International Journal of Physical Distribution & Logistics ManagementReverse logistics information system success and the effect of motivationBenjamin T. Hazen Joseph Huscroft Dianne J. Hall Fred K. Weigel Joe B. Hanna

Article information:To cite this document:Benjamin T. Hazen Joseph Huscroft Dianne J. Hall Fred K. Weigel Joe B. Hanna , (2014),"Reverse logisticsinformation system success and the effect of motivation", International Journal of Physical Distribution &Logistics Management, Vol. 44 Iss 3 pp. 201 - 220Permanent link to this document:http://dx.doi.org/10.1108/IJPDLM-11-2012-0329

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Users who downloaded this article also downloaded:Dianne J. Hall, Joseph R. Huscroft, Benjamin T. Hazen, Joe B. Hanna, (2013),"Reverse logistics goals,metrics, and challenges: perspectives from industry", International Journal of Physical Distribution &Logistics Management, Vol. 43 Iss 9 pp. 768-785 http://dx.doi.org/10.1108/IJPDLM-02-2012-0052Michael Bernon, Silvia Rossi, John Cullen, (2011),"Retail reverse logistics: a call and grounding frameworkfor research", International Journal of Physical Distribution & Logistics Management, Vol. 41 Iss 5 pp.484-510 http://dx.doi.org/10.1108/09600031111138835Joseph R. Huscroft, Benjamin T. Hazen, Dianne J. Hall, Joe B. Hanna, (2013),"Task-technology fit forreverse logistics performance", The International Journal of Logistics Management, Vol. 24 Iss 2 pp.230-246 http://dx.doi.org/10.1108/IJLM-02-2012-0011

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Reverse logistics informationsystem success and the effect

of motivationBenjamin T. Hazen

Department of Supply Chain and Information Systems Management,Auburn University, Auburn, Alabama, USA

Joseph HuscroftDepartment of Operational Sciences, Air Force Institute of Technology,

Wright-Patterson Air Force Base, Dayton, Ohio, USA

Dianne J. HallDepartment of Supply Chain and Information Systems Management,

Auburn University, Auburn, Alabama, USA

Fred K. WeigelArmy-Baylor Graduate Program in Health and Business Administration,

Baylor University, Fort Sam Houston, Texas, USA, and

Joe B. HannaAuburn University, Auburn, Alabama, USA

Abstract

Purpose – Information systems (IS) play a substantial role in managing reverse logistics (RL)processes. However, the RL literature rarely takes a holistic approach to examining the “success” of ISemployment. Drawing on the rich literature base from the IS field, the authors explore IS Successtheory in the context of RL. Considering Diffusion of Innovation theory, the authors also examine theeffect of motivation on IS utilization. In doing so, the authors provide scholars and practitioners withinsight into the factors affecting the success of a RL IS. The paper aims to discuss these issues.

Design/methodology/approach – Based upon DeLone and McLean’s IS Success theory, theauthors develop the model to consider information quality, IS utilization, and RL cost effectiveness(as a proxy for net benefits). The authors disaggregate RL into two processes and thus consider themodel from two perspectives: the process of receiving returns from customers (inbound) and theprocess of returning products to suppliers (outbound). The authors survey 136 RL professionals andemploy partial least squares modeling for data analysis.

Findings – For both inbound and outbound path models, information quality is significantly andpositively related to IS utilization; in turn, IS utilization is a significant predictor of net benefits. Forinbound, RL goals provide significant motivation to drive IS utilization. For outbound, RL challengesprovide significant motivation for IS utilization.

Originality/value – The authors bring IS Success theory into the context of RL. Additionally, byinvestigating the topic from both inbound and outbound perspectives, the findings suggest differencesbetween inbound and outbound RL processes.

Keywords Diffusion of innovation, Reverse logistics, Partial least squares, Information quality,Information systems success

Paper type Research paper

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

www.emeraldinsight.com/0960-0035.htm

Received 7 November 2012Revised 13 February 2013

30 March 2013Accepted 12 May 2013

International Journal of PhysicalDistribution & Logistics Management

Vol. 44 No. 3, 2014pp. 201-220

q Emerald Group Publishing Limited0960-0035

DOI 10.1108/IJPDLM-11-2012-0329

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IntroductionScholars recognize reverse logistics (RL) as one of several important areas within thecontext of supply chain management (Croxton et al., 2001; Lambert, 2008; Rogers et al.,2002), and if managed effectively, RL processes can help firms gain a competitiveadvantage in the marketplace (Stock et al., 2006). Likewise, information systems (IS)capabilities are recognized as playing an important role in managing RL processes(Daugherty et al., 2002, 2005). Research in this area often investigates the effects of ISimplementations on measures of performance to determine if IS adds value in the contextunder examination. However, literature suggests that the success of IS should notnecessarily be based upon measures of performance alone – indeed, the idea of “success”encompasses several dimensions (DeLone and McLean, 1992, 2003; Petter et al., 2008).Unfortunately, extant RL literature rarely takes such a holistic approach. The purpose ofthis study is to extend the tradition set forth in the management information systems(MIS) literature to explore facets of IS Success theory in the context of RL.

