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Article

Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective

by
Madduma Kaluge Chamitha Sanjani Wijewickrama
1,*,
Nicholas Chileshe
1,2,
Raufdeen Rameezdeen
1 and
Jose Jorge Ochoa
3
1
UniSA STEM—Sustainable Infrastructure and Resource Management, University of South Australia, Adelaide, SA 5000, Australia
2
Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2094, South Africa
3
Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, SA 5000, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5493; https://doi.org/10.3390/su14095493
Submission received: 24 March 2022 / Revised: 20 April 2022 / Accepted: 29 April 2022 / Published: 3 May 2022

Abstract

:
Every year, the construction industry produces a large volume of demolition waste (DW) recovered through reverse logistics supply chains (RLSCs). Information-centric QA plays an important role in the RLSC, providing an excellent solution for inferior-quality reprocessed products. However, information deficiency creates epistemic uncertainties that lead to information-processing needs (IPNs) for QA, for which the internal stakeholders in the RLSC should respond by undertaking appropriate information-processing mechanisms (IPMs). Given this, the current study aims to explore how internal stakeholders process information for QA in RLSC of DW through the organizational information processing theory (OIPT) perspective. The study follows a qualitative approach encompassing 30 semi-structured interviews with internal and external stakeholders in the RLSC of DW. The study found eight uncertainties that stem from the internal organizational environment and two uncertainties caused by the interactions with stakeholders in the supply chain. In addition, 15 IPMs were identified, which the demolishers and waste processors could undertake in response to the epistemic uncertainties. The study developed an information-processing management framework that would serve practitioners and academics to understand how effectively process, people, policy and technology elements contribute to responding to the epistemic uncertainties for successful QA in RLSC of DW.

1. Introduction

In recent years, construction and demolition waste (CDW) has created detrimental issues over the environment, economy and society with rapid urbanization. It has been found that the construction industry contributes 40% of total waste globally per annum because of building construction and demolition works [1]. Herein, the amount of the demolition waste (DW) produced at the end-of-life (EoL) phase of the building is comparatively higher than the other means of construction-related waste [2]. The DW constitutes a heterogeneous mixture of surplus, damaged products and components that are mainly composed of inert and non-inert materials [3]. Amid the different waste management options available, the construction industry still prefers landfilling, which is comparatively more economical and convenient [4]. However, increasing levels of landfill levies have stimulated the construction industry to embrace alternative environmentally friendly ways of managing DW [5]. With this, the notion of ‘reverse logistics (RL)’ has captured the attention of the construction industry as a viable option to manage DW efficiently and effectively.
The RL is the reverse of the forward supply chain by making a closed-loop supply chain [6]. From the DW management perspective, the reverse logistics supply chain (RLSC) is a process that focuses on moving materials and related information from the point of dismantling buildings to the point of new construction to recapture the value of waste that would otherwise be disposed of at landfills [7]. The RLSC of DW is complicated by the involvement of multifaceted organizations, mainly the demolition and waste processing organizations [8,9]. These organizations excel in waste recovery by diverting waste from landfills and converting it into usable products to be introduced to the secondary market through reprocessing. Therefore, product quality is a significant win-or-lose factor for RL implementation in the construction industry.
Many previous studies pointed out that the negative perception of the end users of the quality of reprocessed products is a primary issue in the RLSC of DW [10,11,12]. For instance, Wijewickrama et al. [12] criticized that end users have a stigma about buying reprocessed products even if they have the expected quality because of the presence of the labels ‘reuse’ and ‘recycle’. However, Chileshe et al. [13] revealed that the industry experiences quality issues in reprocessed products. It is often argued that product quality primarily depends on how well the quality is assured within the production process [14]. With this, quality assurance (QA), a process-centered, systematic approach to determining whether the product meets the specified end-user requirements, plays an important role in the RLSC of DW [12]. The authors further emphasized that QA in RLSC is a system comprising four elements: people, process, policy and technology integrated within an information-centric environment.
However, it is a well-known fact through literature that the RLSC of DW does not benefit from a well-organized information flow [15,16,17]. For instance, Wijewickrama et al. [16] found that useful information, in some cases, is trapped in organizations and sometimes cannot even be found throughout the supply chain because of the fragmented, cross-functional and multidisciplinary nature of the RLSC of DW. Consequently, Wijewickrama et al. [17] mentioned that information sharing is particularly challenging in complicated supply chains like RLSC, which attempt to close the material loops. Herein, useful information means an organized body of data or knowledge that are relevant, accurate, timely and concise for making an array of decisions and guided actions [16]. Chileshe et al. [4] criticized that the useful information deficiency for stakeholders in the RLSC is a significant root cause for poor-quality reprocessed products. According to the seminal study by Galbraith [18], the circumstances when the useful information required to make decisions is lacking are known as ‘uncertainties’. The later studies defined uncertainties that arise from a lack of information or knowledge regarding how a system should behave, ultimately affecting its outcome as epistemic uncertainties, which could be reduced by processing the required information [19,20]. Since quality issues in reprocessed products are linked to a lack of information [4], it could be argued that the QA system is adversely affected by epistemic uncertainties in the RLSC of DW.
A holistic understanding of the information-centric QA in RLSC of DW is, consequently, presented by the organizational information processing theory (OIPT). The theory posits that uncertainties evolve from the lack of useful information, create information-processing needs (IPNs) to which the organizations should respond by adopting appropriate information-processing mechanisms (IPMs) [18,21]. The theory further argues that the extent to which these organizations’ IPNs and IPMs match determined the organizational performance. Recent studies have used this theory, underpinning empirical findings to provide useful insights into information processing in RLSC in the construction industry. For instance, van den Berg et al. [22] explained how demolition contractors coordinate the EoL activities of buildings based on the OIPT perspective. However, this study only focused on demolishers in the RLSC and not on the waste processors. Wijewickrama et al. [8] found that external stakeholders use seven influence strategies: regulating, monitoring, leading, incentivizing, demolition approval, forming contracts and specifications to provide useful information for QA in RLSC of DW. In extending this study, Wijewickrama et al. [9] asserted that when the influence strategies are not effectively undertaken, the useful information is not available for QA, creating macro-level uncertainties in the RLSC of DW. This study used an integrated lens of stakeholder theory and OIPT to explore the macro-level uncertainties encountered by RLSC for QA. However, this study only examined the epistemic uncertainties for QA, which are caused by the ineffective external stakeholders’ influence. In addition, the same study is limited in identifying the measures that minimize the macro-level uncertainties from the external stakeholder’s perspective but not from the point of internal stakeholders in the RLSC of DW. Due to all these limitations, further study is needed to provide insights into the complete information processing for QA in RLSC of DW [16]. Previous studies revealed that epistemic uncertainties also stem from the internal organizational environment (i.e., micro-level uncertainties) and the interactions with stakeholders within the supply chain (i.e., meso-level uncertainties) [23,24], other than macro-level uncertainties. Given this, a holistic investigation of the epistemic uncertainties for QA (excluding macro-level uncertainties) and how organizations in the RLSC respond to them become timely and relevant. On this note, the current study aims to explore how internal stakeholders process information for QA in RLSC of DW from the perspective of OIPT. This study tries to fill this aim by answering the following research questions:
RQ1. What epistemic uncertainties (excluding macro-level uncertainties) lead to IPNs for QA in RLSC of DW?
RQ2. What IPMs do organizations in the RLSC undertake in response to these epistemic uncertainties?
This study was conducted in south Australia (SA), the leader in CDW management in Australia [25]. It has been found that SA is the jurisdiction that made the highest contribution to the 67% of CDW recycling rate in Australia [26,27]. The diversion rate of CDW in SA was 91.4% in 2020, and the state is expected to achieve a 95% diversion rate by the end of the year 2025 [25]. Therefore, steering this study within such a context is ideal to obtain a valuable stream of knowledge regarding the information processing for QA that is due to the state’s innovativeness and experience in CDW management. The paper is structured as follows: Section 2 and Section 3 present the literature review and research methodology, respectively. Next, Section 4 outlines the findings of the study, followed by the discussion in Section 5 and the implications of the study in Section 6. Finally, Section 7 and Section 8 are sequentially arranged as the limitations and future research and conclusions of the study.

2. Literature Review

For this particular study, the literature review, in general, fulfils a number of functions. First, it presents the study’s theoretical background by demonstrating that the theoretical studies around OIPT have not provided a holistic understanding of information processing for QA with data from real-world projects. Second, it presents the empirical background of the QA in RLSC of DW by outlining that the empirical studies lack a sound theoretical perspective to explain what epistemic uncertainties are encountered for QA in RLSC of DW and how internal stakeholders respond to them.

2.1. Conceptualisation and Theoretical Basis: Organizational Information Processing Theory (OIPT)

OIPT provides a predominant theoretical lens to explain the importance of information processing in an organization. This theory evolved in the 1970s, specifically with an intra-organizational focus, in response to the organizational design problems under uncertainty [18]. Later it was also applied at an inter-organizational level to evaluate information processing between dyadic supply chains [28] such as RLSC of DW. The OIPT views organizations such as demolishers and waste processors as information processors under uncertainty. Here, the term ‘uncertainty’ is defined as “the difference between the amount of information required to perform the task and the amount of information already possessed by the organization” [18] (p. 5). As a foundation for this definition, many previous studies later asserted that epistemic uncertainties are the uncertainties caused by a lack of information [19,20]. The OIPT has developed with the basic proposition that the greater the epistemic uncertainty, the greater the amount of information the organizations should process to achieve a given level of performance [21]. In this regard, information processing is the key concept in OIPT. Information processing encompasses gathering, interpreting and synthesizing information for organizational decision-making [29]. According to Galbraith [21], organizations should undertake appropriate IPMs according to the type of epistemic uncertainty that the organization encounters. Herein, the theory posits that the IPNs should match with the IPMs of the organization to achieve performance in organizational activities like QA, whereas the mismatch jeopardizes the performance [18,21].
OIPT postulates that the organization’s external environment is resistant to change while the internal organizational processes are more adaptable [30]. Consequently, based on the OIPT, many IPMs have been put forward, which could be undertaken by internal organizations in response to the epistemic uncertainties. For instance, the seminal study by Galbraith [21] first mentioned three types of IPMs that an organization could undertake in response to the task uncertainties: ‘rules and programs’, ‘hierarchical referral’ and ‘goal setting’. Herein, the rules and programs are appropriate to provide a known response to predictable task uncertainties that have arisen in the past. When the exceptions to these rules increase, the epistemic uncertainties that evolve afterward would be resolved through hierarchical referral (i.e., the infrequent problems are referred up to the next level in the organizational hierarchy). Instead of specifying everything as rules and procedures, the organizations establish targets and goals for employees to decide the appropriate behaviors they should undertake during epistemic uncertainties that are not routine and predictable. According to Galbraith [21], when the epistemic uncertainties increase, the organizational hierarchy becomes overwhelmed and is left with two options: either reducing IPNs or increasing the information-processing capacity of the organization. To reduce IPNs, the organization could create slack resources (i.e., increasing the planning targets to encounter few exceptions) and/or self-contained groups (i.e., groups including all resources and capabilities for completeness). From these IPMs, only fewer exceptions are expected to occur and even if they occur, lesser factors need to be considered. Besides, to increase information-processing capacity, the organizations could invest in information systems and/or create lateral relations that enable it to process new information for task performance by avoiding the related epistemic uncertainties.
The OIPT that originated from the seminal work by Galbraith [18,21] is a relatively simple but sophisticated formulation [31] that has served as the theoretical foundation for many conceptual and empirical studies in different organizational contexts. However, from the construction industry perspective, only a few scholars have attempted to apply OIPT, including studies that quantify epistemic uncertainties in construction projects [32], identify and differentiate construction clients’ ability to manage epistemic uncertainties [33] and provide insights over the barriers to overcome inertia in client decision-making [34]. Despite the applications of OIPT in the forward supply chain, none of the studies have attempted to adopt it in the RLSC in the construction industry until the recent study by van den Berg et al. [22]. This study focused on EoL building coordination from the perspective of the demolition organizations. According to this study, building, workflow and environment are the epistemic uncertainties that affect the EoL building coordination of demolishers. Importantly, the study discovered a few context-specific IPMs that no previous study had disclosed hitherto (i.e., a collection of drawings, on-site inspections and prolongation of buyer collaboration). Wijewickrama et al. [9] also used OIPT together with stakeholder theory to identify macro-level uncertainties for QA in RLSC of DW. Even though this study has considered the entire RLSC, the focus is only on identifying the epistemic uncertainties that originate from the external environment (i.e., macro-level uncertainties) and measures that the external environment could undertake to minimize these uncertainties. The authors revealed that the internal stakeholders in a supply chain also encounter micro and meso-level uncertainties other than macro-level ones. Therefore, none of the previous studies viewed the holistic representation of information processing in RLSC of DW from an OIPT perspective. Consequently, the current study aims to fulfil this gap in the literature related to the OIPT.

