What makes people more willing to dispose of their goods rather than throwing them away?

Resources, Conservation & Recycling 156 (2020) 104682

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Resources, Conservation & Recycling journal homepage: www.elsevier.com/locate/resconrec

Review

What makes people more willing to dispose of their goods rather than throwing them away?

T

Florence De Ferrana, Eliabeth Robinotb, Myriam Ertzb a b

IAE, La Rochelle University, 39 Rue De Vaux De Foletier, 17024, La Rochelle Cedex 1, France UQAM, Montréal, QC H2L 2C4, Canada

ARTICLE INFO

ABSTRACT

Keywords: Redistribution behavior Disposition Attitudes Motivations Product characteristics

People often need to make decisions regarding the goods around them, particularly when they wish to discard them. Yet, several options are available to them, some being less detrimental to the environment than others. Redistribution practices, in particular, appear as more environmentally friendly than throwing away. This article identifies people's product redistribution behaviors and identifies the factors that account for the selection of various redistribution behaviors over the throwing away of products. We conduct a qualitative study with 18 people followed by a quantitative study involving 507 participants. The findings suggest counterintuitive insights. It appears that product-specific factors such as the perceived condition and the perceived polluting nature of the products are not strong determinants to favor redistributing products rather than throwing them away. Rather, consumer-specific factors such as past experiences, people-specific attitudes and motivations, seem to be the main catalysts favoring redistribution behaviors over the throwing away of products. Besides, donating is significantly more influenced by pro-social motives and attitudes whereas reselling draws on consumers’ market transaction motives. This research has profound implications and potential applications for real world collection of products. The core implications of these findings suggest that managers need to focus on consumer perceptions and the development of programs or systems that nurture the perception of redistribution behavior as being positive for others and for themselves, and instilling redistribution as a habit, instead of focusing on the way consumers perceive the condition or the polluting nature of their products.

1. Introduction Society relates to resources "linearly", through resource depletion and waste accumulation (Lieder and Rashid, 2016). On average, 99% of the natural resources are transformed into waste in less than 42 days.1 In addition, a significant portion of the discarded goods are not recovered, and few mechanisms are in place for recycling or reusing them (Tansel, 2017). This situation engenders environmental, social, and economic problems such as the difficulty of producing more goods with fewer resources, and for a growing world population. Many researchers have suggested transforming the current linear economy into a circular one (Stahel, 2016; Bocken et al., 2017; Geissdoerfer et al., 2017) which aims at “recapturing [the] value of postconsumption products, resources, and packaging by swapping linear material and energy flows with circularity through closed-loop production and consumption systems” (Jabbour et al., 2017, p. 2). Although vast and multilayered (Geisendorf and Pietrulla, 2018), the circular

economy grants particular attention to the improved management of product lifespan. Product lifespan extension has been investigated in (i) industrial ecology; (ii) product design practices; and (iii) environmental, political, and social sciences (Urbinati et al., 2017). While industrial ecology investigates the setting up of eco-systems and eco-industrial networks (Mathews and Tan, 2011; Mattila et al., 2012; Lombardi et al., 2012), product design practices entail design for recycling, for remanufacturing and reuse, for disassembly or for the environment (Bakker et al., 2014a, b; Bocken et al., 2016). Environmental, political, and social sciences address the issue through fostering people’s pro-environmental behaviors, by developing product circularity-based policies such as the new legislation incorporating the principles of extending product life cycle (Xue et al., 2010; Guo et al., 2017). However, although promising (e.g. Gregson et al., 2007a, 2007b; Gregson et al., 2012), this research lacks the capacity to provide a finegrained understanding of consumer, who are responsible for the life course of products. In fact, consumers directly face major trends such as

E-mail address: [email protected] (F. De Ferran). ADEME Report, Déchets Chiffres Clés [Waste key facts and figures] (2016), Paris. Viewed on July 13, 2018: https://www.ademe.fr/sites/default/files/assets/ documents/dechets-chiffres-cles-edition-2016-8813.pdf. 1

https://doi.org/10.1016/j.resconrec.2020.104682 Received 11 November 2018; Received in revised form 23 September 2019; Accepted 3 January 2020 0921-3449/ © 2020 Elsevier B.V. All rights reserved.

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Table 1 Motivations related to redistribution practices. Motivations

Definitions

Related redistribution practices

Comments

Economic

Earning money through an exchange (Ertz et al., 2017d, p. 729)

Donating is the only practice with no economic motivation, unlike exchanging, which entails the expected equal value of the exchanged goods.

Protest-based

Protesting against the traditional commercial system (Ertz et al., 2017d, p. 729)

Hedonistic-recreational

Searching for pleasant stimulations (Ertz et al., 2017d, p. 729)

Social

Forming social bonds with other people (Ertz et al., 2017d, p. 729)

Altruistic

Doing good for others without expecting anything in return (Ertz et al., 2017d, p. 729)

Ecological

Reducing wastage and pollution (Ertz et al., 2017d, p. 729)

Practical

Efficiently getting rid of products and making room (Ertz et al., 2017d, p. 729)

Generative

Obtaining symbolic immortality through handing goods down (Ertz et al., 2017d, p. 729)

Renting (Botsman and Rogers, 2010) Lending (Botsman and Rogers, 2010; Lamberton and Rose, 2012) Swapping (Sherry, 1983; Albinsson and Perera, 2009) Reselling (Wang et al., 2011; Ertz et al., 2015; Lemaître and de Barnier, 2015) Swapping (Sherry, 1983; Albinsson and Perera, 2009) Reselling (Ertz et al., 2015; Lemaître and de Barnier, 2015) Donating (Guillard and Del Bucchia, 2012) Swapping (Sherry, 1983; Albinsson and Perera, 2009) Reselling (Ertz et al., 2015; Lemaître and de Barnier, 2015) Donating (Bergadaà, 2006) Lending (Botsman and Rogers, 2010; Lamberton and Rose, 2012) Donating (Guillard and Del Bucchia, 2012) Lending (Philip et al., 2015) Swapping (Sherry, 1983; Albinsson and Perera, 2009) Donating (Bergadaà, 2006; Guillard and Del Bucchia, 2012) Lending (Botsman and Rogers, 2010; Lamberton and Rose, 2012) Swapping (Sherry, 1983; Albinsson and Perera, 2009) Donating (Bergadaà, 2006; Guillard and Del Bucchia, 2012) Renting (Botsman and Rogers, 2010) Reselling (Ertz et al., 2015; Lemaitre and de Barnier, 2015) Donating (Bergadaà, 2006) Donating (Price et al., 2000; Lastovicka and Fernandez, 2005; Albinsson and Perera, 2009) Reselling (Lemaitre and de Barnier, 2015)

the production of goods with shorter life cycles, cheap products that undermine the repair culture, or the “upgrading" pressures induced by advertising, fashion, and the throwaway society (Packard, 1960; Cooper, 2005; Gregson et al., 2007a; Cooper, 2010a, 2010b). It is thus essential to understand the processes whereby consumers choose to extend the lifespan of their products, i.e. redistribution. To date, research has been confined to typologies of behaviors (Jacoby et al., 1977; Pieters, 1993; Paden and Stell, 2005; Ertz et al., 2018) and model designing (Hanson, 1980; Pieters, 1993; Paden and Stell, 2005; Ertz et al., 2015). Other studies have focused on secondhand selling (Chu and Liao, 2007; Guiot and Roux, 2010; Abdul-Ghani et al., 2017), donating (Guillard and Del Bucchia, 2012; Klug, 2017), swapping (Lee et al., 2014), or using commercial sharing systems (Lamberton and Rose, 2012; Bardhi and Eckhardt, 2012; Armstrong et al., 2015) or social lending systems (Philip et al., 2015). However, few studies have brought to light the factors driving people to choose redistributing rather than scrapping. What are the determining factors of redistribution? What are the potential interactions between these factors that may promote redistribution? This study uses the Extended Model of Goal-directed Behavior (EMGB) (Perugini and Bagozzi, 2001) as a theoretical foundation, as well as a qualitative consumer survey in order to identify the determining factors promoting redistributing rather than scrapping. Theoretically and managerially, the study identifies the intrapsychic variables that foster redistribution behaviors and brings to light the catalysts encouraging redistribution behaviors with a view to fostering a circular economy.