Since the start of the first formal university-level MIS program in 1968, scholarshave sought to develop a better understanding of IS and the value these systemsprovide organizations (Nolan and Wetherbe, 1980). As one such attempt, DeLone andMcLean (1992) developed a model of IS Success, which theorizes the dependentvariable for IS research, from which scholars could better understand what IS Successmeans and how to achieve it. In 2003, DeLone and McLean reassessed their theory,adapting it based on their and other scholars’ studies. In this study, we consider ISSuccess as the theoretical basis for our investigation into the use of IS for RL.In addition, we consider Diffusion of Innovations theory to examine howorganizational goals for and perceived challenges with RL motivate the use of IS in RL.

The remainder of this article begins with a discussion of our study’s contextualbackground (RL). Then, we provide a short background of IS Success theory and,considering Diffusion of Innovation theory, describe how an organization’s motivationfor adoption of IS may affect IS Success. This discussion leads to the presentation ofour research model and hypotheses. We then describe our survey research method andpartial least squares (PLS) regression modeling approach to data analysis. Finally,we report our results and discuss implications for theory and practice.

RL: a matter of perspectiveRL can be defined as:

[. . .] the process of planning, implementing, and controlling the efficient, cost effective flow ofraw materials, in-process inventory, finished goods, and related information from the point ofconsumption to the point of origin for the purpose of recapturing or creating value or properdisposal (Rogers and Tibben-Lembke, 2001, p. 130).

As with any supply chain function, RL involves cooperation from a variety ofstakeholders (Bernon et al., 2011; Carter and Ellram, 1998). Although some researchaddresses RL from multiple perspectives (Nagurney and Toyasaki, 2005; Savaskan et al.,2004), much of the empirical literature in this area is limited to the context of only theorganization receiving the return from their customer or downstream trading partner(Autry et al., 2001; Daugherty et al., 2001, 2003; Hazen et al., 2012a, b; Richey et al.,2005a, b). Nonetheless, just as forward logistics involves cooperation amongst multipletrading partners, the same is true for the reverse channel. The RL process requires

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action from two parties, and both trading partners play similar yet different roles.Supply chain management literature has a rich tradition of examining multipleperspectives when investigating organizational phenomenon (e.g. shipper vs carrier;shipper vs receiver; inbound vs outbound, etc.). Although limiting the scope of RL to theperspective of the firm receiving the returned product may be appropriate in many cases,we propose that examining multiple perspectives when investigating RL may beadvantageous in some circumstances. We suggest that examining IS Success is one suchcircumstance. In this study, we disaggregate RL into both inbound and outboundfunctions to provide a more focused investigation of RL IS Success.

We examine IS Success regarding the process of accepting returns from customersand term this perspective “inbound.” We also examine IS Success regarding the processof returning products to a supplier and term this perspective “outbound.” Although weseparate the RL process into inbound and outbound functions, it must be noted that mostorganizations within a supply chain will fulfill both roles. However, we contend that it isadvantageous to decompose the RL process into these two functions to more accuratelyexamine IS Success. Therefore, for each proposed relationship within our researchmodel, we present two hypotheses: one that addresses the relationship from the inboundperspective and one from the outbound perspective.

Conceptual development and hypothesesAs shown in Figure 1, IS Success theory consists of the following constructs:

. system quality;

. information quality;

. service quality;

. intention to use/use;

. user satisfaction; and

. net benefits.

Several additional studies have corroborated the relationships posed in the theory(Petter et al., 2008; Petter and McLean, 2009). Notably, the concept of IS Success iscontextual and thus, all constructs are rarely examined in aggregate (Petter et al., 2008);instead, the most salient constructs are often chosen to operationalize IS Success, inconsideration of each study’s context. Several studies suggest that while IS Successtheory is both comprehensive and useful, it might behove scholars to pare the model tobetter focus their studies (Garrity et al., 2005; Kulkarni et al., 2006; Seddon, 1997;

Figure 1.Updated theory

of IS success

Information Quality

System Quality

Service Quality

User Satisfaction

Net Benefits

Use (Intention to Use)

Source: DeLone and McLean (2003)

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Velasquez et al., 2009). For our study, we focus on three aspects of IS Success:information quality, use, and net benefits. We posit that these aspects are mostgermane to realizing the most tangible, organizational-level outcomes in the context orRL. For instance, we are not as concerned with whether or not employees intend to usethe IS, but rather with the degree of actual use. In addition, we are not as concernedwith the degree to which users are satisfied as we are with the net organizationalbenefits obtained from using the IS. Finally, we posit that information quality might bethe most relevant quality consideration in the context of RL because of the dynamicnature of returns and the need for high-quality information about returns emphasizedin the RL literature (Hazen et al., 2011; Tibben-Lembke, 2002).