2.2. The Reverse Logistics Supply Chains of Demolition Waste and Its Quality Assurance

The RL is the reverse flow of traditional logistics and this notion has emerged from the manufacturing industry as an established and contemporary supply-chain management concept. From the construction industry perspective, the RLSC starts with the waste generated during construction and demolition [35]. Since the DW accounts for more than 70% of total CDW [36], it is the type of waste that is mostly going through the RLSC in the construction industry. During the RL process, the waste materials could be either reused during the construction of buildings or in other work, or recycled and turned into new products used in construction [37]. If the waste has no economic value for use or is contaminated with hazardous substances, it is directly disposed of in an environmentally correct way.
The RLSC of DW is complicated because, in most cases, it encompasses more than one internal stakeholder representing different organizations with varied interests [38]. Generally, the demolition companies and subcontractors with whom the demolishers are working together are accountable for dismantling, collection, on-site sorting and transportation of salvageable waste to an off-site material recovery facility (MRF), while transferring the contaminated waste and waste with no value to an authorized landfill [22]. On the other hand, the waste processors are entitled to do waste processing and, in most cases, the marketing of reprocessed products [8,9]. Therefore, in general, and as in the case of SA, two organizations for demolition and off-site waste processing are involved in the RLSC of DW, whose cultures, interests, capacities and vulnerabilities contrast.
Previous studies pointed out that the end users have encountered quality issues in reprocessed products, which stimulated their negative perceptions over using them [11,13]. Herein, Chileshe et al. [13], through their empirical study conducted in SA, found that in most cases the high-quality reprocessed products are no longer available in the market because of the issues in quality control (QC) and compliance. Gee [39] stressed that QC is used to verify whether the quality of the output meets the product quality specification by doing various inspections and testing techniques. In contrast, QA relates to how well the process is performed to ensure a desired level of quality in the development, production or delivery of products [40]. Therefore, improving quality in a production process is more effective to prevent quality failures before they occur by assuring quality within the process rather than controlling it at the end [41]. In light of this, the QA process is more crucial to ensure the product quality in RLSC of DW.
Wijewickrama et al. [12] established that QA in RLSC acts as a system of four elements: people, process, policy and technology integrated within an information-rich environment, as shown in Figure 1. The process includes practices and approaches required specifically for QA in RLSC of DW. Experienced and knowledgeable people should undertake the QA practices and approaches by complying with the available policies while using new or available technologies. In addition, these people should always be collaborative and communicate with each other. The QA system should be equipped with innovative equipment and plants while getting support from information and communication platforms [4,12]. When high-tech machines, equipment and ICT-enabled platforms are incorporated into the QA system, both the people and process elements become efficient and effective [42]. From the policy perspective, the QA system should be well-framed within the requirements of current legislation, regulations, standards and guidelines [43,44]. Accordingly, the policies should be stringent to lead the process, people and technologies needed for QA in RLSC of DW. With this, it is comprehensible that all four elements in the QA system are integrated. Interestingly, Wijewickrama et al. [12] asserted that information is the source that integrates these four elements; thus, an information-rich environment should be available for QA in RLSC of DW. Still, the body of literature recurrently outlined that information deficiency is a serious issue in the RLSC of DW [4,15,16] because of the supply chain’s fragmented, unorganized, cross-functional and multidisciplinary nature [10]. Even though the unavailability of useful information, which negatively affects QA in RLSC of DW, is being criticized [4,16], none of the previous studies explain how this happens in detail and what mechanisms the internal stakeholders could undertake to process this deficient information. The current study is a progression in this direction.

3. Research Methodology

The methodological framework employed for this study includes four components, as shown in Figure 2. Accordingly, the first component discusses the research approach of the study. The second one concerns data collection, followed by data preparation and analysis. The final section discusses the strategies taken to improve the trustworthiness of the qualitative study. A detailed explanation of each of these components is presented in the following sub-sections.

3.1. Research Approach

Due to the exploratory nature of the topic, a qualitative research approach was selected to steer the study. According to Busetto et al. [45], the qualitative approach enables the understanding and uncovering of the holistic view, experiences and opinions over the phenomenon under study in greater depth. This approach is considered appropriate for studies in areas that are hardly being researched [46]. From an interpretivism stance, Merriam [47] asserted that “the key philosophical assumption upon which all types of qualitative research are based is the view that reality is constructed by individuals interacting with their social worlds” (p. 6). Furthermore, the authors averred, “reality is not an objective entity; rather, there are multiple interpretations of reality” (p. 22). It is comprehensible from this philosophical assumption that the primary interest of qualitative researchers is to understand the meaning or knowledge raised by people over the issue under study.
The RL is a notion that is barely being researched in the construction industry [11]. In addition, as revealed in Section 2, none of the existing studies provide a holistic view of the information processing for QA, which is an area developed based on two critical issues in RLSC of DW, i.e., (i) inferior quality of end products and (ii) information deficiency through the supply chain. Moreover, the QA in RLSC of DW is influenced by diverse internal and external stakeholders [9]; thus, there could be multiple interpretations of the reality. In light of this, the qualitative approach is appropriate to provide insights into the information processing for QA in RLSC of DW from the perspective of OIPT.

3.2. Data Collection

The study was carried out based on the qualitative approach using semi-structured interviews, which secure rich descriptions supporting the purpose of the study [48]. A total of 30 semi-structured interviews were conducted with both internal and external stakeholders in the RLSC of DW in SA. Of them all, 20 interviews were conducted with internal stakeholders representing building dismantling and off-site waste processing sectors. Besides, 10 interviews were conducted with external stakeholders representing regulatory sectors and the forward supply chain upstream (i.e., the builder or client to commence the demolition) and downstream (i.e., end users to use reprocessed products) organizations. These external stakeholders are not directly involved in the RLSC, but they affect or are affected by the operations in the RLSC of DW [44]. Therefore, they are attentive to the setting of the RLSC and its QA procedures [9,12]. Since external stakeholders exist peripheral to the internal supply chain, they are keener at pointing out the existing deficiencies of the supply chain than internal stakeholders. Therefore, incorporating both internal and external stakeholders ensures adequacy for building up the context behind information processing for QA in RLSC of DW. Table 1 outlines the details of the interviewees’ profiles.
Both purposive and snowball sampling techniques were used to incorporate participants into the study. Since the study’s population is not easily reachable and responsive, 11 seed participants were initially interviewed following the purposive sampling to begin the referral chain for the snowball sampling [49]. As shown in Table 1, all the interviewees had experience ranging from 8 to 32 years. The interviewees’ average experience is 18 years, and noticeably, 12 out of 30 had more than 20 years of experience. Examination of Table 1 further demonstrates that all the interviewees held senior positions in their organizations. Given this, the current study’s quality of data was improved by incorporating experienced and knowledgeable participants [50] who showed their interest in taking part in the study [51]. It was found that the interviewees did not properly respond when they were directly questioned about the uncertainties that they encountered for QA because of their unfamiliarity with the theory-based terminology, i.e., uncertainty. Most interviewees also remained clueless even after defining uncertainty because they could not recall at once the circumstances when they encountered information deficiencies. Therefore, in addition to questions directly enquiring about the uncertainties, indirect questions were also asked about the barriers they encounter for QA and their mechanisms to avoid them. With this approach, the interviewees became more relaxed and confident in pointing out the gaps in the existing supply chain. Therefore, the authors conducted the interviews to identify the gaps in the existing RLSC, which indirectly advocated them to explore the uncertainties that the RLSC encountered for QA.
According to Simms and Rogers [51], in a qualitative study, the sample size becomes trivial when the quality of data is rich. Qualitative studies are always being criticized for small sample sizes; however, Smitt [52] applauded that “rich knowledge and small samples purposefully chosen are thus unique strengths of qualitative research, not weaknesses” (p. 139). Simms and Rogers [51] asserted that the sample size of a qualitative study is determined by the concept of ‘saturation’, which refers to the point at which no new data is available to be added that will enhance or change the current findings of the study. Correspondingly, the interviews were carried out in the study until no new themes were obtained from the last two interviews, other than what was found in the initial interviews. Therefore, after doing 30 interviews, data saturation was achieved in the current study. The number of interviews is within the acceptable range of sample sizes that previous studies asserted as sufficient for reaching saturation in a qualitative study [53,54].
The COVID-19 pandemic disturbed the conduct of interviews; thus, the interviews lasted longer than anticipated (i.e., from September 2020 to May 2021). Herein, the interviewees were primarily questioned about (a) demographic data, (b) the epistemic uncertainties that the internal stakeholders encounter for QA and (c) what mechanisms the internal stakeholders could undertake in response to those uncertainties. During the interviews, the interviewees were encouraged to draw practical examples when explaining their views and discussing issues related to the QA in RLSC. The interviews were conducted following ethical considerations and protocols, and none reported any issues. Each interview lasted between 45–60 min and was recorded with the consent of the interviewee.

