Mining the Drawers to Close the Loop

Mining the Drawers to Close the Loop.

What drives Swedish Consumers to Recycle their Old Cell phone?

MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 ECTS PROGRAMME OF STUDY: International Marketing AUTHOR: Bilal Ahmad & Yi Pu JÖNKÖPING May 2020

Master Thesis in Business Administration

Title: Mining the Drawers to Close the Loop – What drives Swedish Consumers to Recycle their Old Cell phones? Authors: Bilal Ahmad and Yi Pu Tutor: Darko Pantelic Date: 2020-05-18

Key terms: Cell phone recycling in Sweden, determinants of recycling behaviour, theory of planned behaviour, attitude towards recycling scheme, intention to participate in recycling schemes,

Abstract

Background: The cell phone industry causes numerous environmental problems due to its extractive and unsustainable business practices. However, in recent years there has been a shift within the industry towards more circular economy (CE) centric business models. As a result, the industry has introduced a number of recycling schemes which would not only mitigate unsustainable disposal practices but also retrieve the precious material contained in old cell phones. Thereby, reducing the dependence on virgin resource extraction and the associated environmental degradation. Successful implementation however hinges on the willingness of consumers to participate in these schemes. Whereby a better understanding of consumers’ behavioural cues will help not only the producers to increase the effectiveness of these schemes but would from a policy making standpoint help facilitate this transition towards CE centric business models.

Purpose: The purpose of this study is to develop an explanation of the determinants of recycling intention. Since, previous research indicates that intention to perform a behaviour dictates the materialization of the desired behaviour. Within this previous research, factors such as attitude, subjective norm, perceived behavioural control, moral incentive, convenience, awareness of consequences, concern for information security and environmental assessment have been shown to predict a person’s recycling intention. In this study, we empirically test the applicability of these factors in determining the intention to participate in recycling schemes in the context of cell phone recycling in a Swedish setting.

Method: We adopted a positivist research philosophy, a deductive approach and an explanatory research design to conduct this quantitative research. The process started with a comprehensive literature to uncover the determinants of recycling intention to develop 13 hypotheses. Following which we surveyed 268 residents in Sweden to form generalizable insight about the various factors identified in the literature. As a result, 194 surveys were included based on the quality and qualification criteria. Finally, the data was analysed utilising structural equation modelling to test the hypotheses.

Conclusion: We were able to confirm 8 of the 13 hypothesis developed. The results showed that moral incentive and convenience had the greatest influence on intention to participate in schemes.

Acknowledgements

First and foremost, we express our deepest gratitude to Darko Pantelic, for not only providing valuable feedback and lending us his academic expertise but also for making the process intellectually stimulating. Without his continued guidance and patience we would not have been able to complete this thesis.

Secondly, we would like to express our gratitude to our friends in the seminar group for going beyond what was required in providing feedback, for challenging us intellectually and helping us shape this research.

We would also like to thank all 264 of the wonderful people, that took out the time to complete our survey. Without their contribution this study would not have been possible.

Finally, we thank our friends and family, whose support and encouragement made this journey bearable.

Bilal Ahmad & Yi Pu Jönköping, 18th May 2020

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Table of Contents

1. Introduction ...... 1 1.1 Background ...... 1 1.2 Problem Discussion ...... 2 1.3 Previous Research ...... 4 1.4 Purpose ...... 5 1.5 Research Methodology ...... 6 1.6 Delimitations ...... 6 1.7 Thesaurus ...... 7

2. Literature review ...... 8 2.1 Consumers as enablers of circular economy ...... 8 2.1.1 Cell phone industry’s transition towards circular economy ...... 8 2.1.2 Consumer Participation ...... 9 2.1.2.1 Trade-in Schemes ...... 11 2.1.2.2 Recycling Schemes ...... 11 2.2 Determinants of consumers’ recycling behaviour...... 13 2.2.1 Attitude, Subjective Norm and Perceived Behavioral Control ...... 14 2.2.2 Moral Incentive ...... 16 2.2.3 Convenience ...... 17 2.2.4 Awareness of the problem ...... 18 2.2.5 Concern for Information Security ...... 19 2.2.6 Environmental Assessment...... 20

3. Methodology ...... 23 3.1 Research Philosophy ...... 23 3.2 Research Approach ...... 24 3.3 Research Design ...... 24 3.4 Research Method ...... 25 3.5 Data Collection Method ...... 26 3.5.1 Survey Design ...... 26 3.5.1.1 Operationalization ...... 27 3.5.1.2 Questionnaire Design ...... 27 3.5.2 Sampling ...... 29 3.6 Data Analysis Method ...... 29

4. Results and Analysis ...... 33 4.1 Data, Demographics and Descriptive Statistics ...... 33 4.2 Exploratory Factor Analysis ...... 36 4.3 Analysis of the Measurement Model ...... 37 4.4 Analysis of the Structural Model ...... 39

5. Discussion ...... 42 5.1 Moral Incentives ...... 42 5.2 Subjective Norms and Awareness of the problem ...... 43 5.3 Convenience ...... 45 5.4 Attitude and Perceived Behavioural Control (PBC) ...... 46 5.5 Concern for information security ...... 47 5.6 Environmental Assessment...... 48

6. Conclusion, Implications and Limitations ...... 50 6.1 Implications for Academics and Suggestions for Future Research ...... 51

6.2 Implications for Policymakers...... 52 6.3 Implications for producers ...... 53 6.4 Limitations ...... 54

7. Reference list ...... 56

8. Appendix ...... 67

Figures Figure 1 - The proposed theoretical model ...... 14 Figure 2 - What did you do with your old phone? ...... 34 Figure 3 - The structural model with standardized path coefficients ()...... 39

Tables Table 1 - Summary of the hypothetical assumptions ...... 22 Table 2 – Operationalization process...... 28 Table 3 - Classification of latent constructs ...... 30 Table 4 - Criteria for assessing the Goodness-of-Fit ...... 32 Table 5 - Data screeningprocess ...... 33 Table 6 - Sample demographics ...... 34 Table 7 - Frequency distribution and ANOVA results for recycling intention and past behaviour ...... 35 Table 8 - The rotated factor matrix ...... 36 Table 9 - Evidence for Convergent Validity ...... 37 Table 10 - Evidence for discriminant validity ...... 38 Table 11 - Summary for the Measurement Model ...... 38 Table 12 - Summary for the Structural Model...... 39 Table 13 - Summary of the empirical findings ...... 40

Appendix Appendix Table 1 - Questionnarie ...... 67 Appendix Table 2 - Cronbach’s alpha ...... 74 Appendix Table 3 - Correlation Matrix ...... 80

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1. Introduction

______This chapter starts with general introduction about how exponential growth and hyper consumption in the cell phone industry is causing negative consequences for the environment. This is followed by a discussion on Circular Economy and the need for increased consumer participation in producers recycling schemes. We then proceed to provide an overview of the current state of research about recycling. A research gap is identified not only with regards to the lack of a rich analysis about consumers’ cell phone recycling intention but also in consideration to previous research on Swedish consumers. The subsequent section presents the research question and articulates what it entails. The chapter end with a brief comment on the planned methodology, identification of delimitations in order to frame the scope of this study. ______

1.1 Background Information and Communication Technology (ICT) devices, such as cell phones, are for most people an important part of everyday life and influence the way we work, communicate, travel and much more (Belkhir & Elmeligi, 2018). Human population has doubled in the last 50 years and the consumption of electronic devices has grown six-fold during that time (Wann, 2011). In 2020, Including both smart and feature phones the number of phone users in the world reached 4.78 billion, which means 61.62% of all people owns a cell phone; the number for smartphone is 3.5 billion making 45.12% of the world’s population a smartphone owner (Turner, 2020).

Today’s technology enables reduction of human impact on nature, for instance by reducing traveling in favour of video conferencing or through smart buildings by optimizing energy usage (Chwieduk, 2003; Gharavi & Ghafurian, 2011; Yi & Thomas, 2007). However, the negative side of the ICT industry is the rapidly growing energy consumption due to manufacturing and powering our devices (Belkhir & Elmeligi, 2018). In Europe, ICT devices account for approximately 8-10% of the electricity consumption and 4% of the carbon emission (EU, 2018). Important to note is that those numbers do not include energy used for manufacturing the devices which is particularly alarming since they have a comparatively short life span (Belkhir & Elmeligi, 2018). Building a new smartphone, taking mining the rare materials into account, represents 85% to 95% of the device’s total CO2 emissions within two years, which means buying one new smartphone requires the same amount of energy as operating a phone for a decade (Wilson, 2018). Similarly, producing a mobile phone weighing 169 grams gives rise to 86 kg of waste material, in the form of for instance mining waste and slag products (IVL Swedish Environmental Research Institute, 2015). Furthermore, in order to fulfil the ever growing demands for newer better

technology, the mining of rare earth metals have increased significantly and it is estimated that by 2080, the biggest metal reserves will no longer be underground (Ore Streams, 2020).

The fast development of mobile phones follows close to equally rapid replacement of outdated devices (Suckling & Lee, 2015). Over 60 per cent of mobile phone sales are replacements for already-existing phones, 90 per cent of which are still functioning when they are discarded (Mitchell, 2017). With an average life cycle of two years these new cell phone purchases can also be regarded more or less disposable (Wilson, 2018). This leads to the question what happens to the old phones? Consider the fact that the Green House Gas Emissions (GHGE) of smart phones increased 730% between 2010 to 2020 (Belkhir & Elmeligi, 2018) while the recycling rate of e- waste in Sweden decreased overall from 62.4% to 55.4% in a comparable period between 2008 and 2016 (Statista, 2020). These figures illustrate the notion that the discarded devices do not make it back to manufacturers or recycling facilities where at least some of the valuable materials could be extracted for reuse, refurbishing or recycling. Especially when considering that in some instances these discarded devices can yield significantly more rare metals in comparable weight than actual mineral ore (Nogrady, 2016).

1.2 Problem Discussion In recent years Circular Economy (CE) has emerged as one of the most promising paradigms. The CE represents the most recent attempt to conceptualize the integration of economic activity and environmental wellbeing in a sustainable way (Murray, Skene, & Haynes, 2017). In a circular economy we keep resources in use for as long as possible, extract the maximum value from them whilst in use, then recover and regenerate products and materials at the end of each service life (The Waste and Resources Action Programme, 2018). However, a circular economy is not just a paradigm shift by reference to repairing, reusing, refurbishing, recycling and remanufacturing; it is also about the redesign of the future economy and society through new business models and new consumption behaviours (Tse, Esposito, & Soufani, 2015). In essence, ‘a circular economy is an industrial system that is restorative or regenerative by intention and design’ (The Ellen MacArthur Foundation, 2012, p. 7). Here the word ‘intention’ is of particular importance, in that this intention is not only indicative of the manufacturers’ commitment to create production systems that are restorative or regenerative by design but also implies a certain degree of consumer responsibility to participate in the curation of these systems (Charter & Bakker, 2018; Hazen, Mollenkopf, & Wang, 2017; Rizos et al., 2016).

While we acknowledge that within the cell-phone industry current business models are driven by accelerated obsolescence and where the manufacturers might still be resistant towards extending

the use life of their smart phones (Belkhir & Elmeligi, 2018). Recent years have seen an increase in CE initiatives from major cell-phone manufacturers such as increased recyclability, use of recycled materials in production, greater repairability coupled with better take-back schemes and return facilities (Samsung Electronics, 2016, 2019; Watson et al., 2017). We argue that these initiatives suggest a paradigm shift towards a closed loop circular economy within the industry and is certainly reflective of the ‘intent’ towards greater producer responsibility.

In contrast, a plethora of research including that undertaken by academics and independent institution paints a rather bleak picture with regards to consumer involvement in facilitating the closed loop transition in the industry (see for example: Bai, Wang, & Zeng, 2018; Parker, 2019; Welfens, Nordmann, & Seibt, 2016; Welfens, Nordmann, Seibt, & Schmitt, 2013). Almost all of the previous research points to the fact that a large majority of consumers own more phones than they actually use and that a significant proportion of the old phones are stored at home (Bai et al., 2018; Parker, 2019; M. Welfens et al., 2013). In fact, according to a 2015 study conducted by Deloitte, regarding what people do with their old devices, 43% of the respondents indicated they store them as spares, 20% gave them to friends and family and only 19% sold or traded in their old phones (Statista, 2015). More interestingly, in a study undertaken by Bai et al., (2018) it was reported that a significant number of consumers because of information security concerns would instead of using proper channels to recycle, rather smash their phones into pieces before throwing it away.

These examples illustrate the point that for a CE transition to be successful within the cell phone industry, consumers have to play an active role in facilitating this transition (Ghisellini, Cialani, & Ulgiati, 2016; Kirchherr, Reike, & Hekkert, 2017; Yuan, Bi, & Moriguichi, 2006). More specifically, from a producer’s perspective one way to achieve this goal is through greater consumer participation in the recycling initiatives (Sarath, Bonda, Mohanty, & Nayak, 2015). Thereby ensuring continuous flow of materials within the industrial system which will significantly reduce the environmental cost of new productions (Belkhir & Elmeligi, 2018; The Ellen MacArthur Foundation, 2012). Hence, in order for the CE initiatives to be successful organizations have to pay attention to the behavioural cues of their consumers to implement strategies that foster greater consumer participation in the recycling schemes (Echegaray & Hansstein, 2017). More specifically, through uncovering the various factors that determine consumers’ intention to recycle, interventions can be designed to not only get consumers to form intentions to participate in recycling schemes but also to help consumers act on any existing intentions (M Fishbein & Ajzen, 2011).

1.3 Previous Research

Just as there exists a rich tradition of research on consumer behaviour and sustainability ((Fraj & Martinez, 2007; Harris, Roby, & Dibb, 2016; ölander & ThØgersen, 1995; Young, Hwang, McDonald, & Oates, 2009), an ever increasing body of research has also approached recycling from a consumer behaviour perspective (Hornik, Cherian, Madansky, & Narayana, 1995; Sarath et al., 2015). A substantial body of this research has been dedicated to understanding recycling intentions as the immediate antecedent of consumers’ recycling behaviour (Hornik et al., 1995). Furthermore, there appears to be general consensus among a great majority of these academics that people’s recycling intentions and consequent behaviour can ultimately be traced to the consumers’ attitudinal dispositions, normative influences and control considerations about the recycling behaviour (Echegaray & Hansstein, 2017; Lizin, Van Dael, & Van Passel, 2017; Wang, Guo, Wang, Zhang, & Wang, 2018). However, given the diverse nature of recycling as a subject – not only in consideration to the object of recycling but also in regards to the contextual background in which recycling takes place (Botetzagias, Dima, & Malesios, 2015) – these factors are not always successful in predicting recycling intentions (Ramayah, Lee, & Lim, 2012; Tonglet, Phillips, & Read, 2004). As a consequence, many researchers have favoured inclusion of additional factors within their research to improve the prediction of intentions and behaviours (Botetzagias et al., 2015; Echegaray & Hansstein, 2017; F. Khan, Ahmed, & Najmi, 2019). Hence awareness about the issue (Wang, Guo, & Wang, 2016), personal moral norms (Botetzagias et al., 2015; Tonglet et al., 2004), economic incentives (Mak, Yu, Tsang, Hsu, & Poon, 2018) and convenience (Ng, 2019) associated with recycling have surfaced as important factors influencing recycling intention.