As one of the seminal MIS theories, IS Success theory permeates the discipline’sliterature; however, use of IS Success theory outside of the IS discipline is limited.We recognize the utility and applicability of IS Success theory and seek to extend its useto the supply chain management discipline. In evolving IS Success theory toward RL, weseek to not only examine facets of IS Success in the context of RL, but also synthesizetenets of Diffusion of Innovation theory to investigate the effect of motivation on ISSuccess. In the remainder of this section, we describe the IS Success constructs ofinformation quality, use, and net benefits in the context of RL and describe why a firm’smotivation for adopting IS in support of its RL functions might influence IS Success.

Information quality and useLogistics managers rely on timely and accurate information to make effective decisions.Thus, the quality of these decisions depends on the quality of the information available(Dyson and Foster, 1982; Warth et al., 2011). Indeed, poor information quality has beenshown to promote a number of tangible and intangible losses for businesses (Batini et al.,2009). This might be especially true in RL, where processes are less formalized (Autry,2005) and returns are difficult to accurately forecast (Richey et al., 2004; Srivastava andSrivastava, 2006).

The literature suggests several dimensions of information and data quality(Batini et al., 2009; Haug and Arlbjørn, 2011), and includes measures of both accuracyand timeliness. Accuracy refers to information that reflects the “real” state of affairs(Ballou and Pazer, 1985). Timeliness implies that information is as up-to-date aspossible (Blake and Mangiameli, 2011). IS Success theory proffers that the degree towhich information within an IS is accurate and timely is directly related to the degreeto which the IS is used (DeLone and McLean, 2003). This assertion is based, in part,on tenets of the Technology Acceptance literature, which has established usefulness asa direct antecedent to use (Davis et al., 1989; Venkatesh et al., 2012). When aninformation technology is perceived as not being useful (as may be the case wheninformation quality is subpar), then individuals are less likely to use it (Davis et al.,1989; Venkatesh et al., 2003). Indeed, results of a meta-analysis of seven IS Successstudies that investigate the relationship between information quality and use suggest asignificant, positive relationship (r ¼ 0.49) (Petter and McLean, 2009). As a businessprocess, RL is related to many other functions conducted throughout an organization(Dekker et al., 2004; Rogers and Tibben-Lembke, 1999); thus, we have no reason tobelieve that this relationship would not remain consistent in the context of RL:

H1a. RL information quality will be positively correlated with utilization of IS forinbound RL.

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H1b. RL information quality will be positively correlated with utilization of IS foroutbound RL.

Use and net benefitsIS Success theory suggests that, when the determinant conditions are met, IS usage willlead to net benefits. Indeed, some critiques of IS Success theory suggest the only validmeaning for IS use and success is as a proxy for benefits from use (Seddon, 1997). Thus, it isvia use that any effects from information, service, or system quality on organizational-levelnet benefits can be realized. RL literature has long recognized the value that RL can provideto an organization (Blackburn et al., 2004; Blumberg, 1999; Dowlatshahi, 2000). In thisstudy, we operationalize net benefits as RL cost effectiveness, which is defined as thedegree to which RL reduces organizational costs (Christmann, 2000; Richey et al., 2005a, b).Based on a recognized need to directly link logistics performance with firm performance(Fugate et al., 2010), we examine cost effectiveness as a net benefit.

IS Success theory suggests a significant, positive relationship between IS utilizationand net benefits (DeLone and McLean, 2003). Several studies grounded in IS Successtheory have since provided evidence in support of such a relationship (Petter andMcLean, 2009). Additionally, research regarding IS use in support of RL suggestsseveral benefits derived from the employment of IS (Daugherty et al., 2002, 2005).Therefore, it follows that IS utilization will also enhance levels of RL cost effectiveness:

H2a. Utilization of IS for inbound RL will be positively correlated with RL costeffectiveness.

H2b. Utilization of IS for outbound RL will be positively correlated with RL costeffectiveness.

The role of motivationOne area that is not encompassed by IS Success theory is why organizations look toutilize IS. Given the rich and extensive history regarding technology acceptance anddiffusion of innovation (Davis et al., 1989; Venkatesh et al., 2003, 2012) combined withrecent research in the area of supply chain innovation diffusion (Grawe, 2009; Hazen et al.,2012a, b), it follows that there may be a link between the reasons why an IS is adoptedand the degree to which it is utilized. Therefore, we turn to Diffusion of Innovation theoryas a complement to IS Success theory in the context of RL. As a facet of Diffusion ofInnovation theory, the organizational innovation diffusion process explains howtechnologies are diffused throughout organizations. Because we are concerned withunderstanding why organizations look to use IS, we examine the first stage of theorganizational diffusion process.