3.3. Data Preparation and Analysis

The qualitative data were obtained from the semi-structured interviews and were analyzed following the directed content analysis. According to Hsieh and Shannon [55], the directed content analysis describes a phenomenon supported by an existing theory or prior research that is incomplete or would benefit from further research. The five-step data analysis process based on Creswell and Creswell [56] guided qualitative data analysis. This process includes steps such as (i) organizing and preparing data for analysis, (ii) reviewing the data, (iii) coding all the data, (iv) generating a description of themes and (v) presenting the description and themes.
In the first step, the collected data from the interviews were combined and transcribed using audio-recorded files. After preparing the transcripts, they were again checked with the original audio to ensure accuracy. During the second step, an iterative review of transcripts was carried out to get familiar with the data and comprehend the broader meaning of interviewees’ excerpts. Further in this step, relevant notes were also taken while reading whenever a new idea or issue evolved from the data, thinking it would add an interesting finding for the study. The researchers created the preliminary open codes using NVivo 12 software in the third step. Then, these open codes were consolidated into similar groups and formed axial codes. As the fourth step, the axial codes were refined, reviewed and grouped to create selective codes, which are the final themes of the study. In the fifth step, the developed themes were presented with corresponding descriptions and narrative passages to provide comprehensive insights into the issue under investigation.

3.4. Trustworthiness of the Qualitative Study

To be accepted as trustworthy, the qualitative study should demonstrate the appropriateness of the research methodology to answer the research questions and the robustness of the data analysis [57]. There are four criteria that qualitative research should fulfil to ensure its trustworthiness: truth-value, applicability, consistency and neutrality [58,59].
According to Guba [58], truth-value means “the confidence in the truth of the findings” (p. 79). The current study encompasses interviewees who had an average experience of 18 years and held senior and decision-making positions in the external and internal organizations in the RLSC of DW. In addition, all of these interviewees had shown their interest in participating in this study. The relevant literature related to the OIPT was also used to confirm and support the findings from data collection. With all these measures, the researchers endeavored to augment the truth-value of the current study. Second, applicability indicates how the findings could apply in other settings or, simply, how findings could be generalized [58]. Generalizability is often outlined as a criticism rather than a strength in qualitative research [52]. Contrasting to the quantitative studies, the readers have more work appraising how the results could apply to new contexts in qualitative studies [60]. In light of this, generalizability in qualitative studies is named ‘reader generalization’ [52]. Though it is impossible to make a statistical generalization, qualitative research could be generalized by one or more combinations of naturalistic, transferability, analytical and intersectional generalizability [52]. The current study embraced naturalistic and inferential generalization through employing different strategies, as demonstrated in Table 2.
Data triangulation and audit trail contribute to enhancing the consistency and neutrality of the study [58]. Consistency means that if the study is repeated with the same research process, it has the possibility of producing the same results [59]. Further to the author, neutrality refers to how the study’s results avoid the researchers’ biases, opinions, interests and perspectives [58]. Both these criteria were fulfilled through an audit trail by giving a transparent description of the research, from the inception to the development and reporting of findings [61]. In addition, via data triangulation, the researchers demonstrated that the interview findings were consistent with previous literature related to OIPT.

4. Findings

As explained in Section 3.3, the semi-structured interviews were analyzed using the five-step data analysis process introduced by Creswell and Creswell [56]. The first two steps of this process provide a basis for data analysis (see Section 3.3). The directed content analysis was followed in steps three and four to derive the sequentially open, axial and selective codings of the study. A code is a basic information element extracted from the data, which can derive a meaningful proclamation regarding the phenomenon under study [62]. While codes could come from either an extant theory/literature or the collected data [22], they were derived from both of these sources in the current study. For instance, some of the IPMs are new because they are exclusively related to the RLSC of DW; thus, they could not be explained with concepts presented in the literature related to the OIPT (i.e., reflecting the inductive character of the study). Therefore, the final coding structure includes codes that not only originated from OIPT literature (e.g., “planning and goal setting”) but also from the data itself (e.g., “waste acceptance criteria/gate fee”). Then, the preliminary open codes were grouped into forming the potential axial codes. Herein, the open codes were reviewed one by one and identified the potential codes that could be grouped together under one category. In the fourth step, the axial codes were further grouped and formed selective codes, i.e., the study’s final themes. These final themes were reviewed and refined in the final step by cross-checking with the collected raw data [63]. This is an iterative process that helps identify new themes and avoid duplication in the existing themes. Last, the broad categories of themes were presented as excerpts, as in this section, to support the findings of the analysis. Accordingly, this section presents the epistemic uncertainties that lead to IPNs and corresponding IPMs for QA.

4.1. Epistemic Uncertainties That Lead to Information Processing Needs for Quality Assurance during Building Dismantling and On-Site Processing Stage

The current study found one meso-level uncertainty and four micro-level uncertainties encountered by demolishers for QA during the building dismantling and on-site processing stage. The following sections explain each of these uncertainties in detail.

4.1.1. Meso-Level Uncertainties for Quality Assurance

The demolition companies always employ subcontractors for their work that needs specialized expertise. According to Interviewee BD1, in most cases, the demolishers employ subcontractors for manual work, asbestos testing and removal, transportation, excavation, etc. The subcontractors advocate demolishers by bringing specialization to the company that it currently lacks. However, the ‘management of subcontractors’ has become an uncertainty for the demolishers. For instance, Interviewee BD4 explained: “A major issue that we encounter is that the understanding and importance given to assure the quality of work is not the same with every sub-contractor. We cannot guarantee that they would comply with our policies, procedures and guidelines.” Interviewee BD4 further mentioned that, in most cases, subcontractors maintain fewer interactions with the staff of the employer company. Due to this, sometimes they expose less commitment toward their work. Furthermore, Interviewee BD7 mentioned that he has experienced subcontractors whose working styles and approaches do not blend with their company; thus, their work has negatively affected the quality of their work.
So, I have had both good and bad experiences working with subcontractors. Sometimes, they say, they are doing the work. And when I go and have a look on them, and then some just don’t any sounds of it all, and I guess, smash everything. Cause time is money.
(Interviewee BD7)
Therefore, this indicates that on the one hand incorporating subcontractors has become a valuable addition for demolishers. On the other hand, it has become a meso-level uncertainty for QA of demolishers in RLSC.

4.1.2. Micro-Level Uncertainties for Quality Assurance

According to the interviewees, the difficulty to ascertain the ‘as-is condition of the building’ is a micro-level uncertainty experienced by demolishers in the RLSC. Interviewee BD5 mentioned that the demolishers get jobs to demolish buildings or structures that they are unaware of in advance. Thus, their job will be different from one project to another. Due to this, at the beginning of every project, the demolishers are going through the uncertainty over what would be the as-is condition of the building. Noteworthily, the as-is condition of the building could be uncertain for a demolisher not only at the beginning but also during the process of demolition.
Until we enter the site for a new project, we do not know how the building looks. It is quite a lot. So, we’ve just got to ascertain; and see what is involved. Most unusual things arise after opening the building during demolition, even after ascertaining. Therefore, the as-is condition of the building is always uncertain for us.
At the dismantling stage, ‘managing contaminated/hazardous substances’ is another micro-level uncertainty for demolishers. Interviewee BD4 acknowledged that many contaminated/hazardous substances could be present in a building, such as asbestos, rock wool, soil, lead-based materials, etc. Some of these substances could be easily recognizable, whereas others are invisible or hidden. For instance, Interviewee BD2 explained this as follows:
In houses, friable and non-friable asbestos is mostly hidden and difficult to recognize at once. Rock wool is another dangerous product that everybody is not much concerned about. This when you touch it’s a little fine. But now, when you remove them, a lot of dust and invisible loose fibres get contaminated with air, which will not be good for your eyes, mouth, skin and especially lungs. Rockwool should be treated like friable asbestos when you remove it.
Interviewee BD2 pointed out that rather than identifying contaminated/hazardous substances, their evacuation process is the most difficult and riskier. Interviewee BD7 revealed that in most cases, when the contaminated/hazardous substances containing building parts are damaged, dust and fibers could be released and become airborne. If anybody inhaled this dust or fibers, they could encounter serious diseases that can even be life-threatening. Therefore, Interviewee BD7 stressed that the building parts and even soil that is thought to contain contaminated/hazardous substances should not be disturbed, damaged or put into contact with other uncontaminated materials during the entire process from removal to disposal. Given this, Interviewee BD7 underpinned that “removing hazardous substances like asbestos needs more experience, knowledge and procedures; otherwise, it will end up in a menace”. Therefore, managing contaminated/hazardous substances is uncertain for demolishers; thus, they should undertake appropriate responses to overcome it.
Interviewees confirmed that ‘health and safety concerns’ are inevitable uncertainties experienced by demolishers during the dismantling and on-site processing stage. This uncertainty could arise from two factors: operational and external factors. From an operational perspective, the interviewees identified that the demolisher’s health and safety have been uncertain because of hazardous and/or contaminated substances. According to Interviewee BD5, inappropriate management of asbestos and other contaminated materials which contains airborne fiber that could cause harm to human health and safety. The nature of the dismantling operations has also caused uncertainty for the health and safety of the demolisher. For instance, Interviewees BD3, BD4, BD7 and SG2 stated that the entire demolition job is uncertain from the perspective of health and safety because workers on a demolition site are exposed to many hazards that vary depending on the nature of the operation: unplanned structure collapse, work at heights, movement of vehicles, plants and machinery, manual handling and noise and vibration. Herein, Interviewee SG2 asserted:
We are generally receiving many accident cases on demolition sites. Most of them are related to the uncontrolled collapse of the structure or part of the structure due to poor planning of the demolition process. Usually, the demolition sites are small and confined, and thus, the demolishers are exposed to more accidents.
From the external perspective, the interviewees pointed out that demolition is uncertain for the health and safety of adjacent properties, traffic and community. For instance, Interviewee BD1 revealed instances when the mechanical demolition caused slight structural damage to the adjacent properties. Interviewee BD1 further mentioned that complaints from neighbors over disturbances caused by noise and dust are very common in the demolition industry.
Interviewees confirmed the ‘workflow uncertainty of demolishers’ is due to the specific tasks carried out during this stage. This uncertainty has formed as a function of three constructs: (i) task variability, (ii) task interdependency and (iii) task analyzability. According to the interviewees, demolishers are vulnerable to several unanticipated events that demand different methods and procedures for doing the job (i.e., task variability). For instance, Interviewee BD9 mentioned that based on their experience, they could easily identify the locations where asbestos is present. However, there are circumstances in which they find asbestos unexpectedly during the mechanical demolition. Interviewee BD9 further explained this as:
We already know that asbestos is there. Therefore, before demolition, we remove that part first. First, or during, depending on how it’s set up. Sometimes you find it and sometimes don’t know it’s there. Anything is weird that has asbestos; we don’t touch it. Sometimes, we find asbestos from the footings formwork, which is not a common construction method, and this requires additional expertise and supervision to manage safely.
Additionally, Interviewee BD6 pointed out that, in some cases, a building cannot be dismantled as planned because of unexpected constructions that were not recognized through referring to drawings or visiting the site. To the same point, Interviewee BD7 added that sometimes after demolition they have ended up with unexpected loads of waste that could not be managed on-site. Moreover, Interviewee BD2 revealed that unpredicted adverse weather conditions have also been a prime cause for the poor quality of the demolisher’s work.
In the perspective of task interdependency, interviewees highlighted that the entire demolition work constitutes several interdependent sub-tasks and thus, the performance of a particular task depends upon one another. For instance, Interviewee BD5 elucidated:
Suppose we take the demolition of a house. The process should be services gets disconnected, and you need to start with the asbestos removal. Particularly, asbestos is done separately and as the very first task. Once the asbestos is done and cleared, we start salvaging, like timber joist, truss, roof tile, window and roof, bricks and concrete. After that, only the mechanical demolition will be taken place.
Herein, Interviewee BD5 stressed that the interruption in one of these tasks undeniably would disrupt the performance of the subsequent tasks. In terms of task analyzability, Interviewee BD3 criticized that all the tasks in a demolition project except asbestos management are not regulated by any of the legal instruments in SA. Therefore, most demolishers do not have an established procedure for other sub-tasks within their job, such as deconstruction (i.e., soft stripping), mechanical demolition and source separation. With this, the workflow of demolishers is uncertain from the perspectives of the task variability, interdependency and analyzability. Therefore, it is important to undertake appropriate IPMs in responding to the workflow uncertainty of a demolisher in RLSC of DW.

