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Journal of Cleaner Production 108 (2015) 818e829

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Measurement of consumers' return intention index towards returningthe used products

Sarat Kumar Jena, S.P. Sarmah*

Department of Industrial & Systems Engineering, IIT Kharagpur, 721302, India

a r t i c l e i n f o

Article history:Received 15 November 2014Received in revised form23 April 2015Accepted 27 May 2015Available online 11 June 2015

Keywords:RemanufacturingConsumer behaviorReturn intentionReturn intention indexClosed-loop supply chain

Acronyms used in this paper: (CLSC), closed-loopbenefits; (SA), social awareness; (PPB), post purchase b(MC), market characteristics; (SN), subjective normknowledge; (RI), return intention; (RII), return intent* Corresponding author. Tel.: þ91 3222 283734.

E-mail addresses: saratkumar.jena@gmail.com (S.Kernet.in (S.P. Sarmah).

http://dx.doi.org/10.1016/j.jclepro.2015.05.1150959-6526/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

More attention has been given on consumer's behavioral attitude towards remanufactured products inclosed-loop supply chain literature. Till date, no research has been carried out on consumers' intentiontowards returning the used products. The aim of this research is to develop a theoretical model toexamine the intention and perception of consumers' for retuning the used products and to explore thevarious cues that consumers use for returning the Endeof-use and Endeof-life products. The model isexamined through an empirical study and the developed hypotheses are tested by conducting a surveyamong the college graduated students and others (job holder and non-job holder) for specified elec-tronics products. Finally, the extent of return intention index of the consumers is computed using thestructural equation modeling. The results revealed that return intention is positively influenced by theperceived benefit and social awareness, whereas negatively influenced by the perceived risk.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Over the years, the activities of manufacturing industry has beenmostly confined to extracting raw materials from earth,manufacturing them into products and then disposing of theseproducts into landfills or incineration after the end of use. But, thisapproach is not sustainable in the current competitive businessenvironment because of the rising concern of people for clean andsafe environment in the present and for the future. Realizing thegravity of the problem, various initiatives have been undertaken bythe industry, government and academia and they have tried toanalyze this issue from the perspective of the supply chain andmore particularly in the context of closed-loop supply chains(CLSCs) (Guide and Van Wassenhove, 2009; Rubio et al., 2008;Jimenez-Para B et al., 2014). In CLSCs area of research, many au-thors have studied the problems from the supply side of end-of-life(EOL) and end-of-use (EOU) products. A good number of analyticalstudies have also been done on the flow of goods from the

supply chain; (PB), perceivedehavior; (PR), perceived risk;; (RPKW), returns product

ion index.

. Jena), spsarmah@iem.iitkgp.

consumer back to the recovery agent or to the original equipmentmanufacturer (OEM) or to the remanufacturer, e.g., return, EOUcollection, value recovery, and 4-R(reduce, reuse, recycling, rema-nufacturing), inventory management, etc.

Remanufacturing is one such measure which has been carriedout by CLSCs more often. It involves various key activities such asproduct acquisition, reverse logistics, inspection and possibleproduct disposition (Blackburn et al., 2004). It closes the materialuse cycle and focuses on value-added recovery, rather than justmaterial recycling. Guide and Jayraman (2000) have mentionedthat remanufacturing systems is environmental friendly and prof-itable, which has been employed in a number of industries rangingfrom mechanical automotive and aircraft parts to electronics suchas computers, cellular telephones, television etc. One of theimportant tasks of remanufacturing is handling of uncertainquantity and quality of the return items in themost effectiveway. Inthe CLSCs, OEM or remanufacturer may decide to handle the returnprocess individually. Guide and Jayraman (2000) mentioned that82% of firms collect the used products directly from the customers.Manufacturers know that the retailers are the closest players to themarket and can influence the customers, creating knowledge andawareness of environmental concerns, and educate them to returnthe products in good conditions (Giovanni, 2014; Hong and Yeh,2012).

One of the major challenges in CLSCs research is to understandin depth its relationships with themarket and consumers (Jimenez-

S.K. Jena, S.P. Sarmah / Journal of Cleaner Production 108 (2015) 818e829 819

Para et al., 2014). The purchase intention of consumers towardsremanufactured products considering various factors was studiedby Wang et al. (2013). However, till date, no researcher hasunearthed the factors that underpin the consumer's return inten-tion (RI) of the used products which is considered as the strongestpredictors for willingness of consumers. To fill this void in theliterature, a systematic empirical research is needed.

Authors have theoretically developed and empirically validateda research model that identifies the RI of consumers. This devel-oped model not only captures the role of individual towardsreturning the used products, but also informs us, how perceivedbenefits (PB), social awareness (SA), post purchase behavior (PPB),perceived risk (PR), market characteristics (MC), subjective norm(SN) and returns product knowledge (RPKW) would impact onconsumers' attitude and intention about the return of the useditems. This paper provides a comprehensive model consideringrelevant factors by comparing with the existing literature. A scale isdeveloped to compute the return intention index (RII), whichwould bring knowledge regarding the current level of RI onbehavior of the Indian consumers. Here, RII is an index that pro-vides information on howwell the consumers are agreed on PB, SA,MC, and RPKW factors for returning their used products. Apart fromdeveloping a scale for measuring RI of consumers, this study ad-dresses the following research questions.

(RQ1) What is the relationship of various factors with RI?(RQ2) Which are the most important factors of PB and SA thatmotivate the consumers' to return their used products?(RQ3) What is the most important factor that impact on RI?(RQ4) What is the current level of consumers' return intentionindex (RII)?

Particularly, (re)manufacturers are more interested to know therelationships of various factors with RI and how these factors aremotivated to the consumers' for returning their used products.Now-a-days, (re)manufacturers motivate the consumers' to returntheir EOU and EOL products through some benefits such as war-ranty, incentives, exchange offer, etc. However, still they are unableto collect sufficient amount of used products from the markets. Forthat, firms want to know the key factors which impact mostly onthe RI and also return intention index of the consumers. Moreover,these questions are likely to be encountered by the (re)manufac-turer in practice, have not been adequately addressed in the liter-ature. Thus, this study seeks to gain insights by exploring thesequestions. A clear-cut example of this problem is observed in theIndian electronics industry, where, our new theoretical frameworkcan be applied. Electronics and electrical parts included in end-of-use and end-of-life returns are processed through remanufacturingand recycling subsequently by the (re)manufacturer. Acquiring ofused products from the market is a challenging task for a (re)manufacturer under remanufacturing system. By the end of 2012,in India, the electronics waste (e-waste) touched 8, 00,000 metrictonnes, while the annual amount of e-waste was over 3, 30, 000metric tonnes (Raghupathy and Chaturvedi, 2009) and expected totouch 1.72 million metric tonnes by 2020 (Raheja, 2013). As per UNreport, India contributes significantly in generating e-waste and thegrowth is around 500 percentages in just 13 years, closely followedby China at 400 percentages. Sixty five cities in India generate 60percentages of the total e-waste and ten states generate 70 per-centages of the total waste in India (Raghupathy and Chaturvedi,2009). Looking at the growth of e-waste, India government hasmade a series of rules and regulations to return the used electronicsproduct. But the rate of return of electronics used product is only 5%of the total e-waste costing US $12e15billion in India (Thomas,2012). Low RI of consumers has been listed as a major bottleneck

in the development of remanufacturing industry. Based on thedeveloped theory, hypotheses are constructed here and validatedthem by using the data collected from consumers, and alsopostulated that this study can shed light on the consumers forreturning their used products.

