35.

Grawe, S.J. (2009), “Logistics innovation: a literature-based conceptual framework”, InternationalJournal of Logistics Management, Vol. 20 No. 3, pp. 360-77.

Griffith, D.A. and Yalcinkaya, G. (2010), “Resource-advantage theory: a foundation for newinsights into global advertising research”, International Journal of Advertising, Vol. 29No. 1, pp. 15-36.

Guiltinan, J.P. and Nwokoye, N.G. (1975), “Developing distribution channels and systems in theemerging recycling industries”, International Journal of Physical Distribution, Vol. 6 No. 1,pp. 28-39.

Hunt, S.D. and Davis, D.F. (2008), “Grounding supply chain management in resource-advantagetheory”, Journal of Supply Chain Management, Vol. 44 No. 1, pp. 10-21.

Hunt, S.D. and Madhavaram, S. (2006), “Teaching marketing strategy: using resource-advantagetheory as an integrative theoretical foundation”, Journal of Marketing Education, Vol. 28No. 2, pp. 93-105.

Hunt, S.D. and Morgan, R.M. (1995), “The comparative advantage theory of competition”, Journalof Marketing, Vol. 59, pp. 1-15.


385

Hunt, S.D. and Morgan, R.M. (1996), “The resource-advantage theory of competition: dynamics,path dependencies, and evolutionary dimensions”, Journal of Marketing, Vol. 60 No. 4,pp. 107-14.

Kim, S.T. (2011), “Implementation of green supply chain management: impact onperformance outcomes in small- and medium-sized electrical and electronic firms”,available at: http://digitalcommons.unl.edu/dissertations/AAI3412875 (accessed31 May 2011).

Kopicki, R.J., Berg, M.J., Legg, L.L., Dasappa, V. and Maggioni, C. (1993), Reuse andRecycling – Reverse Logistics Opportunities, Council of Logistics Management,Oak Brook, IL.

Kroll, M., Wright, P. and Heiens, R.A. (1999), “The contribution of product quality to competitiveadvantage: impacts on systematic variance and unexplained variance in returns”,Strategic Management Journal, Vol. 20 No. 4, pp. 347-84.

Kwon, T.H. and Zmud, R.W. (1987), “Unifying the fragmented models of information systemsimplementation”, in Boland, R.J. and Hirschheim, R.A. (Eds), Critical Issues in InformationSystems Research, Wiley, New York, NY, pp. 227-51.

Larson, P.D. (1994), “Buyer-supplier co-operation, product quality, and total costs”,International Journal of Physical Distribution & Logistics Management, Vol. 24 No. 6,pp. 4-10.

Lubin, D.A. and Esty, D.C. (2010), “The sustainability imperative: lessons for leadersfrom previous game-changing megatrends”, Harvard Business Review, Vol. 88 No. 5,pp. 42-50.

Markley, M.J. and Davis, L. (2007), “Exploring future competitive advantage through sustainablesupply chains”, International Journal of Physical Distribution & Logistics Management,Vol. 39, pp. 763-74.

Murphy, P.R. and Poist, R.F. (2000), “Green logistics strategies: an analysis of usage patterns”,Transportation Journal, Vol. 40, pp. 5-16.

Nidumolu, R., Prahalad, C.K. and Rangaswami, M.R. (2009), “Why sustainability is now the keydriver of innovation”, Harvard Business Review, Vol. 87 No. 9, pp. 56-64.

Nikbakhsh, E. (2009), “Green supply chain management”, in Farahani, R.Z., Davarzani, H. andAsgari, N. (Eds), Supply Chain and Logistics in National, International and GovernmentalEnvironment, Physica-Verlag HD, Heidelberg, pp. 195-220.

Patterson, K.A., Grimm, C.M. and Corsi, T.M. (2003), “Adopting new technologies for supplychain management”, Transportation Research Part E: Logistics and TransportationReview, Vol. 39 No. 2, pp. 95-121.

Patterson, K.A., Grimm, C.M. and Corsi, T.M. (2004), “Diffusion of supply chain technologies”,Transportation Journal, Vol. 43 No. 3, pp. 5-23.

Persson, G. (1991), “Achieving competitiveness through logistics”, International Journal ofLogistics Management, Vol. 2 No. 1, pp. 1-11.