4.2. Epistemic Uncertainties That Lead to Information-Processing Needs for Quality Assurance during Off-Site Waste Processing Stage

From the waste processors’ perspective, the study found one meso-level and four micro-level uncertainties for QA for which the waste processors should undertake appropriate responses. The following sections present in-detail descriptions of them.

4.2.1. Meso-Level Uncertainties for Quality Assurance

According to the interviewees, ‘mixed waste from demolishers’ received at MRF is the meso-level uncertainty experienced by waste processors in the RLSC. Interviewee WP1 mentioned that his company only accepts concrete, plastic, metal and some green waste that could be easily classified. However, in some cases, they also receive contaminated mixed waste, which will not be accepted and redirected to a licensed landfill facility of the same company. If any contaminated substance is hidden in a waste load and then enters the MRF either intentionally or unintentionally, the entire business of the waste processor will be at risk. Interviewee WP7 stressed that mixed-waste loads, which contain hazardous substances such as asbestos, oxidizing materials and lead-based materials, make other materials also hazardous. Interviewee WP7 further revealed that some of the items like gas bottles and glass items that have not been removed from the waste load could even damage the plant and machinery of the waste reprocessing facility. Herein, Interviewee WP3 stated:
[…] but that’s more about what happens on-site. What we produce is a direct reflection of the material we receive. So, if we get poor-quality material in, our product will be poor quality; it’s not a magic store that we run. So, policy and procedure are crucial, but if the materials are handled on-site correctly at the demolition point, we will get quality material into our gate. Then we can make a quality product out of it.
Interviewee WP4 asserted that small and medium-scale demolishers mostly take more mixed waste to their MRF than large-scale demolishers. In the meantime, Interviewee WP4 highlighted that if they recognized a mixed-waste load, the remedial side is easier with large-scale demolishers who act more professionally when dealing with it than small and medium-scale demolishers.

4.2.2. Micro-Level Uncertainties for Quality Assurance

Most interviewees confirmed that the ‘product description complexities’ are a micro-level uncertainty encountered by the waste processors. Interviewee WP8 criticized that most reprocessed products’ functional and technical performances have not fulfilled the end-user requirements. Besides, Interviewees WP2, WP5 and WP9 pointed out that end users demand cheaper products over quality products in some circumstances. Interviewee WP9 mentioned that it is a significant challenge for waste processors to produce more affordable products because their production cost is considerably higher than that of virgin materials. Herein, Interviewee WP5 stated: “Of course, recycled products are a little more expensive, mainly because of the labor-intensive production process. Also, it has huge processing involved with waste taking, handling, sorting, processing and marketing. You know, all these are pretty expensive.” Interestingly, Interviewee UA2 mentioned that if the price is too low, end users are also reluctant to buy reprocessed products, assuming they are of inferior quality. Consequently, Interviewee WP4 pointed out that the waste processors are unaware of customer expectations over reprocessed products. Therefore, a significant mismatch exists between the demand and supply of reprocessed products. Interviewee WP7 stressed that the unavailability of standard specifications for reprocessed products had compounded waste processors’ uncertainty of understanding the end-user requirements for the reprocessed products. However, Interviewee WP7 further elaborated that complying with standard specifications would not completely avoid the product description complexities of waste processors. Instead, the waste processors require greater interaction with end users to accurately understand their technical, design and function requirements for the reprocessed products.
Interviewees WP1, WP4 and WP5 underpinned that waste processing companies are highly vulnerable to ‘human errors’ despite greater efforts in making the process perfect. In particular, Interviewee WP4 stated: “It’s mainly humans involved nearly 75–80%, so people make mistakes; we all make mistakes. Sometimes, things get through not very often, but we also, like I said, with any manual processes, an area where it can be, it can be a mistake.” Interviewee WP5 identified that human errors in his company had arisen primarily because of the forgetfulness, inattention, negligence and carelessness of workers. All these factors could be categorized as human-related factors. Interviewee WP8 emphasized that the nature of the job compounds the human errors during waste processing. For instance, Interviewee WP8 stressed that waste picking is the most labor-intensive stage in a waste processing plant. At this stage, workers constantly perform the same movements over an extended period, causing fatigue and mistakes. Interviewees WP3 and WP4 pointed out that organizational-related factors have also augmented human errors in waste processing. Herein, the interviewee WP3 explicated that poor procedures, inadequate knowledge, miscommunication and improper supervision could lead to human errors in waste processing; however, they are manageable.
Many interviewees highlighted that ‘health and safety concerns’ during waste processing are uncertain. As per the interviewees, the nature of waste reprocessing operations creates threats to the health and safety of workers in the plant and the external parties who live near the plant. Interviewee WP4 pointed out that most human injuries occur because of exposure to active plants and equipment during waste processing. Interviewee WP4 also revealed that the new workers to the MRF are mostly subjected to these types of accidents because they always try to work hard to impress their superiors. Interviewee WP2 shared some examples of accidents that he has observed in his facility, as follows:
We have many heavy machines operating outside and people walking around, so safety is a huge focus because we are not all sitting under desks. There is quite a good opportunity for people to get hurt, and that couples with using heavy machines and crushers. Another time a piece of the plant fell off, and the person thought it was metal coming through the product. So that is an absolute disaster how it happened, but it happened, and he got hurt on his back and was recovering for a couple of weeks.
An unexpected high risk exists for workers, especially those who engage in waste picking at a MRF because of the receipt of mixed waste blended with contaminated/hazardous substances from demolishers into the facility. Interviewee WP1 explained this as follows:
For instance, if rock wool is present, we cannot even recognize it; we cannot even touch it, but it is dangerous to everybody in the plant if exposed to air. Therefore, we make efforts not to enter any of these harmful materials into the plant, but there are some unexpected situations that we cannot avoid totally.
Interviewee WP6 stated that unsafe walkaways around the plant, prolonged repetitive working arrangements and inappropriate usage of personal protective equipment (PPE) are the potential sources of accidents ranging from mild injuries to fatal incidents in an MRF. Interviewee WP4 emphasized that noise, dust and fumes generated from the waste processing also endanger the health and safety of both workers and the neighborhood. Therefore, health and safety are always uncertain for waste processors, which they could not avoid but manage properly by undertaking appropriate IPMs.
Waste processors encounter ‘workflow uncertainties’ because their job constitutes a series of variable, interdependent and less analyzable tasks. For instance, Interviewee WP7 pointed out that sorting and segregating before reprocessing has sometimes become uncertain because of the hidden materials that go through the conveyor belts. Interviewee WP7 further mentioned that even if some materials like asbestos and rock wool are not permitted to enter the MRF, there are possibilities for them to enter the facility because of improper monitoring at the drop-off station. In the same way, there are circumstances when some large-sized rocks and bricks enter the plant, which cannot be crushed easily for further reprocessing.
The main uncertainty is hidden materials that we are not informed or aware of. For instance, we are vigilant about asbestos, chemicals, hazardous chemicals and oils because if some happen to swift through our system and go into our products, that would be the end of our business. Also, sometimes hidden bricks and rocks falling off the conveyors would damage our equipment.
(Interviewee WP7)
As an example of task variability, Interviewee WP4 mentioned that it is difficult to predict the incoming and outgoing amounts of materials and products in an MRF. Interviewees also highlighted that waste processing constitutes many sub-tasks that are interdependent; thus, the entire process will be disrupted if any issue arises at any stage. In addition, as the waste process is more mechanistic, the whole batch of products must be removed if any contaminated substance enters the plant. Additionally, Interviewee WP2 criticized that there is no known procedure for any other sub-task in this stage except for asbestos and hazardous management. Therefore, the workflow has become uncertain for waste processors in RLSC of DW because of the low task analyzability.

4.3. Information Processing Mechanisms for Quality Assurance in Reverse Logistics Supply Chains of Demolition Waste

The study found 15 IPMs that the internal stakeholders could undertake in response to the epistemic uncertainties for QA in RLSC of DW, as shown in Table 3. The following sections explain each of these IPMs as follows.