1.1. Literature review

Several studies have discussed consumer behavior related todifferent situations. Ajzen (1991) discussed the complexity of hu-man behavior and action using Theory of Planned Behavior model.Author has observed that intention in combination with perceivedbehavioral control can account for the proportion of variance inbehavior. This study examined the consumers' behavior towardsthe used products. Keeping in view of our research work, the re-view of literature is divided into two sections (i) Literature relatedto consumer behavior towards remanufactured/sustainable prod-ucts, (ii) Secondly, literature related to the reverse flow of usedproducts. Both the streams of research are discussed briefly in thefollowing sub-sections.

1.1.1. Literature related to customer perception towardsremanufactured/sustainable products

Consumer may believe that the quality and performance ofremanufactured products are low compared to brand new productsas the remanufacturers may not restore truly the products like tothe new one. Essoussi and Linton (2010) studied the impact ofproduct categories on consumers' willingness to pay underperceived risk. They found that perceived functional risk is animportant dimension of the price which consumers are ready topay for products and also suggested that consumers could switchfrom recycled products to new products under high functional risk.Moreover, consumers might not be positive initially towardsremanufactured products because of uncertainty about the prod-ucts' quality (Ovchinnikov, 2011). On the other hand, some of theconsumers bought remanufactured products due to the linear ef-fect of discounting (Majumder and Groenevelt, 2001 and Debo et al.2005) and positive effect of greenness (Atasu et al., 2008). Agrwalet al. (2012) have examined the effect of the presence of remanu-factured products and also identify the remanufacturer's influenceon the perceived value of new products through behavioral ex-periments. They found that remanufactured products are sold bythe OEM to reduce the perceived value of new products in theabsence of third party remanufacturer. Subramaniam andSubramanyan (2012) examined market factors such as self-reputation, the identity of the remanufacturer, and warrantystrength that explains the differential price between new andremanufactured products. They observed that self-reputation is animportant factor among the other market factors for buying theremanufactured products, and also found consumers to pay higherprice to those remanufactured products, which are produced byOEM or reputed companies.

Further, Hazen et al., (2012a) have studied operational issue andproduct acquisition pertinent to CLSCs considering consumerbehavior. Hazen et al. (2012b) have extended the earlier work toexamine the relationships between ambiguity tolerance, willing-ness to pay and perceived quality for remanufactured products.They found that there is a direct relationship between ambiguitytolerance and willingness to pay of consumers for remanufacturedproducts, and also an indirect relationship is made between themthrough perceived quality. They also observed that perception ofquality of remanufactured products affect the consumers' willing-ness to pay for the remanufactured products. Wang et al. (2013)studied the purchase intention of consumers towards remanufac-ture product. They observed that the purchase intention was

S.K. Jena, S.P. Sarmah / Journal of Cleaner Production 108 (2015) 818e829820

directly influenced by the purchase attitude and perceived behav-ioral control and indirectly influenced by the perceived risk,perceived benefit and product knowledge via attitude. This is thefirst work related to the purchase intention of consumer towardsremanufactured automobile products. Later on, this work wasextended by Abey et al. (2014) taking into account discount andbrand equity under multiple product categories. They examinedvarious cues that consumers use to evaluate the remanufacturedproducts. Gaur et al. (2015) studied the major drivers of consumers'purchase intention towards remanufactured products consideringrelocation. They observed that the level of environmental con-sciousness, individual values, post-use perceptions, nature of pur-chase and socio-cultural norms are the major drivers for buying theremanufactured products. Authors highlighted that personal andcontextual factors affect the consumers' decision making and pur-chase intention.

Michaud and Lierena (2011) have studied the issue of con-sumers' willingness to pay for remanufactured products, whenconsumers were informed about it as Green products. The authorsfound that consumers tend to give value towards remanufacturedproducts more compared to conventional products after knowingthe product as green. Lin and Huang (2012) studied the consumers'choice behavior towards green products. They determined theinfluential factors which impact on the consumers' behavior byapplying the theory of consumption value. The authors alsoexamined the significant difference in consumption value andchoice behavior considering outlooks on environmental concern.Biswas and Roy (2015) studied the relationship between environ-mental concerns and consumers' buying behavior in green prod-ucts in the context of India and also explained consumers'motivation to adopt the sustainable consumption behavior. Theyfound that social value parameter being the dominance parameterinfluence the sustainable buying behavior.

1.1.2. Literature related to reverse flow of used productsRemanufacturing of product depends upon the availability of

returned product in the market (Jena and Sarmah, 2014). The flowof used products from consumers to OEM or remanufacturer isuncertain (Guide, 2000). In the literature, variability in the qualityof return products from consumers has been well documented(Bloemhof-ruwaard et al., 1996). Consumers generally do not returntheir used products through the available collection channels dueto lack of awareness and it ultimately hinders the remanufacturingactivities (Geyer and Doctori Blass, 2010). Promotional offer playsan important role to overcome this hindrance as it motivates theconsumers to return their used products (Guide and VanWassenhove 2001; Guide et al., 2003; Das and Dutta, 2013a).Guide and Van Wassenhove (2001) introduced the incentivemechanism to manage the return quantities in the reverse channel.Recently, Das and Dutta (2013b) stated that exchange offer given bythe companies encourage the consumers to return their usedproducts and also helps in increasing the demand for the rema-nufactured product as well. Ayres et al. (1997) mentioned thatconsumers are motivated to return their used products byproviding higher manufacturer's incentives, making governmentlegislation and awareness about the environmental effect. Theyfound that these factors have positive influence on consumers'behavior for returning their used products. The CLSC literature hasaccepted that some consumers are concerned on environmentregarding the return of the used products. A few researches haveexplored the consumer behavior on remanufactured products.Recently, an empirical and experimental study was carried out toidentify the price difference and willingness to pay for both newand remanufactured products (Hazen et al., 2012).