Observing the low recycling rate of cell phones, many researchers have put their attention into studying the consumer’s cell phone recycling behaviour (Sarath et al., 2015). Nonetheless, cell phone recycling is an emerging area of research within the recycling literature (Welfens et al., 2016). Therefore a large majority of research has primarily focussed on reporting the patterns and trends in consumer’s attitudes and behaviours towards recycling (Bai et al., 2018; Nnorom, Ohakwe, & Osibanjo, 2009; Welfens et al., 2013; Yin, Gao, & Xu, 2014). Here, in addition to the awareness (Yin et al., 2014), convenience (F. Khan et al., 2019) and incentives (Ongondo & Williams, 2011b; Welfens et al., 2016), concern for information security (Bai et al., 2018; Zhang, Wu, & Rasheed, 2020) has been highlighted as an important consideration in the context of cell phone recycling. Furthermore, for the studies that did involve an examination of consumer’s recycling intention (Nnorom et al., 2009; Welfens et al., 2016; Zhang et al., 2020), the discussion and was constrained by the overarching purpose which has generally been geared towards

understanding recycling processes (Ongondo & Williams, 2011b), implications for policy making (Yin et al., 2014) effectiveness of awareness campaigns (Welfens et al., 2016) or individual’s perception of risks (Zhang et al., 2020). No previous research to the best of our knowledge has utilized the full spectrum of factors that have been highlighted in previous recycling literature to present a rich analysis about the determinant of intention to participate in cell phone recycling schemes. Especially in the context of CE from a producer’s perspective.

Finally, While there are a number of studies about recycling in Sweden (Hage, Söderholm, & Berglund, 2009), there exists no previous research that has approached cell phone recycling from a consumer behaviour perspective in Sweden. Previous research has shown that factors that are influential in affecting intention to recycle in one setting (for example developing countries) or for a specific recycling object (such as electronic-waste) may not necessarily be important in another situational context or a different recycling object (Botetzagias et al., 2015; Ramayah et al., 2012; Tonglet et al., 2004). Therefore, insights developed from previous research in Sweden about recycling behaviour in other contexts, for example household recycling (Hage et al., 2009), may not hold true for cell phone recycling in Sweden. To the best of our knowledge, our study is the first that attempts to fill this research gap.

1.4 Purpose

The purpose of this study is to answer the following primary research question:

RQ: What factors determine Swedish consumers’ intention to participate in recycling schemes?

Previous research on recycling has shown a number of factors such as attitude, subjective norm, perceived behavioural control, awareness of the problem, moral norms etc., to predict a person’s recycling intention (Echegaray & Hansstein, 2017; Onel & Mukherjee, 2017; Wang et al., 2016; Welfens et al., 2013). In this study, we identify and empirically test the applicability of these factors in determining the intention to participate in recycling schemes in the context of cell phone recycling in a Swedish setting. According, Geller (2002) promoting behaviour change involves examining which factors cause those behaviours, and applying well-tuned interventions to change relevant behaviours. We believe that a satisfactory answer to our research question would provide proper explanations of influences moulding pro-recycling behaviours. Which in turn offers valuable insights to identify the key touchpoints that not only corporations but other concerned

parties such as governments, academics and grassroots initiatives can explore to design interventions that would increase participation in recycling schemes.

1.5 Research Methodology Given the nature of the research question, the research warrants a positivist research philosophy, a deductive approach and a descriptive research design to conduct this quantitative research. Both secondary and primary data will be collected. Here the secondary data collection in form of a comprehensive literature review will help us identify the various factors previous research in recycling literature has identified as predictors of recycling intention; that might be applicable to our context. While the primary data collection in form of survey will help us gather data from a large sample. This will enable us to (1) deduce generalizable insight about the Swedish populations’ intentions to participate in recycling schemes, and (2) test the applicability of the various factors identified in previous literature through various statistical analysis.

1.6 Delimitations

First and foremost, the study is being conducted from a producer’s perspective i.e. where the ultimate benefactor of the recycling activities is the producer. Therefore, other CE facilitating consumer behaviour such as reuse, resell and repurpose fall outside our scope. Moreover, previous research has also included demographic cues and has shown it relevance in the e-waste recycling context in a developing country. We argue that since this research is being conducted Sweden, a country ranking highly on the human development index (Statista, 2019), factors like income, locality etc., would not display similar variances in regards to the determinants of behaviour. Other factors such as age, gender and education could thus be addressed using the various statistic reporting techniques. Finally, non-random convenience sampling is also a limitation which will directly influence the validity and generalizability of the insights developed as result of this research (Babin & Zikmund, 2016).

1.7 Thesaurus

Circular Economy: an industrial system that is restorative or regenerative by intention and design (The Ellen MacArthur Foundation, 2012). Consumer Participation: A consumer can participate in the producers CE initiatives by ensuring that that materials keep flowing within the producer’s supply loops (Watson et al., 2017; Welfens et al., 2016). Producers: including manufacturers (Belkhir & Elmeligi, 2018; Welfens, Nordmann, & Seibt, 2016) and service providers such as network operators and retailers that play a considerable role in end of life product recovery (D’Antone, Canning, Franklin-Johnson, & Spencer, 2017; Watson et al., 2017). Recycling Schemes: End of life cell-phone return/recovery through voluntary donation routes (D’Antone, Canning, Franklin-Johnson, & Spencer, 2017). Trade-in Schemes: End of life cell-phone return/recovery through commercial route. That is, a recycling scheme with an added economic incentive to recycle (D’Antone et al., 2017). Moral incentive: Incentives that induce people to translate any felt obligation or ‘moral norm’ into recycling action (Hage, Söderholm, & Berglund, 2009). Convenience: consumer’s expectations in consideration to the time space and ease of use of recycling scheme (Bai et al., 2018; F. Khan et al., 2019; Wan, Shen, & Choi, 2017). Attitude: an individual favourable or unfavourable perception of performing a certain behaviour (Ajzen, 1991) Perceived behavioural control: The extent to which a consumer feels that it’s possible, and practically feasible, to recycle their cell phones (Zhang et al., 2020). Subjective norm: an individual’s evaluation whether a certain behaviour is encouraged or not by important people and groups (Ajzen, 1991). Concern for information security: An individual’s subjective evaluation of the objective risk of recycling with regards to information security (Pennings & Smidts, 2003; Zhang et al., 2020). Environmental Assessment: consumer’s perception of the environmental performance of their country ((Echegaray & Hansstein, 2017).

2. Literature review

______This chapter introduces the current state of research on topics being discussed in this study. The first part makes up the case as to why this study is framed in the context of circular economy. We discuss how financial viability is incentivizing companies to introduce various incentives; the success of which depends on greater customer participation. We then introduce the idea of recycling schemes, discuss the current state of research and justify to the reader why it is important to approach the topic from a consumer behaviour perspective. Due to the nature of our research question, the second part is concerned with research model and hypothesis development. We begin by providing justification for why the Theory of Planned Behaviour (TPB) was chosen as a suitable starting point. 5 additional constructs are added to the TPB, including moral incentive, convenience, concern for information security, awareness of the problem and environmental assessment. The remainder of the section is focussed on introducing each of the latent construct to the reader and justifying why it was included in the theoretical model. Finally, hypotheses are developed at the end of each discussion in light of the previous research associated with each latent construct. ______

2.1 Consumers as enablers of circular economy This section builds on the discussion on consumer’s role in enabling the cell phone industry’s transition towards circular economy, something that was touched briefly in the previous chapter. We start this discussion by presenting an overview of the concept of circular economy (CE), why it is important, and perhaps more interestingly the reasons that make this transition towards CE viable for the cell phone industry. The subsequent section then presents the existing literature advocating for greater consumer participation for the development of circular economies. Here, the focus in not only on why consumers should participate in circular economy initiatives but also on how they can participate. We then present participation in trade-in schemes and recycling schemes as two viable alternatives. Here we place more focus on recycling schemes and explain why it is necessary to approach this topic from a consumer behaviour perspective.

2.1.1 Cell phone industry’s transition towards circular economy Circular Economy (CE) is a concept that has been gaining attention in recent years (Ghisellini et al., 2016). Its potential contribution to sustainable development is the main reason. The concept aims at preventing rapid resource depleting and reducing waste generation (Feng & Yan, 2007; Ghisellini et al., 2016; Hazen et al., 2017). Within the concept of sustainable development, CE is viewed as a more tangible part of the broader concept (Ghisellini et al., 2016; Kirchherr et al., 2017; Murray et al., 2017). One of the key features of CE is resource efficiency and

dematerialization of electronic economy (Lieder & Rashid, 2016). In early stage, CE research was conducted largely in the areas of industrial process and applications (Korhonen, Honkasalo, & Seppälä, 2018). The adherence to linear economy concept of “take-make-dispose” was found as one of the major barriers to CE (Hazen et al., 2017). Therefore, scholars have recently shifted their interest to closed-loop supply chain (CLSC) practices, which involve remanufacturing process. The efficient implementation of CLSC systems hinges on the efficient recovery of the end of life products (The Ellen MacArthur Foundation, 2012; Welfens et al., 2016).

The cell-phone industry has in the past been a target of criticism with regards to its negative environmental consequences, such as the depletion of rare natural resources and poor recovery of end-of-life products (Belkhir & Elmeligi, 2018; Ongondo, Williams, & Cherrett, 2011). In this market both manufacturers (Belkhir & Elmeligi, 2018; Welfens et al., 2016) and service providers such as network operators and retailers (hereafter, referred collectively as producers) play an important role in not only new cell-phone sales but also considerably, in end of life product recovery (D’Antone et al., 2017; Watson et al., 2017). Furthermore, as opposed to other waste electrical and electronic equipment (WEEE), return systems for cell-phones yield positive financial outcomes for the producers (D’Antone et al., 2017; Watson et al., 2017). Since not only the recovery and treatment costs are considerably smaller compared to other WEEE items, the revenue generated for example through the reselling of the refurbished cell-phones, offers financial viability to the CE model (D’Antone et al., 2017; Watson et al., 2017). Hence, incentivized by this financial viability and in response to negative consumer sentiments, the cell phone producers over the years have introduced a number of schemes with the aims of increasing end of life product return/recovery (Samsung Electronics, 2016, 2019; Watson et al., 2017). However, for these schemes to be successful, greater consumer participation is required (Gallaud & Laperche, 2016). Consequently, in order to achieve greater participation producers need to develop a better understanding the various factors that influence their consumers’ willingness to participate in these schemes (Borrello, Caracciolo, Lombardi, Pascucci, & Cembalo, 2017). We discuss the concept of consumer participation in the following section.

2.1.2 Consumer Participation Previous research on the topic has advocated for greater consumers responsibility and participation for the promotion of sustainable products and services (Feng & Yan, 2007; Ghisellini et al., 2016). The earlier research generally approached consumer participation from a policy making perspective. For example, Feng & Yan (2007), not only deemed consumer participation as ‘indispensable to the development of a circular economy’ and advocated the use policy as an instrument to encourage the public to acquire attitudes and habits about consumption

that are conducive to CE (Feng & Yan, 2007, p. 108; Geng & Doberstein, 2008). Or consider Matete & Trois (2008), who while analysing a zero waste program in Durban, South Africa, provided evidence that the successful implementation depended on the participation rate of households.

In the recent years the focus has shifted towards the impact of consumer participation on CE at the firm, industry or supply chain level. These researchers argue that while promotion of responsibility is crucial for CE (Ghisellini et al., 2016), little is known about consumers’ willingness to participate in a CE (Borrello et al., 2017). Hence, excluding the consumer and adapting a supply side view involves the risks of developing business models that may be unviable due to a lack of consumer demand (Kirchherr et al., 2017). Therefore successful CE models involves rethinking consumption (Moreau, Sahakian, van Griethuysen, & Vuille, 2017) whereby, supply chains must not only consider the various production and distribution, but also consumption processes. Hence, making the consumer the most central enabler of CE (Gallaud & Laperche, 2016).

In the context of the cell-phone industry a consumer can participate in the producers CE initiatives by ensuring that that materials keep flowing within the producer’s supply loops (Watson et al., 2017; Welfens et al., 2016). Whereby some components can be reused while other material can be recycled (The Ellen MacArthur Foundation, 2012). Hence, reducing the producer’s dependency on exploitative and extractive practices and as a result minimize or eliminate some of the negative consequences associated with new cell-phone production (Belkhir & Elmeligi, 2018). Previous studies have pointed out that the consumers can participate in the creation of circular economies through reusing, repairing, refurbishing or repurposing material good within their consumption cycles (Kirchherr et al., 2017). However, in the context of the cell-phone industry from a producer’s perspective, greater consumer participation is required in the manufacturer’s return schemes for the survival of their closed-loop supply chain (CLSC) systems (Bai et al., 2018; Sarath et al., 2015; Watson et al., 2017).

These schemes can be broadly categorized into commercial routes and donation routes to end of life product return/recovery. The donation routes to return/recovery include cell-phone returns or recovery when a consumer voluntarily disposes a cell-phone using a producer owned return facility such as in-store collection boxes (D’Antone et al., 2017). In this case a moral incentive i.e. personal satisfaction of doing good, seems to be the driving factor (Echegaray & Hansstein, 2017). In contrast, commercial routes generally involve a monetary incentive, such as a cash vouchers or discounts on (future) purchases (D’Antone et al., 2017) but may also include

charitable donations on behalf of customers (Watson et al., 2017; Welfens et al., 2016). For the purpose of this study, end of life cell phone return/recovery through voluntary donation routes are termed as recycling schemes whereas those incentivized through commercial routes are termed as trade-in schemes.

Since this study is primarily focussed on recycling schemes a detailed discussion on trade-in schemes falls outside the scope. However, in order to inform the reader with the major differences between the two concepts, a brief discussion on trade-in scheme is presented in the next section.

2.1.2.1 Trade-in Schemes As mentioned previously, trade-in schemes represent an overarching term which is indicative of the commercial route to cell phone return/recovery (D’Antone et al., 2017). And in the context of this study, trade-in schemes serve as an extension to the concept of recycling schemes by introducing an economic incentive to the construct. Here, an economic incentive is indicative of a system that relies on the immediate material compensation of users that motivates more people to recycle or return their old phones (Welfens et al., 2016, 2013). These incentives generally include discounts and vouchers but could also include charitable donations, such as Telenor’s take-back via sport clubs who receive money for each used cell phone collected (Ongondo & Williams, 2011a; Watson et al., 2017; Welfens et al., 2016). Similarly, trade-in schemes offering cash for returned mobile phones also have a noticeable impact on the collection levels. However, this incentive is generally reserved for newer models that can be resold by the producer (Watson et al., 2017; Welfens et al., 2016).