These behaviors relate to a commercial and consumption system different from the traditional exchange schemes. The pleasure that is being sought is clearly related to what people receive in exchange for redistributing their goods, whether tangible or not. This social motivation is all the more significant as people do not expect financial returns for their redistribution action. This motivation involves those behaviors that bring added value to the receivers without any monetary gain being expected by the owners, even though goods are being definitively disposed of. This motivation is absent, or is present in a very limited way, when monetary gains are expected, as for selling or renting. Knowledge and habits are often related to this lesserperceived effort. People show interest in the goods and the people receiving them such that they may delay disposing of the goods until they find appropriate receivers.

exchange, lend, sell, or throw it away (Albinsson and Perera, 2009). Redistribution behaviors may be classified into four families of generic practices, excluding keeping or scrapping products (Ertz et al., 2017a; 2017b): (1) donating; (2) reselling; (3) swapping; and (4) lending/ renting, namely giving access free of charge or for a fee. Multi-year empirical studies of consumer behavior have shown that the most popular redistribution practices are 1) donating (or gift-giving) and 2) reselling, whereas swapping and lending/renting are more marginal (Kijiji, 2017; 2018). This study thus focuses upon donating and reselling. 2.2. Redistribution behaviors: determining factors The literature has identified three types of factors accounting for redistribution behaviors: individual, product-related, and situational factors. 2.2.1. Individual factors A large corpus of literature has discussed the topic of consumer willingness to redistribute products through various means, such as trade-in programs (Tian et al., 2015) or management systems to control waste (Fraige et al., 2012). Others explored more generally, consumers’ willingness to pay for recycling e-waste (Nixon and Saphores, 2007; Nixon et al., 2009; Song et al., 2012; Afroz et al., 2013; Song and Wang, 2016) or participate in e-waste recycling (Wang et al., 2011; Dwivedy and Mittal, 2014) with a point reward system (Zhong and Huang, 2016). Some, such as Zhong and Huang (2016) used the theory of planned behavior (TPB) (Ajzen, 1991) to identify variables such as attitudes towards the environment (Paden and Stell, 2005), behavioral control, and subjective norms (Harrell and McConocha, 1992; Albinsson and Perera, 2009). Other factors included personality, selfimage, emotions, commitment, societal awareness, risk tolerance,

2. Literature review 2.1. Redistribution behaviors: definition When consumers wish to redistribute a product, they may donate, 2

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experience, creativity, knowledge about toxic materials in waste, strong moral norms and motivations (Jacoby et al., 1977; Hanson, 1980; Paden and Stell, 2005; Saphores et al., 2012). Consumers’ motivations have been thoroughly investigated due to their significant impact on behaviors (Chu and Liao, 2007; Guiot and Roux, 2010; Ertz et al., 2015, 2017a). Table 1 shows that eight motives underlie redistribution behaviors: economic, protest-based, hedonistic-recreational, social, altruistic, ecological, practical, and generative. Table 1 shows the links between each motivation and the related redistribution practice. All behaviors are not necessarily related to each motivation. Each specific redistribution behavior appears to be linked to its own specific motivations and others related to other redistribution behaviors. For instance, donating is related to a distinctive altruistic motivation that is also shared with lending and swapping. The literature established that it is possible to examine redistribution behaviors as a single construct (Harrell and McConocha, 1992; Paden and Stell, 2005; Albinsson and Perera, 2009; Ertz et al., 2017a; 2017b), influenced by generic motivations (Ertz et al., 2017a; 2017b). Consequently, all the documented motivations in Table 1 may thus have an impact upon the donating and reselling behaviors under investigation in this study.

accomplishment of a goal. This modeling bypasses the analysis of instrumental behaviors one after the other and promotes the analysis of the entire set of behaviors related to achieving a goal, hence taking into account the context that contributes to behavior formation. The EMGB accounts for the factors linked to product disposition through the redistribution behaviors that entail definitive disposition such as donating and reselling. In this context, the respondents were first asked about their relationship with the products they wished to get rid of; then, they explained how, on what occasion, and why they had discarded the last product they had redistributed. We then conducted a thematic content analysis of the interviews. 3.2. Results The redistribution behaviors of donating and reselling were regularly evoked during the interviews, along with EMGB issues, in particular, attitudes and previous behaviors. Other variables, specific to both products and people, were also mentioned. 3.2.1. Redistribution behaviors Redistribution behaviors are perceived as useful, smart, effective, or bringing benefits, but also responsible, carrying value, or generating enthusiasm. The interviews revealed that reselling occurs mainly via Internet platforms/applications and flea markets. Donating is directed towards relatives or third-party nonprofits (e.g. donation centers, second-hand shops). We thus consider the following behaviors: reselling on the Internet, reselling in flea markets, donating to one’s close circle, and donating to non-profits.

2.2.2. Product-related factors Product characteristics, such as the overall condition, age, size, style, value, color, innovative aspect, adaptability, replacement cost, reliability, sustainability or obsolescence, or the initial cost of the product, influence the selection of redistribution behaviors (Jacoby et al., 1977; Paden and Stell, 2005). Goods in poor condition or not useful to others are not redistributed (Bianchi and Birtwistle, 2010). Moreover, selecting one redistribution behavior rather than another is influenced by the category of the product, its potential technological obsolescence, or whether it is subject to fashion trends (De Bell and Dardis, 1979).

3.2.2. People-specific determining factors Regarding people-specific variables, the respondents mentioned various redistribution-related motivations corresponding to the literature (e.g. Guiot and Roux, 2010; Ertz et al., 2015).

2.2.3. Situational factors These factors include psychological factors such as the social environment (e.g. close circle of family and friends), and its influence upon behaviors, but also objective factors such as the time of the year, people’s financial situation, the space available to store the goods, changing fashions, legislation, and the circumstances of acquisition (Jacoby et al., 1977; Hanson, 1980; Paden and Stell, 2005). Contrarily to person- and product-related factors, situational ones are numerous and diverse; no comprehensive framework exists for them and none of those factors stands out in importance. Therefore, they are not included in the analysis.

Hedonistic-recreational motivation. “Because I enjoy knowing the people who look good in it” (Hélène); “It was great to be with someone, to be able to talk.” (Aurélia) Economic motivation. “I'm going to sell them because these clothes were expensive” (Aurélia); “This sideboard cost us €500, so we thought we'd sell it.” (Florence) Ecological motivation. “I'd rather clothes had a second life, I don't think it's ecological to throw things away.” (Florence) Altruistic motivation. “It's going to help other people […] it could help students who don't have enough money.” (Aurélia) Generative motivation. “I've kept The Famous Five for my son; they were my books and my father’s and before that they belonged to my father’s sister.” (Aurélia); “Three-quarters of my books are technical books related to my work, and I now think I’m going to give them to my children in priority.” (Alain) Social motivation. “I offered it to the people who helped me move … since I’m giving away, I might as well give to people I love or who have helped me.” (Sarah); “I'm happy that they're happy.” (Hélène); “It's possible to make others and oneself happy.” (Nathalie)

3. Phase 1: exploring redistribution behaviors and related determining factors A qualitative study aimed at identifying the most common redistribution behaviors for donating and reselling activities as well as the factors determining redistribution behaviors over scrapping. 3.1. Method

Redistribution behaviors are viewed by most respondents as an end in itself; the goal of getting rid of unwanted products is seen as less important than the behavior adopted. Some people give for giving’s sake; this behavior has become a habit whose goal is to make others happy. This reveals the non-instrumental nature of some disposition behaviors, especially gift-giving. However, in line with the EMGB, participants frequently evoked attitudes and the role they play in the execution of behaviors (Ajzen, 1991; Sidique et al., 2010; Yuan et al., 2016).