The organizational diffusion process begins when an organization identifies a needor problem and then searches for an innovation to provide the solution (Brown, 1981).However, problem/solution identification is often not always immediate becauseorganizations often require ample time to evaluate their own shortcomings and exploreavailable innovations (Schroeder, 1989). For instance, an organization may realize that agiven process is underperforming or lagging the competition. Because of a number ofproblems or challenges that are driving such subpar results, the organization may look totechnologies that will help solve their problems. Conversely, the solution can sometimesprecede the problem, such as when an organization is made aware of an innovation that is

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fashionable or has the potential to provide a desired opportunity (Wang, 2010; Wildemuth,1992). In this scenario, the organization proactively seeks new technologies to facilitatevisionary goals. Regardless of the motivation (proactively pursue goals or reactivelyaddress challenges), the action of searching for ways to improve the organization isthe first stage in the innovation diffusion process, which Rogers (2003) refers to asagenda-setting.

We posit that organizations look to adopt and use IS in support of RL to proactivelyattain goals and/or reactively address perceived challenges. Therefore, in addition toexamining IS Success, we examine motivation to adopt IS for RL in the form of twoantecedents: organizational goals set forth for RL and perceived challenges with RL. Giventhe literature regarding Diffusion of Innovation theory (Cooper and Zmud, 1990; Rogers,2003) and RL formalization (Autry, 2005; Genchev et al., 2011), it follows that a greaternumber of goals or perceived challenges will influence an organization’s propensity toadopt and use innovation:

H3a. Number of goals for inbound RL will be positively correlated with ISutilization.

H3b. Number of goals for outbound RL will be positively correlated with ISutilization.

H4a. Number of challenges for inbound RL will be positively correlated with ISutilization.

H4b. Number of challenges for outbound RL will be positively correlated with ISutilization.

Figure 2 illustrates our conceptual model – we operationalize tenets of IS Success andinclude motivation (goals and challenges) as an additional antecedent.

Figure 2.Hypothesized model

Goals

H3 H4

H1 H2

Challenges

IS UtilizationInformation

QualityRL Cost

Effectiveness

IS S

ucc

ess

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Data collectionMeasuresWe built our questionnaire using existing measures. To measure RL cost effectiveness,we used items employed by Richey et al. (2005a, b) and Jack et al. (2010). Informationquality was assessed using items based on measures of IS support employed byDaugherty et al. (2002). IS utilization was assessed using measures employed by Chenand Paulraj (2004). We considered each of these constructs as reflective and all itemswere evaluated using a seven-point Likert scale. Our measures are listed in theAppendix; only minor modifications for context were made to the original items.To assess goals and challenges, we asked participants to list the explicit organizationalgoals for RL and list challenges that they face with RL; these questions are also listed inthe Appendix. Our measures of goals and challenges are counts of the number of listedgoals and challenges. Given our desire to examine the degree to which organizations aregoal-oriented and challenge-oriented with regard to their RL practices, literaturesuggests that the use of single-item measures for these concrete attributes is appropriateand valid (Bergkvist and Rossiter, 2007; Wanous et al., 1997).

ParticipantsBecause we are investigating both inbound and outbound functions, we required asample frame comprised of business-to-business trading partners. To meet thisrequirement, we chose to sample the US Department of Defense supply chain, whichconsists of several thousand civilian and military organizations that provide goods andservices to organizations both within and outside the government (House Committee onArmed Services, 2012). We obtained a listing from a professional defense logisticsorganization, which included managers and logistics professionals who are affiliatedwith the RL functions within their organization. Potential participants were e-mailed alink to an internet-based questionnaire.

Participants were first asked to indicate whether their responses would be inreference to an inbound or outbound process. Definitions of both functions wereprovided and, if participants had knowledge of both, participants were asked to choosethe function with which they have more experience. The same measures were used forboth inbound and outbound; however, we adapted the instructions for context.

To determine our target sample size, we conducted an a priori power analysis,considering a power of 0.80 and an a of 0.05. For PLS analysis, this requires a samplesize of 59 (Soper, 2012). To obtain the desired sample, we solicited 450 RL professionalsand received 76 complete responses for outbound and 60 complete responses forinbound for a response rate of 30.2 percent. Participant demographics are reportedin Table I. Of note, the organizational-level demographics listed in Table I werealso employed as controls. Table II reports statistics regarding performance of themeasures.

Analysis and resultsWe used PLS regression modeling for data analysis and hypothesis testing. We chosePLS over linear regression because of the strength of PLS in analyzing pathmodels with multiple item constructs (Ahuja et al., 2003; Gefen et al., 2000;Sambamurthy and Chin, 1994). Additionally, PLS requires fewer observations and hasmore relaxed distributional assumptions than other modeling techniques, such as

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covariance-based structural equation modeling, making PLS most appropriatefor exploratory studies such as ours (Gefen et al., 2011).