To the best of the authors' knowledge, till date, no study hasbeen made empirically in CLSC to understand the RI of the con-sumers for returning their used products. This paper tries tocontribute to the existing literature by exploring this unexploredarea. Practitioners are more interested to know about the con-sumers' behavior towards remanufactured products and intentionof consumers for returning their used products. This study empir-ically examines the consumers' intention of returning the usedproducts and tries to find out various cues that consumers use forreturning the EOL and EOU products and thereby, this paper makesa significant contribution to the existing literature of CLSCs.

The paper is organized as follows. The conceptual backgroundand hypothesis are developed in Section 2. Research methodologyis defined in Section 3and data analysis and results are presented inSection 4. Discussions on the results and managerial implicationsare made in Section 5. Finally, conclusions and future scope of thestudy are presented in Section 6.

2. The conceptual background and hypothesis development

Based on the literature review and consumers' opinion, it isfound that PB, SA, RPKW, SN, PPB, MC, PR, and return attitude (RA)constructs are the main dimension and antecedents that affect RI ofconsumer. Among the eight constructs considered here, PB, PPB,SN, SA, RPKW, andMC are independent variables, PR and RA are themediating variables, and intention to return the used products isthe dependent variable. Here, the conceptual model is based on theTheory of Planned Behavior (Ajzen, 1985, 1991). Jimenez-Para et al.(2014) have used this psychological model to explain the con-sumers' behavior towards remanufactured products. A few otherstudies have also used Theory of Planned Behavior successfully toexplain the consumers' behavior in situations similar to thatconsidered in the present work. The goal of the proposed model isto describe the intention of consumers' to return used electronicsproducts considering eight variables that might influence theintention. The conceptual model and their related hypotheses areshown in Fig. 1 and the arrow shows the relationships among theconstructs and RI.

2.1. RA, SN, PB, SA on RI

Intention is an individual's planning of action to perform thebehavior, and also captures the motivational factors that influencethe behavioral attitude. A person's behavioral intention isconjointly determined by RA and SN, which assists to measure theactual behavior of a person (Fishbein and Ajzen, 1975; Wang et al.,2013).

Return attitude is defined as an individual's overall behavior ofperforming to return their used products. RA of the consumergenerally includes various manifest variables and is given inTable 1. These variables have a direct impact on the RI (Taylor andTodd, 1995; Jimenez-Para B et al., 2014). SNs are based on in-dividuals' perception of whether friend or senior people in theirlife would want them to return the used products. SNs includevarious indicators such as SN_Frnd, SN_Senior and SN_Self, andall these activities' have direct impact on RI. Based on thebehavioral intention of a consumer, the following hypotheses areformulated:

Personal and social benefit can be achieved from retuning usedelectronics products. Apart from social and economic benefit, sig-nificant energy, and resource saving is also achievable by the re-covery of used products (Michaud and Lierena, 2011). Comparedwith environmental benefit, PB is one of the important dimensionsof consumers for returning their used products. PB is defined as thebenefit perceived to self-interest and social interest. Consumers

Fig. 1. The proposed conceptual model.

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develop certain interest to return the used electronics productsafter receiving some financial or non-financial benefit. Perceivedbenefit generally includes various indicators such as incentives interms of money, SF, warranty, discount and exchange of newproducts with used products. The social benefit that consumersperceive is closely related to the environmental awareness. Thougha sizeable portion of Indian consumers are concerned aboutthe environment, many of them consider that an awarenessof environmental issues can affect individuals' attitude andreturn behavior. To make awareness among the consumers forreturning their used products is a challenging issue for the (re)manufacturer.

Table 1Exploratory analysis.

Construct/Indicator Item

Perceived benefit(PB) PB_SFPB_IncentivePB_DiscountPB_WarrantyPB_Exchange

Perceived risk(PR) PR_Performane NewPR_ServicePR_TimeDelPR_EQNew

Market characteristics(MC) MC_BOMC_Mkt sizeMC_CompMC_Mkt avial

Return product knowledge(RPKW) RPKW_EnergySavingRPKW_PriceRPKW_IdeaRPKW_Centre

Return attitude(RA) RA_ChaoiceRA_Ireturning ideaRA_Idea

Social awareness(SA) SA_EducationSA_FriendsSA_Adv

Return intention(RI) RI_Ecno.FrndRI_I will return

Post purchase behaviour(PPB) PPB_GOPPB_WE

Subjective norm(SN) SN_Frnd,SN_Senior

Social awareness is defined to assess the information aboutreturning the used products through different sources. Consumers'assessed this information from various sources such as friends,advertisement and education. Advertisement plays an importantrole for making awareness among the consumers about the returnof used products. Here, the means of getting information of thereturned items is explored such as advertisement or through anyother sources by the consumers. How does consumer returnbehavior change after making awareness through differentsources?

Based on the above discussion, following hypotheses areconstructed:

Mean(SD) Factor loading CPVE

5.47(1.46) 0.823 20.435.63(1.65) 0.8005.78(1.31) 0.7255.43(1.45) 0.5425.89(1.28) 0.5146.10(1.11) 0.844 28.35.87(1.05) 0.8175.51(1.57) 0.6506.03(1.27) 0.6255.26(1.49) 0.799 36.15.38(1.46) 0.7244.91(1.52) 0.7135.46(1.51) 0.6825.54(1.64) 0.768 43.215.50(1.49) 0.7445.04(1.70) 0.7255.22(1.57) 0.6705.59(1.37) 0.959 49.755.60(1.38) 0.9575.67(1.29) 0.6445.56(1.30) 0.949 55.775.67(1.11) 0.9485.65(1.32) 0.6005.39(1.37) 0.820 61.545.56(1.35) 0.8204.44(1.71) 0.949 66.164.40(1.72) 0.9464.20(1.75) 0.971 70.014.33(1.77) 0.970

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H1. RA towards used electronics products positively influencesthe RI.

H2. SNs positively influences the RI of an Indian consumer forreturning their used products.

H3. PB positively influences the RI of the consumers' towardsretuning the used items

H4. PB positively influences the RA of the consumers' to returnthe used products

H5. SA positively influences the RI of the consumers.

2.2. RPKW, MC, PPB on RI

Consumers' returnable behavior is affected by another twoimportant indicators: RPKW, and MC. A significant portion of In-dian consumers' have information for returning the used product.However, they are not returning the used electronics products dueto lack of information about the availability of market for takingback the used products.

RPKW is an individual's knowledge and familiarity aboutretuning the used products. It can be measured in terms of objec-tive or subjective knowledge, which is very difficult to separateoperationally (Rao and Monroe, 1988). Thus, a composite multiplescale knowledge on subjective and objective analysis is used tomeasure the RPKW as quality, performance and price (Rao andMonroe, 1988). Here, the set is extended by considering somespecial features of returned product knowledge like availability ofcollection of used products centre and energy saving. Consumers'behavioral attitude will be affected by the earlier knowledge ofprice, energy saving, market size and number of centers available inthe market which has a direct impact on the RI.

