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Background:
Systematic Review

Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review

by
Amila Kasun Sampath Udage Kankanamge
1,
Michael Odei Erdiaw-Kwasie
1,* and
Matthew Abunyewah
2,3
1
Business & Accounting Discipline, Faculty of Arts & Society, Charles Darwin University, Waterfront Campus, Darwin, NT 0800, Australia
2
The Australasian Centre for Resilience Implementation for Sustainable Communities, Faculty of Health, Charles Darwin University, Casuarina Campus, Darwin, NT 0810, Australia
3
School of Architecture and Built Environment, University of Newcastle, Callaghan, NSW 2308, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(15), 6389; https://doi.org/10.3390/su16156389 (registering DOI)
Submission received: 10 June 2024 / Revised: 4 July 2024 / Accepted: 10 July 2024 / Published: 26 July 2024
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Abstract

:
The role of technology in e-waste management is receiving increasing attention as a dominant strategy to achieve long-term sustainability and well-being goals. However, a lack of comprehensive understanding of the contemporary factors influencing e-waste urban mining technology design and adoption remains. This is the first study to propose a taxonomy to clarify the contemporary factors influencing e-waste urban mining technology design and adoption. The taxonomy comprises four thematic clusters, notably the device cluster, the process cluster, the organizational cluster, and the macro cluster. This study further shares insights on how the taxonomy of e-waste urban mining technology design and adoption can be applied to assess each stage of the technology transition process. Drawing from this study synthesis, this study taxonomy model characterizes the embedded internal and external various states of technology design and adoption and derives informed decisions from a sustainable technology perspective. This study’s taxonomy framework supports the outlook measurement analysis of e-waste urban mining technology factors from both developing and developed countries’ perspectives, which can contribute to broadening the scope and level of the applicability of technologies.

1. Introduction

In recent years, the debate surrounding e-waste urban mining has surged, capturing the attention of an expanding cohort of researchers and practitioners. This phenomenon is largely attributed to the rapid pace of technological advancement, leading to a decreased obsolescence age of electrical and electronic equipment (EEE) [1,2]. Indeed, over the last decade, the volume of e-waste has rapidly escalated from 44.4 million metric tons (MTs) to 53.6 million MTs [3]. The projections indicate further escalation, with the expectations set for an increase to 74.7 million MTs by 2030 [3]. Given the significant shift in contemporary business paradigms towards sustainability, e-waste urban mining has transcended its traditional role as a mere environmental solution. It has emerged as a fundamental business imperative crucial for safeguarding our global ecosystem’s future sustainability and survival [4,5,6]. Considering this shift in perspective, there has been a growing emphasis on collecting, recycling, and treating e-waste within the framework of national and international waste and environmental management strategies [5]. Given the increasing coverage of e-waste urban mining, clarifying the complex thematic clusters they encompass and pursuing the underlying factors influencing each cluster are critical to supporting the achievement of sustainable e-waste urban mining. The current e-waste urban mining methods are characterized by outdated methods, inadequate end-of-life (EoL) management practices, an insufficient capacity to cope with the escalating volumes of e-waste generated, and the proliferation of unethical transboundary movement, which poses substantial threats to the efforts towards sustainable e-waste management [2,7,8,9,10,11,12,13,14].
In this context, the role of technology in e-waste urban mining stages is pivotal in promoting sustainable outcomes [2,11,15,16]. Therefore, the integration of sustainable technologies into the e-waste urban mining processes offers diverse benefits, including heightened efficiency and productivity, access to real-time data for informed decision making, decreased energy consumption, mitigated health risks for workers and society, minimized climate change effects, reduced human errors in processes, improved output quality and process agility, and the curtailment of illegal transboundary movements of e-waste across regions [1,17,18,19,20,21]. While sustainable technologies are indispensable for enhancing organizational performance, safeguarding the environment, protecting health, promoting social well-being, and fostering economic prosperity on a global scale, it is important to acknowledge the contemporary factors that influence technology design and adoption [22,23]. The previous studies have identified the drivers of computer vision technology [24], the barriers and drivers of e-waste recycling [25], the technological challenges, and the opportunities of e-waste disposal [14], and the barriers to achieving sustainable e-waste management [26]. However, the available evidence reveals inconsistencies in the findings and a lack of a cohesive narrative regarding the contemporary factors influencing technology design and adoption from an e-waste urban mining perspective. This oversight underscores the need for a systematic review to address this notable gap. Such a review would be a critical resource, providing valuable insights and guidance to e-waste urban mining businesses worldwide. In light of this, this systematic review is guided by two research questions (RQs).
RQ1. What are the emerging technology typologies in urban mining?
RQ2. What are contemporary factors that influence designing and adopting sustainable technologies?
We conducted the comprehensive content analysis of 51 research studies to delineate the research profile and thematic clusters. In doing so, this paper contributes significantly to the discourse on e-waste urban mining in three primary ways. Firstly, amidst the proliferation of research endeavours in e-waste urban mining, this study fills a notable gap by clarifying the contemporary factors that influence the technology design and adoption, presenting a taxonomy of e-waste urban mining technology design and adoption. Secondly, this study develops a framework that offers a holistic perspective on the critical aspects of the technology transition process that require attention in this domain. Thirdly, this study proposes potential directions for future researchers.
The subsequent sections of the paper are structured as follows: Section 2 describes the methodology employed in this study, while Section 3 presents the research profile. Section 4 elaborates on the synthesis results and taxonomy, whereas Section 5 delves into discussions and presents the technology transition framework. Finally, Section 6 concludes this study, suggesting directions for future researchers.