4.3.1. Information Processing Mechanisms of Both Demolishers and Waste Processors

The interviewees identified that demolishers and waste processors had developed ‘rules and programs’ to guide their work in response to the epistemic uncertainties. Interviewee WP7 pointed out that most demolishers and waste processors have obtained third-party accreditations for their organizations, which they have used as the guide to develop their in-house policies and procedures. Interviewee WP3 raised the importance of involving operational-level workers when developing these company-based policies and procedures, which does not essentially happen when regulatory bodies develop regulations and codes of practices. However, criticisms were raised by the interviewees, such as that “policies are nothing more than the statements” (Interviewee WP5) and an unnecessary cost for them (Interviewee BD8). In contrast, another cluster of interviewees highlighted that rules and programs are the ‘backbone’ of the organizations (Interviewee SG1), which advocate developing a ‘self-discipline’ (Interviewee BD8), as they stipulate the standard rules and guidelines that the demolishers and waste processors should follow to deliver quality output.
‘Planning and goal setting’ is an IPM that internal stakeholders are adopting in response to the epistemic uncertainties for QA. On the one hand, the demolishers do extensive planning to complete their jobs within the strict time and budget allocated. Interviewee BD1 pointed out that demolishers develop a demolition management plan, asbestos removal control plan, safe work method statement, safety checklist, environmental plan and resource allocation plan for each demolition job based on their company’s rules and programs. On the other hand, the waste processors have developed internal plans and set goals to produce quality reprocessed products in compliance with the in-house specifications. For instance, Interviewee WP4 asserted that they had developed a product-quality management plan based on their company’s quality policy, which outlined the approaches adopted by the company in manufacturing and supplying each reprocessed product to the industry.
Interviewees identified ‘business partnering’ as an IPM that internal stakeholders are undertaking to avoid epistemic uncertainties. Interviewee WP2 highlighted that large-scale companies have partnerships with several waste management companies because of SA’s limited government financing options (i.e., incentivizing uncertainties). For example, Interviewee WP2 explained:
We have partnerships for different projects with […]. All of us were bringing cash into the projects and, thereby, we were able to raise more capital to grow our business. Also, each of these companies has any specialization that we lack. Thus, we could expand our business using their know-how, technologies and experience.
Interviewees revealed that ‘forming contracts’ is also an IPM that internal stakeholders employ in response to epistemic uncertainties, especially the meso-level ones. Waste processors form contracts to inform the demolishers what sort of waste they will accept for their MRF and assigned gate fees. The gate fees in this type of contract are lower than the general gate fees, making a win-win situation for demolishers and waste processors. However, only large-scale waste processors adopt this mechanism with only a selected number of demolishers. Interviewee WP1 shared one of his experiences where 80% of the waste recovery was yielded from a demolition project by forming a contract: “In the past, we had a contract with […]. As per the contract, all of that demolition material was brought to us, and we processed that material into rubble, which went back to the site as the base for the new buildings.” Demolishers also form contracts with subcontractors, as in any form of subcontracting work in the construction industry. Interviewee BD4 articulated that a subcontract provides proof of what has been agreed and, thus, helps prevent disputes between parties. Therefore, forming a contract ensures that the subcontractors perform better to ensure their job quality.
Interviewee BD2 pointed out that they frequently have ‘group meetings’ at demolition sites, incorporating all the in-house employees and subcontractors to enhance coordination between tasks. Interviewee WP7 expressed that in addition to the toolbox and quality meetings, the waste processors have fortnightly management meetings to discuss the issues and progress of different departments in the company. These direct meetings enable the organization’s employees to exchange their opinions, perceptions and judgements in person. In addition, they permit the employees to coordinate with each other, which helps them avoid disruptions because of their firmly arranged task interdependencies.
Interviewees pointed out that ‘hierarchical referral’ is an IPM where internal stakeholders could use the chain of command in response to epistemic uncertainties, especially micro-level ones. For instance, Interviewee BD9 stated that the continuous supervision by senior management over the worker’s performance is essential to ensure that they are working in a safe environment. Similarly, Interviewee WP8 revealed that the top management of his waste-processing company follows an open-door policy encouraging employees to consult them whenever they encounter an issue over the plant. Herein, Interviewee WP8 explained:
We tend to run the company with an open-door policy; teams work on-site pretty much. I go to the site a fair bit; obviously, COVID made it a bit harder. But if somebody wants to ask a question, we’ll answer it where possible. And it’s still got that informal communication level.
The interviewees identified the ‘incorporation of self-contained groups’ as an IPM for QA in RLSC of DW. It is important to incorporate self-contained teams for demolition and waste processing, especially to avoid micro-level uncertainties. Therefore, frequent training, competency development and licensing are imperative for workers engaged in RLSC of DW. For instance, Interviewee BD1 revealed that the demolishers employ a separate self-contained team to manage contaminated/hazardous substances on-site. As per the regulatory requirements, all the workers involved in removing the asbestos-containing materials should be licensed in SA.
We have our trained group of people for managing hazardous materials. We usually have training every six months. We go in and assess the building or the structure; whatever we’re looking at, we assess that. And we make sure that we’ve identified hazardous materials in the particular building.
(Interview BD1)
Furthermore, ‘investment in technologies’ promotes information processing for task performance. For instance, Interviewees BD2 and WP3 pointed out that near-infrared spectrometers and air monitoring technologies ensure that the demolition and waste-processing premises are safe and free from asbestos. In addition, optical sorters (Interviewee WP1) and online analyzers (Interviewee WP8) are the newest technologies that waste processors incorporate to enhance the accuracy of the sorting and segregation of waste in their plants. These technologies, in turn, support confronting the micro-level uncertainties of both demolishers and waste processors.
The interviewees confirmed that the ‘creation of slack resources’ is an IPM for QA in RLSC. For example, Interviewee BD3 proclaimed that if they found that their job is likely to threaten health and safety, they would indeed reduce the progress of the work, even if it matters for their time and cost targets.
If we think it’s risky, we’re going to pull the wall down; if it’s a particularly high wall, if it’s a boundary wall, we’re going to bring that down by hand. So, without or with the machine, we can control the wall, so the wall doesn’t fall over and cause some damage to property or people.
Interviewee WP5 revealed that since the task interdependency is high in waste processing, there is a need for a waste processor to allocate sufficient space to stock waste either on or off the MRF. If not, the waste processors would experience difficulty managing the waste inflow that comes into the MRF if any disruption to the production process occurs because of either health and safety or any other serious concern.

4.3.2. Information Processing Mechanisms That Are Exclusive to Demolishers

According to the interviewees, the ‘collection of building documents and asbestos registers’ is an appropriate IPM that responds to the micro-level uncertainties of demolishers. Interviewee BD5 mentioned that if they have provided documents related to the building, such as drawings and specifications and the asbestos register of the building, they can ascertain much of the important information about the as-is condition of the building. Interviewee BD7 pointed out that thorough examination of drawings and the asbestos register also advocate managing contaminated/hazardous substances and thence the health and safety concerns of the demolition job. Interviewee BD1 stated that demolishers develop conceptual drawings if the drawings are not provided. Similarly, if the asbestos register is not available, Interviewee UA01 stated that: “the demolisher just gets an asbestos register prepared by an asbestos specialist consultant. Typically, another second sort of review is being done just in case this asbestos register is available but may not be 100% up to date or complete.”
The demolishers are also doing ‘site visits/pre-demolition audits’ to gather the required information for building dismantling. First, it would be helpful for the demolisher to offer a competitive bid for the dismantling project if the client/developer permits him to do a site visit before tendering. Interviewee BD1 highlighted that pre-demolition site visits and audits enabled them to effectively assess the dangers and plan their jobs accordingly. Second, after getting the job, the demolisher needs to do a site visit to understand the as-is condition of the building and the presence of hazardous materials. Interviewee BD2 explained below how he ascertains the as-is condition of the building during a site visit:
You need to conduct a site visit and measure everything in your mind. It needs experience. Now I always measure it. How it is calculated is you need to transfer the building or the demolition; whether it’s trees, bitumen, or building, you have to convert that to tons of cubic meters. This is my methodology.
On the same note, Interviewee BD3 echoed that repeated site visits and audits, even during the demolition, are more recommended in their company.
Interviewees identified ‘performance evaluation of subcontractors’ as an IPM for QA in RLSC of DW. Interviewee BD1 stated that demolition companies incorporate a lot of subcontractors from different work trades whenever they offer a demolition job. Therefore, before recruiting the subcontractors for the job, a comprehensive performance evaluation should be undertaken to ensure they possess the necessary skills.
I would just interview the people and see their general experience, how they look, and what they say. Whether they got good experience in what they do, how they’re going to treat their machines or our equipment, there are several things we look at, to see whether we want to have that person. They’ve got to have some experience to work with us.
(Interviewee BD1)
Interviewee BD4 mentioned that incorporating a skillful and experienced workforce guarantees that they possess the knowledge and are aware of the information required to do the job effectively and efficiently.

4.3.3. Information-Processing Mechanisms That Are Exclusive to Waste Processors

Interviewees revealed that waste-processing organizations are ‘creating integrator roles’ known as ‘accounts managers’ whose primary responsibility is to develop relationships with demolishers. Interviewee WP3 pointed out the importance of developing prolonged relationships with demolishers because they are the main input customers of a waste-processing company. On the one hand, since the accounts managers follow an educational approach, demolishers would become aware of the importance of adopting QA practices such as deconstruction and source separation. On the other hand, the accounts managers make sure for the waste processor that the demolisher is delivering a clean mix of the waste stream to the MRF.
Accounts Managers will get to the job site, ensuring that demolisher would come to us. Yeah, so they’re very proactive in that sense. So one, they’re responsible for setting the price for that customer, for that job. And two, they’re responsible for everything that happens with that. And the idea of that is you have to have good relationships with them so that when things go wrong, or things go right, you’ve got someone who understands their business and can mediate between our business and their business.
(Interviewee WP3)
The interviewees acknowledged that the ‘waste acceptance criteria/gate fee’ is an effective IPM that waste processors employ to ensure that they are not receiving mixed waste from demolishers. Interviewee WP6 explained that the waste acceptance criteria outline the different types of waste streams that the waste processor should accept based on the regulation and organizational requirements and their corresponding gate fee.
Waste acceptance criteria is a really strict protocol around what we can accept. We can’t accept soil, asbestos and green waste, but we could accept general waste. We have a landfill for contaminated soil. So again, if a customer comes over the weighbridge, we can’t accept all of that. There are rules around how much we can dispose of without being tested. So, we stick to those protocols. It is a 100 tons per site.
(Interviewee WP6)
On the other hand, the waste acceptance criteria acknowledge for the demolishers on what basis they should sort and deliver the waste to the MRF. Interviewee WP5 divulged that professional demolishers are always concerned and attempt to transfer waste based on waste acceptance criteria to avoid paying more money as gate fees to waste processors.
Whereas now on-site, demolishers segregate and stuff out and then make sure the loads go because they’d certainly not want to be paying us. They want to make sure that they pay the least possible for each trip. So, the demolition companies have gotten better at it through economic incentives. And conversely, it’s been less of an issue for us.
(Interviewee BD5)
Interviewees identified that obtaining feedback about the quality of reprocessed products from end users (i.e., ‘creation of special reports’) is a significant IPM to reduce the micro-level uncertainties of a waste processor. According to Interviewee WP6, developing specifications is important, yet not adequate to overcome the product description complexities. Given this, he raised the need for collecting feedback about the reprocessed products from the market to recognize the areas where improvements should be made in the production process. Interviewee WP1 shared one of the experiences when his company changed the production process because of continuous negative feedback from customers over a reprocessed product: “A while ago, our product was, people were saying it’s too dry. So, you have to supply a product at OMC, which is optimum moisture content. So, we had some feedback that our product was a bit dry, which impacts compaction. And we made some adjustments.”
With all these proclamations, it is evident that collecting feedback from the end users is an IPM for the waste processor to avoid issues in reprocessed products while improving their production process.

5. Discussion

This qualitative study has adopted a conceptual information-processing perspective to explain the information-centric QA in RLSC of DW with empirical data from 30 semi-structured interviews. Building upon the results from the directed content analysis, this section discusses the findings as follows in three sections: (i) epistemic uncertainties for QA, (ii) IPMs for epistemic uncertainties and (iii) information processing for QA in RLSC of DW.