MC is defined as the individuals' perception of secondary/pri-mary market of the retuned items. Consumers' behavioral attitudewill change with different manifest variables like market size,business opportunity and market competition. Consumers' can bemotivated through financial and non-financial benefits to returnthe used products by the market competition.

PPB is defined as the Individual's decision to return the usedproducts after purchasing the new products. Consumer can returnthe used products if he/she is not satisfied with the desiredexpectation, failure of the new products, or if warranty period isover. Besides these factors, consumer can also return the usedproducts because of competitions or due to the advancement intechnology of the products though the used product is in goodcondition. Considering these aspects, following hypotheses aredeveloped.

H6. RPKW positively influences the RI of the consumers'.

H7. MC positively influences the RI of the consumers'.

H8. PPB positively influences the RI of the used products.

2.3. PR on RI

PR has generally been defined as a combination of uncertaintyplus serious outcome involved, and the expectation of lossesassociated with return and acts as an inhibitor to return behavior(Peter and Ryan, 1976). Grewal et al. (1994) studied the six di-mensions or components of perceived risk from different angles.McCorkle (1990) has suggested the resource risk, which takes placefrom the loss caused by the dishonest behavior of the sellers. But,authors have considered PR from the consumers' point of view ofreturning their used items. The different dimensions of risk are

faced by the consumers during returning of their used products.They have analyzed five types of risk such as performance, service,quality, time and social risk. Used products return generally doesnot obtain any potentially loss of control over personal information.Therefore, measures of privacy risk are excluded from this analysis.Though, risk perception is defined as a potential expected loss, inthis case, it has a negative impact on the return attitude and RI of acustomer. Hazen et al. (2012b) stated that uncertainty in quality ofused products exploited in the past is a major risk, which werefollowed by the remanufacturer. Featherman and Pavlou (2003)have stated that risk perception has a negative influence on con-sumers' purchase intention. Consumers' attitude has a directimpact on RI as like purchase intention because of the samebeneficial attitude of the consumers.

Based on the above discussion, following hypotheses aredeveloped:

H9. PR has a negative impact on RA of consumers towardsretuning the used products.

H10. PR has a negative impact on RI of consumers in retuning theused products.

3. Research methodology

The RI model comprises of nine constructs, which are derivedfrom the literature review and consumers' opinion. It depicts thatRI may be influenced by various constructs, and that can vary fromconsumer to consumer perception. The constructs of the RI modelare unobservable (latent) variables (RA, SN, SA, PPB, PB, MC,RPKNW, and PR) and they are indirectly described by a number ofobservable variables, which are called as manifest variables or in-dicators. The list of construct variables and their observable vari-ables are given in Table 2. Questionnaires were distributed torespondents located in India using convincing sampling techniqueas the method allows the representativeness of the sample. Theparticipants responded to a self-administrated questionnaire dur-ing December 2013 to April 2014. Their participationwas voluntaryand there were no response bias.

3.1. Questionnaire development and data collection

To confirm the questionnaire design, pre-test was conducted.The questionnaire was evaluated by a group of experts in the dis-ciplines of consumer behavior towards remanufactured products toensure that all the variables were correctly represented in thequestioner. The experts were chosen from one batterymanufacturing company, one cartridge remanufacturing unit, andfrom three technical Institutes. Three sales managers and threeassociate professors were questioned about the present position ofthe remanufacturing industry and RI of the used products by theconsumers.

A two-phase pilot study of the survey was conducted fordeveloping the questionnaire. First, four academicians completedthe review on the basis of content appropriateness and clarity. Aftercomplying minor modifications, these questionnaires were sent to42 participants for further pilot testing. Aforementioned researchsuggests that young adults are more knowledgeable and have in-formation about return of the used products and remanufacturedproducts than older adults because of environmental consciousnessand availability of remanufactured products such as mobile, andpersonal electronic goods (Ko and Hwang, 2009; Hazen et al., 2012).Hazen et al. suggested that university students may have ampleexperience with remanufactured products. From the aforemen-tioned research, it is analyzed that they may have more idea about

Table 2Convergent validity and construct reliability.

Construct/Indicator Item Standardizedloading/weight

t-value Squared multiplecorrelation

Compositereliability (CR)

Average varianceextracted (AVE)

Cronbanch's a

PB PB1 0.868 0.754 0.764 0.519 0.760PB2 0.836 15.355*** 0.698PB3 0.577 10.533*** 0.333PB4 0.412 7.228*** 0.270PB5 0.404 6.518*** 0.240

PR PR1 0.936 0.877 0.842 0.580 0.806PR2 0.762 16.650*** 0.580PR3 0.552 10.881*** 0.312PR4 0.742 16.014*** 0.550

MC MC1 0.731 0.535 0.779 0.475 0.776MC2 0.828 12.190*** 0.686MC2 0.600 9.705*** 0.360MC3 0.565 9.163*** 0.319

RPKW RPKW1 0.727 0.529 0.761 0.451 0.760RPKW2 0.608 9.221*** 0.370RPKW3 0.677 10.019*** 0.459RPKW4 0.650 9.725*** 0.423

RA RA1 0.995 0.990 0.869 0.710 0.831RA2 0.981 36.228*** 0.963RA3 0.423 8.290*** 0.279

SA SA1 0.402 6.514*** 0.260 0.836 0.654 0.814SA2 0.955 7.419*** 0.911SA3 0.943 7.482*** 0.890

RI RI1 0.841 0.706 0.885 0.795 0.765RI2 0.939 13.715*** 0.882

PPB PPB1 0.978 0.957 0.960 0.923 0.740PPB2 0.943 16.772*** 0.889

SN SN1 0.987 0.974 0.970 0.941 0.969SN2 0.953 13.732*** 0.908

***Significant at p < 0.001, N ¼ 329.

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the return of used products than older adults. Therefore, not only tocarry out our survey but also to identify potential sources of error(e.g. poorly expressed items, aspect hard to understand, notsequential question given, etc.) of using a sample of younger adults,a pilot test of the survey was conducted among under graduate,post graduate students and others within the age group of 20e30years. Before conducting the main survey, a pre-test was performedwith the variables of the instrument. The pre-test involved 42colleges' students who had heard about or already have returnedthe used products. Taking the suggestion of the participants, thefinal questionnaire was prepared for the data collection. This allowsthe definitive version of questionnaire to be drafted. The structuredclosed-ended questionnaire was designed on the basis of theobjective of the work. The instrument was designed to develop thefinal questionnaire and divided the study into two parts as pre-sented in Appendix A. The first part of the questionnaire consists ofthe questions related to respondents' profile information such asname, sex, age, education, profession and experience of return ofused products. The second part includes a seven-point Likert scale,ranging from “Strongly disagree” (1) to “Strongly agree”(7), relatedto various constructs of consumers' RPKW, perceived social andindividual benefit, RA, PR, SN, MC, SA, PPB and RI.