We sought to allay common method bias via careful design of our questionnaire(Podsakoff et al., 2003). Nonetheless, we also tested statistically for indications ofcommon method bias using three analyses. First, we employed Harman’s one factor test(Harman, 1960; Podsakoff and Organ, 1986), which showed that no single factoraccounted for a significant amount of the variance. Table III shows the factor analysisresults, which indicate that variance is distributed among each of the factors, rather thanbeing concentrated on one factor. Second, Table II shows the correlations betweenfactors; the highest correlation is r ¼ 0.62, which is below the suggested maximum

ExperienceMean years in current position 6.21Mean years in organization 11.28Mean years RL experience 7.75PositionSenior management 30%Operations manager 40%Logistics professional 21%Other 9%GenderFemale 22%Male 78%Organization size (count of employees)Mean organization size 11,588Mean organization unit size 89Scope of operationsRegional 8%National 45%International 47%IndustryTechnology 41%Aerospace 22%Medical 14%Heavy equipment/automotive 18%Other 5%

Note: n ¼ 136

Table I.Individual andorganizationaldemographics

Construct Items Mean SDCronbach’s

a CR AVE CE IQ ISU Goals

Cost effectiveness (CE) 4 4.73 1.58 0.896 0.926 0.758 0.871Information quality (IQ) 5 4.93 1.16 0.905 0.929 0.724 0.395 0.851IS utilization (ISU) 6 5.11 1.34 0.864 0.898 0.597 0.473 0.624 0.773Goals 1 1.63 1.56 – – – 0.322 0.098 0.323 –Challenges 1 1.73 1.76 – – – 0.313 0.186 0.320 0.510

Notes: n ¼ 136; CR – composite reliability; AVE – average variance extracted; square root of AVE initalics

Table II.Construct descriptivesand correlations

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threshold of r ¼ 0.90 and suggests that common method variance is not significantlymanifest in our data (Podsakoff et al., 2003). Third, we followed the procedure describedby Liang et al. (2007, pp. 85-87), where a latent method factor is included in our PLS modelsuch that all indicators load on both their substantive construct and the method factor(Podsakoff et al., 2003). We then compared variances explained by the method factor andthe substantive construct. For each indicator, we found that method factor loadings wereeither non-significant, or that the variance attributed to the method factor was less thanthe variance attributed to the substantive construct. These findings suggest thatcommon method bias is not a significant threat to the validity of this study’s findings(Williams et al., 2003). Next, to assess non-response bias, we used a wave analysis(Wagner and Kemmerling, 2010). Examination of a random selection of constructs anddemographics indicated no significant differences in responses between early and lateresponders, suggesting that non-response bias is not a significant validity threat.

We assessed convergent validity by examining the outer model (Gefen and Straub,2005). As demonstrated in Table III, each of the measurement items load highly ontheir intended construct and below 0.60 on other constructs. The composite reliabilityscore and average variance extracted (AVE) values can also be used to indicateconvergent validity. As shown in Table II, all composite reliability scores are wellabove the suggested minimum value of 0.70 (Chin and Newsted, 1999) and all AVEvalues exceed the minimum threshold of 0.50 (Fornell and Larcker, 1981). In summary,the heuristics noted above suggest convergent validity of our measures.

Three procedures were employed to examine discriminant validity of our multi-itemmeasures. To begin, we further examined AVE values and item loadings (Fornell andLarcker, 1981; Gefen and Straub, 2005). Discriminant validity is indicated when thesquare root of a construct’s AVE is larger than its correlations with other constructs. It isalso indicated when the square of the correlation is less than the AVE. Both criteria areshown in Table II where the square roots of the AVE scores are in italics; the correlationsbetween constructs are shown to be less than the corresponding square root ofthe AVE. Additionally, the square of the correlations are all less than the AVE.

Challenges Goals Info quality IS utilization Cost effective

Challenges 1.000 0.510 0.186 0.320 0.313Goals 0.510 1.000 0.098 0.323 0.322Info_Quality1 0.251 0.123 0.925 0.572 0.390Info_Quality2 0.199 0.061 0.825 0.539 0.342Info_Quality3 0.107 0.234 0.839 0.542 0.339Info_Quality4 0.066 20.121 0.846 0.391 0.183Info_Quality5 0.124 0.060 0.815 0.433 0.392IS_Utilization1 0.311 0.421 0.582 0.826 0.431IS_Utilization2 0.152 0.182 0.573 0.829 0.349IS_Utilization3 0.278 0.218 0.523 0.855 0.513IS_Utilization4 0.370 0.274 0.309 0.635 0.421IS_Utilization5 0.038 0.194 0.333 0.694 0.150IS_Utilization6 0.261 0.153 0.494 0.774 0.200Cost_Effective1 0.302 0.342 0.417 0.531 0.858Cost_Effective2 0.290 0.292 0.308 0.402 0.915Cost_Effective3 0.211 0.200 0.294 0.335 0.813Cost_Effective4 0.265 0.243 0.316 0.292 0.892

Table III.Factor loadings

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Third, discriminant validity is evidenced when the items used to measure a constructload higher on their intended construct and lower on all other constructs (Straub et al.,2004). Table III, which shows the factor analysis results, demonstrates suchdiscriminant validity; all cross-loadings are below 0.60 (Cook and Campbell, 1979).