2. Methodology

2.1. Planning the Review

This section explains and defines the methodological approach employed in research. Before this study commenced, a review panel was convened, comprising authors and one industry practitioner possessing expertise in circular economy and waste management. The purpose of this review panel was threefold: (i) to deliberate on the boundaries of e-waste urban mining research, (ii) to define the scope and research questions of this study, and (iii) to establish the necessity for and contribution of this study. During the initial stage, two brainstorming sessions were conducted, each lasting between 45 and 60 min. The first session involved generating and categorizing ideas in person, with all emergent concepts meticulously recorded. Subsequently, a further discussion was conducted via Zoom to consolidate the primary ideas, clearly outlining the research questions and the anticipated contributions of this study.

2.2. Search Strategy

The studies underlined the significance of systematic reviews as a fundamental method for synthesizing the findings of a specific body of research investigations [27,28]. Due to its comprehensive nature, reproducibility, and impartiality, a systematic literature review is typically preferred over other approaches to fulfil this study objective [27]. The researchers conducted this systematic review by adhering to the PRISMA protocol, as outlined by [29]. The study provides PRISMA checklists as Supplementary Materials. Figure 1 illustrates the logical framework guiding the methodological approach of this study. As depicted, the researchers initiated the process by defining the identification of the dataset. In this regard, the researchers searched two prominent research databases, Scopus and Web of Science (WOS), to ensure comprehensive coverage of the literature, given their widespread usage by scholars worldwide [30]. These databases serve as repositories for essential academic literature sources on e-waste urban mining, encompassing both conventional and non-conventional studies. Subsequently, a preliminary database search was executed employing the initial keywords to identify the publications pertinent to this SLR. The selection of keywords holds significance in ensuring the inclusiveness of relevant publications for this review. This study utilized various terminologies, guided by previous review articles [27,31], to formulate an initial keyword list. Additionally, searches were conducted on Google Scholar using selected keywords to identify existing scholarly works at the intersection of e-waste urban mining and sustainable technologies, thereby updating the keyword list. Utilizing relevant keywords and adhering to the guidelines outlined by [32], the research publications were identified in the selected databases. Subsequently, the research publications that did not meet the inclusion criteria were eliminated by employing well-defined inclusion and exclusion criteria; refer to Table 1. Additionally, a snowballing methodology was employed to include research articles that were similar to those that had been previously chosen.

2.3. Identification of Records

The selected keywords were transformed into search strings utilizing Boolean logic, which employs operators, such as “OR” and “AND” connectors. The symbol “*” captures words that begin with the prior prefix. Then, we searched the Scopus and Web of Science databases on May 2023 to retrieve research publications using the following search strings, ((“electr* waste” OR e-waste OR “urban mining” OR “e-waste recover*” OR “resource recovery” OR “electronic and electrical waste” OR weee OR “value recovery” OR “smart e-waste management”) AND (digital* OR “digital technolog*” OR “Information Communication Technology” OR “Information Technology” OR “internet of things” OR RFID OR blockchain OR “deep learning” OR “machine learning” OR “big data” or “artificial intelligence” OR “cloud computing” OR 4IR OR “wireless communi*” OR robo*)), to find the relevant publications. The search used the TITLE-ABS-KEY function in Scopus and the “ALL fields” function in WOS. The search returned 2040 publications from the databases, with 1257 and 783 in Scopus and WOS, respectively (refer to Figure 1).

2.4. Screening of Records

Two thousand and forty publications were examined in the initial screening using the inclusion and exclusion criteria (refer to Table 1). In the first screening, duplicates (n = 368), review articles (n = 236), proceeding papers (n = 623), book chapters (n = 69), notes (n = 13), books (n = 5), editorials (n = 4), data papers (n = 2), letters (n = 1), and short surveys (n = 1) were excluded, leaving 716 articles for the second screening. During the second screening, articles without the full text (n = 3), articles not in English (n = 16), and articles not falling in the subject area (n = 654) were excluded. After excluding 673 articles in the second screening, 43 remained for further screening. The snowballing method was employed to locate the other relevant articles, encompassing both the reverse and forward citation analyses of the selected articles. This technique ensures comprehensive coverage, leaving no relevant studies overlooked in developing this review’s argument [27,33]. After conducting the snowballing method, eight related and relevant articles were identified. Therefore, in total, 51 articles were analyzed in this review.
The inclusion and exclusion criteria highlighted in the screening process consist of four guidelines: the type of document, language, the availability of the full text, and most importantly, the subject of the paper.