The data collection involved individual semi-structured interviews with 18 individuals of varying age and professional situation, recruited through the snowball sampling technique (see Appendix A). The interviews were structured according to the extended model of goal-directed behavior (EMGB) (Perugini and Conner, 2000; Perugini and Bagozzi, 2001) which is designed to investigate behaviors from the abstract level of motivation. In fact, the Extended Model of Goal-Directed Behavior (EMGB) (Perugini et Conner, 2000; Perugini et Bagozzi, 2001) seems to be better adapted to explain redistributive behaviors as it improves the TPB by adding other variables to the model such as emotional, motivational and behavioral ones. Also, the model posits that behavior is not an end in itself; rather, the behavior entails the

3.2.3. Product-specific determining factors The condition of the products is a determining factor regularly mentioned. Other factors such as the lack of use of the products, their 3

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current economic value, the emotional bond with them, and their potential for further use are also quoted, but less regularly. The condition of the products and their economic value encourage redistribution (Paden and Stell, 2005; Guillard and Del Bucchia, 2012) just as their perceived residual value does (Kréziak et al., 2016).

4.1.1.3. Frequency of past behaviors. Adding the frequency of past behaviors to the TPB model enhances behavior prediction (Ajzen, 1991; Ouellette and Wood, 1998). Under certain stable conditions, the degree of execution of past behaviors is a strong predictor of future behaviors (Sidique et al., 2010) as in the context of e-waste recycling (Wang et al., 2011; Dwivedy and Mittal, 2013). Identifying past behaviors indicates the operations of internal and external factors that influence the controlled performance (or non-performance) of past behaviors (Bamberg et al., 2003). Steg and Vlek (2009) suggest that the frequent execution of a behavior generates a habit such that this behavior is systematically repeated without any significant cognitive effort. Hence, we propose the following hypothesis:

“When it's broken, I throw it away”, “They're not completely full of holes, they can be used some day.” (Aurélia) “I give only stuff in very good condition.” (Florence) “They still had some value. I would have hated giving them.” (Florence) “There are things I’d hate her to throw away, like the kimonos for instance. They have both monetary and artistic value for me.” (Alain)

H3. The frequency of redistribution behaviors influences the subsequent practice of these behaviors positively.

The polluting nature of the products also encourages redistribution behaviors.

4.1.2. Product-specific determining factors 4.1.2.1. The condition of products. As shown in the literature (Ertz et al., 2017b) and the qualitative analysis, the condition of the products, namely consumers’ perception that the products are in a good or poor condition significantly influences the redistribution decisions. Products deemed to be damaged, worn, or broken down may be thrown away as they are obsolete and useless to anyone else (Bianchi and Birtwistle, 2010). If products are still functioning, people can choose other alternatives, such as redistribution (Kréziak et al., 2016). It might appear as counter intuitive to give products that are still in good condition. The literature indicates, however, several conducive factors and events such as life events (e.g. move, bereavement, weight gain) (McAlexander, 1991) or post-consumption situations (e.g. wrong purchase, duplicates, compulsive or impulse purchases) (Chu and Liao, 2007) that may explain the redistribution of products in good condition. Hence, we propose the following hypothesis:

“This is electronic equipment, there are metal-based components, well, stuff like that, and I can't possibly put that in the bin.” (Aurelia) 4. Phase 2: factors determining redistribution behaviors 4.1. Factors viewed as determining redistribution behaviors In view of the significant number of determining factors identified through the literature review and the qualitative phase of the study, an integrative conceptual framework was developed that would incorporate people- and product-specific determinants. 4.1.1. People-specific determining factors 4.1.1.1. Intrapsychic variables. Individual intrapsychic variables may influence redistribution behaviors because of their non-instrumental nature, as our qualitative phase shows. These variables have also been frequently explored in past literature resulting in the provision of large empirical support (Nixon and Saphores, 2007; Nixon et al., 2009; Tian et al., 2015; Zhong and Huang, 2016). Attitudes, subjective norms, and perceived behavioral control influence intentions, then behaviors (Ajzen and Fishbein, 1980; Ajzen, 1991). In light of the qualitative analysis results, only attitudes were examined. The influence of attitudes on people’s conative dimension has been empirically validated in various contexts in terms of behavioral intentions (De Leeuw et al., 2015) and behaviors (Sidique et al., 2010; Yuan et al., 2016). Several studies emphasized the crucial role of attitudes, in general, and attitudes toward the environment, in particular, to explain the tangential behavior of recycling products (e.g., Nixon and Saphores, 2007; Nixon et al., 2009; Zhong and Huang, 2016). Hence, the following hypothesis is proposed: H1. People's attitude towards redistribution behaviors positively.

redistribution

behaviors

H4. The good condition of products influences the practice of redistribution behaviors positively. 4.1.2.2. The perceived polluting nature of product. Products have a varying polluting nature, which induces firms to devise specific mechanisms to curb pollution (Roussel et al., 2015). It is thus likely that people would be aware of this feature and would consider it. Our qualitative study appears to confirm this conclusion; some people do not wish to throw their mobile phone away because they see it as polluting. The perception of the polluting nature of products appears to be more significant than objective criteria (e.g. environmental labelling) that do not seem to engender more environmentally friendly behaviors (Bernard, 2009). Hence, we propose the following hypothesis: H5. The perceived polluting nature of the product influences the practice of redistribution behaviors positively.

influences

4.1.2.3. Influence of product-related variables on people-related variables. Product-related variables are based upon people’s perceptions, but they are clearly related to the products and their characteristics. Thus, they may influence the people-related variables determining redistribution. Products in poor condition may reduce the motivation to make happy or to hand down. People assume that donating or reselling products in poor condition might make the recipients unhappy rather than delighted (Ertz et al., 2017b). The motivation of a market transaction would be similarly reduced, since it is unlikely that products in poor condition might lead to effective exchanges (Chu and Liao, 2007). Utilitarian (or non-utilitarian) attitudes would be weakened because of the unlikelihood of the rapid and effective (or pleasing) availability of products in poor condition (Ertz et al., 2018). Finally, the impact of frequent past behaviors would also be weakened since the chances of success of redistribution as

4.1.1.2. Motivations. Motivations are antecedents to the conative dimension of attitudes (Ertz et al., 2017c). Motivations to engage in a particular redistribution behavior correspond to the energy used to bring tangible goods to be redistributed. As subjective factors, motivations exert a significant influence on willingness to participate in e-waste recycling with a points reward system (Zhong and Huang, 2016). Further, several factors of motivation such as economic, convenience or environmental motives have been associated with disposal methods (Tian et al., 2015). In the qualitative phase of the study, these motivations appeared to be significant factors fostering people’s redistribution behaviors as an alternative to throwing goods away. Hence, the following hypothesis is proposed: H2. Motivations to redistribution behaviors influence adopting these behaviors positively. 4

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experienced in the past would be expected as lessened if products are in poor condition. Hence:

with one item about the occurrence of each redistribution behavior over the previous 12 months. The condition of the product and its polluting nature were each assessed with one item. These measures used a 5point Likert-type scale (from 1 = “strongly disagree” to 5 = “strongly agree”).