In contrast, a number of studies have provided evidence that the willingness to recycle is negatively influenced when the direction of these economic incentives is reversed (Ongondo & Williams, 2011b, 2011a; Yin et al., 2014). That is, when the consumer is required to pay for the recycling services or when the consumers perceive the producers to be deriving greater economic benefit (Bai et al., 2018; Ongondo & Williams, 2011a; Yin et al., 2014). However, as pointed out previously incentives of any kind – cash, discounts or charity – are generally influential on consumers’ willingness to return their old cell phones (Bai et al., 2018; Sarath et al., 2015; Welfens et al., 2016).

2.1.2.2 Recycling Schemes By definition, recycling refers to the disposal of waste (unwanted materials) into a material cycle, in order to minimize environmental pollution (Geiger, Steg, van der Werff, & Ünal, 2019). In essence, sufficient recycling can recover valuable materials, and these materials can be put into

new products for reuse, which can reduce the consumption of raw materials, save energy, and help prevent air pollution and water pollution (Tanskanen, 2013). It is also worth mentioning that in the context of circular economies in the cell phone industry, although recycling mobile phones and other ICT products is perhaps not the most sustainable option, it is the best available solution – in addition to prolonging the use phase – for tackling the resource problem at present (Welfens et al., 2016). However, the small size of cell-phone devices makes it susceptible to being stored at home, forgotten in drawers, particularly when there is a lack of intention to recycle (Bai et al., 2018; Sarath et al., 2015). This storability of cell phone devices requires extra motivation on part of the consumer since not only proper consumer awareness is required for a recycling system to perform efficiently (Sarath et al., 2015), but also sufficient consumer participation is imperative for a successful mobile phone recycling system (Bai et al., 2018).

According to Sarath et al., (2015), previous research on cell phone recycling has generally focused on five distinct areas: Generation and Management of mobile phone waste, Consumer Behavioural studies, Economics of mobile phone recycling, Toxicity assessment and finally, Material Identification and Recovery. While material recovery is perhaps the largest area of interest for researchers, consumer behaviour is arguably the most crucial parameter to improve the recycling rate of waste mobile phones (Sarath et al., 2015). Since the future of cell phone recycling rests mainly at the hands of cell phone users (Kirchherr et al., 2017; Sarath et al., 2015).

In the recycling literature with regards to consumer behaviour, three constructs are widely used by researchers: consumer attitudes towards recycling, behavioural intentions, and actual recycling behaviour (Hornik, Cherian, Madansky, & Narayana, 1995). Where, successful large-scale adoption of recycling practices results as a function of these constructs (Bai et al., 2018; Echegaray & Hansstein, 2017; Welfens et al., 2016). Therefore, proper understanding of influences moulding pro-recycling behaviours offers valuable insights for policymaking and helps to identify the key touchpoints that government, corporations and grassroots initiatives can explore to address environmental pressures more effectively (Echegaray & Hansstein, 2017).

In conclusion, CE presents a viable alternative to eliminate or at least mitigate some of the negative environmental consequences of the cell phone industry (Belkhir & Elmeligi, 2018). This idea is further reinforced by the fact that producers are now realizing that implementing recycling schemes presents opportunities that offer both financial gains and intangible benefits (such as pacifying dissatisfied consumers) (D’Antone et al., 2017; Watson et al., 2017). However, in order to capitalize on this opportunity, a better understanding of consumer’s willingness to participate in recycling scheme is required. In the next section, we discuss the determinants of consumer’s

recycling behaviour by first presenting a theoretical model and then developing an understanding of each of the influencing factors identified in the theoretical model.

2.2 Determinants of consumers’ recycling behaviour To understand human behaviour it is important to understand their psychological process (F. Khan et al., 2019). In fact the premise of understanding the determinants of recycling behaviour implies a research building socio-psychological model which helps to understand socio-psychological influences, captured by latent variables, on people's recycling behaviour (Lizin et al., 2017). For this purpose, we propose using the theory of planned behaviour as a suitable baseline model. Especially since this theory is largely considered to be the dominant theory in most the studies recorded on determinants of recycling behaviour (Ramayah et al., 2012; Wan et al., 2017) and has been used extensively in the past to study recycling behaviour in various contexts (Wan et al., 2017; Wang et al., 2018).

Theory of Planned Behaviour (TPB) is an extension of Theory of reasoned action (TRA) (Martin Fishbein & Ajzen, 1975). According to TRA, behavioural intention determines a person’s performance of a specified behaviour, while the person’s attitude and subjective norm jointly determine the behavioural intention concerning the behaviour in question (Fishbein & Ajzen, 1975). TPB extends the TRA by introducing an additional construct, specifically perceived behavioural control (PBC) to account for situations where the control over a specific behaviour is not necessarily fully volitional (Ajzen, 1985). Much like TRA, a person’s behavioural intention provides explanation for their behaviour, while the behavioural intention itself is influenced by perceived behavioural control, attitude and subjective norms (M Fishbein & Ajzen, 2011).

Furthermore, according to Ajzen (1985), TPB is open to the inclusion of additional predictors if it can be shown that they capture a significant proportion of the variance in intention or behaviour after the theory’s current variables have been taken into account. This flexibility offered by the TPB has allowed previous research to develop additional novel constructs that are particularly useful in explaining determinants of recycling behaviour. For example, Tonglet, Phillips, & Read’s (2004) study on recycling intentions and behaviour found that TPB’s three components could not adequately explain recycling behaviour unless a construct for moral norms was also incorporated in the model. Similar studies for example by Echegaray and Hannstien (2017) incorporated the concepts of Awareness of the problem and environmental assessment to extend the conventional TPB model. Whereas Khan et al., (2019) argued in favour of including convenience as a separate factor, independent of perceived behavioural control.

For our purpose we extend the TPB model further by introducing awareness (Welfens et al., 2016), environmental assessment (Echegaray & Hansstein, 2017), concern for information security (Bai et al., 2018, Zhang et al., 2020), convenience (F. Khan et al., 2019) and moral incentive (Botetzagias et al., 2015) as additional latent constructs. Figure 1 illustrates our proposed model for the determinants of recycling behaviour. In following section, we aim to explain each construct and use the previous research to guide the hypothesis development.

Figure 1 - The proposed theoretical model

2.2.1 Attitude, Subjective Norm and Perceived Behavioral Control Attitude is defined as an individual favourable or unfavourable perception of performing a certain behaviour (Ajzen, 1991). In the context of predicting consumers’ intention to recycle their cell phones, attitude refers to the positive or negative evaluation of recycling their cell phones (Zhang et al., 2020). Researchers have found that attitude is positively related to individuals’ recycling behaviour. The findings of a number of studies showed that the attitude has a positive influence on recycling intention (Bai et al., 2018; Echegaray & Hansstein, 2017; Wang et al., 2018; Zhang et al., 2020). When individuals have a more positive attitude, they are more likely to perform a certain behaviour (Ajzen and Fishbein, 2005). More specifically in the study conducted by Zhang et al., (2020) attitude was shown to have the greatest influence on intention. Hence, we propose the following hypothesis:

H1: Favourable attitude towards recycling will positively influence the intention to participate in recycling schemes.

While attitudes are an effective predictor of intention (Zhang et al., 2020), some previous research has indicated the existence of an attitude-behaviour gap (Conner & Armitage, 1998), especially when the desired behaviour has some ethical consideration (Geiger et al., 2019; Kim & Rha, 2014). Which is in fact the basic premise of the TRA, i.e. behaviour is not only determined by attitude but also subjective norm (M Fishbein & Ajzen, 2011). Ajzen (1991) defines subjective norm as to whether a certain behaviour is encouraged or not by important people and groups. In this context the subjective norm refers to the social pressure consumers experience in relation to recycling their smartphones. According to Echegaray & Hansstein (2017) social norm is the most significant factor predicting electronic waste recycling intention. More interestingly, in a research conducted by Hage, Söderholm & Berglund (2009), studying 2800 Swedish Households, it was reported that the higher one perceived the degree of recycling by one’s neighbours to be, the greater the rise in recycling rate. In fact, when consumers perceive social pressure to perform a certain behaviour, they are more likely to do so (Ajzen & Fishbein, 2000). Therefore, we propose the following hypothesis:

H2: Subjective norm will positively influence intention to participate in recycling schemes.

Conner & Armitage (1998) argue that the TRA restricts itself to volitional behaviours by suggesting that behaviour is solely in control of intention. Since behaviours requiring skills, resources, or opportunities not freely available are likely to be poorly predicted by the TRA (Conner & Armitage, 1998). Furthermore, the intention-behaviour relationships are moderated in such a way that when actual control is high rather low, the effect of intention on behaviour is stronger rather than weaker (Fishbein & Azjen, 2012). Since it is not possible to assess what constitutes actual control over behaviour, the TPB attempts to also predict nonvolitional behaviours by incorporating perceptions of control over performance of the behaviour as an additional predictor. Perceived Behaviour Control (PBC), according to Ajzen (1991) be explained as the extent of ease or difficulty consumers perceive to perform a certain behaviour. In the context of e-waste recycling it captures to which extent the consumers feel that it’s possible, and practically feasible, to recycle their cell phones (Zhang et al., 2020). In short, consumers are more likely to recycle when they have the resources or ability to do so (Axsen and Kurani, 2013). A number of studies have shown PBC to have a significantly positive influence over recycling

intention (Botetzagias et al., 2015; Lizin et al., 2017; Wan, Shen, & Yu, 2014). Therefore, we propose the following hypothesis:

H3: PBC will positively influence intention to participate in recycling schemes.

As mentioned previously, attitude, subject norm and perceived behavioural control as the three constructs of TPB have been shown as effective predictors of recycling intention within the previous literature (Dixit & Badgaiyan, 2016; Echegaray & Hansstein, 2017). However previous research has also criticized TPB for only being able to explain a limited amount of variance in both behavioural intention and behaviour (Botetzagias et al., 2015; Ramayah et al., 2012; Tonglet et al., 2004). For example, while PBC was shown to be an influential factor in predicting intention for battery pack recycling in Belgium (Lizin et al., 2017), it was shown to be completely insignificant in predicting intention to recycle plastic waste in Pakistan (F. Khan et al., 2019). Therefore, given the diverse nature of recycling as a subject not only in consideration to the object of recycling but also in regard to the contextual background in which recycling takes place. A number of researchers have advocated for inclusion of additional factors to more adequately explain intentions and behaviour. (Botetzagias et al., 2015; Conner & Armitage, 1998; Ramayah et al., 2012). The following section is aimed at explaining the five additional constructs included in our extension to the TPB, starting with moral incentive.

2.2.2 Moral Incentive With regards to an incentive affecting attitude, the concept of perceived usefulness may serve as an appropriate point of departure. Perceived usefulness is defined as ‘the degree to which a person believes that using a particular system would enhance his or her performance’ (Davis, 1989, p. 320) and is indicative of the consumer perceptions of consumption outcomes or the use- performance relationship (Davis, 1989; Eriksson, Kerem, Bank, & 2008, n.d.). It is considered an important factor shaping consumers’ attitudes (Davis, 1989).

Here, perhaps at the lowest level are the moral incentives – for example: satisfaction of fulfilling a societal obligation – consumers receive as a result of voluntary donations to recycling scheme (Echegaray & Hansstein, 2017). While higher levels include charitable donations and more importantly the economic benefits consumer avail through their participation in the trade-in schemes (Welfens et al., 2013). More interestingly, previous research has shown that in the absence of incentives consumers might adopt rigid attitudes when participating in a recycling scheme is below desired levels of convenience (Bai et al., 2018). However, when offered desired

incentives, consumers’ attitudes displays greater flexibility in over-looking inconvenience (Bai et al., 2018).

Since the recycling of household waste is a behaviour likely to contain elements of personal morality and social responsibility (Tonglet et al., 2004), it was considered appropriate to include this dimension within the model. Especially when considering that in previous studies of recycling behaviours, inclusion of a moral factor has been found to be particularly effective in predicting intention (Bai et al., 2018; Hage et al., 2009). In this study we include this dimension as moral incentive. We argue that moral incentives are incentives that induce people to translate any felt obligation or ‘moral norm’ into recycling action (Hage, Söderholm, & Berglund, 2009). Here, these moral norms relates to the individual’s personal beliefs about the moral correctness or incorrectness of performing a specific behaviour (Tonglet et al., 2004). Furthermore Botetzagias, Dima & Malesios (2015), proved empirically that moral norms are an important and largely independent predictor of recycling and have influence on both attitude and intention (Botetzagias et al., 2015) Therefore, since previous research has shown this moral dimension to be influential on both attitude (Bai et al., 2018; Vicente & Reis, 2008) and intention (Wan et al., 2017). We argue that incentivizing the fulfilment of these moral norms will have similar effects. Hence, we propose:

H4: Moral incentive will positively influence consumer’s attitude towards participation in recycling schemes. H5: Moral incentive will positively influence consumer’s intention to participate in recycling schemes.

2.2.3 Convenience Convenience is considered as the time, space and the perceived ease of an individual in managing waste (Wan, Cheung, & Shen, 2012). In context of the cell-phone industry convenience of use of a return scheme was shown to be one of the most important factors influencing both consumers attitudes (Bai et al., 2018; Welfens et al., 2016) and intention (F. Khan et al., 2019; Wan et al., 2017) towards recycling. It also worth mentioning that some of the previous research has also attributed convenience as internal characteristic of perceived behavioural control and have represented it as such in their research (Wang et al., 2018). Khan et al., (2019) argue that convenience in the context of recycling presents a completely different conceptualization, whereby PBC is representative of an intrinsic construct while convenience is extrinsic by nature.

An important concept is the idea of ease of use i.e. ‘the degree to which a person believes that using a particular system would be free of effort’ where by ‘an application perceived to be easier than other is more likely to be accepted by users’ (Davis, 1989, p. 320). Ease of use plays an important role in shaping consumer attitudes (Ajzen, 1991) since at times, seemingly useful systems are rejected by consumers due to the perceived difficulty of use (Davis, 1989; Venkatesh & Davis, 2000). In fact, as a consequence of the rapid development of e-commerce, consumers are demanding door-to-door service solutions (Bai et al., 2018) and are generally aversive towards solutions that are time consuming (Van Beukering & Van Den Bergh, 2006) or perhaps inconvenient in term of accessibility (Echegaray & Hansstein, 2017). In the context our study we assume the factor convenience to be an extrinsic measure of consumer’s expectations in consideration to the time, space and ease of use of a recycling scheme (Bai et al., 2018; F. Khan et al., 2019; Wan et al., 2017) whereas PBC is an intrinsic measure of personal control and ability over the performance of behaviour itself (F. Khan et al., 2019). In addition to the influence of convenience on attitude, many previous studies on recycling behaviour have also shown convenience to be significantly related to recycling intention (F. Khan et al., 2019). Therefore, we propose the following hypothesis:

H6: Consumer’s perception of convenience associated with recycling will positively influence consumer’s attitude towards participate in recycling schemes. H7: Consumer’s perception of convenience associated with recycling will positively influence consumer’s intention to participate in recycling schemes.