In order to maximize the response rate, different approacheswere employed. The participants were guaranteed privacy of theirdata and were free to opt online or offline survey. For online survey,respondents received an e-mail message containing an informationletter and an active link to an anonymous Web-based survey,whereas in the offline survey, authors visited the respondents'location personally and collected data through hard copy of ques-tionnaire survey form. This survey was conducted among differentbackgrounds of graduate, and post graduate students of differentIndian institutes and universities. Most of the data were collectedthroughWeb-based process. In fact, it is found that the relationshipbetween age and heard/information available/about returning the

used products was significant and negative (t¼�14.115, p¼ 0.000),which provides further evidence of chosen sample to represent thesuitable respondents for returning of the used items. The ques-tionnaires were sent to 800 students and 400 others (job holderand non-job holder) through email and surface mail to differentparts of India randomly. Here, non-job holders are taken as thepeople who are stayed at home after completion of graduation.

3.2. Measurement of RII

Anderson and Fornell (2000) and Fornell et al. (1996) proposedthe formula as given in Equation (1) for overall value of customersatisfaction index. The present study used this formula for calcu-lating the overall value of RII because of similar evaluation ofmodel.

RII ¼Pn

j¼1 sjyj �Pn

j¼1 sj

ðr � 1Þ Pn

j¼1 sj

! � 100 (1)

where, sj is the weight of measurement variable of jth construct, r isthe range of the scale, and yj is the average value of jth construct,and n is the number of construct.

4. Data analysis and results

From data analysis, it is observed that out of the 1200 potentialrespondents, 358 responses (about 29.83 percentages) from thesurvey were received. Out of which 19 responses were eliminateddue to errors in completing the questionnaire, and 10 were unus-able because of not satisfying the education as well as age criteria.Therefore, the effective sample size was 329, which represents ausable response rate of approximately 28 percentages. Among the

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329 respondents, 58.13 percentages who have heard of the returnof the used electronic products, 41.87 percentages of them have noteven heard of returning the used products. Again, 19.2 percentagesof the 329 respondents have returned the used products. Further,2.12 percentages of total respondents were from the rural siteswhereas 73.86 percentages respondents were from urban areas ofIndia. A total of 77.45percentages of the respondents weremale andrest was female. The biggest group of respondents (about 49.42percentages) was between the age group of 25e30 years. Regardingeducation, 55.6 percentages of the respondents were postgraduatedegree holder followed by undergraduate degrees (44.4 percent-ages) holder. Finally, in the total respondents, students' communityaccounted for 45.24 percentages while academia professionalaccounted for 3.2 percentages. In this survey, it is found that manypeople in India do not know about returning of the used products.

Here, four-steps approach was adopted for analysing the data. Inthe first step, exploratory factor analysis (EFA) was carried out tofind out the most significant components (constructs) from themanifest variables and also to know whether the constructs areexplained fully or not by the respective indicators. Most of themanifests variables considered here are taken from different earlierstudies except SA and MC. In order to accomplish the task, the in-dicators were analyzed by IBM SPSS 19 software. In the second step,confirmatory factor analysis (CFA) is conducted to measure theconvergent and discriminate analysis. Third, the structural model isconstructed to investigate the model fit and to test the hypotheses.Both second and third measurement models and structural modelare assessed by maximum likelihood parameter estimatorAmos12.0. Finally, it calculates the Return Intention Index (RII) ofconsumers' for returning their used products.

4.1. Factor analysis: EFA

EFA was performed to purify the scale of return intention withthe hope of deleting “unusable indicators” which don't have thecommon core. All the indicators with respect to common factor ofthis study are presented in Appendix A. Before starting the EFA, aBartlett sphericity test was conducted to determine the appropri-ateness of the data for factor analysis and for that, the value of KMO(Kaiser-Meyer-Olkin) had been used for measuring adequacy. Thevalue of KMOwas found as 0.72, and a significance level of 0.00 wasobtained using Bartlett's sphericity test and it suggests the intercorrelation matrix contains sufficient common variance to makethe factor analysis worthwhile. It is important to note that theminimum acceptable value of KMO is 0.5 (Nunnally, 1978). There-fore, it can be argued that the matrix neither suffers from themulti-co linearity nor singularity (Chattopadhyay et al., 2012). For EFA, thePrincipal Component Analysis, with varimax rotation and Eigenvalue greater than 1 and factor loading greater than 0.4 was used(Kiser, 1958). For the analysis of items, the adjusted indicatoretotalcorrelation coefficient less than 0.4 were used as the criterion to

Table 3The analysis of discriminant validity.

PPC PB PR MC

PPC 0.984PB 0.124 0.647PR 0.385 0.441 0.762MC 0.228 0.318 0.373 0.689RPKW 0.179 0.169 0.403 0.346RA 0.016 0.021 0.080 �0.005SA �0.016 0.111 0.081 �0.026RI 0.157 0.313 0.340 0.488SN 0.073 0.176 0.144 0.156

delete indicators. From the result, it is found that PPB has notexplained well by the government legislation (the value of indica-toretotal correlation coefficient 0.324 is less than 0.4). From thisresult, one can infer that Government legislation in India is not thatmuch effective to compel consumers to return their used item. Forthat, government legislation from this factor was removed. Allitems loaded on their respective factors with most of them loadingabove 0.70 are shown in Table 1. The cumulative percentage ofvariance explained (CPVE) experienced by nine factors is 70.01percentage.

To test the normality assumptions underlying the maximumlikelihood procedure, all the constructs were tested individuallythrough both box plot and multivariate normality test. The resultsindicated that the data were normally distributed.