2.5. Data Analysis Method

The analysis of the 51 articles included in this review proceeded in two stages. Firstly, this study examined the research profile of the selected articles based on three criteria: (i) the timeline of publication, (ii) the journals of publication, and (iii) the knowledge domain. Secondly, the research team conducted manual content analysis to address the research questions by identifying the emerging urban mining technologies and contemporary factors influencing sustainable technology design and adoption. Qualitative manual content analyses were conducted by creating an Excel data extraction form to capture information, such as the author(s), title, year of publication, knowledge domain, ranking of the publication and research findings of each paper, in response to the research questions [34]. This study followed a three-step process to derive the thematic clusters [35]. First, this review employed a deductive coding approach to analyse the content and organize the findings into the research questions. Second, this study identified 52 factors that emerged from the content of the selected articles when performing the deductive coding approach. The authors repeatedly reviewed the data, and further summarized the emerging factors into twenty-two factors, where the authors began labelling the factors. In the final step, the twenty-two factors were grouped into four thematic clusters and interpreted in terms of meaningful themes, representing the research questions. This was a non-linear and recursive process involving continuous discussion among the authors, during which the themes gradually became clear. Subsequently, the thematic clusters and their subcategories, along with the relationships between them, were explored and interpreted through a thematic map. The results from the analysis conducted by two independent researchers were compared, and consensus was reached in cases of disagreement with the assistance of a third researcher. This process aimed to ensure the validity and reliability of the findings extracted from the content of papers.

3. Research Profiling

E-waste urban mining represents an emerging concept globally, offering an appealing alternative to traditional virgin mining practices by sustainably exploiting the mineral resources from e-waste [36]. Indeed, the selected research articles indicate that discussions regarding sustainable technologies in e-waste urban mining have commenced relatively recently and have gradually increased over time with better practices (refer to Figure 2). However, the average number of research articles identified per year was approximately three, indicating a steady, but modest growth in scholarly interest in the intersection of e-waste urban mining and sustainable technologies over the studied period (2010–2023). The statistics show that 75% (38 articles out of 51 articles) of the selected articles were published between 2020 and 2023. The year 2021 recorded the highest number of publications, with 12 research publications. Additionally, 2020 and 2022 followed closely, with a substantial number of publications, nine articles per year. At the beginning of publication, researchers made at least one publication per year until 2019, except for there being no publications in 2011 and 2012. The timeline of research publications suggests that scholars have predominantly contributed to the discourse on the transition of sustainable technologies in e-waste urban mining within the last four years. This indicates that the intersection of sustainable technologies and urban mining is a recent phenomenon.
Additionally, publications from 32 journals were included in this systematic review. Among them, Resources, Conservation & Recycling had six publications, followed by the Journal of Cleaner Production with four publications, Waste Management with three publications, and Waste Management & Research with three publications. In that sense, the contribution for the total number of selected articles from the top four journals is 32% (refer to Figure 3). Further, Applied Sciences, Environmental Technology & Innovation, IEEE Access, IEEE Transactions on Automation Science and Engineering, Minerals Engineering, Science of the Total Environment, and Sensors followed with two publications each. The remaining journals had one publication each (refer to Figure 3). Then, this study analyzed the ranking of the selected publications through https://www.scimagojr.com/ accessed on 29 May 2023. The statistics show that 96% of the publications fall under either Q1 or Q2, which is a sign of the quality of the content of this review paper.
Other Journals: Advances in Engineering Software; Applied Sciences; Chemosphere; Data; Environmental Impact Assessment Review; Environmental Monitoring and Assessment; Environmental Pollution; Environmental Science and Pollution Research; Environmental Technology & Innovation; e-Prime—Advances in Electrical Engineering; Electronics and Energy; Heliyon; IEEE Access; IEEE Transactions on Automation Science and Engineering; the International Journal of Automation Technology; the International Journal of Computer Integrated Manufacturing; the International Journal of Integrated Engineering; the International Journal of Production Research; the Journal of Chemical Information and Modeling; the Journal of Environmental Management; the Journal of Intelligent Manufacturing; the Journal of Material Cycles and Waste Management; the Journal of the Air & Waste Management Association; Metals; Minerals; Minerals Engineering; Science of the Total Environment; Sensors; and Transportation Research.
Further, preliminary analysis indicated that the selected studies contributed to four knowledge domains: the collection process, the disassembly process, the recovery process, and the integrated process (refer to Figure 4). Disassembly and recovery are the two processes with the highest number of contributions over the period, which was recorded in 15 (29%) publications each, followed by the collection process and integrated process, with 11 (22%) publications and 10 (20%) publications, respectively. The knowledge domain of integrated processes primarily focuses on providing comprehensive solutions for businesses to enhance e-waste urban mining practices by adopting sustainable technologies. Also, the publications on the disassembly process dominate the period of 2020–2023, especially in 2023. The recent publications focus more on the disassembly process (fourteen articles), followed by the recovery process and the collection process (nine articles each).

4. Synthesis Results

This section delves into the insights drawn from the 51 selected articles in response to the research questions posed in this systematic review. Through the systematic analysis of the content of the selected studies, this study explores the technologies used in e-waste urban mining and groups them into various typologies addressing RQ1 (i.e., what are the emerging technology typologies in urban mining?). Further, this study comprehensively analyses the factors influencing the design and adoption of sustainable technologies, categorizing them into four thematic clusters addressing RQ2 (i.e., what are contemporary factors that influence designing and adopting sustainable technologies?).