2.2.4 Awareness of the problem The topic of awareness in the context of cell-phone recycling and consumer behaviour has been one of the most frequently discussed factors (Hornik et al., 1995; Sarath et al., 2015). However, the idea of what incorporates awareness seems to have evolved slightly. While the earlier research classified awareness as consumers’ understanding of the risk associated with improper waste disposal and knowledge about recycling channels (Hornik et al., 1995). More recent research on the topic has also included awareness about resource (over)consumption into the construct (Bai et al., 2018; Welfens et al., 2016; Yin et al., 2014).

Previous research has suggested that, mobile phone recycling is very low in both developing countries and developed countries owing to the awareness level of the public (Sarath et al., 2015). In fact, in one of the earliest meta-studies on recycling behaviour conducted by Hornik et al., (1995, p. 109), it was suggested that “awareness of the importance of recycling and knowledge about recycling programs” was the determining factor between those who recycled and those who

didn’t. Subsequent studies on the topic have validated this proposition. For example, Nnorom, Ohakwe, & Osibanjo (2009) studied the response of Nigerian population toward mobile phone recycling and concluded that since the population is aware of the environmental deterioration taking place as result of waste generated by increased mobile production, they are very willing to take part in recycling programs. In contrast, Bai et al., (2018) argued that while customer awareness with regards to the existence of recycling channels has improved significantly in China, awareness level of pollution risk from improper disposal of waste mobile phones and its value in resource conservation need to be improved considerably.

In summary, awareness of environmental issues and concern for the state of the environment predicts a favourable disposition towards specific pro-eco-friendly standings and behaviours (Kim and Choi, 2005, Vicente-Molina et al., 2013). Previous research has shown awareness of the consequences to have influence on both attitudes towards recycling (Ramayah et al., 2012) and intention to recycle (Echegaray & Hansstein, 2017). Which leads to our hypothesis:

H8: Awareness of the problem will positively influence attitude towards recycling schemes. H9: Awareness of the problem will positively influence intention to participate in recycling schemes.

2.2.5 Concern for Information Security Smartphones is for most consumers an important tool of everyday life, enabling several activities and services (Zhang et al., 2020). The versatility and personalization that comes with a smartphone is beneficial to the user in many ways but creates problems in terms of recycling, mainly since the phone contains plenty of personal and private information (Bai et al., 2018). According to Bai et al., (2018) 63.7% of consumers did not want to recycle due to low trust for recycling parties and fear of leakage of personal information. Unlike traditional mobile phones most parts of the smartphones are integrated and cannot be removed by the consumer without difficulties, which means the entire device needs to be sent to be recycled (Zhang et al., 2020). Even if the personal information has been deleted there has been cases of information being recovered illegally adding to security becoming a priority of consumers looking to recycle (Zhang et al., 2020). Thereby increasing consumers' perception of risk associated with cell phone recycling.

Risk perception can in this context be perceived as the subjective evaluation of the objective risk of recycling, especially information security (Pennings & Smidts, 2003; Zhang et al., 2020). Zhang et al. (2020) found that risk perception moderated the relationships between

conscientiousness and the TPB variables in such a way that these relationships will be weak for individuals high in risk perception. Furthermore, previous research outside the recycling domain has shown the perceived security risk associated with a systems to be a dominant factor in determining intention to adapt a new system(Lee, 2009; Rezaie, Abadi, Ranjbarian, & Zade, 2012). For example, Lee (2009) showed empirically this risk factor to exert a stronger effect on customers’ decision-making than the benefit factors, when they consider using online banking. Therefore, as risk perception with regards to information security plays an important role in predicting both attitude (Bai et al., 2018) and intention (Lee, 2009), we propose the following:

H10: Concern for information security will negatively influence attitude towards recycling old cell phones. H11: Concern for information security will negatively influence intention to recycle cell phones.

2.2.6 Environmental Assessment Environmental assessment is representative of consumer’s perception of the environmental performance of their country (Echegaray & Hansstein, 2017). This is a novel construct introduced in the recycling literature by Echegaray & Hannsstein (2017) arguing that people’s assessment of the environmental situation of one’s country is a factor predicting recycling behaviour. Echegaray & Hannsstein (2017, p. 185) in the context of e-waste in Brazil, proved empirically that as evaluations about the state of the ecology go grimmer, the more motivated individuals are to embrace responsible waste disposal practices, which is reflected by their increased intentions to recycle.

We argue that as compared to Brazil, Sweden has a long tradition of environmental democracy (OECD, 2014). In fact as compared to the European average, residents of Sweden place a higher value on the environment by assigning greater importance to environmental protection (OECD, 2014). They also appear to be more satisfied with their country’s environmental quality than people in other European countries (OECD, 2014). Based on these insights, we argue that not only will people report higher level of satisfaction with the current state of environmental performance but that this positive assessment would in fact serve as a reinforcing factor on both attitude and intentions to participate in recycling. This concept is not so different than the idea of perceived policy effectiveness, which implies that people’s perception of an effective policy measure will increase the attractiveness of a pro-environmental behaviour (Wan & Shen, 2013). As a result people would form a positive attitude towards that behaviour and subsequently, when

a policy is perceived to be effective it will induces a higher level of intention to perform that behaviour (Wan & Shen, 2013). Hence, we propose the following

H12: Positive environmental assessment positively influences attitude towards participation in recycling schemes. H13: Positive environmental assessment positively influences intention to participate in recycling schemes

To sum up, in the beginning of the chapter we provided the arguments for why customer participation is imperative for the success of producers CE initiatives. We argued in favour of approaching the topic of recycling from a consumer behaviour perspective in order to develop understanding of the various factors moulding consumer pro-environmental behaviours. The second part of the chapter was aimed at utilizing the previous research to develop an understanding of the determinants of recycling intention. Which is the premise of our initial research question. Here, we followed the recommendations in the recycling literature to not only use TPB as a theoretical framework but also to extend the TBP by utilising the various factors that previous research has shown to be influential determinants of recycling intention. Furthermore, since there is evidence from the previous research that the applicability of these factors varies with the recycling object and the context, 13 hypotheses were developed to test if these factors would in fact predict intention to participate in recycling schemes in a Swedish setting. The table 1 presents a summary of the hypothesis developed. In the next chapter we discuss the methodological framework adopted in service of finding a satisfactory answer to our research question.

Factor Hypothesis Reference Attitude H1: Favourable attitude towards recycling will positively (Bai et al,2018; Echegaray & influence the intention to participate in recycling schemes. Hansstein, 2017; Wang et al.,2018; Zhang et al.，2020) Subjective Norm H2: Subjective norm will positively influence intention to (Echegaray&Hansstein,2017; participate in recycling schemes. Hage,Söderholm & Berglund ,2009) Perceived H3: PBC will positively influence intention to participate in (Zhang et al., 2020; Axsen and Behavioural recycling and/or trade in schemes. Kurani, 2013) Control Moral Incentive H4: Moral incentive will positively influence consumer’s (Bai et al., 2018; Botetzagias attitude towards participation in recycling schemes. et al., 2015; Vicente & Reis, 2008) H5: Moral incentive will positively influence consumer’s (Bai et al., 2018; Botetzagias intention to participate in recycling schemes. et al., 2015; Wan et al., 2017) Convenience H6: Consumer’s perception of convenience associated with (Bai et al., 2018; Welfens et recycling will positively influence consumer’s attitude towards al., 2016） participation in recycling schemes. H7: Consumer’s perception of convenience associated with (F. Khan et al., 2019; Wan et recycling will positively influence consumer’s intention to al., 2017；F. Khan et al.， participate in recycling schemes. 2019） Awareness H8: Awareness of the problem will positively influence attitude （ Nnorom, Ohakwe, & towards participation in recycling schemes. Osibanjo 2009；Ramayah et al., 2012） H9: Awareness of the problem will positively influence （ Nnorom, Ohakwe, & intention to participate in recycling schemes. Osibanjo 2009；Echegaray & Hansstein, 2017） Concern for H10: Concern for information security will negatively （Pennings & Smidts, 2003; information influence attitude towards participation in recycling schemes. Zhang et al., 2020; Bai et al., security 2018) H11: Concern for information security will negatively influence (Zhang et al., 2020; Lee, 2009) intention to recycle cell phones. Environmental H12: Positive environmental assessment positively influences (Echegaray & Hansstein, assessment attitude towards participation in recycling schemes. 2017; Wan & Shen, 2013) H13: Positive environmental assessment positively influences (Echegaray & Hansstein, intention to participate in recycling schemes 2017; Wan & Shen, 2013)

Table 1 - Summary of the hypothetical assumptions

3. Methodology

______This chapter is aimed at familiarizing the reader with the methodological framework applied in this study. The first 4 section provides justifications the research philosophy, approach, design and method implemented in the study. The next section is aimed at introducing the data collection process. Here, particular emphasis is placed on explaining the choices made during the survey design and sampling process. The final section not only provides justification for the chosen data analysis method but also presents a detailed overview of the 6 steps process implemented to analyse the collected data.

3.1 Research Philosophy

Research philosophy can be defined as “overarching term relating to the development of knowledge and the nature of that knowledge in relation to research” (Catterall, 2000, p. 107). This research philosophy is indicative of the researcher’s epistemological assumptions – what constitutes acceptable knowledge in the field of study (Catterall, 2000, p. 111) – ontological assumptions – i.e. the nature and reality – and axiological assumptions – the role of values and ethics in the research – that shape how we understand our research questions, the methods we use and how we interpret our findings (Bryman, Bell, & Harley, 2007; Saunders, Lewis, & Thornhill, 2016). According to Saunders et al., (2016), there exists five major research philosophies: positivism, critical realism, interpretivism, postmodernism and pragmatism. For the purpose of our study we adopt a positivist research philosophy, the reasons for which are explained below.

As pointed out in the earlier chapter, promoting behaviour change involves examining which factors cause those behaviours, and applying well-tuned interventions to change relevant behaviours (Geller, 2002). Our research question is aimed at that examination of factors. Therefore, the premise of this research project is not new theory generation but rather using the existing research to uncover factors that might predict intention to participate in cell phone recycling schemes in a Swedish setting. Ergo, to answer this question we take a realist ontological stance underpinned by universalism (Saunders et al., 2016), i.e. either a factor influences recycling intention or it doesn’t. Similarly our epistemological stance warrants the use of observable and measurable facts in order to make law like generalisations (Saunders et al., 2016). That is, we are more interested in generating causal explanation and prediction as contribution rather than developing new understanding (as in interpretivism) or challenging of dominant views (as in postmodernism) (Bryman et al., 2007). Finally, our axiological assumptions are based on

maintaining an objective stance by remaining neutral and independent of what is being researched (Saunders et al., 2016). Especially since an acceptable answer to the research question does not necessarily warrant any interpretation on our part as researchers.

3.2 Research Approach Research approach is important for determining the design of the research project (Saunders et al., 2016). These research approaches (or reasonings) can be classified as: deductive, inductive and abductive (Saunders et al., 2007). Deductive reasoning occurs when the conclusion is derived logically from a set of premises, the conclusion being true when all the premises are true (Saunders et al, 2016). Deductive reasoning implies collecting data to evaluate propositions to generalize from the general to the specific and aimed at theory falsification or verification (Becker, Bryman, & Ferguson, 2012). Whereas in an inductive approach known premises are used to generate untested solution (Saunders et al, 2016). Inductive reasoning is aimed collecting data for new theory generation from the specific to the general (Dubois & Gadde, 2002). Moreover, the abductive represents a research approach where known premises are used to generate testable conclusions (Saunders et al, 2016). Abductive reasoning in contrast incorporates existing theory to build new theory or modify existing theory (Dubois & Gadde, 2002).

For the purposes of our study we take a deductive approach because deduction possesses several important characteristics are relevant to our research purpose. Firstly, deduction is used when the researcher aims to explain causal relationships between variables (Saunders et al., 2016). Secondly, since the applicability of the various factors identified in previous research depend on the research object and the research context, taking a deductive approach would allow us to test the various hypothesized relationships concerning the factors and their applicability for our research (Saunders et al., 2016). Finally deductive approach allows generalisations about target population through collecting data from a large sample size (Becker et al., 2012; Saunders et al., 2016). In contrast inductive reasoning is more concerned with individual narratives and perceptions (Bryman et al., 2007; Catterall, 2000). Therefore a study of small sample is considered more appropriate, which in turn might affect the generalizability of the findings (Saunders et al., 2016). Hence, given our research question, adopting a deductive approach is reasonable.

3.3 Research Design Research design form the basis for the conduct of a research project and provides a roadmap for the collection and analysis of data (Bryman, Bell, & Harley, 2007; Yin, 2009). According to Ghauri and Grønhaug & Strange (2020), an appropriate research design allows a researcher to

find solutions to a research problem within limited time and resources through ensuring the relevance of the collected empirical data. Babin & Zikmund (2016) articulates three types of research design which include exploratory, descriptive and causal. Here, exploratory research design is concerned with observing that which is already in existence (Babin & Zikmund, 2016; Saunders et al., 2016). This design is underpinned by the greater flexibility and is generally applied in studying novel phenomenon or while exploring an existing one from a fresh insight (Saunders et al., 2016). In descriptive research design, the basic premise is to describe the characteristic of objects, people, groups, organizations or environments (Babin & Zikmund, 2016) in order to formulate an understanding of events, situations or individuals (Saunders et al., 2009). This requires defining the measurements and population in a clear manner in order to gather and analyse the behaviours and opinions of the sample (Saunders et al., 2016). Explanatory or causal research design on the other hand is concerned with identifying and developing an understanding of the relationship between different variables to offer explanations about a specific issue (Robson, 2002).

This study investigates the relationship of different potential factors that might affect consumer intentions towards recycling initiatives. In particular, in order to identify the proper influence between individual factors on consumer intention, if it was positively or negatively affected. As a consequence, the formulation of Swedish consumer intention towards recycling cell phones could be explained. For this purpose, a population was specified, and different measurements were constructed to measure each theoretical concept. As a result this study can be categorized as an exploratory (or causal) research design, attempting to investigate the relationship between the different independent and dependent variables (Saunders et al., 2016).

3.4 Research Method Business research can be classified in to quantitative and qualitative methods (Bryman et al., 2007). Here qualitative research represents a research strategy where the researched is concerned developing comprehensive understanding of some phenomena. In other words, qualitative studies are generally aimed at providing explanations and insight into problem through offering ideas and solutions, which in turn paves the way for further quantitative examination (Bryman et al., 2007). Quantitative research on the other hand utilizes a deductive approach that emphasizes quantification in the collection and analysis of the data (Bryman et al., 2007). Furthermore the aim of a quantitative research is to generalize findings to population (Saunders et al., 2016). The idea is to collect data from large sample sizes and perform statistical analysis to form generalization about the chosen population in line with the corresponding theoretical framework (Bryman et al., 2007).