H6. The condition of the products reduces the positive relation between (a) attitudes, (b) motivations, (c) frequency of redistribution behaviors, and redistribution behaviors. A similar line of argument may be proposed for the polluting nature of the products, although the latter - as with capital-intensive products (Ertz et al., 2017b) - is likely to magnify attitudes, motivations, and the effect of the frequency of past behaviors upon redistribution behaviors. Hence:

4.2.3. Measurement model, reliability, and validity An exploratory factor analysis determined the psychometric properties of the attitude and motivation scales (see Table 2). The measurement model of the attitude and motivation variablesexcluding behavior frequency and product condition-shows a good 2 level of overall fit (81) ( = 223.49, p = 0.00, CFI = .95, RMSEA = .06, SRMR = .063, GFI = .97, AGFI = .94). These indices meet the required statistical thresholds (Hu and Bentler, 1998). Factor loadings meet the local fit conditions and contribute to convergent validity (Bagozzi et al., 1991). The common method bias (CMB) was controlled a priori through randomized questions and varying the presentation order of the items. Harman’s single-factor test was used to examine the CMB a posteriori; the unifactorial model produced significantly lower results 2 ( (87) = 1286.17, p = 0.00, CFI = .55, RMSEA = .16, SRMR = .15) than the multidimensional model. Hence, CMB is not an issue. Discriminant validity is ensured since the square root of the average variance extracted (AVE) of any given factor (e.g. market transaction motivation) is superior to all the correlations of the factor with the other factors in the model (Fornell and Larcker, 1981) (See Table 3). Second, as shown in Table 4, a pairwise restriction of models (Anderson and Gerbing, 1988) determined sequentially the correlation related to each pair of constructs at 1.0, and a test of the significance of the Chisquare change was carried out. All the Chi-square changes were

H7. The polluting nature of the products strengthens the positive relation between (a) attitudes, (b) motivations, (c) the frequency of redistribution behaviors, and redistribution behaviors. 4.2. Method 4.2.1. Data collection method Five hundred and seven (507) questionnaires were collected from people in France through an online survey. The quota sampling technique ensured a representative sample of the French population, in terms of gender, age, and profession (see Appendix B). 4.2.2. Measures used A 7-item scale adapted from Perugini and Conner (2000) measured attitudes. An 8-item scale, devised from the qualitative phase and the literature (e.g. Guiot and Roux, 2010; Ertz et al., 2015), measured motivations. The frequency of redistribution behaviors was assessed

Table 2 Descriptive statistics and psychometric properties.

People-specific determining factors Attitudes Utilitarian attitudes Useful Effective People-specific attitudes Smart Bringing value Responsible Pleasant Generating enthusiasm Motivations/benefits sought Making happy and handing down Making one’s close circle happy Making others happy Handing down Giving products a second life Motivations related to market transactions Money Meeting people Experiencing something new Allowing to talk and bargain Frequency of behavior Throwing away Reselling on the Internet Reselling in flea markets Donating to one’s close circle Donating to non-profits Product-specific determining factors Perceived product condition Perceived product’s polluting nature

Descriptive statistics

Factor loadings

Mean

Standard deviation

Factor 1 loading

3.957 3.974

1.020 0.793

3.779 3.586 3.929 3.807 3.619

0.801 0.718 0.726 0.818 0.763

0.716 0.763 0.812 0.831 0.806

2.961 3.716 3.639 2.811

1.472 1.326 1.26 1.446

0.773 0.760 0.574 0.689

2.128 2.142 2.101 1.931

1.465 1.129 1.116 1.097

2.077 2.134 2.174 3.568 3.355

0.999 1.228 1.217 1.003 1.292

2.000 2.288

0.861 1.427

Reliability Factor 2 loading

0.919 0.622

0.792 0.679 0.718 0.827

5

Validity

Cronbach alpha

Composite reliability (CR)

Average variance extracted (AVE)

0.600

0.630

0.500

0.865

0.860

0.553

0.708

0.820

0.534

0.802

0.867

0.640

6

* p < .05. ** p < .01. *** p < .001.

Making happy and handing down Motivations related to market transactions People-specific attitudes Utilitarian attitudes Perceived product condition Perceived product's polluting nature Frequency of behavior Throwing away Frequency of behavior Reselling on the Internet Frequency of behavior Reselling in flea markets Frequency of behavior Donating to one's close circle Frequency of behavior Donating to nonprofits

Table 3 Correlation matrix.

1.000

.096* 0.008 −0.051

.088*

−.124**

.503***

.468***

0.030

−0.046

.294*** −0.011 −.245***

−0.072

−.224***

−.185***

−0.055

.432***

.359***

Motivations related to market transactions

0.000

1.000

Making happy and handing down

.313***

.303***

.115*

0.073

−.249***

−0.069

1.000 0.000 −.256***

Peoplespecific attitudes

−.140**

−0.050

−0.079

−0.007

−0.016

−0.039

1.000 −.102*

Utilitarian attitudes

−0.066

−.212***

−0.062

−.121**

.335***

.120**

1.000

Perceived product condition

−.148**

0.012

0.069

0.064

0.011

1.000

Perceived product's polluting nature

−0.061

−.207***

−0.052

−0.082

1.000

Frequency of behavior Throwing away

−.095*

0.057

.586***

1.000

Frequency of behavior Reselling on the Internet

0.066

.241***

1.000

Frequency of behavior Reselling in flea markets

.307***

1.000

Frequency of behavior Donating to one's close circle

1.000

Frequency of behavior Donating to nonprofits

F. De Ferran, et al.

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Table 4 Paired constraints of the models to examine the discriminant analysis. Correlation constraint

Chi-square

Change in the Chi-square

DDL

P value

Basic model Correlation constraint in the model Utilitarian attitudes – People-specific attitudes Utilitarian attitudes – Motivation: making happy Utilitarian attitudes – Motivation: market transaction People-specific attitudes – Motivation: making happy People-specific attitudes – Motivation: market transaction Motivation: making happy – Motivation: market transaction

223.49

—

—

—

261.73 349.92 506.70 349.37 498.59 329.79

38.24 126.43 283.21 125.88 275.10 106.30

1 1 1 1 1 1

0.000 0.000 0.000 0.000 0.000 0.000

Note: DDL = Degrees of freedom.

significant, which means that each construct shows discriminant validity.

product polluting nature, as independent variables. The odds ratios (OR) and their confidence interval (CI) at 95% are shown in Table 5. The model shows good fit ( 2 = 1776.226, p < .001) with adequate explanatory power for the redistribution behaviors in relation to scrapping (Pseudo-R²Cox et Snell = 0.880).