4.1. Technology Typologies in E-Waste Urban Mining

The transition towards sustainable technologies in urban mining has increasingly become a strategic priority among urban mining businesses in the modern context, notwithstanding the conventional methods. This section discusses the prevalent technologies in the e-waste urban mining nexus.
Despite the increasing recognition of the strategic importance of transitioning towards sustainable technologies in urban mining globally, most businesses still rely on unsustainable conventional methods for e-waste urban mining (refer to Table 2). Research acknowledges that the conventional methods, such as the manual approaches, remain extensively utilized for e-waste urban mining, particularly in developing countries where the informal sector predominates. This process has adverse impacts on the health of workers and society and diminishes the recovery rate of valuable materials from e-waste [8,37,38,39]. Further, the conventional process leads to the discharge of e-waste residues into open areas and water sources, contributing to environmental pollution and health hazards [7,8,39,40,41]. In response to these challenges, the recent studies have deeply criticized these unsustainable technologies [37,41]. In light of that, researchers and practitioners increasingly recognize the strategic importance of emerging sustainable technologies in e-waste urban mining globally [37,38,41]. Sustainable technologies such as smart trucks transport the e-waste from collection points to processing facilities utilizing optimized vehicle routes, minimizing the travel time and fuel consumption [42,43]. The process, therefore, facilitates an efficient collection process, while concurrently reducing the transport costs and emissions [44]. The automatic and robotic technologies equipped with online sensing and machine learning capabilities to dismantle e-waste components efficiently and accurately [21,45,46,47,48] reduces the operational costs and mitigates the health risks associated with human intervention [1,19]. Subsequently, intelligent recycling processes support reducing the experimental costs and environmental risks, while enabling sustainable recycling practices in the e-waste urban mining nexus [20].

4.2. Taxonomy of E-Waste Urban Mining Technology Design and Adoption

To address RQ2, this study systematically digests the selected studies’ content to explore the factors that influence sustainable technology design and adoption and present in four clusters, (i) the device cluster, (ii) the process cluster, (iii) the organizational cluster, and (iv) the macro cluster, to pave the way for facilitating the taxonomy of e-waste urban mining technology design and adoption. Table 3 and Table 4 present the thematic clusters related to technology design and adoption.

4.2.1. Device Cluster

The device cluster encompasses attributes or characteristics intrinsic to a specific device attached to the primary technology. The review synthesis revealed four underlying factors driving the device cluster of the e-waste urban mining technology taxonomy model, such as device capability (e.g., the battery life of smart tags, the capacity of robotic arms, and the computational power of devices), device accuracy (e.g., the accuracy of signals over smart tags and GPS and the complexity of e-waste forms), device robustness (e.g., the probability of the accidental and deliberate separation of tags), and device compatibility (e.g., the compatibility of technology accessories). These factors are pivotal in determining the effectiveness, efficiency, and sustainability of the devices employed in the e-waste urban mining process. The device cluster significantly focuses device capability on tracking e-waste movements throughout the entire life cycle of electronic products and beyond to enable transparency in e-waste transportation and provide a viable means of investigating end-of-life paths for e-waste around the globe. For instance, this study revealed that the limited battery life of the smart tags disrupts the continuous monitoring of e-waste movement and global tracking [67]. In fact, this study suggests that e-waste urban mining businesses should include long-lasting batteries in tracking devices with appropriate intervals. Further, the limited capacity of robotic arms constrains the transition to mobile robotic systems from manual labour to collecting and classifying e-waste [13]. This study suggests that businesses should increase the capacity of robotic arms to facilitate the use of heavy e-waste appliances that handle e-waste and increase the efficiency of collection. Moreover, the device cluster pays attention to the degree of device compatibility with advancing technologies. For instance, this study indicated that the incompatibility of technology accessories with sustainable technologies makes the process unproductive [45]. This study recommends using compatible accessories to achieve the fullest capacity and for the undisrupted contribution of sustainable technologies, which is equally important.