In this study, quantitative research was chosen as an appropriate approach since the primary focus of this research is to investigate relationship of different potential factors that might affect consumer intentions towards recycling initiatives. In order to achieve this, we the test hypotheses derived from the adapted theoretical model instead of gaining in-depth insights into the phenomenon. More specifically the goal is to identify the underlying relationships and influences of the different latent constructs outlined in the theoretical model on intention to participate in recycling schemes.

3.5 Data Collection Method For the purpose of this study, both secondary and primary data was collected. The secondary was conducted through examining the previous research on the topic. This primarily included scientific and academic research papers but other credible resources such as newspapers, sustainability reports of relevant companies and research by government and independent institutions were also considered.

The primary data was collected using an online survey platform. An online survey in simple terms is a self-administered survey administered using a web-based questionnaire (Babin & Zikmund, 2016, p. 186) and is ideal in situations where the researcher is interested in collection responses from large population samples in a time and cost effective manner (Babin & Zikmund, 2016; Sue & Ritter, 2007). For the purpose of this study Qualtrics survey platform was utilized since it also enables digital processing of the stored responses through statistical software to derive analytics (Sue & Ritter, 2007). The following sections (3.5.1 and 3.5.2) presents a detailed overview of the survey design and sampling process.

3.5.1 Survey Design According to Saunders et al., (2016), the design of the questions, the structure of the questionnaire as well as pilot testing will not only affect the internal validity and reliability of the data that is collected but also the response rate the survey achieves. Here for a question to be valid and reliable, firstly the researcher must be clear about data requirements while designing the question, (Saunders et al., 2016). Secondly, not only the respondent should be able to decode the information just as the researcher intended but also the answer given by the respondent must be understood by the researcher in a way intended by the respondent (Saunders et al., 2016). We address these requirement during the operationalization and questionnaire design process in the following sections.

3.5.1.1 Operationalization Operationalization refers to the process of developing logic variables from abstract theories within a research framework (Bryman et al., 2007; Ghauri et al., 2020). Or simply put, to connect real world practices with the study (Bryman et al., 2007). Operationalization generally constitutes four steps: creating a theoretical framework, outlining potential variables, selection of most appropriate variables and ultimately collecting data for the research (Bryman et al., 2007). The existing research on similar and relevant topics (for example: Bai et al., 2018; Echegaray & Hansstein, 2017) were leveraged as conceptual definition and an operational definition – in service of the research objective – was created in order to measure the different latent constructs outlined in our theoretical model. Table 1 visualizes the operationalization process and articulates the linkage between the proposed model and previous research.

3.5.1.2 Questionnaire Design The survey questionnaire consisted of 44 questions in total. 2 questions are related to the respondent qualification criteria (discussed later in 3.5.2). 3 questions are related to previous behaviour of the survey respondents in the context of cell-phone recycling. Furthermore, 3 questions are related to socio-demographics factors of the participants. The remaining 36 question are concerned with the theoretical model and are articulated in table 2. The questionnaire was constructed by leveraging the items from the existing studies on other similar and relevant topics as outlined in Table 2. The subsequent items were then adapted to the context of our study. For example, questionnaire item ‘It's wrong to dispose electronic waste and regular waste together’ (Echegaray & Hansstein, 2017) was rephrased to ‘It's wrong to dispose old or broken cell phones and regular waste together’. The survey participants were asked to give their individual opinion on each item which as measured using a 5-point Likert scale ranging from (1) strongly disagree/very unlikely/much worse/never heard to (5) strongly agree/very likely/much better/know a lot. All responses were collected and stored using the Qualtrics survey platform. Please refer to table 1 for an overview of the questionnaire used for the purpose of this study.

Furthermore, the survey was pretested before distribution by separate researchers (non- participants) and as a result some items were rephrased and the format was adjusted for some questionnaire items (Babin & Zikmund, 2016). The basic premise of pretesting is to refine the questionnaire and to ensure the items are relevant and comprehensible, thereby laying down a strong foundation for the data collection process (Ghauri and Grønhaug, 2020).

Leveraged Construct Conceptual definition Operational definition Item Questionnaire item from Convenience An individual’s beliefs A measure to reflect CN1 Convenience of return location is important for me. (Bai et al., about the degree to how perceived 2018) which using a system convenience of return CN2 Convenience of return method is important for me. would be free of effort method and location CN3 I will return my old phone if the producers made it easier (Davis, 1989) would affect attitude for me to do so and intention to recycle. Moral incentives that induce A measure to reflect MI1 Recycling my old/broken cellphone will give me (Czajkowski Incentive people to translate any how moral incentive satisfaction et al, 2017) felt moral obligation into would affect attitude MI2 Recycling my old/broken cellphone is my moral duty (Ramayah recycling action (Hage et and intention to recycle. MI3 Recycling my old/broken cellphone will reduce my et al., 2012) al., 2009) environmental impact MI4 I feel good about myself when I recycle.

Attitude Sets of beliefs about a A measure to reflect AT1 Taking my old cellphone to recycling is good for the (Echegaray certain object or an act how consumer attitude environment & which may translate into towards recycling AT2 Taking my old electronic appliances to recycling is good Hansstein, intention to carry out the schemes affects AT3 fIo bre mlieyv he etahlatht manyd r emcy cflainmgi lbye'sh haevaioltuhr will help reduce 2017) act (Schwartz, 1992) intention to participate. wasteful use of landfills. (Ramayah AT4 I believe that my recycling behaviour will help conserve et al., 2012) natural resources. Percieved A person’s perception A measure to reflect PBC1 There are no collection sites for old/broken cellphones (Echegaray Behavioral regarding his ability and how control beliefs nearby my home or work & Control (PBC) capability to engage in towards recycling PBC2 My old/broken cellphone is not eligible for a recycling Hansstein, the behavior under affects intention to scheme 2017) consideration (Ajzen, participate PBC3 Electronic waste recycling is someone else's responsibility 1991) PBC4 As a consumer, I can influence a cellphone manufacturer to be responsible for collecting and reusing old/broken cellphones disposed as waste Subjective Collective effect of A measure to reflect SN1 Some of my friends recycle cellphones because it is the (Echegaray & Norm social influences on how social influences right thing to do Hansstein, behavioral intention towards recycling SN2 Some of friends have made use of a cellphone recycling (Lam et al., 2007) schemes affects scheme. intention to participate SN3 I already do my share for the environment SN4 I want others to see me as environmental conscious SN5 Everybody should care about what our unused cellphones do after end of use/life Concern for The propencity of an A measure to reflect IS1 information security is important to me (Bai et al., information individual to avoid the how personal beleifs IS2 Information security influences my decision to recycle or 2018) Security negative consequences regarding information trade in old/broken cell-phones. resulting from security affects intention IS3 To protect my information, I would rather keep the compromises in to participate old/broken phone than to recycle and/or trade in. information security of IS4 For information security, used mobile phones should be their devices (Bai et al., disposed as household garbage after pollutants (eg: 2018) batteries) are taken out Awareness An individual’s A measure to reflect AW1 It's wrong to dispose old or broken cellphones and regular (Echegaray knowledge and how awareness about waste together. & understanding of the the issue affects AW2 I know where I can return my old electronics no longer in Hansstein, larger environmental intention to participate AW3 uI saem. aware that parts and components of old cellphones 2017) concerns can be recycled or reused. AW4 I am aware of the impact of electronic waste on the environment and society. Environment An individual’s A measure to reflect EA1 Compared to 2 years ago, the current situation of electronic- (Echegaray al Assesment assessment regarding the how personal beliefs waste pollution in Sweden is... & environmental regarding the state of EA2 Compare to 2 years ago, the current situation of cell-phone Hansstein, performance of their environment in their recycling in Sweden is... 2017) country ((Echegaray & country affects intention EA3 Compared to 2 years ago, the environmental impact of new Hansstein, 2017) to participate cell phone production in Sweden is... Intention Individual’s subjective A measure that reflects IN1 I’m willing to separate trash for recycling on a regular basis. (Echegaray & likelihood of performing consumers intention to IN2 I'm willing to spend some time taking/sending my old or Hansstein, a certain behavior participate in recycling broken cellphone to recycling. 2017) (Fishbein and Ajzen, IN3 I am willing to speak to my friends about appropriate (Kikuchi- 1975, p.289) modes of disposing old or broken cellphones. Uehara et IN4 I am willing to get more information about appropriate al., 2016) modes of disposing old or broken cellphones. IN5 I am willing to participate in a return scheme.

Table 2– Operationalization process.

3.5.2 Sampling Sampling in simple terms represents the selection of participants or respondent in a study (Babin & Zikmund, 2016). Where the representative sample yields results that reflect what is generalizable to the population as a whole (Djurfeldt, Larsson, & Stjärnhagen, 2010; Saunders et al., 2016) As compared to qualitative research, quantitative research involves collecting responses from a larger sample since the aim of quantitative study is to derive statistical generalization while qualitative study is aimed at developing deeper insights (Bryman et al., 2007). There exist two types of sampling procedures: Probability sampling and Non-probability sampling (Babin & Zikmund, 2016; Djurfeldt et al., 2010). Where the former implies a random selection method where each individual in the target population has an equal chance of being selected (Babin & Zikmund, 2016; Bryman et al., 2007). In contrast, non-probability sampling employs techniques such as convenience and snowball sampling where some individuals have greater probability of being chosen (Babin & Zikmund, 2016; Bryman et al., 2007). While these procedures impact the generalizability of the finding they offer a low cost and time efficient option for researchers to obtain large amount of data, for example through a survey (Babin & Zikmund, 2016). For our purposes we employed both convenience sampling and snowball sampling to collect responses for the online Qualtrics survey. The motivation was to generate a large number of responses on the online survey within a short time frame and with limited resources at our disposal.

With regards to the qualification criteria, since we are interested in uncovering the determinants of intention of the Swedish cell-phone users to participate in recycling initiatives, the respondent criteria were individuals residing in Sweden, having purchased a new cell phone within the last 24 months. Since, Belkhir & Emiligi (2018) argued that people on average buy a new cell-phone every 24 months. Further In order to increase the volume of responses, respondents that responded negatively to the first question, were subjected to second qualification criteria: if they own an old cell phone that they did not use. Since, easy storability of old cell phones is also a factor that affects the successful implementation of recycling schemes (Sarath et al., 2015). The survey was distributed primarily through email and social media platforms Facebook and LinkedIn. The respondents were also encouraged to forward the survey to their friends and colleagues. The survey was distributed between 3rd April 2020 to 13th April 2020.

3.6 Data Analysis Method For the purposes of our study we chose to implement Structural Equation Modelling (SEM) to analyse the data. SEM is a multivariate technique combining aspects of factor analysis and multiple regression that enables the researcher to simultaneously examine a series of interrelated dependence relationship among the measured variables and latent constructs, as well as between

several latent constructs (Hair, Black, Babin, & Anderson, 2014). Furthermore, these dependence relationships or structural relations can be modelled pictorially to enable a clearer conceptualization of the theory under study (Byrne, 2016). Here, the theoretical model developed as a result of our literature review represents the structural model (or the path model), which relates independent to the dependent variables (Hair et al., 2014). SEM as a chosen technique for our analysis is appropriate because our analysis contains multiple dependence relationships. Since the latent variable Attitude is both a dependent and an independent variable, other techniques (for example: multiple regression) would not enable us to assess the key theoretical relationships outlined in our proposed model.

Since Hair et al., (2014) argue that a complete SEM analysis involves both the test of a measurement theory and the structural theory that links the constructs together in a logically meaningful way. These recommendations were thus incorporated in our 6 steps process to analyse our data using SEM. We made use of IBM SPSS and Amos version 26 to perform the various analysis mentioned in the proceeding section.

Step 1 included classifying the latent constructs in to endogenous and exogenous variables, as seen in Table 3. Here endogenous variables are synonymous with dependent variables and are influenced by the exogenous variables in the model, either directly or indirectly. The important distinction is as opposes to exogenous variables, fluctuations in the values of endogenous variables can be explained by the model since all latent variable that influence them are included in the model specification. In contrasts fluctuations in the values of exogenous variables are influenced by factors (such as age, gender etc.) external to the model (Byrne, 2016).

Endogenous Variables Exogenous Variables Attitude Moral Incentive Intention Convenience Subjective Norm Perceived Behavioral Control Concern for information security Awareness Environmental Assessment

Table 3 - Classification of latent constructs

Step 2 involved ensuring quality of the measurement data. Here again we followed the criteria suggested by Hair et al., (2014) where all survey responses with more than 10% missing data was excluded. Similarly, with responses containing less than 10% data, we employed data estimations technique of replacing missing entries with the median of all nearby values using SPSS.

In step 3, an exploratory factor analysis (EFA) utilizing maximum likelihood method of extraction with PROMAX rotation was performed to uncover the underlying structure of the dimensions in the data. The idea here is to determine the extent to which the observed variables were related to the nine latent constructs. Since all items are free to load on any factor, observed variable designed to measure a specific latent construct (for example: Attitude) should exhibit high loadings on that specific factor, and low or negligible loadings on the other factors (Byrne, 2016). At this point, observed variables displaying significant cross-loadings and or low factor loadings (<0.4 as suggested by Hair et al., (2014)) were excluded.

Step 4 involved performing a confirmatory factor analysis (CFA) in order to test the measurement model. That is, to evaluate how well the observed variables represent the latent constructs. At this point, using the item short-listed from the EFA, observed variables intended to measure a specific construct were allowed to load freely on that factor, but restricted to have zero loadings on other factors (Hair et al., 2014). The model was then evaluated by statistical means to determine the adequacy of its Goodness-of-Fit (GOF) to the sample data as well as the criteria for construct validity. In assessing model fit, the Chi-square (X2) test provides the most convincing evidence of a good model fit. Here, an X2 value with an associate p-value greater than 0.05 would suggest a good model fit (Hair et al., 2014). Other suggested criteria for assessing the GOF is presented in table 4. The resulting latent constructs were imputed into variables using IBM Amos, to be utilized in testing the structural model.

Measure Fit Description Cut-off for good fit Research study Absolute fit indices: Measure of overall goodness-of-fit Relative amount of Variance and covariance Goodness o Fit Index Hair et al., (2014) in the sample data that is > 0.90 (GFI) Byrne (2016) jointly explained by the hypothesized model

The error of apprixmation Root Mean Square Error < 0.8 to 0.10 in the population per Hair et al., (2014) of Approximation (mediocre) degrees of freedom (df) Byrne (2016) (RMSEA) < 0.6 (good) expected to occur

Incremental Fit indices: Group of Goodness of Fit indices that assesses how well a specified model fits relative to some alternative baseline model. Adjested GFI by a ratio Adjusted Goodness of Fit of df used in a model to Hair et al., (2014) > 0.80 Index (AGFI) the total degrees of Byrne (2016) freedom available Hair et al., (2014) Comparitive Fit Index >.90 Byrne (2016) Parsimony Fit Indices: Measures of overall goodness of fit representing the degree of model fitper estimated coefficient < 5 (acceptable) Hair et al., (2014) Normed Chi-Square Chi-Square (X2) / df 1 to 3 (good) Byrne (2016)

Table 4 - Criteria for assessing the Goodness-of-Fit

Step 5 involved checking against the assumptions of multicollinearity by performing multiple regression analysis to evaluate the Tolerance and VIF values against the respective >0.1 and <10 criteria suggested by Hair et al., (2014). Furthermore, the correlation matrix obtained as results of CFA is also evaluated for correlations between latent constructs exceeding the 0.7 threshold (Hair et al., 2014; Kline, 2005).