4.3. Results 4.3.1. Redistribution behaviors and determinants Redistribution (90.0%) is relatively more frequent than scrapping (10.0%), and donating (65.1%) is more prevalent than reselling (24.9%). Donating to nonprofits (36.1%) is the most frequent redistribution behavior followed by donating to one’s close circle (29.0%). Reselling is less prevalent with around one seventh of respondents indicating to resell on the Internet (15.8%), and one eleventh in flea markets (9.1%). The alternative solution involving consumers keeping the product for potential use or another use was not included in the analysis since it does not involve product redistribution. A series of four multinomial logistic regressions were carried out for each redistribution behavior. In each of the four multinomial logistic regressions, the disposition classification was the dependent variable. Throwing away was used as the reference category. The predictors included utilitarian and people-specific attitudes, motivations to make happy, motivations to conduct a market transaction, frequency of past disposition behaviors, perceived product condition, and perceived

4.3.1.1. Attitudes. People-specific attitudes encourage all redistribution behaviors over scrapping. Consumers whose attitude toward redistribution is less related to utilitarian considerations have 7.656 times more chances to donate to nonprofits rather than throwing away, 7.499 times more chances to resell on the Internet, 5.353 times more chances to donate to their close circle, and 4.119 times more chances to resell in flea markets. Conversely, Utilitarian attitudes (useful, effective) encourage reselling on the Internet only and with much lower probabilities in relation to people-specific attitudes. Overall, H1 is thus partly validated. 4.3.1.2. Motivations. Motivations related to market transactions, represent mainly a factor determining reselling in flea markets, then on the Internet, and to a much lesser degree, to donating to nonprofits. Motivations to make happy/hand down (making one’s close circle happy, making others happy, handing products down, and giving them

Table 5 Relation between people-specific and product-specific determining factors and the four redistribution types using the multinomial logistic regressions (N = 507). Donating to one’s close circle

Donating to non-profits

Reselling in flea markets

Reselling on the Internet

CI (95%)

OR (standard error)

CI (95%)

OR (standard error)

CI (95%)

OR (standard error)

CI (95%)

(1.675–17.114) (0.190–1.646)

7.656 (0.001) 0.565 (0.224)

(2.432–24.105) (0.225–1.418)

4.119 (0.034) 0.353 (0.075)

(1.112–15.248) (0.112–1.112)

7.499 (0.002) 0.303 (0.033)

(2.157–26.069) (0.101–0.909)

(4.768–80.107) (0.102–1.152)

8.098 (0.003) 0.248 (0.025)

(2.027–32.361) (0.073–0.838)

0.859 (0.855) 9.586 (0.007)

(0.167–4.422) (1.841–49.911)

0.822 (0.803) 3.586 (0.080)

(0.175–3.852) (0.858–14.988)

(0.098–0.661) (0.622–6.857)

0.282 (0.007) 1.840 (0.308)

(0.113–0.706) (0.569–5.946)

0.148 (0.002) 0.831 (0.815)

(0.044–0.504) (0.177–3.898)

(0.054–0.520) (4.232–68.867)

(0.960–14.474)

3.454 (0.069)

(0.908–13.146)

(4.285–144.036)

(0.575–10.589)

(0.274) (0.419)

(0.616–5.510) (0.251–1.776)

0.188 (0.002) 7.612 (0.000)

(0.064–0.551) (2.729–21.237)

24.843 (0.000) 0.339 (0.111) 0.975 (0.967)

0.167 (0.002) 17.072 (0.000) 2.468 (0.224)

(0.090–1.282) (0.301–3.157)

0.241 (0.029) 0.844 (0.767)

(0.068–0.862) (0.275–2.591)

(0.000)

(0.030–0.330)

0.080 (0.000)

(0.025–0.260)

0.052 (0.000)

(0.013–0.205)

0.026 (0.000)

(0.007–0.104)

(0.005)

(0.210–0.751)

0.448 (0.011)

(0.242–0.830)

0.606 (0.206)

(0.279–1.318)

0.348 (0.005)

(0.168–0.721)

a

OR (standard error) People-specific determining factors Attitudes People-specific attitudes 5.353 (0.005) Utilitarian attitudes 0.487 (0.133) Motivations Motivation: making happy 19.543 (0.000) Motivation: market transaction 0.342 (0.083) Frequency of redistribution behaviors Throwing away 0.254 (0.005) Reselling on the Internet 2.066 (0.236) Reselling in flea markets

3.728 (0.057)

Donating to one’s close circle 1.842 Donating to non-profits 0.668 Product-specific determining factors Perceived product condition Perceived product condition (from 0.100 poor to good) Perceived polluting nature of the product Perceived polluting nature of the 0.397 product (from less to more polluting)

Notes: OR = Odds Ratio; CI = Confidence Interval. The odds ratios and their confidence interval at 95% in bold indicate that the confidence intervals exclude 1.0 and thus are significant. a Alpha of .05 is used for the statistical significant levels. 7

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a new life) are exclusively related to donating. Consumers who are more motivated by the act of making happy and handing down are thus 19.543 times more likely to give to their close circle and 8.098 times more likely to give to a nonprofit rather than scrapping, Hence, these results led to a partial validation of H2.

5. Discussion and implications The study contributes significantly to the literature on the circular economy through examining it from the consumers’ perspective, as the consumers’ role in facilitating circularity is often overlooked (Kirchherr et al., 2017). The study presents empirical evidence regarding the impact of various factors and their interactions to encourage product redistribution rather than scrapping. The frequency of past behaviors appears to be a particularly strong determinant as it shows the highest likelihood of redistribution vs. scrapping. Habitual behavior has been identified as a particularly powerful predictor in other areas such as pro-environmental behaviors (Steg and Vlek, 2009). This study adds evidence to this phenomenon in the redistribution context. With the exception of giving to one’s close circle, redistribution behaviors are strongly facilitated by the previous execution of those behaviors. Selling second-hand products is strongly dependent on past behaviors. For selling on the Internet and in flea markets, the likelihood of adopting this behavior as opposed to scrapping is 17 and 24 times higher, and it is 7 times higher for donating to nonprofits. Conversely, donating second-hand products to one’s close circle is not dependent on past behaviors. It thus appears that people exhibit habitual redistribution behaviors that are activated more or less automatically (Ouellette and Wood, 1998; Albinsson and Perera, 2009). Although donating to one’s close circle does not increase the likelihood of favouring this behavior over product scrapping, this behavior engenders a modest yet significant reverse effect upon other behaviors such as reselling on the Internet and donating to nonprofits. Interestingly, consumers who have thrown products away in the past are slightly more likely to redistribute. In fact, they may experience psychological discomfort in the form of cognitive dissonance (Festinger, 1957) or hypocrisy (McGrath, 2018) that they try to mitigate by engaging in redistribution when they next dispose of products. Future studies may investigate the potential existence of such avoidance strategies in disposition. Motivations appear as significant catalysts for redistribution behaviors. Motivations to make others happy and hand products down is very important for donating behaviors, particularly those involving one’s close circle, and market transaction motivations for reselling behaviors. These results are congruent with well-known motivations in the domain of reselling (Lemaitre and de Barnier, 2015). When no gain is expected, as in product donating, the pleasure this behavior brings to oneself or to others is an important catalyst to execute it. Similarly, powerful market transaction motivations exist for people selling used products. It is important to note that it is only for donating to nonprofits that the motivations of making happy and market transaction are combined. Potentially, because some nonprofits provide financial incentives to bring about donations. Attitudes toward redistribution behaviors also play a role, but the latter is comparatively more modest than that of past behavior frequency or motivations. Surprisingly, it is people-specific attitudes that foster redistribution most at the expense of scrapping; this phenomenon could be explained by a Beckerian cost-benefit analysis whereby consumers perceive making more effort and receiving lesser gains (Lamberton and Rose, 2012) in the case of redistribution as opposed to scrapping. Conversely, redistribution brings obvious people-specific compensations through social, recreational, and experiential stimulation absent from scrapping. Beyond technological obsolescence and product-related fashion trends (De Bell and Dardis, 1979), the condition products are in and their polluting nature also play their part in people’s adoption of these behaviors, although this impact is limited compared to that of peoplerelated variables. The main influence of these two variables remains modest, but their interaction with antecedents, in particular past redistribution behaviors, is relatively more important. The positive effect of past redistribution behaviors upon the likelihood of repeating them instead of scrapping, is determined by the condition products are in and