4.2.2. Process Cluster

The process cluster encompasses a multitude of elements or variables that exert influence over a specific process. The review synthesis revealed six underlying factors driving the process cluster of the e-waste urban mining technology taxonomy model, such as process performance (e.g., the efficiency of the process, the accuracy of the processes, and the data processing time), the process cost (e.g., the data acquisition cost, energy consumption, the operational cost of the process, and the cost of data generation and acquisition), the process characteristics (e.g., the degree of non-destructiveness, the quality of processes, and the credibility of the process), the process capacity (e.g., the ability to trace the lifecycle of e-waste, the capacity to manage e-waste volume, the ability to monitor the e-waste movements, and the recycling return of e-waste), process exertion (e.g., effort in the process and the availability of data for the process), and the process impact (e.g., the risk to environment and health, and human errors during the process). These factors are pivotal in shaping the methodologies and practices implemented within the e-waste urban mining process. The process cluster significantly focuses on the performance of the e-waste urban mining process. For instance, Nowakowski et al. (2020) [42] highlighted the limited payload capacity of conventional collection vehicles, such as lorries and vans, resulting in inefficient collection and lower collection rates. This study pointed out that businesses should use smart trucks with a flexible loading capacity to mitigate inefficiencies, particularly in densely populated areas and urban cities with apartments and tall buildings. Further, this study indicated that e-wastes with a similar appearance, the appearance of an unforeseen colour, and negligible differences in the external characteristics decrease the accuracy of automatic detection of e-waste [47]. This study recommends that businesses continuously train machine learning algorithms with different images to improve the detection effectiveness. Moreover, Wang & Wang (2019) [62] focused on issues of data interruption in manual data-feeding structures, resulting in low efficiency and poor data integrity. This study urges businesses to apply digital twin technology in the e-waste urban mining process to access the actual product data of individual e-waste items, integrating the physical world (e.g., smart tags) and the cyber world (e.g., the cloud). Also, the process cluster pays significant attention to the process characteristics. For instance, Ueda et al. (2020) [70] examined the limitations of disfigured or over-crushed e-waste components in the demanufacturing process. This study encourages businesses to establish non-destructive demanufacturing technologies to minimize destructive detachments of e-waste components and enhance output quality. Further, Khan and Ahmad (2022) [18] indicated that the existing e-waste urban mining processes ignore the appropriate data destruction procedures for e-waste items, posing risks to sensitive information, including government-classified data. This study emphasizes the need for a smart e-waste management system incorporating valid data destruction certificates to improve the credibility of the e-waste urban mining process.

4.2.3. Organizational Cluster

The organizational cluster encompasses various aspects related to an organisation’s structure, culture, policies, and strategies that influence its process and initiatives. The review synthesis revealed six underlying factors driving the organizational cluster of the e-waste urban mining technology taxonomy model, such as support for organizational decisions (e.g., the degree of intelligent decisions), support for organizational functions (e.g., the ability to integrate processes, the cognitive abilities of technologies, the feasibility of collaborative robots, and the possibility of workplace accidents), support for organizational strategies (e.g., the ability to substitute human workers), employee attitude (e.g., the attitude towards sustainable technologies), capital investment (e.g., the capital requirement for technology and the cost of installation of technology), and technological know-how (e.g., the technical knowledge of employees, the knowledge of sustainable technologies, and the technical skills of workers). These factors influence the implementation and outcomes of e-waste urban mining initiatives. The organizational cluster significantly focuses on support for organizational function and strategies, while simultaneously focusing on the capital requirement for technologies. For instance, this study revealed that the limited cognitive abilities of collaborative robots are unproductive and create conflict with workers on the floor [50]. This study stresses that businesses need to improve the sensing capabilities of collaborative robots to guide their decisions on the floor, increase productivity, and reduce conflicts with workers. Further, Madhav et al. (2021) [13] indicated that the conventional methods continuously cause human health and safety issues in the e-waste urban mining industry. In fact, this study guides businesses in the industry to shift to alternative solutions, such as mobile robotic technology, to replace manual labour for collecting toxic e-waste to reduce the harmful effect on workers. Moreover, Adanu et al. (2020) [7] highlighted that the high capital costs of acquiring and maintaining such technologies present obstacles to technology uptake by businesses, particularly for businesses motivated primarily by profit, which are less inclined to invest substantial sums of money to address the detrimental effects of conventional methods or to prioritize environmental protection efforts. This study urges the government and NGOs to offer financial support to facilitate the establishment of sustainable technologies. Additionally, this study revealed that the limited technical capacity of workers restrains technology advancements in organizations [41]. This study stresses that businesses should introduce upskilling programs and provide technical guidelines for workers to improve their technical capacity to support technology initiatives in the e-waste urban mining businesses.

4.2.4. Macro Cluster

The macro cluster encompasses broad external influences that affect an organization and its decisions. The review synthesis revealed six underlying factors driving the macro cluster of the e-waste urban mining technology taxonomy model, such as data protection (e.g., data security and privacy, the intellectual rights of product design, and access to the technical data of manufacturers), regulatory framework (e.g., the objectives of return strategies, the regulations and policies of countries, and global e-waste schemes), facilitating conditions (e.g., the availability of infrastructure facilities and the condition of e-products at their EOL), stakeholders’ behaviour (e.g., the acceptance of technologies and the attitude towards sustainable technologies), stakeholders’ characteristics (e.g., the knowledge of stakeholders), and industry traits (e.g., the degree of formality of businesses). These factors are pivotal in shaping e-waste urban mining business decisions. The macro cluster highly focuses on the data protection and regulatory framework. For instance, Yu et al. (2020) [43] pointed out that manufacturers hesitate to disclose confidential and copyright information, hindering e-waste urban mining businesses from making informed decisions for each item. This study urges cohesive efforts of urban mining actors and authorities to develop a big data-driven information-sharing platform to facilitate information sharing among industry participants involved in the e-waste urban mining process, while ensuring manufacturers’ copyright information protection. This study further unveiled that the emerging return policies and regulations are prompting businesses to implement effective take-back technologies to meet the increasingly stringent collection targets [52,66]. This study urges businesses to search for sustainable forms, such as customer-centric mobile collection systems, to increase the collection rates through on-demand collection facilities to meet the objectives of the emerging return policies and align with the social and legislative requirements. Moreover, this study indicated a lack of knowledge about e-waste urban mining among users to understand the importance of their contribution, leading to the low engagement of users with technology initiatives [62]. This study stresses that businesses should educate and motivate their target groups on the importance of their engagement with technology initiatives to improve active participation in technology initiatives. Therefore, the synthesis results clearly indicate that four clusters, (i) the device cluster, (ii) the process cluster, (iii) the organizational cluster, and (iv) the macro cluster, significantly impact technology design and adoption, as illustrated in Figure 5.