Finally, step 6 involved the estimation of the structural model. Here, the structural model indicative of the relationships specified between the endogenous and exogenous variables in the hypothesized model (Byrne, 2016; Hair et al., 2014). Where, the model estimation would either confirm or reject the theory backed hypothesis outlined in our structural model. The idea here is to evaluate these hypothesized dependence relationships between the latent constructs – represented by the standardized path coefficient () at p<0.05 criteria – with the objective of achieving an acceptable model fit (Byrne, 2016; Hair et al., 2014). The criteria for assessing the model fit for the structural model is the same as that for the measurement model outlined in table 4.

4. Results and Analysis

______This chapter presents the finding of our research. Since recycling within the cell phone industry is an emerging topic and little is known about cell phone recycling in the Swedish context, we begin by providing the reader with a detailed report on the demographics and past behaviour of our sample. We then present a comparative analysis to show how the demographic cues are related to past behaviour. The next section informs the reader about the findings from the EFA and explains why certain variables were excluded from the study. The next section presents the results from the evaluation of the measurement model. The final scale items are identified, construct validity is established, and model fit is justified. Finally, we move on to the evaluation of the structural model. We start by presenting the reader with a summary of the model fit before moving on to evaluate the hypothesis developed in the literature review. 8 of the 13 hypotheses are confirmed, while moral incentive is identified as the most influential determinant of both attitude and intention to participate in recycling schemes.

4.1 Data, Demographics and Descriptive Statistics

Out of the total 264 responses collected, 75 had not Criteria N purchased a new phone in the last 24 months. Qualification Criteria Total responses collected 264 Interestingly, 45 of these respondents reported Qualified according to criteria 1 189 having an unused old phone and were therefore Qualified according to criteria 2 45 Total qualified responses 234 included in our study. As result 234 entries were Quality Criteria initially qualified. Applying the measures for 100% completed 190 Less than 10% missing 4 ensuring quality of the measurement data, 40 More than 10% missing 40 Total respnses included 194 responses were deleted because they were missing more than 10% of the data. While, for responses Table 5 - Data screeningprocess missing less than 10% of the data (N=4), we utilized SPSS to replace the missing values with the median of all nearby points. Based on the qualification criteria and the measures for ensuring quality of the measuring data, 194 of the 234 total surveys qualified responses were included in our study.

Table 6 presents the results with regards to the demographics of our sample. As can be observed the sample is somewhat evenly distributed with regards to gender. However, in terms of age group and education, our sample seems to be dominated by the younger and highly educated demographic. At this point it might be prudent to assume that given this imbalance, the results of this study might be somewhat biased.

Criteria N Percent With regards to past behaviour 47.9% of all Gender participants reported having recycled a cell phone Male 89 45,9% Female 105 54,9% in the past. This insight is somewhat significant Age given that previous studies in similar domains have 18-30 159 82,0% 30-45 26 13,4% reported much more modest figures. For example, 45-60 7 3,6% Echegaray and Hannstein (2017) in the context of 60+ 2 1,0% Education electronic waste in Brazil, reported this figure to be High School 14 7,2% a meagre 6%. Figure 2 presents a visual insight Bachelors 48 24,7% regarding the participants response to the current Masters 131 67,5% status of their old cell phone. As can be observed, a Table 6 - Sample demographics large majority (46%) of our respondents reported still owning their old cell phones but not using it. For the 19.6% of the respondents that did use their old phones, only a small majority (32%) used their old phone every day. Furthermore, while 47.9% of the respondents reported having recycled an old phone in the past, only 9.5% of the respondents actually recycled their old cell phone after their latest purchase. This could be due to the fact that the newer models of smartphones generally have some monetary value and or useful life left even after the first use phase (Bai et al., 2018). This is represented by the various other options like gave it friends or family (12.7%), sold it (5.3%) or traded in for a new phone (5.8%) in figure 2. At this point, it is worth noting these behaviours are also conducive to the proliferation of circular economies.

Figure 1 - What did you do with your old phone?

Furthermore, given the paucity of knowledge about pro-recycling behaviour in the context of cell phone recycling in Sweden, we offer a further analysis comparing the participants sentiments with regards to recycling intention as well as previous recycling behaviour. Differences are further identified by reporting the F-statistic and the associated p-value from the one-way ANOVA. Table 7 presents the findings of this analysis.

Previous behavior Intention to recycle old adequate for cellphones ANOVA (N=194) recycling ANOVA F-test Not F-test Positive Neutral Negative Adequate (p-value) Adequate (p-value) Gender 3,616 3,362 Female 89,5% 2,9% 7,6% 41,9% 58,1% Male 83,1% 3,4% 13,5% 55,1% 44,9% Age group 5,567* 0,755 18-30 89,9% 1,3% 8,8% 50,3% 49,7% 30-45 76,9% 11,5% 11,5% 34,6% 65,4% 45-60 71,4% 14,3% 14,3% 42,9% 57,1% 60+ 0,0% 0,0% 100,0% 50,0% 50,0% Education 8,710** 0,184 High School 50,0% 14,3% 35,7% 42,9% 57,1% Bachelors 87,5% 6,3% 6,3% 45,8% 54,6% Masters 90,8% 0,8% 8,4% 49,6% 50,4% Note: p-value: **p<0.001, *p<0.05

Table 7 - Frequency distribution and ANOVA results for recycling intention and past behaviour

As can be observed, the great majority of the respondents hold a positive intention to recycle. However, not all respondents have been successful in translating these intentions into actual behaviour in the past. Closer examination of table 7 reveals three interesting insights. (1) While females’ respondents displayed marginally greater positive intention to recycle, the male respondents in the past recycled roughly 13% more than the female respondents. (2) The positive intention to recycle seemingly decreases as people get older, this is signified by a drop in intention to recycle as we move from the youngest to the oldest demographic. (3) Educations also seems have an effect on recycling intention, this is signified by an increase in positive intention as we move from the lowest education criteria to the highest. The latter 2 insights are further confirmed by the one ANOVA indicated by the significant F-statistic values. However, it is worth mentioning that the sample demographics are unevenly skewed in terms of age group and education which may affect the validity of these latterr two insights. At this point further examination exceeds the scope of this study, however these insights may provide a starting point for future research.

4.2 Exploratory Factor Analysis An EFA was conducted utilizing the maximum likelihood method for extraction and PROMAX rotation. The, Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) value generated was 0.842 which exceeded both the 0.6 threshold articulated by Pallant (2016), and the 0.8 ‘meritorious’ value suggested by Spicer (2011). Similarly, Bartlett’s test was significant at p=0.000, which implies that that factor analysis is appropriate (Pallant, 2016). Based on the Kaiser’s Criterion of having an eigenvalue of 1 or more (Pallant, 2016), nine factors were extracted. These nine factors accounted for 59.3% of the cumulative variance which is marginally short of 60% criteria suggested by Hair et al., (2014).

Table 8 presents the rotated factor Factor 1 2 3 4 5 6 7 8 9 matrix generated as a result of the MI1 0,721 EFA. According to the guidelines MI2 0,772 MI3 0,722 proposed by Hair et al., (2014), in a MI4 0,839 EU1 0,688 sample size of 200, a factor loading of EU2 0,754 0.40 are required for significance. EU3 0,613 AT1 0,677 Since our sample size is relatively AT2 0,757 AT3 0,674 close to this value, only items AT4 0,694 displaying a loading of more than 0.4 PBC1 0,562 PBC2 0,682 were included. Furthermore, items PBC3 0,638 PBC4 0,737 displaying significant cross-loadings SN1 0,792 SN2 0,688 with other factors were also excluded. SN3 0,707 Cross loading occurs when a variable IS1 0,664 IS2 0,792 is found to have more than one IS3 0,526 AW1 0,707 significant loading (Hair et al., 2014). AW3 0,600 The presence of cross loading AW4 0,724 EA1 0,815 indicates distinctly measured EA2 0,799 EA3 0,856 variables do not represent the intended IN2 0,484 IN3 0,862 construct appropriately (Hair et al., IN4 0,729 2014). Hence, observed variables IN5 0,607 Extraction Method: Maximum Likelihood. SN4, SN5, IS4, AW2 and IN1 were Rotation Method: Promax with Kaiser Normalization. a Rotation converged in 8 iterations. excluded from the study at this stage.

At this point a correlation matrix was Table 8 - The rotated factor matrix also evaluated (Appendix Table 1) to check if all of the correlation values were below the 0.85 intercorrelation threshold suggested by Kline (2005). This criterion was also fulfilled.

4.3 Analysis of the Measurement Model A CFA was performed using IBM AMOS in order to assess the model fit of measurement model but also to establish construct validity. The initial CFA results did not provide a reasonably good fit. This meant, shifting attention towards issues related to construct validity and model diagnostic to improve the model fit.

Factor According to the criteria suggested by Hair et Construct C.R. AVE Loadings Environmental Assesment 0,850 0,654 al., (2014), the standardized loading estimates EA3 0,852 or factor loadings should be greater than 0.5, EA2 0,764 EA1 0,807 the measure for average variance extracted Moral Incentive 0,853 0,659 MI4 0,792 (AVE) should be greater than 0.5 and the MI3 0,851 construct reliability (C.R.) should be greater MI2 0,792 Convenience 0,765 0,522 than 0.7 to suggest adequate convergent CN3 0,648 validity. Our evaluation of the measurement CN2 0,707 CN1 0,803 model on this criterion suggested that Attitude 0,761 0,520 AT3 0,585 observed variables MI4, AT4, PBC1, PBC2 AT2 0,732 and IS1 displayed factor loadings lower than AT1 0,825 Percieved Behavioral Control 0,708 0,549 0.5 and were thus excluded from the model. PBC4 0,771 PBC3 0,710 Table 9 presents the results of AVE, C.R. and Subjective Norm 0,828 0,617 SN3 0,707 factor loadings for remainder of the model. SN2 0,835 As can be noticed the criteria for factor SN1 0,799 Concern for Information 0,784 0,655 loadings and C.R. was met. The AVE for the IS3 0,959 IS2 0,627 latent construct Awareness was marginally Awareness 0,736 0,485 short of the 0.5 criteria. However, we decided AW4 0,680 AW3 0,636 to keep the construct, since it was shown to AW1 0,762 Intention 0,811 0,519 reliable measure in previous research. Based IN5 0,666 on the findings in table 9 and the criteria IN4 0,722 IN3 0,793 specified earlier we can argue that an IN2 0,695 adequate degree of convergent validity is Table 9 - Evidence for Convergent Validity established.

With regards to discriminant validity, Hair et al., (2014) argue that the correlations between any two latent constructs should be lower than the square root of a construct’s AVE. The idea here is that the latent construct should explain more of the variance in its item measures that it shares with another construct (Hair et al., 2010). Table 10 provides evidence of this discriminant validity. In this correlation matrix, the highlighted values represent the square root of each construct’s

AVE. As can be observed none of the correlation values exceed this value for each of the latent construct. Hence discriminant validity is established.

Correlation N Mean SD EA MI EU AT PBC SN IS AW IN EA 194 3,330 0,589 0,809 MI 194 4,282 0,697 0,415 0,812 EU 194 3,987 0,589 0,255 0,529 0,723 AT 194 4,138 0,651 0,262 0,579 0,338 0,721 PBC 194 3,566 0,719 0,541 0,377 0,299 0,301 0,741 SN 194 4,570 0,935 0,545 0,542 0,241 0,519 0,624 0,785 IS 194 2,180 0,607 0,383 0,045 0,005 0,300 0,546 0,451 0,809 AW 194 4,037 0,616 0,310 0,399 0,196 0,380 0,247 0,351 0,179 0,696 IN 194 3,809 0,616 0,419 0,631 0,597 0,349 0,540 0,515 0,284 0,321 0,720

Table 10 - Evidence for discriminant validity

The model summary is presented in table 11. As can be observed, the Chi-square test yielded a significant (p < 0.05) X2 value which at first glance doesn’t seem to suggest a good model fit. However, this may not be the case. Hair et al., (2014) argue that very simple models with small samples have a bias towards a non-significant X2 even though they do not meet other standards of validity or appropriateness, likewise the X2 sometimes penalizes larger sample sizes and larger number of indicator variables. Hair et al., (2014) as a rule of thumbs suggest that the researcher should rely on at least one absolute fit index and one incremental fit index in addition to the X2 results. More specifically for models with N < 250 and number of observed variables (m) between 12 and 30, and RMSEA value of less than 0.07 with CFI of .92 or higher would indicate adequate goodness-of-fit (Hair et al., 2014).

Evaluating the remainder of the model fit indices, we note that Model Summary 2 403,219 RMSEA (0.053 < 0.6), AGFI (0.824 > 0.8), CFI (0.933 > 0.9) TLI Chi-square (X ) Degrees of Freedom 262 (0.917 > 0.9) and X2/df (1.539 < 3.00), all fulfil the criteria Probablity Level 0 mentioned in the previous section. Furthermore, the values for GFI Model Fit Index X2/df 1,539 is only marginally short (0.869 < 0.9). Hence, we conclude that the CFI 0,933 TLI 0,917 CFA results generally support the measurement model and the RMSEA 0,053 overall fit statistic suggest that the estimated model reproduces the GFI 0,869 sample covariances matrix reasonably well. AGFI 0,824 Table 11 - Summary for the Measurement Model Finally, before moving on to analysing the measurement model, the conditions for multi collinearity were evaluated. We used IBM SPSS to perform multiple regression analysis on the imputed latent variables to extract the Tolerance and VIF values. Based on our analysis all tolerance and VIF values were below the >0.1 and <10 criteria suggested by

Hair et al., (2014). Furthermore, an evaluation of the correlation matrix in table 11 also confirms that none of the correlations between any latent variables exceeded the 0.7 criteria suggested (Hair et al., 2014; Kline, 2005). Hence the conditions for multicollinearity are satisfied.