5.1. Epistemic Uncertainties for Quality Assurance

The current study established that the internal stakeholders in the RLSC encounter different epistemic uncertainties stemming from the supply chain (meso-level) and internal organizational interactions (micro-level). Accordingly, each demolisher and waste processor in the RLSC encounter one meso-level and four micro-level uncertainties. This study was developed based on the recent study by Wijewickrama et al. [9], which was only limited to identifying the macro-level uncertainties for QA that evolve because of the lapses in the information-centric influence strategies of external stakeholders of the RLSC of DW. As an interesting finding, the current study found that all these three levels of uncertainties have interactions; thus, they have not appeared as distinct levels but as an onion diagram. Figure 3a,b demonstrate the onion diagrams, representing the multilayered context of the epistemic uncertainties of demolishers and waste processors, respectively.
Wijewickrama et al. [9] found three distinct macro-level epistemic uncertainties for QA in RLSC of DW: (i) regulatory uncertainties, (ii) incentivizing uncertainties and (iii) contractual uncertainties. Noteworthily, these three macro-level uncertainties are common to demolishers and waste processors. According to Figure 3a,b, the macro-level uncertainties are positioned at the outer layers of the onion, indicating that they are the root causes for the remaining meso- and micro-level uncertainties. As an interesting side of the findings, the regulatory uncertainties that evolve because of the ineffective aggressive influence of government agencies acting as root causes, propagating through incentivizing to the contractual uncertainties [9]. Given this, the macro-level uncertainties in the onion diagram were represented as three sections (see Figure 3a,b), indicating regulatory uncertainties as to the outer layer and subsequently the incentivizing and contractual uncertainties.
After macro-level uncertainties, the next layer of the onion diagrams indicates the meso-level uncertainties of each demolisher and waste processor (see Figure 3a,b). The study found that ‘management of subcontractors’ and ‘mixed waste from demolishers’ are the meso-level uncertainties experienced by demolishers and waste processors, respectively. Many authors asserted that subcontracting made quality management more difficult in the construction industry, especially because it is problematic, conflict-oriented [64] and absorbs profit at the different levels [65]. While adding a different view for this, the current study elaborated that subcontracting is an epistemic uncertainty for QA because the unavailability of information regarding the performance of sub-contractors has made their management difficult for demolishers. This study argues that the macro-level uncertainties, especially the regulatory and contractual uncertainties, augments the difficulties of managing subcontractors, which will, in turn, raise the micro-level uncertainties of the demolisher. On the other hand, Tennakoon et al. [7] asserted that the business link between demolisher and waste processor in the RLSC completely relies upon the bulks of the waste received from the demolishers. The current study found that both these organizations may meet a win-win situation if the receiving waste is clean and well-sorted. However, there are circumstances when waste processors receive contaminated or mixed waste because of the poor QA of demolishers and/or issues in the existing regulatory requirements. When mixed waste is received from demolishers, it is considered a meso-level uncertainty for waste processors that compounds their micro-level uncertainties. With this explanation, the position of meso-level uncertainties at the intermediate level of the onion diagrams is permissible, indicating that they influence the micro-level uncertainties while getting influenced by macro-level uncertainties.
In addition to meso-level uncertainties, the internal stakeholders encounter eight micro-level uncertainties as four per each demolisher and waste processor. Herein, ‘health and safety concerns’ and ‘workflow uncertainties’ are common to demolishers and waste processors. Corresponding with Banihashemi et al. [66], the current study asserted that depending on the product or material flows and aspects of different RL activities, implications for the health and safety dimensions differ for demolishers and waste processors. With this, the ‘health and safety concerns’ have created epistemic uncertainties to which the demolishers and waste processors should respond by undertaking suitable IPMs. van den Berg et al. [22] found that the demolishers encounter workflow uncertainties for EoL coordination that stem from the ability to assign skilled personnel (task capabilities) and the number of interdependent tasks (task interdependencies). However, the current study found ‘workflow uncertainties’ also affect the QA of not only demolishers but also of waste processors because of the nature of their RL activities and working procedures, i.e., the higher number of exceptions or unanticipated events (i.e., task variability), the extent of having the known procedure (task analyzability) and the extent of tasks dependent upon one other (task interdependencies). Furthermore, each demolisher and waste processor encounters two micro-level uncertainties exclusively particular to their job. Similar to what van den Berg et al. [22] found from the perspective of EoL coordination, the current study elaborated that a lack of information about the existing condition of the building (i.e., as-is condition of the building) is an epistemic uncertainty for the QA of demolishers. In addition, the current study emphasized that ‘management of contaminated/hazardous substances’ is also an epistemic uncertainty for demolishers that no previous study has disclosed hitherto. From the perspective of waste processors, since the production process of reprocessed products is highly labor-intensive, ‘human errors’ are frequent because of the unavailability of information and poor information sharing. Besides, the complexity of reprocessed products without standard specifications (i.e., product description complexities) also demands the waste processors to interact highly with and consolidate information from the end users.
In Figure 3a,b, the micro-level uncertainties appeared at the inner layers of the onion diagrams because they are getting influenced by both macro- and meso-level uncertainties. Noteworthily, the study found that these micro-level uncertainties are interrelated; thus, they have cause-and-effect relationships. For instance, when considering the micro-level uncertainties of the demolisher, if the as-is condition of the building is unable to be ascertained, the management of contaminated/hazardous substances will undeniably be uncertain for demolishers. Furthermore, if the management of contaminated and/or hazardous substances is not properly performed, health and safety will certainly be concerns for demolishers. If all these uncertainties exist, there is no way for the workflow to be smooth and effective for demolishers. In the same way, if the product specifications are unavailable, there is a high possibility of increasing human errors, and thereby the health and safety concerns and workflow uncertainties of the waste processors. Given this, the micro-level uncertainties of each demolisher and waste processor are positioned layer by layer in the onion diagrams, indicating how they relate to each other.

5.2. Information Processing Mechanisms for Epistemic Uncertainties

The study posits 15 diverse IPMs that the internal stakeholders could deploy in response to the macro-, meso- and micro-level uncertainties. Complying with the definition of information processing by Tushman and Nadler [29], these IPMs can be described as the mechanisms that the internal stakeholders undertake to gather, interpret and synthesize information to respond to the epistemic uncertainties successfully. Wijewickrama et al. [9] highlighted three measures: (i) reforming regulatory instruments, (ii) employing effective incentivizing schemes and (iii) active employment of forward supply chain actors to minimize macro-level uncertainties from the external stakeholders’ perspective. Herein, the authors argued that it would be effective to minimize macro-level uncertainties at their source rather than before they become a menace to the internal stakeholders. However, Namada [67] claimed that organizations should always scan their environments to understand their changes, new technologies and changing customers and adapt accordingly. Correspondingly, Wijewickrama et al. [9] criticized that even if the external stakeholders undertake mechanisms at their source to avoid macro-level uncertainties for QA, there is no guarantee of evading them completely. Still, the study has not underpinned how internal stakeholders in the RLSC of DW could respond to macro-level uncertainties [9]. In shedding light on this, the current study found that in response to the macro-level uncertainties, both demolishers and waste processors could develop internal policies, procedures, guidelines and frameworks (rules and programs), plan extensively and define targets (planning and goal setting) and make alliances with each other (business partnering). Noteworthily, it was also found that these three IPMs could be undertaken in response to all the macro-, meso- and micro-level uncertainties of demolishers and waste processors. Previous authors highlighted that IPMs such as ‘rules and programs’ and ‘planning and goal setting’ are appropriate to respond to known issues that recurrently occurred in the past [18,31]. The current study compounded this proposition and underpinned that these IPMs are also suitable for responding to uncertainties that originate for reasons beyond the control of internal stakeholders, such as macro-level uncertainties.
The study found that demolishers and waste processors form contracts in response to their meso-level uncertainties, other than undertaking common IPMs such as rules and programs, planning and goal setting and business partnering. Previous studies highlighted that, on the one hand, forming contracts is an information-centric influence strategy that provides essential information [8], while on the other hand, lapses in this strategy would create uncertainties because of the missing information [9]. Therefore, it is important to underpin that while forming contracts is an appropriate IPM, there is also a tendency to be uncertain if it is not properly compiled. In addition, there are IPMs particular to demolishers and waste processors that could be taken on against their meso-level uncertainties. For instance, ‘group meetings’ and ‘performance evaluation of subcontractors’ are acceptable to undertake in response to the difficulties in managing subcontractors. On the other hand, implementing a strict ‘waste acceptance criteria/gate fee’ and appointing ‘integrator roles’ (accounts managers) are effective IPMs that waste processors could undertake in response to their meso-level uncertainty, i.e., ‘mixed waste from demolishers’.
There are many IPMs other than ‘rules and programs’, ‘planning and goal setting’ and ‘business partnering’ that the demolishers and waste processors employ in response to their micro-level uncertainties. Herein, the current study found and corroborated with van den Berg et al. [22] that the ‘collection of building documents and asbestos registers’ and ‘site visits/pre-demolition audits’ are IPMs that only the demolishers undertake in response to the micro-level uncertainties. On the other hand, ‘creation of special reports’ (i.e., studies and/or surveys that gather data about an issue) is an IPM that the waste processors employ in response to their micro-level uncertainties, especially the product description complexities. In addition, there are many IPMs that both demolishers and waste processors undertake in response to the micro-level uncertainties such as ‘group meetings’, ‘hierarchical referral’, ‘incorporation of self-contained groups’, ‘investment in technologies’ and ‘creation of slack resources’. Noteworthily, Galbraith [18,21] described all these IPMs as organizational design actions that advocate the stakeholders respond successfully when the uncertainty level increases. The authors further mentioned that some of these IPMs, such as ‘creation of slack resources’ and ‘incorporation of self-contained groups’, reduce the amount of information to be processed. On the other hand, the remaining IPMs increase information-processing capacities. Furthermore, Daft and Lengel [31] underpinned that some IPMs such as ‘group meetings’ and ‘hierarchical referral’ are face-to-face (i.e., personal) IPMs that use rich media to respond promptly, particularly depending on the issue. Therefore, those IPMs are more effective to implement when the uncertainty level increases since they enable immediate feedback.
Figure 4 presents the summary of the discussion under this section. As shown in Figure 4, rules and programs, planning and goal setting and business partnering are less rich and impersonal (i.e., do not have any reference or connection to a particular person) IPMs that provide standard information [31] for internal stakeholders in the RLSC of DW. Therefore, they are applicable to undertake, especially in response to the macro-level uncertainties that are recurrent and/or beyond the control of internal stakeholders. However, they also have an effect on the subsequent meso- and micro-level uncertainties. As an interesting finding, it is clear that when moving from macro-level to meso- and micro-level uncertainties, the communication media of IPMs increases the capacity to process rich information that is prompt and situational.