For our single item measures (goals and challenges), we sought to assurediscriminant validity between the two constructs during our study design by carefullywording our questions to tap unique constructs. Indeed, if participants listed challengesas simply being opposite of stated goals, or vice versa, then we would have problemswith validity. However, our initial review of the raw data found very few similaritiesbetween participant responses. We verified this quantitatively via including directrelationships between goals and challenges in both of our hypothesized models; theserelationships were not significant. Although we do not include these relationships in ourmodels used for hypothesis testing, this analysis provides evidence of divergencebetween the goals and challenges constructs. In summary, the validity checks describedabove indicate adequate convergent and divergent validity.

Hypothesis testingFirst, we examined the inbound model, as shown in Figure 3. Each path is labeled withits respective hypothesis, path loadings, and t-values. The paths for H1a, H2a, and H3aare significant at the 0.01 level and are therefore supported. H4a is not supported,t ¼ 0.706. The control variables (organization size, organizational unit size, scope ofoperations, and industry type) were shown to be non-significant; they affected neitherthe nature nor the magnitude of the relationships considered. The model explains50.1 percent of the variance in IS utilization and 18.9 percent of the variance in RL costeffectiveness.

Figure 4 shows the results for outbound RL. Each path is labeled with its respectivehypothesis, path loadings, and t-values. For outbound, the paths regarding H1b, H2b,and H4b are significant at the 0.01 level. The path regarding H3b is shown to benon-significant, t ¼ 1.382. As with the inbound model, the control variables

Figure 3.Inbound model results

Goals

H3a

Challenges

Note: Standardized bs

IS Utilization0.501

InformationQuality

β = 0.209t = 3.431

H1aβ = 0.692t = 9.223

H2aβ = 0.435t = 4.811

H4aβ = –0.060t = 0.706

RL CostEffectiveness

0.189

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(organization size, organizational unit size, scope of operations, and industry type)were shown to be non-significant. The model explains 46.6 percent of the variance in ISutilization and 22.3 percent of the variance in RL cost effectiveness (Table IV).

Notably, our models suggest mediation. This is based on IS Success, where fullmediation is theorized. That is, it is only via use or user satisfaction (in our study, use)that antecedents such as information quality, or even goal/challenge orientation canlead to net benefits because such net benefits are a proxy for benefits from use (Seddon,1997). Thus, we do not go into depth to develop mediation hypotheses or describeresults of mediation tests in this article. However, we did examine mediation viaadditional analysis in accordance with extant literature (Baron and Kenny, 1986). Ouranalysis suggests full mediation and that all effects of the exogenous variables(information quality, goals, and challenges) on RL cost effectiveness are indirect.

Discussion and implicationsOur study demonstrates IS Success in the context of RL and indicates that motivation to useIS for RL differs between inbound and outbound. These findings contribute to theory andpractice in several ways. First, we extend the MIS theory of IS Success into the

Standardized coefficientInbound Outbound Difference

H1. Information quality ! IS utilization 0.692 * * 0.584 * * 0.108H2. IS utilization ! cost effectiveness 0.435 * * 0.473 * * 0.038H3. Goals ! IS utilization 0.209 * * 0.102 0.107H4. Challenges ! IS utilization 20.060 0.213 * * 0.219IS utilization R 2 0.501 0.466 0.035Cost effectiveness R 2 0.189 0.223 0.034

Note: Significant at: *p , 0.05 and * *p , 0.01

Table IV.Results and model

comparisons

Figure 4.Outbound model results

Goals

Challenges

IS Utilization0.466

InformationQuality

H4bβ = 0.213t = 3.106

H2bβ = 0.473t = 6.904

H1bβ = 0.584t = 7.968

H3bβ = 0.102t = 1.382

RL CostEffectiveness

0.223

Note: Standardized bs

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logistics domain. Within the logistics literature, IS outcomes are generally described in thecontext of operational efficiencies or effectiveness (Hazen and Byrd, 2012), with littleconsideration for the holistic concept of “success.” In fact, of the 181 unique theoriesidentified in Defee et al.’s (2010) inventory of theory used in logistics research, IS Successwas conspicuously absent despite being one of the most cited theories in IS literature(Lowry et al., 2007; Petter and McLean, 2009). This research helps to bridge this obvious gapin the logistics literature. In addition, this study contributes to the literature on IS Successtheory by demonstrating the theory’s efficacy in the context of RL, thus further validatingthe generalizability of the theory. We foresee that future logistics research will see thebenefits of and continue to use IS Success theory as a basis for examining the role of IS.