4.4 Analysis of the Structural Model

The structural model yielded a good fit to the data. The results are Model Summary 2 presented in table 12. As can be observed the X2 test was Chi-square (X ) 4,338 Degrees of Freedom 2 insignificant at p = 0,114 with 2 degrees for freedom. Evaluating Probablity Level 0,114 the remainder of the model fit indices, we note that GFI (0,990 > Model Fit Index X2/df 2,169 0.9), AGFI (0.889 > 0.8), CFI (0.995 > 0.9), TLI (0.961 > 0.9), CFI 0,995 X2/df (2,169 < 3.00) and RMSEA (0.078 < 0.08), all fulfil the TLI 0,961 RMSEA 0,078 criteria specified in the previous section. Figure 3 illustrates the GFI 0,990 standardized path coefficients () for the structural model. AGFI 0,889

Table 12 - Summary for the Structural Model. As can be observed, the  values are significant for 9 of the 13 hypothesis specified initially. Our estimation of the structural model confirms that latent constructs, moral incentive (=0.475, p<0.001), convenience (=0.415, p<0.001), attitude (=0.227, p<0.001) and PBC (=0.186, p<0.05), all positively predict intention to participate in recycling schemes. Similarly, as predicted in the literature review, concern for information security (=-0.228, p<0.001) negatively influenced the intention to participate in recycling schemes. The measure for squared multiple correlation (R2) suggests that these variables together explain 74,7% (R2=0.747) of the total variance in intention. As a result, hypothesis 1, 3, 5, 7 and 11 are supported. Moreover, subjective norm (=0.127, p>0.05) environmental assessment (=-0.09, p>0.05) and awareness about the issue (=-0.063, p>0.05) did not significantly predict intention to participate in recycling schemes. Therefore, hypothesis 2, 9 and 13 are rejected.

Furthermore, moral incentive (=0.658, Figure 2 - The structural model with standardized p<0.001) and awareness (=0.151, p<0.05) path coefficients (). positively predicted attitude towards recycling. In contrast, both concern for information security (=-0.351, p<0.001) and environmental

assessment (=-0.228, p<0.05) had a negative effect on attitude towards recycling. These four latent variables together explained 55.9% (R2=0.559) of the variance in attitude towards recycling. As a result, hypothesis 4, 8, 10 are confirmed. Furthermore, convenience (=-0.033, p>0.05) did not significantly predict attitude. While EA (=-0.197, p<0.05) did show a significant relationship with attitude, the negative  value indicates an inverse relationship which is contrary to our initial hypothesis. As a result, hypothesis 6 and 12 are rejected. A summarized version of the results of this hypothesis testing are presented in table 13.

Hypotheses b p-value Result H1 Favourable attitude towards recycling will positively 0,277 0,000 Confirmed influence the intention to participate in recycling schemes. H2 Subjective norm will positively influence intention to 0,127 0,062 Rejected participate in recycling schemes. H3 PBC will positively influence intention to participate in 0,186 0,006 Confirmed recycling and/or trade in schemes. H4 Moral incentive will positively influence consumer’s attitude 0,658 0,000 Confirmed towards participation in recycling schemes. H5 Moral incentive will positively influence consumer’s 0,475 0,000 Confirmed intention to participate in recycling schemes. H6 Consumer’s perception of convenience associated with 0,033 0,593 Rejected recycling will positively influence consumer’s attitude towards participation in recycling schemes. H7 Consumer’s perception of convenience associated with 0,415 0,000 Confirmed recycling will positively influence consumer’s intention to participate in recycling schemes. H8 Awareness of the problem will positively influence attitude 0,151 0,007 Confirmed towards participation in recycling schemes. H9 Awareness of the problem will positively influence intention 0,063 0,146 Rejected to participate in recycling schemes. H10 Concern for information security will negatively influence -0,351 0,000 Confirmed attitude towards participation in recycling schemes. H11 Concern for information security will negatively influence -0,228 0,000 Confirmed intention to recycle cell phones. H12 Positive environmental assessment positively influences -0,197 0,001 Rejected attitude towards participation in recycling schemes. H13 Positive environmental assessment positively influences -0,090 0,083 Rejected intention to participate in recycling schemes

Table 13 - Summary of the empirical findings

In conclusion, our results show that moral incentive was the most influential latent construct that not only displayed the highest and significant and positive influence on attitude but also on recycling intention. The second most significant finding was the fact that out of the three TPB constructs, subjective norm showed an insignificant influence on recycling intention. This is in contrast to previous research which has generally shown subjective norm to be a significantly influential determinant of recycling intention (Echegaray & Hansstein, 2017). Interestingly, while convenience showed the second most significant positive effect on intention to participate in

recycling scheme (=0.415, p<0.001), it did not significantly predict attitude towards recycling (=0.197, p>0.05). Similarly, two other exogenous constructs awareness and environmental assessment only predicted either one of the two endogenous constructs included in the model. Finally, to the best of our knowledge, ours is the first study that has utilized TPB to prove empirically a negative influence of concern for information security on both attitude and intention to recycle. In the following section we aim to provide a discussion on our findings with an aim to not only elaborate on our results but also explain some of the discrepancies between our findings and the previous research.

5. Discussion

______I this chapter we present a discussion on our empirical findings with an aim to not only elaborate on our results but also explain some of the discrepancies between our findings and the previous research.

The purpose of this study was to examine the determinants of customers intention to participate in recycling schemes. In line with previous research, we argued that the materialization of a desired behaviour is dictated by intention to perform said behaviour (Conner & Armitage, 1998; M Fishbein & Ajzen, 2011). Similarly based on the recommendations by previous researchers (Echegaray & Hansstein, 2017), and because of being considered the dominant theory in recycling literature (Ramayah et al., 2012; Wan et al., 2017), TPB was utilized to develop our theoretical model. In addition to the TPB’s default constructs – attitude, subjective norm and perceived behavioural control – we not only included the more frequently used constructs in recycling literature – moral incentive, convenience, awareness of the problem – but also included more novel constructs such as concern for information security and environmental assessment to emphasize cell phone recycling being a distinct form of recycling. We then used these previous studies to not only develop 13 hypotheses but also measurement models to test these hypothesized relationships in the context of participation in cell phone recycling in a Swedish setting. As a result, Attitude, PBC, moral incentive, convenience and concern for information security were shown to predict Swedish consumers’ intention to participate in recycling schemes. Whereby moral incentive and convenience were identified as the most influential constructs. What follows is a discussion considering the implications of findings with regards to each of the constructs, starting with moral incentive since it was the most influential construct.

5.1 Moral Incentives Based on our confirmed hypothesis, moral incentive had the most positive influence on attitude towards participation in recycling schemes (=0.658, p<0.001) as well as intention to participate in recycling schemes (=0.475, p<0.001). This significant influence on attitude can be explained as following. Thøgersen (1996), argues that in affluent nations, a clean environments is a highly valued goal and influences people’s attitude, therefore when people are incentivized to act through proper information and opportunities, they display positive attitudes even when doing good for the environment comes at some cost. Hence for the Swedish consumer, the perceived usefulness of acting in accordance to their moral norm has positive effect on their attitude towards participation in recycling schemes.

Similarly, moral incentives were also the most influential predictor of participation in recycling schemes (=0.475, p<0.001) over and independently of the attitude towards toward recycling. This result adds to the increasing body of research that argues in favour of including a moral dimension as a necessary and conceptually distinct construct within recycling research utilizing TPB (Botetzagias et al., 2015). However, the researcher should also take into account the larger context in which a behaviour occurs before making the decision to include such a construct (Botetzagias et al., 2015). For example, Khan et al., (2019) showed that moral norms were an insignificant predictors of recycling plastic waste in Pakistan, where financial considerations supersede moral inclinations. Since standards of living are low and extra cost associated with recycling is considered a financial burden on the household (F. Khan et al., 2019). Hence, perhaps one of the reasons why our results in our study were significant was because it was undertaken in Sweden, a country with high living standards which affords consumers the luxury to focus on things beyond the fulfilment of immediate needs. Moreover, we argue that this significant and positive influence of moral incentive on intention has implications beyond the direct effect on intention and may help explain the insignificance of subjective norm and awareness of the problem.

5.2 Subjective Norms and Awareness of the problem The previous research on topic has shown subjective norm to be important predictor of recycling behaviour (Echegaray & Hansstein, 2017; F. Khan et al., 2019; Ramayah et al., 2012), which led to our (rejected) hypothesis that subjective norm will positively influence intention to participate in recycling. The non-significant impact of subjective norm on intention in our study (=0.127, p>0.05) can be explained by the overwhelming influence of moral incentive on intention (=0.475, p<0.001). Since this relationship infers that the intention to recycle is based on internalized feelings, personal goals of acting in accordance to one’s moral norms of ‘doing what feel rights’ rather than a compulsion to conform with social standards and avoiding social injunctions (Botetzagias et al., 2015). While collectivist ideals and conformity to social norm may be the modus operandi in countries like Malaysia (Ramayah et al., 2012), in individualistic countries like Sweden, self-imposed moral norms take precedence (Hage et al., 2009). Furthermore recycling cell phones is not a visible type of behaviour which further reduces the attractiveness of upholding such norms (Lizin et al., 2017).

In addition, while social norms may have the potential to influence recycling behaviour, it may be effective for only a temporary period. At the initial stage, people may engage in recycling behaviour, but they may change their recycling behaviour at a later stage due to social and

demographic changes stemming from the effects of globalization of business, education and life styles (Ramayah et al., 2012). In contrast, our sample is comprised mostly of highly educated residents of Sweden, they most likely have not only been exposed to education and information on the benefits of recycling but they might themselves have been involved in a long-standing recycling schemes (Hage et al., 2009). This is also supported by the fact that 47.9% in our study reported having recycled a phone in the past which is considerably higher than most previous research (Bai et al., 2018; Echegaray & Hansstein, 2017). As a result, these individuals would have had the time to internalize these societal effects to form their own personal moral norms (Thøgersen, 1996). As a result, adherence to these morals take precedence over considerations for societal scrutiny and injunctions.

With regards to awareness of the problem, we had initially hypothesized awareness to positively influence both attitude and intention to participate in recycling schemes. Our results suggests that while awareness of the problem may have indirect effect on intention as through its significant influence on attitude (=0.151, p<0.05), awareness itself is not a determining factor for recycling intention (=0.063, p>0.05). As we mentioned previously, our population seems to have internalized moral norms guiding their behavior (Hage et al., 2009; Thøgersen, 1996). In light of this, while introduction of new information that increases awareness may have some immediate indirect effect through positive evaluations of behavior (Ramayah et al., 2012). In the long run through repetition and with experience this new awareness will be internalized with in the personal moral norm. In fact, Ajzen (2002) argues that individuals do not necessarily review their behavioural beliefs prior to every enactment of a frequently performed behaviour. Instead, they are activated automatically and guide behaviour without the requirement of conscious thought once such beliefs are formed and well-established (Ajzen, 2002).

This is not so different that the idea of forming habitual behavior. That is, the more frequently people act in the same way in a particular situation, the more that situation becomes mentally associated with the relevant goal-directed behavior (Steg & Vlek, 2009). That is, the more a person recycle, the more moral norms are reinforced. The more frequently this occurs, the stronger and more accessible the association becomes, and the more likely it is that an individual act accordingly (Steg & Vlek, 2009). Based on the arguments presented above, we propose that: when a person that has had long-term exposure to recycling is faced with the prospect of recycling a cell phone, increased awareness might reinforce positive evaluations towards participating in recycling scheme. Actual behaviour might only come about in pursuit of the fulfilment of their moral convictions, aided by the convenience associated with the scheme as well as their

perception of control over that behaviour. We discuss this role of convenience in the following section.

5.3 Convenience We had initially hypothesized that convenience would have a positive influence on both attitude and intention. However our findings showed that while convenience was an influential predictor of intention (=0.415, p<0.001) , it did not significantly predict attitude (=-0.033, p>0.05). We argue that moral incentive may also help explain the insignificance of convenience in influencing attitude. Bai et al., (2018) study in China showed a substitution effect of incentive and convenience on consumer’s attitude towards cell phone recycling. That is, when incentives were low people showed a preference towards convenience but in the presence of right incentives people were more than willing to overlook convenience. Keeping in line with this rationale, as compared to their Chinese counterparts, Swedish consumers have stronger moral norms, which by themselves are sufficient to offset the requirements for convenience.

Furthermore Thøgersen (1996) argue that when people are offered an economic incentive to compensate for the private cost – in this case inconvenience associated with the recycling – for behaving in an environmentally responsible manner – acting in accordance with their moral norms – the framing of behaviour in the mind of the mind of factor may change in way that weakens or destroys the felt moral obligation. Since our study did not involve an inspection of the effects of economic incentive, the lack of which may have inflated the sense of moral obligation felt by the respondents in our study.

Finally, an alternative explanation could perhaps be the storability of the cell phone (Sarath et al., 2015), that is, it is more convenient to store an old cell phone than it is perhaps an old refrigerator. In fact in Bai et al., (2018) study the majority of the participants expected a home pickup service for their old phone, especially when considering the alternative to recycling is storing it in a drawer. As a result, when consumers are forming their evaluation about whether or not to participate in a return scheme, convenience of the return method is taken for granted (Bai et al., 2018). However, when the consumer moves from the evaluative phase to actual performance of behaviour, the inconvenience associated to the location or method of return does become important. In contrast, when the individual find the recycling is comfortable, they tend to participate actively in recycling (F. Khan et al., 2019). This is reflected by a high  value (0.415, p<0.001) between convenience and intention. Furthermore we argue that this significant and positive influence of convenience on intention has implications beyond the direct effect on intention and may help explain the modest influence of attitude and PBC.

5.4 Attitude and Perceived Behavioural Control (PBC) We had initially hypothesized that attitude had a positive influence on intention, which was confirmed by our findings. However the total effect of attitude on intention (=0.227, p<0.001) is somewhat modest especially in relation to moral incentive (=0.475, p<0.001) and convenience (=0.415, p<0.001), which have the greatest influence intention. The explanation for this could lie in the previous argument, that is while moral incentive (=0.658, p<0.001) and awareness (=0.151, p<0.05) might entice consumers to form positive evaluations, practical considerations such as convenience which perhaps are not considered at first, may inhibit attitude to sufficiently translate into intention. This factor in tandem with negative influence of concern for information security (=-0.351, p<0.001) and environmental assessment (=-0.228, p<0.05) on attitude may help explain the comparatively modest influence of attitude on intention.

Other explanation of this weaker relationship could be attributed to inclusion of additional constructs within the TPB. Botetzagias et al., (2015), also found moral norms to not only be an influential predictor of attitude but similar to our research, the effect of moral norm on intention exceeded that of attitude. They argued that moral intention is not only a distinct construct but for their sample, the perception of recycling as the morally right thing to do was a much more important factor than the evaluation of the recycling behaviour itself. Other studies argue that the additional constructs, simply take over the predictive power from attitude (Lizin et al., 2017; Wan et al., 2014). Since our study, did not test the default TPB independent of the additional construct, this possibility cannot be discounted.