4.2. Measurement model: CFA

The nature of the data has been checked by computing themeanand standard deviation (SD) of each indicator, which representsthose respondents who have deputed high importance to highmean value factors as shown in Table 1. The low SD with respect tomean value of a factor illustrates the reliability of the data, i.e., lowcoefficient variation. The reliability and validity of the measure-ment variables have been done by using program IBMSPSS 19 andalso multicollinearity tests were performed (Henseler et al., 2009).Here, this model has tested and observed that high level of corre-lation does not exist between constructs and their indicatorsbecause the Variation Inflation Factor does not exceed thethreshold value of 3.3 (Diamantopoulos and Siguaw, 2006;Jimenez-Para et al., 2014). In this case, the Cronbach's scores (a)and Composite Reliability Index (CRI) of each sub-construct wereevaluated using the program as recommended by Fornell andLarcker (1981). From Table 2, it is observed that the CRI of all thenine constructs exceeds the threshold value of 0.6 (Bagozzi and Yi,1988) and Cronchbach's alpha exceeds the recommended value of0.7 (Nunally and Bernstein, 1994). This indicates acceptable internalconsistency of the data. The convergent validity of the measure-ment scales of constructs was analyzed by means of AverageVariance Extracted (AVE). The AVE values of construct exceeds therecommended value of 0.5 (Fornell and Larcker, 1981) with theexception of MC and RPKW, which had an AVE slightly below 0.5.Standardized item loadings for all were greater than 0.5 and sig-nificant <0.001, indicates good convergent validity (Bagozziet et al.,1991; Carbonell &Rodriguez, 2006) as given in Table 2. Here,squared multiple correlations indicated that the percentage ofconstruct variable is explained by the indicators.

Finally, the discriminate was evaluated in terms of the averagevariance extracted (AVE) as shown in Table 3. It is observed that AVEfor each constructs or latent reflective constructor (average vari-ance shared between the shared construct and its indicators) wasgreater than the square of the estimated correlation between

RPKW RA SA RI SN

0.6670.111 0.849

�0.036 �0.037 0.8340.441 0.059 �0.005 0.8910.183 0.079 �0.039 0.015 0.970

Fig. 2. The results of the structural model.

S.K. Jena, S.P. Sarmah / Journal of Cleaner Production 108 (2015) 818e829 825

constructs indicating discriminate validity (Fornell and Larcker,1981; Hair et al., 2010). All the diagonal values exceeded theinter-constructed correlations and therefore, the results confirmthat the instrument was satisfactory to construct validity.

4.3. Structural model: structural equation analysis

After confirming adequate fit for the measurement model, thestructural model is evaluated and the hypotheses are tested asshown in Fig. 2. The result of the overall goodness-of-fit was eval-uated by applying the c2 test. The measurement model yielded a c2

value of 798.5 with 341 degree of freedom. The ratio of c2 to dfwas2.342 which is lower than the suggested cut off value 5 (Bagozziet al., 1991; Hair et al., 2006, 2010). In addition, the results of thegoodness of fit measure support the proposed model (IFI ¼ 0.921,CFI ¼ 0.920, c2/df ¼ 2.342; RMSEA ¼ 0.62). RMSEA, GFI and IFI arealso satisfied the recommended value as shown in Table 4. TheRMSEA is a more effective measure in evaluating the overall fit andthe proposed value of RMESA supports the recommended value.Therefore, there is an acceptable fit between the model and theobserved data.

4.3.1. Test of hypothesesThe structural equation of the model was studied, including the

estimation and statistical test values of the standardized path

Table 4Goodness-of-fit measures for theoretical model.

Measure Measurement

c2/Degree of Freedom (df) Chi square in relation to Degree of FreedomCFI Comparative Fit IndexIFI Incremental Fit IndexRMSEA Root mean square of approximation

coefficient (b) between the constructs and the coefficient ofdetermination (R2). The results of the analysis show all the signif-icant path coefficients marked with * and non-significant pathsindicated as dotted lines. The standardized path coefficient be-tween the constructs is shown in Fig. 2. The standardized pathcoefficients are significant in all paths except path from PB to RA, PRto RA, SN to RI, and RA to RI.

The proposed hypotheses were tested with SEM and reportedaccordingly in Table 5. RI of used products is jointly predicted by PB(b ¼ 0.313, p < 0.1), SA (b ¼ �0.001, p < 0.001), RPKW (b ¼ 0.440,p < 0.001), MC (b ¼ 0.487, p < 0.001), and PR (b ¼ 0.340, p < 0.001)and these variables jointly explain 60.7 percentages of the varianceof intention to return of used products. Among them, MC andRPKW have strongest impact on RI. As a result, H6 and H7 are allsupported. H1 stated that RA positively influences the RI. But ourresult has not supported H1 (b ¼ 0.067, P > 0.05). H2 stated that SNpositively affects the RI. Again, our result has not supported H2(b ¼ 0.014, P > 0.05). H3 states that PB positively influences RI. But,PB is jointly influenced by the discount (b ¼ 0.557, P < 0.001), ex-change (b ¼ 0.404, P < 0.001), incentive (b ¼ 0.836, P < 0.001),warranty (b¼ 0.414, P < 0.001), and SF (b¼ 0.868, P < 0.001). Thesevariables explain 56.8 percentages of total variance in PB(R2 ¼ 0.568). Among them, SF and incentive have strong impact onthe PB due to higher standard weight value. Again our results havesupported hypothesis 3 (b ¼ 0.313, P < 0.001).

Recommended value (Hair et al., 2006) This study

Recommended value <5 2.342CFI acceptable >0.9 0.920IFI acceptable >0.9 0.921RMSEA acceptable <0.08 0.062

Table 5Sequential model.

Hypothesis b t p Decision

0.014 0.239 0.811 Not supported

0.313 4.619 0.000 Supported

0.006 0.056 0.955 Not supported

�0.001 �1.719 0.087 Supported

0.440 5.051 0.000 Supported

0.487 6.141 0.000 Supported

0.161 2.697 0.007 Supported

0.093 1.587 0.112 Not supported

0.340 5.111 0.000 Supported

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RA is jointly predicted by PB (b ¼ 0.006, P > 0.05) and PR((b¼ 0.093, P > 0.05). However, the path coefficients between thesetwo constructs with RA are not significant and therefore, H4 and H9are not supported. SA is jointly influenced by advertisement((b ¼ 0.402, P < 0.001), Education (b ¼ 0.955, P < 0.001), andAdvertisement (b ¼ 0.943, P < 0.001). All these indicators explain65.33 percentages of total variance in SA (R2 ¼ 0.653). As a result, itis found that the proposed relationship between SA and RI (H5) issignificant (b ¼ �0.001, P < 0.1). H8 states that PPB positively in-fluences the RI of used products. The results of our model sup-ported H8 ((b ¼ 0.161, P < 0.001) and this study also found that theproposed relationship between PR and RI (H10) is significant(b ¼ 0.340, P < 0.001). As a result, H10 is supported.

4.4. Results of RII

The RII is calculated considering intention of the consumer forreturning their used products. For calculation of RII for used elec-tronics products, n¼ 8 and r¼ 7 is taken because of 8 constructs forRII, which ranges from 1 to 7. The construct mean (yj) wascomputed as the average of measurement variables of eachconstruct. The overall value of RII was obtained as 87.7 on a scale of1e100.