Second, by demonstrating the impact of a firm’s motivation to adopt on achievingsuccess with the innovation, we contribute to the Diffusion of Innovation literature. Theorganizational innovation diffusion process begins when an organization scans theenvironment in search of new products, processes, or ideas (Cooper and Zmud, 1990).However, to our knowledge, research has never before examined the effects of this initialmotivation on the outcomes of innovation adoption; herein, we examine the reasonsbehind this motivation. Some organizations proactively adopt IS to facilitate their RLprocesses and stay ahead of the competition; other organizations adopt IS to maintaincompetitive parity or even catch up to competitors. Our findings suggest that, althoughthe reason a firm adopts an innovation may vary (i.e. pursue a goal or address achallenge) the outcome of adoption may remain the same. This finding contributes toDiffusion of Innovation theory by providing evidence to suggest that the reason foradoption does not affect outcomes; instead, perhaps it is only the magnitude by which theorganization is motivated to adopt that might affect outcomes. Future research shouldfurther examine additional factors that might contribute to why organizations adopt ISand if these factors might affect the degree to which organizations are motivated to adopt.

Similarly, we found differences between inbound and outbound regardingmotivation to adopt IS for RL. This finding is important for both scholars andpractitioners. For inbound, goals are significantly and positively related to use andchallenges are non-significant; for outbound, challenges are significantly and positivelyrelated to use and goals are non-significant. This suggests, when handling inboundreturns, organizations are goal-driven to utilize IS in support of their RL operations.Perhaps because the inbound side is the more traditional conceptualization of RL,organizations are beginning to establish goals for their processes and, consistent withRL literature (Genchev et al., 2011), are working to formalize their processes with thesupport of IS. Conversely, the process of returning products back up the supply chain(outbound) is seen as being an ancillary, unformalized process that presents a challengeto the outbound firm. However, the results suggest that organizations need tobe motivated by some means (goals or challenges) to support utilization of IS. That is, theresults suggest that if a firm were to have no formal goals and perceive no challengeswith RL, then it will be less inclined to utilize IS to support RL. Again, these findingssupport and extend Diffusion of Innovation theory to suggest that firms require somesort of motivation to adopt a technology; if not, they are less likely to adopt. The findingsalso support our assertion that inbound and outbound RL processes are distinct.We encourage future RL research to examine this distinction further.

The differences between inbound and outbound noted above have additionalimplications for practitioners. Our findings suggest that, because goals are a significant

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indicator of use, inbound firms have a greater interest in the RL process than outboundfirms. Whereas the inbound trading partner has goals established for managing theirproduct returns, the outbound trading partner might see their part of the RL process asbeing more of a hassle or afterthought – and thus, a challenge. However, our resultssuggest that, regardless of a firm’s orientation (inbound or outbound), using IS insupport of their RL efforts will produce tangible performance outcomes. This isconsistent with the preponderance of the logistics literature, which suggests severalbenefits of enhanced integration and collaboration that are facilitated by IS(Bowersox et al., 2000; Frohlich, 2002; Stank et al., 2001). We thus encouragepractitioners to enhance the formalization and integration of their outbound RLprocesses (particularly via IS) in order to enhance performance.

Our findings offer additional insight for logistics professionals considering employingIS in support of RL processes by corroborating previous research implications regardingthe impact of information sharing on RL (Kumar and Putnam, 2008; Olorunniwo and Li,2010). Additionally, our study demonstrates the importance of information quality to ISutilization and, ultimately, the net benefits derived from usage. This finding expandsupon previous research (Daugherty et al., 2002, 2005), which we hope will motivate use ofIS that are tailored specifically toward the needs of RL processes. These results can beused to qualify and motivate information quality improvement efforts and investment inemerging IS technologies to support RL functions.

Limitations and conclusionsThere are some limitations to this study that can be addressed in future research. Forinstance, we examined only three of the constructs represented in IS Success. Although wechose to investigate what we believe are the most salient constructs within our context ofRL, future research might examine additional dimensions of quality (system quality andservice quality) that are addressed in IS Success theory. Also, we examined RL costeffectiveness as the net benefit of IS Success. However, there are other net benefits thatmight be worthwhile considering in this context – such as customer satisfaction orvarious measures of efficiency – that might be examined in future studies.

Our study is also limited by our chosen sample. We chose to examinebusiness-to-business trading partners within a non-retail sector. The findings may notfully generalize to the retail supply chain or within the business-to-consumer context.However, we believe the relationships validated in a multitude of IS Success studies (Petterand McLean, 2009), including ours, will remain consistent in these additional contexts.Future research should verify such validity.