Finally, as with attitude, we had initially hypothesized that PBC has a positive influence on intention, which was also confirmed by our findings (=0.186, p<0.05). A number of previous studies have shown results similar to ours and have termed PBC as a week predictor of recycling intention (Echegaray & Hansstein, 2017). Furthermore, as with attitude similar arguments can be made for the significant but weaker influence of PBC on intention. That is, since we had included an additional construct of convenience, which did exert a greater influence on intention, it may have overtaken the power from PBC. For example, Ramayah et al, (2012) results showed PBC to be the most influential predictor of intention when convenience was considered to be inherent dimension of PBC. While Khan et al., (2017) showed PBC to be ineffective in predicting intention when other variables – including convenience – that more accurately captured the context were included in the study.

Based on the arguments presented previously we can argue that while the fulfilment of moral convictions might provide the incentive to participate in recycling schemes. (1) The presence of easy to use and convenient recycling systems and methods, (2) consumer’s positive perception of their ability to use these systems as well as (3) the overall positive evaluations toward participating in recycle schemes, amalgamate to create the necessary facilitating conditions that foster greater participation in recycling schemes. Finally, so far our discussion has been focussed on factors that displayed a positive relationship with intention. In the following section we discuss the factors that displayed a negative relationship, starting with concern for information security.

5.5 Concern for information security We had initially hypothesized that concern for information security will have a negative influence on both attitude and intention. Our findings show that consumers’ perception of risk with regards to information security is an important predictor of both attitude (=-0.351, p<0.001) and intention (=-0.228, p<0.001) to participate in recycling schemes. Since, cell phone recycling is an emerging research area, previous studies have only studied the influence of this risk perception in relation to attitude. In contrast, research outside the recycling domain has long established perceived information security risk to be a major (negative) influence on both attitudes and behavioural intention (Lee, 2009; Rezaie et al., 2012). The negative value associated with this relationship suggest that as individual’s risk perception of risk associated with a recycling scheme goes up, the less likely they are to participate in such schemes.

This idea has some interesting implications. It presents a preview of the modern realities. (1) To put it in simple terms, gone are the days where recyclables were for the most parts considered garbage, at least for the average consumer (Belkhir & Elmeligi, 2018). There is value attached to waste products such as cell phones and some consumer may perceive this new cost-benefit equation to contain a substantial cost related to compromise of personal information. (2) Economic incentive, and awareness may not be enough to change this perception. As our results indicate, for the Swedish consumer at present, pursuit of one’s moral convictions is more lucrative than perceived cost associated to comprises of personal information. (3) In other contexts, the value attached to morality may not be as substantial. This would create considerable imbalances in the cost-benefit evaluations that guide consumers decision making (Bai et al., 2018; Zhang et al., 2020).

5.6 Environmental Assessment We had initially hypothesized that positive environmental assessment will positively influence both attitude and intention to participate in recycling scheme. Our results showed that Environmental assessment significantly predicts attitude (=-0.228, p<0.05) but not intention (=-0.09, p>0.05) . However the negative  value implies that both hypothesis are rejected. Before discussing the implications of the rejected hypothesis we have to clarify what this negative  value entails. We interpret the negative relationship between environmental assessment and the two endogenous constructs differently than Echegaray and Hannstien (2018). In Echegaray and Hannstein’s (2018) study, the measuring instrument, a 5-point Likert scale, was framed 1 = much better and 5 = much worse. Hence, the negative correlation between environmental assessment and recycling intention indicated that negative environmental assessment of their country leads to positive intentions to embrace responsible waste disposal practices (Echegaray & Hansstein, 2017). However, in our study the scale was framed 1 = much worse and 5 = much better. Therefore, the negative  value representing the relationship between environmental assessment and attitude implies that, for the Swedish population, positive evaluations about the environmental conditions in fact has a negative influence on attitude towards recycling.

Here, the negative effect on both endogenous constructs could be explained by a combination of two factors. Firstly the Sweden is what is termed as a welfare state, which means people look towards the authorities to make important decision about their wellbeing and are happy to obey policies that are enforced, (Hage et al., 2009). Thereby, reducing the need for increased personal regulation. Secondly, positive evaluations of the policies implemented and the profound satisfaction with the current state of affairs (OECD, 2014) induces in consumers a sense of complacency which perhaps further disincentivizes any extra effort on part of the individual. This found to be true about consumers in other context, where complacency with current way of life hinders environmentally responsible behaviour especially when there is a lack incentives to act otherwise (Anable, 2005). We argue that for our sample, the relative importance attached to moral incentives and facilitating conditions was enough to offset effect of environmental assessment on intention to participate in recycling scheme. In contrast, since attitude represent an evaluative construct by definition, the significant and negative environmental assessment accounts for this element of complacency and represents the counterarguments for exerting additional effort when the environmental situation is already good.

To sum up these negative relationship represent the cost associated with participating in the recycling schemes. While moral incentives and facilitating conditions may have been enough to offset the negative effect of environmental assessment on intention. It wasn’t enough to outweigh

the cost of personal compromise associated with breaches of personal information. In the following section we present concluding remarks and articulate the avenues for future research as well as the various implications this research has for academics, policy maker and producers. The thesis is concluded with presenting the limitations of the study.

6. Conclusion, Implications and Limitations

______In this section we present the concluding remarks and present the reader with the key take-aways from the study. This is followed by presenting the implications for academics, policymakers and producers. We conclude the thesis by acknowledging the various limitations of the study.

Considering the severity of the environmental consequences associated with the exponential growth of the cell phone industry. This study, framed in context of circular economy and using TPB as conceptual framework, aimed at shedding light on Swedish consumers intention towards participation in cell phone recycling schemes. Since a proper examination of factors that influence intention would not only allow the producers but also policy makers to devise schemes, incentives that encourage pro-environmental behaviours. While at the same time help develop policy, legislation or business model innovations to address factors that inhibit responsible disposal practices.

Furthermore, this study extended the conventional TPB model to include additional variables moral incentive, convenience, awareness of the problem, concern for information security and environmental assessment, in order to better explain the novelty associated with cell phone recycling. The CFA and the subsequent SEM analysis conducted on data collected from 194 Swedish consumers provided reliable and valid results. 8 out of the 13 initial hypotheses were confirmed. The results establish the validity of the explanatory power of the conceptual framework, reflected by its ability to predict behavioural intentions. We then made a considerable effort in explaining some of the more novel insights from a number of different theoretical perspectives. As result, we answer our research questions as following:

RQ: What factors determine Swedish consumers’ intention to participate in recycling schemes?

For the Swedish consumer, incentives and convenience are the most influential determinant of their intention to participate in recycling schemes, followed by concern for information security, attitude and PBC. Here, moral incentives not only have both direct and indirect effects on consumer’s recycling intention but could also explain some of the insignificance of factors such as subjective norms and awareness about the issue. Particularly when considering this effect in tandem with convenience, attitude and PBC that create the necessary facilitating conditions. Here, while these factors are enough to offset the negative influence of environmental assessment on intention, concern for information security encapsulates a far greater threat of personal

compromise. Hence, concern for information security remain the only perceived cost associated with participating in recycling intention. Similarly, moral incentive and awareness out-weigh the negative evaluations about the environmental situation and the perceived risk associated with cell phone recycling, resulting in positive attitude towards participation in cell phone recycling schemes.

We argue that this explanation provides readers, producers, policymakers and future researchers a starting point for further exploration. Therefore, the authors consider the purpose of this study to be fulfilled. In the following sections we discuss the implications of this study as it relates to academics, policymakers and producers.

6.1 Implications for Academics and Suggestions for Future Research This study also highlights a few academic implications in regard to the applicability of TPB in this new and emerging research domain. While previous studies on recycling in various contexts have been vehement about their support regarding the theoretical appeal of TPB to explain recycling behaviour (Dixit & Badgaiyan, 2016; Echegaray & Hansstein, 2017), our findings showed that additional constructs such as moral incentive, convenience and information security better explained the behavioural intention. Hence our study lays support arguments by previous researchers who argue that the researchers should pay attention to the contextual background in which the behaviour occurs (Botetzagias et al., 2015; Ramayah et al., 2012), rather than implementing a one size fits all approach of TPB. We argue that should we have not included additional constructs like moral intention and convenience, our results would have conformed to the generally accepted norm of explaining recycling behaviour through the lens of these three generally acceptable constructs. In our opinion, academic discourse should not be conformed to set boundaries; it is in novelty of ideas where progress thrives. It is perhaps time to rethink constructs like PBC that were introduced in a time when use of technological devices was a novelty. The realities of the modern consumer are different and should be thus reflected in the theories explaining consumer behaviour.

Furthermore, our study not only provides support to the argument of including a moral dimension in studies explaining recycling behaviour but also shows that this is a distinct concept over and independently of one’s attitude towards recycling. Morality in context of cell phone recycling should be further investigated, since a better understanding of consumers moral norms would help policy makers and producers devise appropriate moral incentives. Similarly, previously routinized or habitual behaviour of the Swedish consumer may have had an impact on our findings by

eliminating the impact of subjective norm and awareness. Furthermore the habit of recycling one kind recycling object may actually encourage recycling of other types recycling object. Since we did not test these relationships empirically, we would suggest this line of inquiry as an excellent future research project.

Moreover, we showed that concern for information security was an important concern. This addition of risk perception is measuring recycling intention could be further explored and refined. In addition to this, our study did not include any economic element both in terms of incentives or cost. Future researcher may want to include an economic variable. Especially since our proposition that presence of economic incentive will not mitigate the effect of concern for information security, is virtually untested.

Finally, we also reported that intention to participate in recycling schemes varied with demographic factors such as age and education. We did not pursue this line of inquiry because firstly it fell outside our predefined scope of research and secondly, because our sample was unevenly distributed. However we would strongly recommend future researchers to further explore the effect of these demographic cues in relation to recycling intention.

6.2 Implications for Policymakers With regards to implications for the policymakers. First and foremost, while the environmental performance of Sweden is commendable, perhaps the manner in which this message of exceptional performance is communicated to the populace is leading to complacent dispositions of Swedish consumers. Policy makers and especially institutions responsible for disseminating information should communicate the message in a manner that frames attainment of environmental goals as continuous improvement project in the minds of their consumers. Furthermore this should be complemented with frequent awareness campaign that are not just limited to the disadvantages of discarding cell phones, but also focus on recycling knowledge, recycling channels, and other rich content- Since Swedes look to authorities for guidance in such matters the state show promote the significance of recycling by portraying recycling as not just as a behaviour or trend that is socially desirable but also the moral obligation of its citizen. This strategy will help develop moral norms in short term, whose effects will be internalized in to personal moral norms in the long run.

Secondly, infrastructure improvement such as availability of collection boxes in metro stations and bus stops, or places of communal gatherings such as parks, shopping districts etc., may also

increase perceived convenience of consumers to participate in recycling schemes (Ongondo & Williams, 2011b; Tonglet et al., 2004; Yin et al., 2014). Another opportunity exists in the form of public private partnerships where the government can utilize its existing resources to enable sort old cell phones along with regular household recycling. While the proceeds from the sale of the collected phones could help finance this operation. Finally, at present, moral norms of the individuals out-weigh the privacy concern. This equation needs not only be maintained but also improved. Policymaking has to be proactive and premeditated. Stricter legislation in regards to privacy violations as well as better regulation and monitoring of recycling facilities would also help alleviate some of the consumers’ concerns for information security.

6.3 Implications for producers Finally in regards to implications for the producers, relevant marketing departments should increase their publicity efforts to improve the frequency of publicity dissemination with regards to various return schemes (Wang et al., 2018). Our research shows that information publicity cannot directly influence residential recycling intentions, but indirectly influence them through mediating variables (Ramayah et al., 2012). These type of publicity campaigns have been extremely useful in the past in other contexts. For example, Returpak’s ‘Pantamera’ campaign boasts of enforcing recycling rate of 87% of various beverage container. In fact, rational and emotional appeals when used in advertising may be used to link morality with returning e-waste and therefore strengthen consumer participation. Furthermore these Campaigns must be undertaken as long-term endeavour to change consumer’s personal norms (Welfens et al., 2016), which will ultimately facilitate the long term viability of producers’ CE systems. However, short term and more immediate benefits of these awareness campaigns may be realised through the attitude-intention route.

Furthermore, in addition to abundant and strategically placed collection facilities, the producers should look to increase perceived convenience of through identifying consumer pain points and developing automated and web based system that significantly reduces transaction costs associated with participation in a recycling scheme (Bai et al., 2018; Dixit & Badgaiyan, 2016).

The producers might also need to consider if they are utilizing all of their customer touchpoints effectively. For example, one way producers can create ‘facilitating conditions’ is by including a post-paid return package in the actual packaging of new phones. This could be complemented by including information on how to use their return scheme in the actual product manual. This way

the producers would not only increase consumers perception of convenience and PBC but also help them develop positive attitudes towards both the behaviour but also the organization itself.

In addition to this since concern for information is an important factor, producers need to implement stricter measures in order to avoid any malpractice by their employees or partner organisation. Furthermore, the organisation should also be transparent in reporting how the cell phones are processed, once collected for the process of recycling. This should include details on what components of the cell phone are put to reuse, how is personal information erased from such components and what parts are broken down to extract the materials.

Finally, one business model innovation that would drastically improve the return rates of old cell phone is leasing schemes. In leasing schemes the customer pays for the utility of using a product while the manufacturer retain ownership of the product, also referred to as a product service system (Amaya, Lelah, & Zwolinski, 2014; Tukker & Tischner, 2006). We argue that since the Swedish sample is driven by moral incentives, they would not only be more willing to participate in such a scheme but this moral incentive might help them overcome some financial considerations, such as not being able to sell the phone after contract expires or paying upfront rather than paying every month (Tukker & Tischner, 2006). Similarly, the contractual obligation will arguably also strengthen the felt moral obligation of ensuring the return of their old phones back to the producers.

6.4 Limitations A number of limitations may influence the validity and generalizability of this research’s findings. First and foremost, we applied a convenience sampling method, where certain individuals are more likely to be selected. This was certainly the case for our sample, where the young adults and masters’ students made up the bulk of the sample population. The one-way ANOVA also found significant difference on recycling intention with regards to education and age. Hence the results may be biased and may not necessarily represent the attitudes and behaviours of the larger Swedish population.

Secondly, previous research has shown that when constructs such as moral incentive and convenience are included as additional latent constructs, the effect of PCB and subjective norms is considerably diminished (Botetzagias et al., 2015). Due the limited scope of this research we did not test how these constructs related to intention in the absence of the additional constructs of

moral incentive and convenience. As a result, there exists some degree of ambiguity with our analysis of the empirical findings.

Thirdly, no socio-economic considerations were included in the study. Some research has shown this socio-economic factors to be influential determinant of intention in other recycling contexts (Echegaray & Hansstein, 2017). Finally, no construct representing the economic cost of recycling was included in this study. Previous research has also shown economic considerations to be an important factors in mediating the relationship between the TPB construct (M. A. Khan, 2013). This is an important limitation since the absence of economic incentive and costs may have inflated the effect of moral incentives in our study.

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8. Appendix

Appendix Table 1 – Questionnaire

Appendix Table 2 – Cronbach’s alpha

Appendix Table 3 - Correlation Matrix

3 0

5 0

0 0