5. Discussion on results and managerial implications

Several insights drawn from the results of this study can besummarized as follows:

First, consumers' RI is influenced by their PB and also via RA.The benefits that consumers perceive in deciding to return theused products come from the retailer or manufacturer. Discount,exchange, warranty, incentive and SF are emerged as positivefactors in the RI. Among them SF and incentive are the biggestbenefit while discount benefit becomes second and warrantybenefit and exchange benefit come third. If consumers get goodservice facilities in new products by giving used products, thenthey will be motivated to return their used products. Some con-sumers want incentives in the form of money rather than otherbenefits to return their used products. Very few consumers focuson exchange of used products with new one. From this study, it isfound that SF in new products and incentives in terms of moneyplay a vital role for returning of electronics used products by theconsumers.

Second, consumers can return their used products if they areaware of this information from friends, education and advertise-ment. Education and advertisement are the biggest source forreturning of used products. However, very few consumers want toget this information through friends. But from this study, it is

observed that path coefficient between SA and RI is negative.Therefore, the impact of SA on RI is less as compared to PB. Thestudy also found that advertisement plays an important role forreturning of used products like SF and incentive.

Third, RPKW and MC played an important role among theconstructors for returning of used products by the consumers. Theyshould get the knowledge of different benefits such as energysaving, social and economical benefit, for returning their usedproducts. Most of the consumers are unable to return their usedproducts because of market unavailability, lack of business oppor-tunities, and the small size of the market means fewer numbers ofbuyer's interest, although consumers are ready to return their usedproducts. For that, MC also played an important role for returning ofused products by the consumers.

Fourth, SN and RA constructs do not have positive impact on RIof consumers. Here, one interesting observation is that RA is notimpacted positively on RI, although it was a major part of RI. Itreveals that consumers are not receiving information aboutreturning the used products and Indian government has notmade astrong rule on consumers/users for returning their used products.For that, Indian consumers have not shown any concern towardsretuning of used products. It is also analyzed that consumers canreturn their used product, if they are aware about the offer given bythe manufacturer and availability of the MC. Consumers are notbothered about how the seniors or friends are behaving towardsused products.

Fifth, RI of used products is influenced by their PR. The risk thatconsumers perceive in deciding to return the used product comesfrom their doubts about quality and performance of the newproducts after exchange. Time risk, quality risk, performance riskand social risk, all are emerged as negative indicators in RI but PRhas no impact on RA.

Sixth, PPB positively influences towards returning the usedproducts. Functional failures, expiry of warranty period, guaranteeperiod over are all emerged as positive indicators in RI. These in-dicators have played a vital role for retuning of the used products.

From this analysis, it is observed that SEM has given first andsecond priority to MC and RPKW respectively whereas, PR, PB andSA hold third, fourth and fifth priority with SEM approach. RA andSN have not held any priorities with this approach. There are stillmany factors that lead to people's low perception and attitude ofreturning the used products in India, e.g., an unregulated market,improper and weak government policy, lack of product returnsinformation, and limited market size. Therefore, consumers havenegative attitude towards returning their used products. Thisshows that respondents' judgments are not completely random. Itis valuable to know the present Indian people's perception aboutreturning of used products.

The above insights about consumers' attitude and RI of usedproducts will help remanufacturer/OEM to develop a collectionstrategy for the used products and the government to make astrong return policy. The government can help the industry forcollecting the used products in the followingways. Firstly, the studyfound an important result that SN as well as RA does not have asignificant impact on the RI. This suggests that the effective mea-sure of consumers' RI relies on coordinated policy built on multiplepillars rather than on a single pillar. The industry and governmentmust make policy jointly and both the parties play a crucial role formotivating the consumers towards retuning the used products.Government and OEM/remanufacturer should work together tobuild a positive image on consumers for returning the used prod-ucts. They should strengthen the publicity regarding the concept ofretuning used products, e.g., by informing the social benefits andeconomic advantages of returning used products. It is also foundthat consumers can hardly take an interest to return the used

S.K. Jena, S.P. Sarmah / Journal of Cleaner Production 108 (2015) 818e829 827

products. In fact, they are throwing the used products outsides,instead of returning them to the market. This is also called forregulation to force OEM to use a uniform eco-label issued by thegovernment. On the other hand, different public serviceannouncement should be made on eco-labels on different channelto achieve more public exposure.

Secondly, the consumers have no idea about returning the usedproducts after purchasing the new one. Similarly, OEMs are also notinterested to bring back the used products from the consumers'because of lack of exact information about the customer towardsacquiring the products. Therefore, government should speed up theprocess of imposing strong laws/regulation regarding taking backthe used products from the customers by the OEMs orremanufacturer.

Thirdly, consumers PB can promote a significant direct effect onRI of the used products. In order to attract the consumers who arenot interested to return their used products, remanufacturer/OEMmust offer at least equal or higher incentive than the consumers'expectation. OEM and Government together should develop agreen marketing strategy and published the environment-friendlyapplication of used products.

Fourthly, both RPKW and PPB also have significant impact onRI. The government and OEM should work jointly to set upcollection network or collection distribution channel for the usedproducts. In this survey, it is found that many consumers do notknow where to return the used products. Therefore, it is suggestedthat more promotions for retuning of the used products should beconducted by setting up exclusive counters or creating businessopportunities for the used products. Government should provideadequate knowledge about returning the used products as wellbenefit of using the remanufactured products to peoples througheducation.

The study contributes to the CLSC literature by exploring thereasons underlying the key assumptions that consumers have alower RI of used products using the integrated research model. Ourmodel also states that the value of R2 for RI is 60.7 percentagestowards retuning the used products. The value of RII was 87.7 on a1e100 scale, i.e. the value lies between 80 and 100. The study willbenefit the researchers as well as people from industry as it pro-vides preliminary idea about consumers' intention/or attitude,which can be used to innovate collection strategy to enhance thereturn of used products.

6. Conclusions

In the literature, much attention has been given to studyempirically on consumers' behavioral attitude towards remanu-factured products or willingness to pay for remanufactured prod-ucts. However, discussion about the consumer's RI of the used-products which is considered as the strongest predictors for will-ingness of consumers is absent in the existing literature. This paperhas tried to bridge this gap in the literature and studied consumers'intention towards returning the used products considering variousperceived factors. The aim of this research is to develop acomprehensive theoretical model to examine the intention ofconsumers' for retuning their used products and to find out variouscues that consumers use for returning the EOL and EOU products.The research model is validated empirically and tests the hypoth-esis considering a survey from the college graduated students andothers. Furthermore, authors have made an attempt to develop ascale and method to measure the RI of the consumers. The signif-icant contribution of this paper to the existing literatures as follows,First, this study identified the various dimensions of RI regardingused products from the literature review and then apply this

theoretical analysis in Indian context. Based on these dimensions,this study developed a scale for measuring the RI of the consumers.Second, this study explores the relationships of different identifiedconstructs with RI by adding the new factor of MC and SA. Third,this study proposed a method of computing the extent of RII.Finally, based on the insights, the factors according to the impor-tance on RI were arranged, and also some managerial insights areprovided such as imposition of strict government legislation andformulation of policy by Government coordinating with the in-dustry etc.