4.3.1.3. Frequency of the redistribution behaviors. Frequently used redistribution behaviors are more likely to be done in the future. These results indicate that redistribution behaviors become habitual. Consumers who have frequently resold products in flea markets are 24.843 times more likely to repeat this behavior rather than throwing products away. This is the highest ratio found in the study. Similarly, reselling behaviors on the Internet (or donating to nonprofits) increases by 17.072 (7.612) times the likelihood of repeating the behavior rather than throwing away. Only donating to one’s close circle is not repeated as often. Moreover, only donating to one’s close circle increases, though slightly, the chances of performing other redistribution behaviors such as reselling on the Internet (0.241 times) and donating to nonprofits (0.188 times), which signals a reversal effect inherent to the practice. H3 is thus partially validated. 4.3.1.4. Condition of the products. The good condition of products fosters redistribution behaviors. Nevertheless, though significant, its impact is limited; consumers are between 0.026 and 0.100 times more likely to have a redistribution behavior rather than throwing away. It is for donating to one’s close circle - and donating in general - that the condition of the products increases most the chances of redistribution. H4 is thus validated. 4.3.1.5. Polluting nature of products. The more products are perceived as polluting, the more they induce redistribution behaviors, except in the case of reselling in flea markets. Although this impact is very weak, H5 is partially validated. 4.3.2. Analysis of interactions To test hypotheses H6a-c and H7a-c, we tested a model showing the interactions between the perceived condition and the polluting nature of the product on the one hand and the determining factors of attitudes, motivations, and past behaviors on the other. The condition of the products influences positively the relation between the frequency of reselling on the Internet and reselling on the Internet (OR = 2.292, CI = [1.058, 4.964]); the frequency of reselling in flea markets and donating to one’s close circle (OR = .587, CI = [.367, .3937]) and donating to nonprofits (OR = .576, CI = [.372, .891]); the frequency of reselling on the Internet and donating to nonprofits (PR = 1.647, CI = [1.022, 2.653]); and between Utilitarian attitudes and donating to one's close circle (OR = .563, CI = [.355, .893]). Hence, H6a and H6c are partially validated, but H6b is not. The polluting nature of the products influences positively the relation between the frequency of reselling in flea markets and reselling in flea markets (OR = 2.486, CI = [1.250, 4.944]) and the frequency of reselling on the Internet and reselling on the Internet (OR = .490, CI = [.268, .895]); the frequency of reselling in flea markets and donating to nonprofits (OR = 1.775, CI = [1.096, 2.875]); and the frequency of executing all the redistribution behaviors and donating to nonprofits (reselling on the Internet : OR = .588, CI [.369, .938] ; reselling in flea markets: OR = 1.800, CI = [1.118, 2.898] ; donating to one's close circle: OR = .532, CI [.354, .801]; donating to nonprofits: OR = 2.253, CI [1.530, 3.319]). The perceived polluting nature of products strengthens the positive relation between motivations for market transactions and donating to nonprofits (OR = .556, CI = [.328, .942]); and between motivations to make happy and donating to one’s close circle (OR = 1.621, CI = [1.039, 2.531]). Overall, H7b-c are partially validated, but H7a is not. 8

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their polluting nature. The better the condition and the more salient their polluting nature, the more likely the products will be redistributed rather than thrown away, when redistribution is a frequent behavior.

our waste. Renting products when it is possible is an example as it could allow a better conception of products and avoid a large pollution. 7. Limitations and research avenues

6. Managerial implications

Beyond the contributions exposed above, this study has limitations. Amongst them, only one goal of redistribution behaviors has been investigated whereas people may desire to donate products even though they did not wish to get rid of them. The determining factors examined in the study are not exhaustive. They are related to the desire of getting rid of products. The goal considered fosters behaviors inducing a definitive separation from products, and other potential time-based solutions to redistribution were not investigated. Moreover, adopting redistribution behaviors rather than throwing away appears to be based strongly upon similar past behaviors. It would be worth investigating the catalysts promoting the initialization of the behaviors, hence contributing to making them habitual. Furthermore, future studies should determine the role of situational and normative variables in the context of redistribution behavior as the structure of our data did not allow us to investigate these variables in details. Finally, it would also be worth examining the entire consumption cycle, from product acquisition to disposition, in the same way the life cycle of products is researched. This would help identify criteria involved when acquiring products, as they could play a significant role when people wish to get rid of products.

From a managerial perspective, some of the catalysts regularly discussed and used in the domain of public policy regarding waste management (e.g. financial incentives) do not necessarily induce the desired routine behaviors (Katzev and Johnson, 1984). This study further shows that Utilitarian attitudes have little impact upon redistribution behaviors compared to people-specific attitudes. It is thus necessary to raise awareness of the issues from within people-specific perspectives, following an intrinsic and autotelic logic, rather than developing extrinsic mechanisms inducing opportunistic behaviors. It is also important to get inspired by public policies such as Extended Producer Responsibility Programs (Saphores et al., 2007) or waste management system implemented in different countries around the world to implement the more efficient actions guiding them. However, we need to take it cautiously regarding the socio-economic situation of the country, the regulations, the infrastructures developed, the efficiency and accessibility of waste management system or secondhand market, that makes transposition of policies or waste management not always relevant (Van Beukering and Van den Bergh, 2006). Regulations and a clear identification of the actors responsible financially and materially of goods are crucial (Dwivedy and Mittal, 2013). Moreover, in high-income developed countries as Europe where the infrastructures and regulations are present, the growth of disposal behavior relies on a better awareness of citizens or a constrain like a cost if not adopted. But it is still better to work on the limitation of goods replacement by giving information about product life cycle and favoring the keeping, the repairing, instead of replacing thanks to nudging techniques or to a shift in social norms. Moreover, brand should go for product eco-conception and favor their reparability as a large part of customers are considering those dimensions (Afroz et al., 2013). A shift in the way firm do business and people consume will allow us to reduce

Funding This research was part of a project called Rechange funded by the French environmental agency, Ademe. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Socio-demographic characteristics of the sample for Phase 1 Characteristics Gender Male Female Age Under 25 years old 25 to 45 years old 46 to 60 years old Over 60 years old Family status Single Married Common-law union Widow/er Divorced Separated Place of residence Rural Urban Type of residence Lives in a flat Lives in a house

Proportion (%) 28% 72% 5.5 66.6% 16.6% 11,11% 20% 50% 20% 0% 10% 0% 42% 58% 35,7% 64,3%

Appendix B. Socio-demographic characteristics of the sample for Phase 2 Characteristics Gender Male Female

Proportion (%) 53,8% 46,2%

9

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F. De Ferran, et al. Age Under 25 years old 25 to 45 years old 46 to 60 years old Over 60 years old Family status Single Married Common-law union Widow/er Divorced Separated Place of residence Rural Urban Type of residence Lives in a flat Lives in a house