5. Discussion

The conventional e-waste urban mining methods are outdated and fraught with critical issues. The transition towards sustainable technologies in e-waste urban mining is widely recognized as essential for the progress and sustainability of the industry [38,81]. However, the extant literature lacks a cohesive narrative regarding the contemporary factors that influence technology design and adoption perspectives. Given the lack of comprehensive understanding of the contemporary factors influencing the technology design and adoption in the e-waste urban mining nexus, this study is the first to address that notable gap by presenting a taxonomy of e-waste urban mining technology design and adoption (refer to Figure 5). The synthesis results of this study support the taxonomy model, which is anchored by four thematic clusters: the device cluster, the process cluster, the organizational cluster, and the macro cluster. The synthesis results further develop a framework to guide e-waste urban mining businesses in designing and adopting sustainable technologies (refer to Figure 6). The developed framework offers a holistic perspective on the critical aspects of the technology transition process, integrating the taxonomy of e-waste urban mining technology design and adoption that require attention. The framework is, therefore, anchored within the taxonomy of e-waste urban mining technology design and adoption, and it assumes the interconnectedness of the four clusters.
The developed technology transition framework (TTF) comprises three stages that guide e-waste urban mining businesses through technology adoption decisions: stage one indicates the businesses that use conventional methods; stage two encompasses the technology transition process from conventional methods to sustainable technologies; and stage three indicates the businesses that benefit the advantages of sustainable technologies. In light of this, the framework has several applications for practitioners, policymakers and researchers. Previous research on the e-waste urban mining domain has often overlooked technology adoption decisions from the customer’s perspective [82,83,84]. The present framework addresses this gap by supporting organizational-level technology adoption decisions. Since this study’s most significant contribution is identifying the contemporary factors influencing technology design and adoption, the present framework offers valuable insights for managers to effectively develop and execute technology adoption decisions at the organizational level, considering the contemporary factors influencing technology design and adoption. This study stresses that practitioners should consider a holistic perspective before adopting technologies to minimize the challenges of adopting sustainable technologies. Further, this study identified emerging factors encapsulated within each cluster. For instance, device capability and device accuracy are the most significant factors in the device cluster, garnering significant attention in the synthesis results. Notably, the process impact and process capacity in the process cluster and support for organizational functions and strategies in the organization cluster are emerging. Therefore, e-waste urban mining actors, including recyclers, processing businesses, and entrepreneurs, can leverage these findings to understand the dynamic factors in the business environment that should be considered in deciding how best to overcome adoption dilemmas. For instance, businesses who are alerted to the emerging factors encapsulated within the device cluster can design sustainable business solutions such as blockchain-based e-waste tracking systems with robust reception devices to facilitate global e-waste tracking, considering the signal interference generating inconsistent data, the lack of reception beyond coastal regions affecting the accuracy of e-waste location tracking, and the possible accidental and deliberate separation of tags affecting the loss of data [66,67].
Moreover, policymakers can leverage this holistic perspective on the critical aspects of technology design and adoption to formulate technology policies that stimulate action within the e-waste urban mining sector. For instance, Madhav et al. (2021) [13] highlighted that the informal sector practices undermine the performance of robotic technologies, as the informal sector can acquire a more significant amount of e-waste compared to that of the formal sector initiatives. In this context, the macro cluster (e.g., informal sector practices) significantly influences the performance of technology initiatives. Policymakers can address this gap by integrating sustainable technologies with extended producer responsibility (EPR) to encourage end-of-life (EOL) disposal through the sustainable technology initiatives of formal actors, thereby minimizing disruptions caused by the informal sector. Moreover, policymakers can integrate the critical insights (e.g., an emerging need for facilitating conditions, such as sustainable infrastructure facilities that synergize the technology design and adoption efforts) of this study into their national development agenda, which requires more customized approaches to meet sustainable development goals and promote circular and sustainable cities in respective countries and continents [85,86].
This study further proposes to extend the application of frameworks for assessing technology transition readiness in e-waste urban mining, which is currently lacking. Businesses can evaluate their technology transition readiness based on the twenty-two factors in the e-waste urban mining technology design and adoption taxonomy. This evaluation provides a clear indication of the readiness of the respective e-waste urban mining businesses for the sustainable technology transition. Future researchers can further enhance this area by developing a technology readiness score, particularly for the e-waste urban mining industry.