5.3. Information Processing for Quality Assurance in Reverse Logistics Supply Chains of Demolition Waste

With the urgent need to yield quality reprocessed products in the construction industry, this qualitative study contributed insights into improving QA in RLSC of DW through information processing. Building upon the findings from the directed content analysis, the study developed an information-processing management framework for QA in RLSC of DW, as shown in Figure 5.
According to Figure 5, three types of epistemic uncertainties, such as macro-, meso- and micro-level uncertainties, create IPNs for QA in RLSC of DW. Noteworthily, three macro-level uncertainties are common to both demolisher and waste processor. As shown in Figure 5, the regulatory uncertainties propagate their influence via incentivizing uncertainties to the contractual uncertainties [9]. The macro-level uncertainties have impacted the meso- and micro-level uncertainties of both demolisher and waste processor (see Section 5.1). Each internal stakeholder encounters a meso-level uncertainty that is particular to their business, which has impacted their corresponding micro-level uncertainties. As in Figure 5, the micro-level uncertainties are also related and affect one another (see Section 5.1). Noteworthily, the study established that the compounded effect of the demolisher’s epistemic uncertainties has a significant impact on enhancing waste processors’ epistemic uncertainties. This is because the poor QA of demolishers directly causes the waste processor’s meso-level uncertainty, i.e., ‘mixed waste from demolishers’.
Wijewickrama et al. [9] found three measures to minimize macro-level uncertainties from the perspective of the external stakeholders (see Figure 5). In contrast, as per the arguments in the current study, the internal stakeholders should confront macro-level uncertainties by undertaking internally developed mechanisms: ‘rules and programs’, ‘planning and goal setting’ and ‘business partnering’. Similarly, Figure 5 shows the IPMs that could be undertaken in response to the meso- and micro-level uncertainties of demolishers and waste processors, which are also discussed in Section 5.2. As an interesting finding, the study found that these IPMs represent each of the elements in the QA system: process, people, policy and technology. An information-rich environment should be available to integrate the process, people, policy and technology elements of the QA system [16]. However, while being corroborated with Wijewickrama et al. [16], the current study elucidated a new interpretation as the attributes that were considered in each of the elements of a QA system are regarded as mechanisms that enable information processing to confront epistemic uncertainties for QA in RLSC of DW. In light of this, the current study empirically proved that an information-rich environment is needed to evade epistemic uncertainties that confront effective and efficient implementation of QA in RLSC of DW. Noteworthily, the integrated system of process, people, policy and technology is responsible for creating this information-rich environment for QA in RLSC of DW.

6. Implications for Theory and Practice

This paper provides new insights explaining information processing for QA in RLSC of DW. As such, it has a plethora of theoretical and empirical implications, explained as follows.

6.1. Theoretical Implications

Despite many theoretical studies around the OIPT (e.g., [22,32,33,34]), none has provided a holistic understanding of information processing for QA, which is a significant criterion for operational performance in any supply chain [40]. On the other hand, the existing empirical studies around RLSC have not used a sound theoretical perspective to explain the epistemic uncertainties in the supply chain; it has even been found that the information deficiency is a significant cause for quality issues in reprocessed products [4]. In this regard, the current study focused on addressing these gaps in the literature by exploring how internal stakeholders process information for QA in RLSC of DW from the perspective of OIPT. Given this, many significant theoretical implications could be deduced from the results of the current study.
First, the study advances the previous research that outlined how information deficiency severely impacts quality efforts [4,68] with a concrete understanding of how this happens (i.e., epistemic uncertainties) and how internal stakeholders cope with such deficiencies (i.e., IPMs) by considering the RLSC of DW. Herein, the information processing perspective introduced by the current study could be adapted to explain the effect of information deficiency on any performance criteria within different contexts in the real world. Second, the current study also advances previous research that used theoretical underpinnings in explaining the information deficiency within the RLSC of DW [9,22] by providing a holistic view of information processing, including all the epistemic uncertainties (i.e., macro-, meso- and micro-level uncertainties) and IPMs of both demolisher and waste processor. In addition, the current empirical study suggested some supplements to the literature based on OIPT. That is, third, the study added three sources of uncertainties based on the context of RLSC of DW that have not been identified hitherto: ‘management of contaminated/hazardous substances, ‘health and safety concerns’ and ‘human errors’. Fourth, it advances the literature on OIPT by outlining organizational responses (i.e., IPMs) to the epistemic uncertainties that are particular to the context of RLSC of DW: ‘performance evaluation of sub-contractors’, ‘business partnering’, ‘waste acceptance criteria/gate fee’ and ‘forming contracts’. Even though many previous studies on OIPT identified epistemic uncertainties and their corresponding IPMs (e.g., [22,28,29]), none found how they relate. In light of this, as the final implication, the current study found that the macro-level uncertainties propagate through meso-level to micro-level uncertainties. Importantly, the uncertainties within macro- and micro-levels are also orderly influences on one another (see Section 5.2).

6.2. Practical Implications

As for the practical implications, the study provides a holistic understanding of information-centric QA for demolishers and waste processors by developing an information-processing management framework, as depicted in Figure 5. According to Dale et al. [69], a management framework can act as a mechanism for improving the integration of science (i.e., knowledge) into policy and practice. Similarly, the management framework in the current study informs the internal stakeholders in the RLSC of the epistemic uncertainties that confront the successful implementation of QA in their organizations. Through pursuing the framework, the internal stakeholders could absorb that these uncertainties do not independently appear on occasion; instead, they could arise gradually as a chain of uncertainties since they are related to each other (see Figure 3a,b). For instance, the demolisher should understand that when the as-is condition of the building is uncertain, there is a high tendency for the management of contaminated/hazardous substances to be uncertain. Furthermore, the framework also demonstrates how internal stakeholders could cope with different epistemic uncertainties. Accordingly, 15 IPMs were found that could be undertaken in combination in response to each uncertainty type. Even though the external stakeholders are accountable for minimizing the occurrence of macro-level uncertainties at their source [9], the current study stressed that the internal stakeholders should respond to them internally, undertaking appropriate IPMs. In this regard, the internal stakeholders should understand that some IPMs (i.e., rules and programs, planning and goal setting and business partnering) are standard and impersonal, providing solutions for recurrent and/or beyond-control problems. However, they are significant to undertake because the other IPMs could only become successful with them. The study also underpinned that when compounding uncertainties from the macro- and meso- to micro-levels, the IPMs undertaken should be rich in media and personal to provide situational and prompt information. Finally, the study posits that all the IPMs stem from the process, people, policy and technology elements in the organization; thus, they should be robust and resilient. With this, the study implies that rethinking attributes that were considered under each of the above elements as information-processing activities can thus help internal stakeholders to find the mechanisms that appropriately respond to the epistemic uncertainties for QA in RLSC of DW.

7. Limitations and Future Research

The current study holds some limitations. First, the results of this study should be absorbed considering the limitations of a qualitative study. Qualitative studies have been widely criticized for their inability to be generalized into other contexts [52]. However, as explained in Section 3.4, the current study’s findings could be generalized into different settings through transferability and naturalistic generalizations. Even if it is not the purpose of a qualitative study, the inability to make statistical generalizations is a limitation of the current study. In response, it is recommended to do future study by deriving the quantitative measurements of the identified key concepts and extending the analyses by testing those in other contexts where information deficiency is a significant issue. Second, the study is limited to SA with reasonable justifications (see Section 1). Future studies could apply the current study’s findings to varied geographical contexts while distinguishing the differences in the results based on varied technical, legal and socio-economical attributes between SA and those of the alternate context of interest. Third, the current study found that the macro-level uncertainties propagate through meso- to micro-level uncertainties. In addition, it was revealed that the uncertainties within the macro and micro levels are also related to each other. Given this, the current study stressed interactions between uncertainties; thus, a quantitative approach to modeling epistemic uncertainties while examining their interdependent influences would be an interesting future research area.

8. Conclusions

With the growing attempts to promote reprocessed products in secondary markets, there is a timely need for better insight over the epistemic uncertainties that confront successful QA in RLSC of DW. Nevertheless, no concrete study has hitherto been done on investigating information processing for QA in RLSC of DW. While taking this into account, the current study aims to explore how internal stakeholders process information for QA in RLSC of DW through elaborating on OIPT. The current study was undertaken to fulfil this aim by answering the two research questions: (i) What epistemic uncertainties (excluding macro-level uncertainties) lead to IPNs for QA in RLSC of DW? and (ii) What IPMs are the organizations in the RLSC undertaking in response to these epistemic uncertainties? Herein, the study steered a qualitative study encompassing 30 interviews with internal and external stakeholders in the RLSC of DW.
Since the typical RLSC of DW is a dyadic supply chain, the current study completely builds upon the conceptualization that the demolishers and waste processors are information-processing systems that encounter epistemic uncertainties. With this, in response to the first research question, the study found that each internal stakeholder is experiencing one meso-level uncertainty and four micro-level uncertainties, other than the three macro-level uncertainties (i.e., regulatory, incentivizing and contractual uncertainties) found in the previous study by Wijewickrama et al. [9]. Accordingly, ‘management of sub-contractors’ and ‘mixed waste from demolishers’ are the meso-level uncertainties encountered by demolishers and waste processors, respectively. As micro-level uncertainties, the demolishers encounter ‘as-is condition of the building’, ‘management of contaminated/hazardous substances’, ‘health and safety concerns of the demolisher’ and ‘workflow uncertainties of the demolisher’, while waste processors encounter ‘product description complexities’, ‘human errors’, ‘health and safety concerns of the waste processor’ and ‘workflow uncertainties of the waste processor’. As an interesting finding, the study found that these epistemic uncertainties no longer exist independently but propagate and compound accordingly from macro-level uncertainties to meso- and micro-level uncertainties.
As for the second research question, the study found 15 IPMs that the internal stakeholders could undertake in response to the epistemic uncertainties for QA in RLSC of DW. Some IPMs are common to demolishers and waste processors, while others are particular. It was found that demolishers and waste processors are undertaking a few IPMs (i.e., rules and programs, planning and goal setting and business partnering) in response to all the epistemic uncertainties irrespective of whether they originate from the macro-, meso- or micro-levels. However, these IPMs are impersonal and provide standard solutions for recurrent and/or beyond-control issues. The media richness of IPMs, i.e., personally provision of situational and prompt information, enhances when moving from macro-level to meso- and micro-level uncertainties. As an interesting finding, the study found that all the IPMs represent any of the elements of the QA system. With this, the study posited that the integrated system of process, people, policy and technology is accountable for creating an information-rich environment that successfully responds to epistemic uncertainties. Based on all these findings, the study developed an information-processing management framework (see Figure 5), which would serve as the starting point for practitioners and academics to have insights over how effectively the process, people, policy and technology elements contribute to responding to the epistemic uncertainties for successful QA in RLSC of DW.
Overall, this study offers theoretical and empirical contributions, which can be outlined as follows. As mentioned by Ågerfalk [70], a theoretical contribution is about advancing the prevalent understanding of concepts and interrelationships in an extant theory. Therefore, one of the main contributions of this study is that it advances the existing understanding of the OIPT by applying the theory in addressing a practical problem of ‘how internal stakeholders process information for QA in RLSC of DW’. In this regard, the study supplemented the theory by introducing several new sources of uncertainties (e.g., management of contaminated/hazardous substances, health and safety concerns and human errors) and organizational responses to them (performance evaluation of subcontractors, business partnering, waste acceptance criteria/gate fee and informing contracts) that are particular to the context of RLSC of DW. The study revealed interesting interrelationships between epistemic uncertainties that have not been identified hitherto in previous studies around OIPT. Regarding empirical contributions, the study presented an information-processing management framework for internal stakeholders in the RLSC, enabling them to understand the epistemic uncertainties they may encounter in the job that hinder their QA efforts. Additionally, they can decide what mechanisms they should undertake in response to the epistemic uncertainties using this framework. As a result, this management framework assists the managers in RL organizations in decision-making to enhance the QA in their businesses.