7,3% 36.1% 37,3% 19,3% 19,3% 49,9% 19,7% 2,0% 7,1% 2,0% 40,6% 59,4% 37,7% 62,3%

Ertz, M., Durif, F., Arcand, M., 2017a. Life after death? Study of goods multiple lives practices. J. Consum. Mark. 34 (2), 108–118. Ertz, M., Durif, F., Arcand, M., 2017b. Towards multilife marketing: how goods multiple lives practices create value for consumers. J. Promot. Manag. 1–32. Ertz, M., Durif, F., Arcand, M., 2018. Business at the fingertips of consumers: a scale for measuring resale motivations in online settings. Int. Rev. Retail. Distrib. Consum. Res. 28 (1), 92–114. Ertz, M., Huang, R., Jo, M.S., Karakas, F., Sarigöllü, E., 2017c. From single-use to multiuse: study of consumers’ behavior toward consumption of reusable containers. J. Environ. Manage. 193, 334–344. Ertz, M., Lecompte, A., Durif, F., 2017d. Dual roles of consumers: towards an insight into collaborative consumption motives. Int. J. Mark. Res. 59 (6), 725–748. Festinger, L., 1957. A Theory of Cognitive Dissonance. Stanford University Press, Stanford, CA. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 39–50. Fraige, F.Y., Al-khatib, L.A., Alnawafleh, H.M., Dweirj, M.K., Langston, P.A., 2012. Waste electric and electronic equipment in Jordan: willingness and generation rates. J. Environ. Plan. Manag. 55 (2), 161–175. Geisendorf, S., Pietrulla, F., 2018. The circular economy and circular economic concepts—a literature analysis and redefinition. Thunderbird Int. Bus. Rev. 60 (5), 771–782. Geissdoerfer, M., Savaget, P., Evans, S., 2017. The Cambridge business model innovation process. Procedia Manuf. 8, 262–269. Gregson, N., Metcalfe, A., Crewe, L., 2007a. Identity, mobility and the throw-away society. Environ. Plan. D 25, 682–700. Gregson, N., Metcalfe, A., Crewe, L., 2007b. Moving things along: the conduits and practices of divestment. Trans. Inst. Br. Geogr. 32, 187–200. Gregson, N., Watkins, H., Calestani, M., 2012. Political markets: recycling, economization and marketization. Econ. Soc. 13 (1), 1–25. Guillard, V., Del Bucchia, C., 2012. “How about giving my things away over the internet?” When internet makes it easier to give things away. Adv. Consum. Res. 40, 630. Guiot, D., Roux, D., 2010. A second-hand shoppers’ motivation scale: antecedents, consequences, and implications for retailers. J. Retail. 86 (4), 355–371. Guo, B., Geng, Y., Sterr, T., Zhu, Q., Liu, Y., 2017. Investigating public awareness on circular economy in western China: a case of Urumqi Midong. J. Clean. Prod. 142, 2177–2186. Hanson, J.W., 1980. A proposed paradigm for consumer product disposition processes. J. Consum. Aff. 14 (1), 49–67. Harrell, G.D., McConocha, D.M., 1992. Personal factors related to consumer product disposal tendencies. J. Consum. Aff. 26 (2), 397–417. Hu, L.T., Bentler, P.M., 1998. Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychol. Methods 3 (4), 424. Jabbour, C.J.C., de Sousa Jabbour, A.B.L., Sarkis, J., Godinho Filho, M., 2017. Unlocking the circular economy through new business models based on large-scale data: an integrative framework and research agenda. Technol. Forecast. Soc. Change. https:// doi.org/10.1016/j.techfore.2017.09.010. Jacoby, J., Berning, C.K., Dietvorst, T.F., 1977. What about disposition? J. Mark. 22–28. Katzev, R.D., Johnson, T.R., 1984. Comparing the effects of monetary incentives and foot‐in‐the‐door strategies in promoting residential electricity conservation. J. Appl. Soc. Psychol. 14 (1), 12–27. Kijiji, 2017. Kijiji Secondhand Economy Index 2016 Report. . Available at: https://www. kijiji.ca/kijijicentral/app/uploads/2016/08/2017-Index1.pdf (Accessed 1-11-2018). Kijiji, 2018. Kijiji Secondhand Economy Index 2016 Report. Available at: https://www. kijiji.ca/kijijicentral/app/uploads/2016/08/Kijiji-Index-Report-2018_EN_Final_web2.pdf (Accessed 1-11-2018). . Kirchherr, J., Reike, D., Hekkert, M., 2017. Conceptualizing the circular economy: an analysis of 114 definitions. Resour. Conserv. Recycl. 127, 221–232. Klug, K., 2017. A gift for a stranger: freecycling as a current lifestyle of sustainable consumption. In: Osburg, T., Lohrmann, C. (Eds.), Sustainability in a Digital World: New Opportunities Through New Technologies. Springer, Cham, pp. 201–207. Kréziak, D., Prim-Allaz, I., Robinot, E., Durif, F., 2016. Obsolescence perçue, décision de renouveler et destinée des produits : le cas du téléphone portable. Décisions

References Abdul-Ghani, E., Marshall, R., F Hyde, K., 2017. Engagement–experiencing magic in the C2c online auction. ACR Latin Am. Adv. 61–62. Afroz, R., Masud, M.M., Akhtar, R., Duasa, J.B., 2013. Survey and analysis of public knowledge, awareness and willingness to pay in Kuala Lumpur, Malaysia - a case study on household WEEE management. J. Clean. Prod. 52, 185–193. Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211. Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behaviour. Prentice-Hall 278 pages. Albinsson, P.A., Perera, B.Y., 2009. From trash to treasure and beyond: the meaning of voluntary disposition. J. Consum. Behav. An Int. Res. Rev. 8 (6), 340–353. Anderson, J.C., Gerbing, D.W., 1988. Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103 (3), 411. Armstrong, C.M., Niinimäki, K., Kujala, S., Karell, E., Lang, C., 2015. Sustainable productservice systems for clothing: exploring consumer perceptions of consumption alternatives in Finland. J. Clean. Prod. 97, 30–39. Bagozzi, R.P., Yi, Y., Phillips, L.W., 1991. Assessing construct validity in organizational research. Adm. Sci. Q. 421–458. Bakker, C., den Hollander, M.C., Van Hinte, E., Zijlstra, Y. (Eds.), 2014. Products That Last: Product Design for Circular Business Models. Delft: TU Delft Library/Marcel den Hollander IDRC. Bakker, C., Wang, F., Huisman, J., den Hollander, M., 2014b. Products that go round: exploring product life extension through design. J. Clean. Prod. 69, 10–16. Bamberg, S., Ajzen, I., Schmidt, P., 2003. Choice of travel mode in the theory of planned behavior: the roles of past behavior, habit, and reasoned action. Basic Appl. Soc. Psychol. 25 (3), 175–187. Bardhi, F., Eckhardt, G.M., 2012. Access-based consumption: the case of car sharing. J. Consum. Res. 39 (4), 881–898. Bergadaà, M., 2006. Le don d’objet: l’exploration de ses dimensions et des profils de donneurs aux œuvres de bienfaisance. Recherche et Appl. en Marketing (French Edition) 21 (1), 19–39. Bernard, Y., 2009. L’étiquetage environnemental des produits en magasin. Revue Française du Marketing 221 (1–5), 63–83. Bianchi, C., Birtwistle, G., 2010. Sell, give away, or donate: an exploratory study of fashion clothing disposal behaviour in two countries. Int. Rev. Retail. Distrib. Consum. Res. 20 (3), 353–368. Bocken, N.M., de Pauw, I., Bakker, C., van der Grinten, B., 2016. Product design and business model strategies for a circular economy. J. Ind. Prod. Eng. 33 (5), 308–320. Bocken, N.M., Ritala, P., Huotari, P., 2017. The circular economy: exploring the introduction of the concept among S&P 500 firms. J. Ind. Ecol. 21 (3), 487–490. Botsman, R., Rogers, R., 2010. What’s Mine Is Yours - the Rise of Collaborative Consumption. HarperCollins Publishers. Chu, H., Liao, S., 2007. Defining and categorizing consumer resale behavior in consumerto-consumer (C2C) e-commerce. Int. J. Bus. Inf. 2 (2), 159–184. Cooper, T.H., 2005. Slower consumption: reflections on product life cycles and the throwaway society. J. Ind. Ecol. 9, 51–67. Cooper, T.H., 2010a. Longer Lasting Products. Ashgate., Farnham. Cooper, T.H., 2010b. Burying the ‘refuse revolution’: the rise of controlled tipping in Britain, 1920–1960. Environ. Plan. A 42, 1033–1048. De Bell, M., Dardis, R., 1979. Extending product life: technology isn’t the only issue. ACR North American Advances 6, 381–385. De Leeuw, A., Valois, P., Ajzen, I., Schmidt, P., 2015. Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: implications for educational interventions. J. Environ. Psychol. 42, 128–138. Dwivedy, M., Mittal, R.K., 2014. Attitudes, behavior and willingness of residents towards e-waste recycling: case study of an Indian University. Waste Manag. 34 (6), 1094–1095. Ertz, M., Durif, F., Arcand, M., 2015. Online product disposition on the rise - the specific case of online resale. Mark. Rev. St Gall. 32 (5), 66–76.