6. Conclusions

The transitioning from conventional methods to sustainable technologies in the e-waste urban mining nexus requires significant contributions from scholars, practitioners and policymakers. The present study recognized that research in this field is highly fragmented, and there is a lack of comprehensive understanding of the contemporary factors that influence the technology design and adoption in the e-waste urban mining nexus. Given the lack of comprehensive understanding of the contemporary factors influencing the technology design and adoption in the e-waste urban mining nexus, this study addresses a notable gap by exploring the following research questions: RQ1. What are the emerging technology typologies in urban mining? RQ2. What are contemporary factors that influence designing and adopting sustainable technologies? Therefore, the rewards of this systematic review are manifold: (i) it provides a comprehensive understanding of the contemporary factors that influence the technology design and adoption, presenting a taxonomy of e-waste urban mining technology design and adoption; (ii) it develops a framework that offers a holistic perspective on the critical aspects of the technology transition process that require attention—technology transition framework (TTF); (iii) it provides implications for e-waste urban mining businesses and policymakers to facilitate technology transitions from conventional methods to sustainable technologies; and (iv) it proposes a potential direction for future researchers. The taxonomy model offers an analytical lens for understanding the factors influencing technology design and adoption. The use of evidence in policymaking in e-waste urban mining is, therefore, improved by a proficient understanding of why and how the identified factors come into play. Further, understanding the four thematic clusters that shape sustainable technology design and adoption shall guide the technology policies in different contexts, countries, and continents. Additionally, this study has its limitations. Firstly, the literature search was restricted to two databases (i.e., Scopus and Web of Science). Secondly, books, book chapters, and grey literature were excluded as they typically undergo a less-rigorous review process [27,87]. Review articles were also excluded in line with the inclusion criteria to focus on original papers for analysis. Thirdly, this study only considered papers written in English. Finally, because the literature search was conducted using a specific combination of keywords, some articles related to the subject area may have been omitted if they used different keywords than those employed in this study. Notwithstanding these limitations, this SLR offers a holistic perspective of the contemporary factors influencing technology design and adoption and critical aspects of the technology transition process that require attention. Moreover, the findings establish a robust foundation for future research in this area, highlighting the opportunity for more comprehensive studies that address these limitations.
Moreover, the present study has suggested several potential directions for future researchers. To begin with, the present study encourages the researchers to test the relationships proposed in the technology transition framework in the real-world context. In fact, researchers should also adopt a more integrated perspective, focusing on the interaction between the four clusters. Further, it is worth noting that sustainable technology typologies are correlated with e-waste urban mining business processes and performance [88]. Moreover, it is evident from the papers included in this systematic review that these two activities complement each other. However, further research is needed to determine the extent and way e-waste urban mining business processes and performance benefit from the transition to sustainable technologies, especially from the perspective of developing countries where conventional methods are in use. Also, e-waste urban mining process environment and health analysis should take a more dynamic approach to evaluate the impact of sustainable technologies. Considering the length, complexity, and multilevel nature of integrating sustainable technologies into e-waste urban mining, researchers should primarily focus on longitudinal analysis in the future to test the impact of four clusters on the e-waste urban mining business process and performance, especially the environmental and health impacts. Furthermore, sustainable technology design and adoption are also influenced by some contingent factors, including, but not limited to, device capacity, emerging return strategies, government regulations, copyright information, and credible data destruction procedures. Therefore, more empirical research is needed to explore these contemporary factors’ impact on sustainable technology design and adoption in the dynamic business environment, which will help businesses better predict the e-waste urban mining business performance. Additionally, future researchers can identify more factors that influence technology design and adoption. In an effort to integrate sustainable technology culture into e-waste urban mining, future studies should explore the grey literature to expand knowledge on these factors that drive businesses towards sustainable e-waste urban mining practices through continuous improvements in technologies.
Declaration of generative AI and AI-assisted technologies in the writing process.
During the preparation of this work, the authors used ChatGPT by OpenAI in order to enhance sentence structures for fluent reading, improve grammar, and refine the overall clarity and cohesiveness of the written content. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16156389/s1.