Despite the aforementioned limitations, our study contributes to the body ofknowledge within the interface of RL and IS, primarily, by extending IS Success theoryinto the context of RL. In doing so, we demonstrate a degree of generalizability of ISSuccess while examining the importance of considering a more holistic perspective ofIS in RL. By providing insight to information quality and the relation to net benefits,our study also assists organizations practicing RL functions.

AcknowledgementsThe views expressed in this article are those of the author and do not reflect the officialpolicy or position of the United States Air Force, Department of Defense, or theUS Government.

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Appendix. Measurement itemsIS utilization (Chen and Paulraj, 2004)To what extent are these systems utilized to assist with RL?

(1) Direct computer-to-computer links with key suppliers.

(2) Inter-organization coordination w/electronic links.

(3) IT-enabled transaction processing.

(4) Electronic mailing/sharing capabilities with our key suppliers.

(5) Electronic transfer of purchase orders, invoices and/or funds.

(6) Advanced IS to track and/or expedite package movement.

Cost effectiveness (Richey et al., 2005a, b)To what extent do you agree with the following statements:

(1) We incur lower compliance costs with environmental regulations because of our returnshandling method.

(2) Our strategy for dealing with returned merchandise improves our cost position relativeto our closest competitors.

(3) Our RL program reduces our cost.

(4) We are realizing cost savings because of our RL activities.

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Information quality (Daugherty et al., 2002)Please assess the usability of your firm’s reverse logistics information systems in the followingareas:

(1) Receive timely information.

(2) Availability of information.

(3) Exception reports.

(4) Receive real-time information.

(5) Delivering accurate information within the organization.

Goals

For outbound. What are your organization’s goals for managing RL when returning productsto your suppliers?

For inbound. What are your organization’s goals for managing RL when receiving returnedproducts from customers?

Challenges

For outbound. What challenges does your organization face in regard to managing RL for thereturn of products to suppliers?

For inbound. What challenges does your organization face in regard to managing RL forreceiving returned products from customers?

About the authorsBenjamin T. Hazen is an active duty US Air Force Maintenance Officer and a recent PhDgraduate of Auburn University. His research interests include reverse logistics, innovation, andinformation systems. His research has appeared in several journals, to include InternationalJournal of Production Economics, International Journal of Logistics Management, andInternational Journal of Physical Distribution and Logistics Management. Benjamin T. Hazenis the corresponding author and can be contacted at: benjamin.hazen@auburn.edu

Joseph Huscroft is an Assistant Professor in the Department of Operational Sciences at theAir Force Institute of Technology and an active duty Aircraft Maintenance Officer. As a careerAir Force Officer, he has maintained aircraft in various capacities, on-equipment andoff-equipment; including sortie support, sortie generation, and maintenance supervisor. Mostrecently, he commanded the 18th Maintenance Operations Squadron and 18th ComponentMaintenance Squadron at Kadena Air Base, Japan. A graduate of the United States Air ForceAcademy, Lt Col Huscroft holds a Masters in logistics management from the Air Force Instituteof Technology and a PhD in management from Auburn University. His primary researchinterests and areas of publication include reverse logistics and logistics information systems.

Dianne J. Hall is an Associate Professor of management information systems at AuburnUniversity. She holds an undergraduate degree in business from the University of Texas,a Master’s degree in business administration with a minor in accounting and a minor incomputer science, and a doctorate in information and operations management, both from TexasA&M University. Her work appears in academic and practitioner journals such as DecisionSupport Systems, Communications of the Association of Computing Machinery, Communicationsof the Association for Information Systems, International Journal of Physical Distribution andLogistics Management, International Journal of Logistics Management, and KnowledgeManagement Research and Practice. Her current research interests include applications ofinformation technologies in support of knowledge management, healthcare, supply chainresiliency, and contingency planning.

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Fred K. Weigel is an active duty Army Biomedical Information Systems Officer, AviationOfficer, and Assistant Professor for the Army-Baylor Graduate Program in Health andBusiness Administration, Fort Sam Houston, Texas. His research interests lie in diffusion ofinnovations, quantitative text analysis, and medical informatics. He earned his AA in businessadministration from Brookdale Community College, BS in professional aeronautics fromEmbry-Riddle Aeronautical University, and PhD in management information systems fromAuburn University.

Joe B. Hanna (PhD, New Mexico State University) currently serves as the Associate Dean forResearch and Outreach in the College of Business at Auburn University. Dr Hanna has authoredor co-authored numerous journal articles and a logistics textbook and has participated ingovernment-funded transportation research. Joe is also an active member of several professionalorganizations and regularly conducts professional training seminars for various organizations.Dr Hanna’s area of interest in supply chain management allows him to instruct undergraduate,graduate, and executive education students at Auburn University. Prior to entering academia,Joe gained professional experience working for Phillips Petroleum (now ConocoPhillips), Phillips66 Chemical Company (now ChevronPhillips Chemical Company), and Coopers and Lybrand(now Pricewaterhouse Coppers).

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