Podsakoff, P.M. and Organ, D.W. (1986), “Self reports in organizational research: problems andprospects”, Journal of Management, Vol. 12 No. 4, pp. 531-44.

Pohlen, T.L. and Farris, M.T. II (1992), “Reverse logistics in plastics recycling”,International Journal of Physical Distribution & Logistics Management, Vol. 22 No. 7,pp. 35-48.

Porter, M.E. (1980), Competitive Strategy: Techniques for Analyzing Industries and Competitors,The Free Press, New York, NY.

IJLM22,3

386

Prahinski, C. and Kocabasoglu, C. (2006), “Empirical research opportunities in reverse supplychains”, Omega, Vol. 34 No. 6, pp. 519-32.

Premkumar, G., Ramamurthy, K. and Nilakanta, S. (1994), “Implementation of electronic datainterchange: an innovation diffusion perspective”, Journal of Management InformationSystems, Vol. 11 No. 2, pp. 157-86.

Rao, P. and Holt, D. (2005), “Do green supply chains lead to competitiveness and economicperformance?”, International Journal of Operations & Production Management, Vol. 25Nos 9/10, pp. 898-916.

Rogelberg, S.G. and Stanton, J.M. (2007), “Introduction: understanding and dealing withorganizational survey nonresponse”, Organizational Research Methods, Vol. 10,pp. 195-209.

Rogelberg, S.G., Church, A.H., Waclawski, J. and Stanton, J.M. (2002), “Organizational surveyresearch”, in Rogelberg, S.G. (Ed.), Handbook of Research Methods in Industrial andOrganizational Psychology, Blackwell, Malden, MA, pp. 141-61.

Rogers, D.S. and Tibben-Lembke, R. (2001), “An examination of reverse logistics practices”,Journal of Business Logistics, Vol. 22 No. 2, pp. 129-48.

Rogers, D.S., Lambert, D., Croxton, K. and Garcia-Dastugue, S.J. (2002), “The returns managementprocess”, International Journal of Logistics Management, Vol. 13 No. 2, pp. 1-18.

Rogers, E.M. (2003), Diffusion of Innovations, The Free Press, New York, NY.

Roy, J., Nollet, J. and Beaulieu, M. (2006), “Reverse logistics networks and governancestructures”, Supply Chain Forum, Vol. 7 No. 2, pp. 58-67.

Safizadeh, M.H. and Ritzman, L.P. (1996), “An empirical analysis of the product-process matrix”,Management Science, Vol. 42 No. 11, pp. 1576-91.

Sarkis, J. (2003), “A strategic decision framework for green supply chain management”, Journalof Cleaner Production, Vol. 11 No. 4, p. 397.

Sarkis, J., Zhu, Q. and Lai, K. (2011), “An organizational theoretic review of green supply chainmanagement literature”, International Journal of Production Economics, Vol. 130 No. 1,pp. 1-15.

Skipper, J., Hanna, J. and Cegielski, C. (2009), “Supply chain contingency planning and firmadoption: an initial look at differentiating the innovators”, Transportation Journal, Vol. 48No. 2, pp. 40-62.

Souza, G.C., Ketzenberg, M.E. and Guide, V.D.R. Jr (2002), “Capacitated remanufacturingwith service level constraints”, Production and Operations Management, Vol. 11 No. 2,p. 231.

Srivastava, S.K. (2007), “Green supply-chain management: a state-of-the-art literature review”,International Journal of Management Reviews, Vol. 9 No. 1, pp. 53-80.

Staikos, T. and Rahimifard, S. (2007), “A decision-making model for waste management in thefootwear industry”, International Journal of Production Research, Vol. 45 Nos 18/19,pp. 4403-22.

Stanton, J.M. (1998), “An empirical assessment of data collection using the internet”, PersonnelPsychology, Vol. 51 No. 3, pp. 709-25.

Stock, J.R. (1998), Development and Implementation of Reverse Logistics Programs, Council ofLogistics Management, Oak Brook, IL.

Stock, J.R. (1992) in Stock, J.R. (Ed.), Reverse Logistics, Council of Logistics Management, OakBrook, IL.


387

Tan, A.W.K. and Kumar, A. (2006), “A decision-making model for reverse logistics in thecomputer industry”, International Journal of Logistics Management, Vol. 17 No. 3,pp. 331-54.