10

Resources, Conservation & Recycling 156 (2020) 104682

F. De Ferran, et al. Marketing 81, 41–59 Janvier-Mars. Lamberton, C.P., Rose, R.L., 2012. When is ours better than mine? A framework for understanding and altering participation in commercial sharing systems. J. Mark. 76 (4), 109–125. Lastovicka, J.L., Fernandez, K.V., 2005. Three paths to disposition: the movement of meaningful possessions to strangers. J. Consum. Res. 31 (4), 813–823. Lee, H.M., Chen, T., Hung, M.L., 2014. Online bartering motivations. Psychol. Rep. 115 (1), 75–90. Lemaitre, N., De Barnier, V., 2015. Quand le consommateur devient commerçant: motivations, production d’expérience et perspectives. Décisions Marketing 78, 11–28. Lieder, M., Rashid, A., 2016. Towards circular economy implementation: a comprehensive review in context of manufacturing industry. J. Clean. Prod. 115, 36–51. Lombardi, D.R., Lyons, D., Shi, H., Agarwal, A., 2012. Industrial symbiosis: testing the boundaries and advancing knowledge. J. Ind. Ecol. 16 (1), 2–7. Mathews, J.A., Tan, H., 2011. Progress toward a circular economy in China: the drivers (and inhibitors) of eco‐industrial initiative. J. Ind. Ecol. 15 (3), 435–457. Mattila, T., Lehtoranta, S., Sokka, L., Melanen, M., Nissinen, A., 2012. Methodological aspects of applying life cycle assessment to industrial symbioses. J. Ind. Ecol. 16 (1), 51–60. McAlexander, J.H., 1991. Divorce, the disposition of the relationship, and everything. ACR North Am. Adv. 18, 43–48. McGrath, A.L., 2018. Encouraging ecological behaviour through induced hypocrisy and inconsistency: a commentary. J. Environ. Psychol. 56, 1–2. Nixon, H., Saphores, J.D.M., 2007. Financing electronic waste recycling Californian households’ willingness to pay advanced recycling fees. J. Environ. Manage. 84 (4), 547–559. Nixon, H., Saphores, J.D.M., Ogunseitan, O.A., Shapiro, A.A., 2009. Understanding preferences for recycling electronic waste in California The influence of environmental attitudes and beliefs on willingness to pay. Environ. Behav. 41 (1), 101–124. Ouellette, J.A., Wood, W., 1998. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol. Bull. 124 (1), 54. Packard, V., 1960. The Waste Makers: a Startling Revelation of Planned Wastefulness and Obsolescence in Industry Today. David McKay Company., New York, NY. Paden, N., Stell, R., 2005. Consumer product redistribution: disposition decisions and channel options. J. Mark. Channels 12 (3), 105–123. Perugini, M., Bagozzi, R.P., 2001. The role of desires and anticipated emotions in goal‐directed behaviours: broadening and deepening the theory of planned behaviour. Br. J. Soc. Psychol. 40 (1), 79–98. Perugini, M., Conner, M., 2000. Predicting and understanding behavioral volitions: the interplay between goals and behaviors. Eur. J. Soc. Psychol. 30 (5), 705–731. Philip, H.E., Ozanne, L.K., Ballantine, P.W., 2015. Examining temporary disposition and acquisition in peer-to-peer renting. J. Mark. Manag. 31 (11–12), 1310–1332. Pieters, R., 1993. Consumers and their garbage a framework, and some experiences from

the Netherlands with garbage separation programs. ACR E-European Adv. Consum. Res. 1, 541–546. Price, L.L., Arnould, E.J., Curasi, C.F., 2000. Older consumers’ disposition of special possessions. J. Consum. Res. 27 (2), 179–201. Roussel, I., Charles, L., Rambaud, J.M., 2015. L’évaluation et la perception de l’exposition à la pollution atmosphérique: une interrogation sociétale Analyse critique des résultats du programme. PRIMEQUAL 205, 2268–3798. Saphores, J.-D.M., Nixon, H., Ogunseitan, O.A., Shapiro, A.A., 2007. California households’ willingness to pay for’ green’ electronics. J. Environ. Plan. Manag. 50 (1), 113–133. Saphores, J.-D.M., Ogunseitan, O.A., Shapiro, A.A., 2012. Willingness to engage in a proenvironmental behavior: an analysis of e-waste recycling based on a national survey of U.S. Households. Resour. Conserv. Recycl. 60 (0), 49–63. Sherry Jr., J.F., 1983. Gift giving in anthropological perspective. J. Consum. Res. 10 (2), 157–168. Sidique, S.F., Lupi, F., Joshi, S.V., 2010. The effects of behavior and attitudes on drop-off recycling activities. Resour. Conserv. Recycl. 54 (3), 163–170. Song, Q., Wang, Z., Li, J., 2012. Residents’ behaviors, attitudes, and willingness to pay for recycling e-waste in Macau. J. Environ. Manage. 106, 8–16. Stahel, W.R., 2016. The circular economy. Nature News 531 (7595), 435. Steg, L., Vlek, C., 2009. Encouraging pro-environmental behaviour: an integrative review and research agenda. J. Environ. Psychol. 29 (3), 309–317. Tansel, B., 2017. From electronic consumer products to e-wastes: global outlook, waste quantities, recycling challenges. Environ. Int. 98, 35–45. Tian, X., Wu, Y.F., Gong, Y., Agyeiwaa, A., Zuo, T.Y., 2015. Residents’ behavior, awareness, and willingness to pay for recycling scrap lead-acid battery in Beijing. J. Mater. Cycles Waste Manag. 17 (4), 655–664. Urbinati, A., Chiaroni, D., Chiesa, V., 2017. Towards a new taxonomy of circular economy business models. J. Clean. Prod. 168, 487–498. Van Beukering, P.J.H., Van den Bergh, J.C.J.M., 2006. Modeling and analysis of international recycling between developed and developing countries. Resour. Conserv. Recycl. 46, 1–26. Wang, Z.H., Zhang, B., Yin, J.H., Zhang, X., 2011. Willingness and behavior towards ewaste recycling for residents in Beijing city, China. J. Clean. Prod. 19 (9–10), 977–984. Xue, B., Chen, X.P., Geng, Y., Guo, X.J., Lu, C.P., Zhang, Z.L., Lu, C.Y., 2010. Survey of officials’ awareness on circular economy development in China: based on municipal and county level. Resour. Conserv. Recycl. 54 (12), 1296–1302. Yuan, Y., Nomura, H., Takahashi, Y., Yabe, M., 2016. Model of Chinese household kitchen waste separation behavior: a case study in Beijing city. Sustainability 8 (10), 1083. Zhong, H., Huang, L., 2016. The empirical research on the consumers’ willingness to participate in E-waste recycling with a point reward system. Energy Procedia 104, 475–480.

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