Author Contributions

Conceptualization, A.K.S.U.K., M.O.E.-K. and M.A.; methodology, A.K.S.U.K., M.O.E.-K. and M.A.; validation, A.K.S.U.K., M.O.E.-K. and M.A.; formal analysis, A.K.S.U.K., M.O.E.-K. and M.A.; data curation, A.K.S.U.K., M.O.E.-K. and M.A.; writing—original draft preparation, A.K.S.U.K., M.O.E.-K. and M.A.; writing—review and editing, A.K.S.U.K., M.O.E.-K. and M.A.; visualization, A.K.S.U.K.; supervision, M.O.E.-K. and M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. PRISMA flowchart.
Figure 1. PRISMA flowchart.
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Figure 2. Timeline of publication.
Figure 2. Timeline of publication.
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Figure 3. Journals of publications.
Figure 3. Journals of publications.
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Figure 4. Knowledge domain of publications.
Figure 4. Knowledge domain of publications.
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Figure 5. Taxonomy of e-waste urban mining technology design and adoption.
Figure 5. Taxonomy of e-waste urban mining technology design and adoption.
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Figure 6. Technology transition framework (TTF).
Figure 6. Technology transition framework (TTF).
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Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
FacetInclusion CriteriaExclusion Criteria
Type of the documentPeer-reviewed journal articlesReview articles, proceeding papers, books, book chapters, notes, editorials, retracted, data papers, letters, short surveys
LanguageAll papers in EnglishPapers in other languages, such as German and Chinese
Availability of full textAvailableNot available
SubjectCentral topic on e-waste urban mining sustainable technologies
Focus on two aspects of e-waste urban mining: technologies and factors that influence technology design and adoption
Original papers focus on empirical evidence		Out-of-scope articles
Other streams of waste than e-waste
Articles focus on either e-waste urban mining or sustainable technologies (not intersection)
Papers focused on theoretical arguments
Table 2. Urban mining technology typologies.
Table 2. Urban mining technology typologies.
Process StageTechnology TypologyExamplesReference
CollectionSmart collectionSmart bins, smart boxes, smart tucks, smart route planner[42,43,44,49]
Robotic initiatives Robotic arms, mobile robots[13,50]
Smart communicationMobile apps, hazard level communicator[51,52,53]
Conventional methodsHand-driven carriages, push carts, small trucks[7,41]
DemanufacturingAI-driven technologiesYODO (You Only Demanufacture Once), automated detection system, vision-based object finder, deep learning object detector, FrHHGO classifier[1,21,47,54,55,56]
Robotic initiativesRobotic arms, collaborative robots, robotized devices, mixed model robotic disassembly[46,48,57,58]
Hybrid technologiesCyber-physical system, smart screw remover[19,45]
Conventional methodsBy hand, screwdrivers, hammers, pliers, tin scissors, motor pestle, de-shouldering rods, manual sorting[8,37,41]
RecoveryAI-driven technologiesAI-enabled metal leaching, AI-enabled bio fenton[20,59,60]
Personalized technologiesCyber-physical system, digital twin[61,62]
Collaborative technologiesRecycling return estimator, electrostatic separation[22,63]
Conventional methodsShredding, grinding, acid leaching, open burning, smelting, blast furnace[7,8,40,41]
Table 3. Thematic cluster analysis for technology design.
Table 3. Thematic cluster analysis for technology design.
Thematic ClustersFactors Influence Technology DesignExemplary Studies
Device cluster
  • Battery life of smart tags
  • The capacity of robotic arms
  • Accuracy of signals over smart tags
  • Accuracy of signals over GPS
  • Probability of accidental separation of tags
  • Probability of deliberate separation of tags
  • Compatibility of technology accessories
[12,13,19,20,40,41,43,44,45,47,48,51,52,53,56,58,61,62,63,64,65,66,67,68,69,70]
Process cluster
  • Efficiency of the process
  • Accuracy of the processes
  • Data processing time
  • Data acquisition cost
  • Degree of non-destructiveness
  • Quality of processes
  • Ability to trace the lifecycle of e-waste
  • The effort in the process
  • Capacity to manage the e-waste volume
Organizational cluster
  • Degree of intelligent decisions
  • Ability to integrate processes
  • Cognitive abilities of technologies
  • Feasibility of collaborative robots
  • Possibility of workplace accidents
  • Attitude towards sustainable technologies
Macro cluster
  • Data security and privacy
  • Objectives of return strategies
  • Intellectual rights of product designs
  • Access to technical data of manufacturers
  • Availability of infrastructure facilities
  • Condition of e-products at EOL
  • Acceptance of technologies
Table 4. Thematic cluster analysis for technology adoption.
Table 4. Thematic cluster analysis for technology adoption.
Thematic ClustersFactors Influence Technology AdoptionExemplary Studies
Device cluster
  • Computational power of devices
  • Complexity of e-waste forms
[1,7,8,19,20,21,22,37,38,40,41,42,45,46,47,49,50,54,55,56,57,59,60,61,65,67,71,72,73,74,75,76,77,78,79,80]
Process cluster
  • Availability of data for the process
  • Ability to monitor e-waste movements
  • Credibility of the process
  • Risk to environment and health
  • Energy consumption in the process
  • Cost of data generation and acquisition
  • Recycling returns of e-waste
  • Operational cost of the process
  • Human errors during the process
Organizational cluster
  • Ability to substitute human workers
  • Capital requirement for technology
  • Cost of installation of technology
  • Technical knowledge of employees
  • Knowledge of sustainable technologies
  • Technical skills of workers
Macro cluster
  • Global e-waste schemes
  • Regulations of countries
  • Policies of countries
  • Attitude towards sustainable technologies
  • Knowledge of stakeholders
  • Degree of formality in businesses
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Udage Kankanamge, A.K.S.; Erdiaw-Kwasie, M.O.; Abunyewah, M. Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review. Sustainability 2024, 16, 6389. https://doi.org/10.3390/su16156389

AMA Style

Udage Kankanamge AKS, Erdiaw-Kwasie MO, Abunyewah M. Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review. Sustainability. 2024; 16(15):6389. https://doi.org/10.3390/su16156389

Chicago/Turabian Style

Udage Kankanamge, Amila Kasun Sampath, Michael Odei Erdiaw-Kwasie, and Matthew Abunyewah. 2024. "Towards a Taxonomy of E-Waste Urban Mining Technology Design and Adoption: A Systematic Literature Review" Sustainability 16, no. 15: 6389. https://doi.org/10.3390/su16156389

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