This study identified eight constructs/factors namely PB, SA,RPKW, SN, PPB, MC, PR, and RA that impact on RI. The reliability andvalidity of these constructs are checked by CRI, Cronbach's scores(a), AVE and discriminate analysis. The theoretical model is thenvalidated and the hypothesis was tested by CFA approach. Further,authors have computed the value of RII based on the weightagederived from the CFA. The results show that the overall value of RIIis 87.7 on 1e100 scale, which indicates that consumers are ready toreturn their used products. The study concludes from this analysisthat RI is positively influenced by PB, MC, RPKW, SA, PPB, andnegatively influenced by PR. However, RI is not influenced directlyby RA and SN. PB plays an important role for motivating thecustomer followed by RPKW and MC. SF and incentive also impacthigher among the others' factors on RI. From this analysis, it isfound that advertisement also plays an important role for makingsocial awareness among the consumers.

Like all the research effort, this study has also suffered fromcertain limitations and that offers new research direction for nextresearchers. First, limitation is the sample size used in this study.This study considered major percentages of respondents onlyfrom university students. Although, it provides fair representationof consumers' of return used products, yet, other segment ofpopulation is left out. Second, the average age and subsequentawareness about returning the used products of participants maylimit the conclusion of this study. For example, other age groupsmay have more knowledge about returning the used products,which may or may not influence the behavioral attitude towardsretuning the used products. Third, the survey was conducted inIndia regarding consumers' intention towards retuning of elec-tronics used products, whereas this work can be extended to otherindustries, such as automobile, battery, paper, etc. Fourth, theresults obtained from this study may not be the generalized,because of the limited samples. For that, this study can beanalyzed again by considering cultural difference, economic dif-ference, geographic difference, and demographics difference sothat research results and model could be generalized to the situ-ation in other countries.

Finally, it can be noted that one of the unique finding of thisstudy in the context of India is that consumers are interested toreturn their used products, if consumers get all the informationregarding PB, SA, MC, and RPKW. However, Government legislationin India is not focused on consumers to return their used items. Thisstudy has also identified key factors that define the behavior of theconsumers, from which (re)manufacturer can design and imple-ment the market strategies. The results obtained from this analysisare novel contribution to the existing literature. No such workshave been discussed in aforementioned literature and it addressessome of the “gaps” in research that have been identified in therecent CLSC literature. The existing studies have only discussedabout the consumers' perception and intention towards theremanufactured and sustainable products, not for the return usedproducts. For that this study is a unique contribution to the liter-atures. The managerial insights of this work can be implemented inEOU products such as laptop, television, toner cartridge, single usecamera, computer etc.

S.K. Jena, S.P. Sarmah / Journal of Cleaner Production 108 (2015) 818e829828

Appendix A

Part1: respondents' profile information

(1). Name and Place(2) Gender:

A. Male B. Female.

(3) Educational Qualification

A. Under or equal 10th b. 12th C. Under Graduation D. PostGraduation E. Under or equal to Ph.D F. PDF.

Sr. No Construct Operational definition Measurement variables Reference

1 Perceived benefit (PB) Benefit perceived to self interestand social interest

1.1. Good service facilities for new product(SF)1.2. Warranty1.3. Desired incentives in term of money (Incentives)

Guide and Li (2012),Rahman and Subramanian (2012),Wang et al. (2013),

1.4. Discount on the new product Ayres et al. (1997),1.5. Exchange

2 Perceived risk(PR) Individual's risk perception ofthe new product after returningused items

a. Performance of new productsb. Provided service on new product(Service)c. Time to deliver the new products(Time Del)

McCorkle (1990),Grewal et al. (1994),Wang et al. (2013)

d. Exchange of quality (EQ_New)e. Friends will laugh at me if I return the usedproduct (Social risk)

3 Return ProductKnowledge(RPKW)

An assessment of the informationin the memory and perception offamiliarity about the returningused items

3.1. The effect of used-product on the energy saving3.2. Price level of the used product3.3. The idea of returning the used productbefore your elementary introduction (Idea)

Liu et al.,(2009),Benjamin T. Hazen et al. (2012),Jimenez-Para et al. (2014)

3.4. The center where the return product is received4 Social awareness (SA) To assess the information about

returning used products throughdifferent sources

4.1. Friend(Frnd)4.2. Education4.3. Advertisement(Adv.)

Ayres et al. (1997)

5 Return attitude (RA) Individuals positive or negativeevaluation of the return

5.1. Returning used electronics products is a good idea(RA_Idea)5.2. Returning used products is a wise choice (RA_Choice)5.3. I like the idea of retuning used electronics products

Chen (2007),Wang et al. (2013),Jimenez-Para et al. (2014)

6 Subjective norm(SN) Individuals' perception of whetherfriend or senior people would wantor tell to return

6.1. If my friend are returning the used product (SN_Frnd)6.2. If I will decide my self to return the used product(SN_Self)6.3. If importance people (senior, guide, and teacher etc)will tell to return(SN_Senior)

Ajzen (1991),Taylor and Todd (1995),Jimenez-Para et al. (2014),

7 Market characteristics(MC)

Individuals' perception of secondarymarket availability for returningused products

7.1. If the size of the return market is available (Mkt_size)7.2. If business opportunity Is available(BO)

*

7.3. If buyer competition is available(Comp)7.4. If the demand of the return market is available(D avail)

8 Post purchase behavior(PPB)

Individual's decision to return theused product after purchasing thenew products

8.1 When my product will not function well8.2 When its warrantee period will expire8.3 When the guarantee and service is over, inspite of theproduct functioning well8.4 When government will do strictly legislation on this

Bansal and Taylor (2002)

9 Return intention (RI) Individual assessment of futurewillingness to return

9.1. I will return the used product(RI_I will return) Chen (2007), Wang et al. (2013)

9.2. I will encourage to my friends and society to returnthe used product (RI_Ecno.Frnd).9.3. It may take long time to return the use products(RI_Long time).

(4). Age:

A. <20 B. 20e24 C. 25e30 D. 31e40 E. >40.

(5) Profession

A. Student B. Service Industry C. Manufacturing Industry D.Academician.

E. Business F. Other.

(6) Have you ever heard about “returning the end ofuse electronics product through to retailer, distributoror manufacturer ? If yes, plz specify the item u heardabout.

A. Computer B. Television C. Mobile phone D. printer E. other.F. No, I have not heard about it.

Part 2: various constructs with measurement variables

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