Teunter, R.H. (2001), “A reverse logistics valuation method for inventory control”, InternationalJournal of Production Research, Vol. 39 No. 9, pp. 2023-35.

Toktay, L.B., Wein, L.M. and Zenios, S.A. (2000), “Inventory management of remanufacturableproducts”, Management Science, Vol. 46 No. 11, pp. 1412-26.

Van Hoek, R.I. (1999), “From reversed logistics to green supply chains”, Supply ChainManagement, Vol. 4 No. 3, pp. 129-35.

Van Loon, P.P.J.C. (2010), “Green reverse logistics”, available at: http://alexandria.tue.nl/extra2/afstversl/tm/Van%20Loon%202010.pdf (accessed 19 June 2011).

Vickery, S.K., Droge, C. and Markland, R.E. (1997), “Dimensions of manufacturingstrength in the furniture industry”, Journal of Operations Management, Vol. 15 No. 4,pp. 317-30.

Vorasayan, J. and Ryan, S.M. (2006), “Optimal price and quantity of refurbished products”,Production and Operations Management, Vol. 15 No. 3, pp. 369-84.

Williams, L.R. (1994), “Understanding distribution channels: an interorganizational study of EDIadoption”, Journal of Business Logistics, Vol. 15 No. 2, pp. 173-203.

Woodruff, R.B. (1997), “Customer value: the next source for competitive advantage”, Journal ofthe Academy of Marketing Science, Vol. 25 No. 2, pp. 139-53.

Zhu, Q. and Sarkis, J. (2004), “Relationships between operational practices and performanceamong early adopters of green supply chain management practices inChinese manufacturing enterprises”, Journal of Operations Management, Vol. 22 No. 3,pp. 265-89.

Zhu, Q., Sarkis, J. and Lai, K. (2008), “Green supply chain management implications for closingthe loop”, Transportation Research: Part E, Vol. 44 No. 1, pp. 1-18.

Zmud, R.W. and Apple, L.E. (1992), “Measuring technology incorporation/infusion”, Journal ofProduct Innovation Management, Vol. 9 No. 2, pp. 148-55.

About the authorsBenjamin T. Hazen is a PhD student in the Department of Management at Auburn Universityand an active duty US Air Force Maintenance Officer. His research interests include reverselogistics, remanufacturing and sustainability. His research has appeared in the proceedings ofregional, national and international conferences and currently he has several manuscripts underreview at journals. He earned his MBA from the California State University in Dominguez Hills,California, his MA in Organisational Leadership from Gonzaga University in Spokane,Washington and his BS in Business Administration from Colorado Christian University inLakewood, Colorado. Benjamin T. Hazen is the corresponding author and can be contacted at:[email protected]

Casey Cegielski, PhD, CISA, CISSP, is an Associate Professor of Management InformationSystems and former KPMG Faculty Fellow in the College of Business on the faculty of AuburnUniversity in Auburn, Alabama. His current research interests are in the areas of innovationdiffusion, emerging information technology, information security, and the strategic use ofinformation technology. His research has appeared in several international informationsystems journals including Communications of the ACM, Information & Management, DecisionSupportSystems, and InformationSystemsJournal. Additionally, DrCegielski has more than 15 yearsof professional experience within the domain of information technology. He has served as a SeniorExecutive and an Executive Consultant in the financial, healthcare, and manufacturing sectors.

IJLM22,3

388

Joe B. Hanna (PhD, New Mexico State University) currently serves as DepartmentChairperson and Professor of Supply Chain Management in the College of Business at AuburnUniversity. Dr Hanna has authored or co-authored numerous journal articles and a logisticstextbook and has participated in government-funded transportation research. He is also an activemember of several professional organizations and regularly conducts professional trainingseminars for various organizations. Dr Hanna’s area of interest in supply chain managementallows him to instruct undergraduate, graduate, and executive education students at AuburnUniversity. Prior to entering academia, he gained professional experience working for PhillipsPetroleum (now ConocoPhillips), Phillips 66 Chemical Company (now ChevronPhillips ChemicalCompany), and Coopers and Lybrand (now Pricewaterhouse Coppers).


389

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

diffusion of green supply chain management

Documents