Author Contributions

Conceptualization, M.K.C.S.W., N.C., R.R. and J.J.O.; methodology, M.K.C.S.W., N.C., R.R. and J.J.O.; validation, N.C., R.R. and J.J.O.; formal analysis, M.K.C.S.W.; writing—original draft preparation, M.K.C.S.W.; writing—review and editing, N.C., R.R. and J.J.O.; visualization, M.K.C.S.W.; supervision, N.C., R.R. and J.J.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data generated or analyzed during the study are available from the corresponding authors by request.

Acknowledgments

The authors would like to acknowledge the Australian government’s financial support through an Australian Government Research Training Program (RTP) Scholarship for PhD studies and support from the University of South Australia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Jin, R.; Li, B.; Zhou, T.; Wanatowski, D.; Piroozfar, P. An empirical study of perceptions towards construction and demolition waste recycling and reuse in China. Resour. Conserv. Recycl. 2017, 126, 86–98. [Google Scholar] [CrossRef]
  2. Yu, B.; Wang, J.; Li, J.; Zhang, J.; Lai, Y.; Xu, X. Prediction of large-scale demolition waste generation during urban renewal: A hybrid trilogy method. Waste Manag. 2019, 89, 1–9. [Google Scholar] [CrossRef]
  3. Hossain, M.U.; Wu, Z.; Poon, C.S. Comparative environmental evaluation of construction waste management through different waste sorting systems in Hong Kong. Waste Manag. 2017, 69, 325–335. [Google Scholar] [CrossRef]
  4. Chileshe, N.; Jayasinghe, R.S.; Rameezdeen, R. Information flow-centric approach for reverse logistics supply chains. Autom. Constr. 2019, 106, 102858. [Google Scholar] [CrossRef]
  5. Bao, Z.; Lu, W.; Chi, B.; Yuan, H.; Hao, J. Procurement innovation for a circular economy of construction and demolition waste: Lessons learnt from Suzhou, China. Waste Manag. 2019, 99, 12–21. [Google Scholar] [CrossRef]
  6. Govindan, K.; Bouzon, M. From a literature review to a multi-perspective framework for reverse logistics barriers and drivers. J. Clean. Prod. 2018, 187, 318–337. [Google Scholar] [CrossRef]
  7. Tennakoon, G.A.; Rameezdeen, R.; Chileshe, N. Diverting demolition waste toward secondary markets through integrated reverse logistics supply chains: A systematic literature review. Waste Manag. Res. 2021, 40, 274–293. [Google Scholar] [CrossRef]
  8. Wijewickrama, M.K.C.S.; Chileshe, N.; Rameezdeen, R.; Ochoa, J.J. Information-centric influence strategies for quality assurance in reverse logistics supply chains: External stakeholders’ perspective. Benchmarking Int. J. 2021. [Google Scholar] [CrossRef]
  9. Wijewickrama, M.K.C.S.; Rameezdeen, R.; Ochoa, J.J.; Chileshe, N. Minimizing Macro-Level Uncertainties for Quality Assurance in Reverse Logistics Supply Chains of Demolition Waste. Sustainability 2021, 13, 13069. [Google Scholar] [CrossRef]
  10. Chileshe, N.; Rameezdeen, R.; Hosseini, M.R. Drivers for adopting reverse logistics in the construction industry: A qualitative study. Eng. Constr. Archit. Manag. 2016, 23, 134–157. [Google Scholar] [CrossRef]
  11. Pushpamali, N.N.C.; Agdas, D.; Rose, T.M.; Yigitcanlar, T. Stakeholder perception of reverse logistics practices on supply chain performance. Bus. Strategy Environ. 2021, 30, 60–70. [Google Scholar] [CrossRef]
  12. Wijewickrama, M.K.C.S.; Chileshe, N.; Rameezdeen, R.; Ochoa, J.J. Quality assurance in reverse logistics supply chain of demolition waste: A systematic literature review. Waste Manag. Res. 2021, 39, 3–24. [Google Scholar] [CrossRef] [PubMed]
  13. Chileshe, N.; Rameezdeen, R.; Hosseini, M.R.; Lehmann, S.; Udeaja, C. Analysis of reverse logistics implementation practices by South Australian construction organizations. Int. J. Oper. Prod. Manag. 2016, 36, 332–356. [Google Scholar] [CrossRef]
  14. Kotsanopoulos, K.V.; Arvanitoyannis, I.S. The role of auditing, food safety, and food quality standards in the food industry: A review. Compr. Rev. Food Sci. Food Saf. 2017, 16, 760–775. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Rahimi, M.; Ghezavati, V. Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (CVaR) for recycling construction and demolition waste. J. Clean. Prod. 2018, 172, 1567–1621. [Google Scholar] [CrossRef]
  16. Wijewickrama, M.K.C.S.; Chileshe, N.; Rameezdeen, R.; Ochoa, J.J. Information Sharing in Reverse Logistics Supply Chain of Demolition Waste: A Systematic Literature Review. J. Clean. Prod. 2021, 280, 124359. [Google Scholar] [CrossRef]
  17. Wijewickrama, M.K.C.S.; Rameezdeen, R.; Chileshe, N. Information brokerage for circular economy in the construction industry: A systematic literature review. J. Clean. Prod. 2021, 313, 127938. [Google Scholar] [CrossRef]
  18. Galbraith, J. Designing Complex Organizations; Addison-Wesley: Reading, UK, 1973. [Google Scholar]
  19. Oberkampf, W.L.; DeLand, S.M.; Rutherford, B.M.; Diegert, K.V.; Alvin, K.F. Error and uncertainty in modeling and simulation. Reliab. Eng. Syst. Saf. 2002, 75, 333–357. [Google Scholar] [CrossRef]
  20. Walker, W.E.; Harremoës, P.; Rotmans, J.; van der Sluijs, J.P.; van Asselt, M.B.A.; Janssen, P.; Krayer von Krauss, M.P. Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model Based Decision Support. Integr. Assess. 2003, 4, 5–17. [Google Scholar] [CrossRef] [Green Version]
  21. Galbraith, J.R. Organization design: An information processing view. Interfaces 1974, 4, 28–36. [Google Scholar] [CrossRef] [Green Version]
  22. Van den Berg, M.; Voordijk, H.; Adriaanse, A. Information processing for end-of-life coordination: A multiple-case study. Costr. Innov. 2020, 20, 647–671. [Google Scholar] [CrossRef]
  23. Busse, C.; Meinlschmidt, J.; Foerstl, K. Managing information processing needs in global supply chains: A prerequisite to sustainable supply chain management. J. Supply Chain Manag. 2017, 53, 87–113. [Google Scholar] [CrossRef] [Green Version]
  24. Premkumar, G.; Ramamurthy, K.; Saunders, C.S. Information processing view of organizations: An exploratory examination of fit in the context of interorganizational relationships. J. Manag. Inf. Syst. 2005, 22, 257–294. [Google Scholar] [CrossRef]
  25. Green Industries South Australia [GISA]. South Australia’s Waste Strategy 2020–2025. 2020. Available online: https://www.greenindustries.sa.gov.au/resources/sa-waste-strategy-2020-2025 (accessed on 24 January 2021).
  26. Pickin, J.; Randell, P.; Trinh, J.; Grant, B.J. National Waste Report 2018; Department of the Environment and Energy: Melbourne, VIC, Australia, 2018.
  27. Zhao, X.; Webber, R.; Kalutara, P.; Browne, W.; Pienaar, J. Construction and demolition waste management in Australia: A mini-review. Waste Manag. Res. 2022, 40, 34–46. [Google Scholar] [CrossRef]
  28. Bensaou, M.; Venkatraman, N. Configurations of interorganizational relationships: A comparison between US and Japanese automakers. Manag. Sci. 1995, 41, 1471–1492. [Google Scholar] [CrossRef] [Green Version]
  29. Tushman, M.L.; Nadler, D.A. Information processing as an integrating concept in organizational design. Acad. Manag. Rev. 1978, 3, 613–624. [Google Scholar] [CrossRef]
  30. Sousa, R.; Voss, C.A. Contingency research in operations management practices. J. Oper. Manag. 2008, 26, 697–713. [Google Scholar] [CrossRef] [Green Version]
  31. Daft, R.L.; Lengel, R.H. Organizational information requirements, media richness and structural design. Manag. Sci. 1986, 32, 554–571. [Google Scholar] [CrossRef] [Green Version]
  32. Chang, A.S.; Tien, C.C. Quantifying uncertainty and equivocality in engineering projects. Constr. Manag. Econ. 2006, 24, 171–184. [Google Scholar] [CrossRef]
  33. Levander, E.; Engström, S.; Sardén, Y.; Stehn, L. Construction clients’ ability to manage uncertainty and equivocality. Constr. Manag. Econ. 2011, 29, 753–764. [Google Scholar] [CrossRef]
  34. Engström, S.; Hedgren, E. Sustaining inertia? Construction clients’ decision-making and information-processing approach to industrialized building innovations. Constr. Innov. 2012, 12, 393–413. [Google Scholar] [CrossRef]
  35. Nunes, K.R.A.; Mahler, C.F.; Valle, R.A. Reverse logistics in the Brazilian construction industry. J. Environ. Manag. 2009, 90, 3717–3720. [Google Scholar] [CrossRef] [PubMed]
  36. Ding, Z.; Wang, Y.; Zou, P.X. An agent based environmental impact assessment of building demolition waste management: Conventional versus green management. J. Clean. Prod. 2016, 133, 1136–1153. [Google Scholar] [CrossRef] [Green Version]
  37. Hammes, G.; De Souza, E.D.; Rodriguez, C.M.T.; Millan, R.H.R.; Herazo, J.C.M. Evaluation of the reverse logistics performance in civil construction. J. Clean. Prod. 2020, 248, 119212. [Google Scholar] [CrossRef]
  38. Schamne, A.N.; Nagalli, A. Reverse logistics in the construction sector: A literature review. Electron. J. Geotech. Eng. 2016, 21, 691–702. [Google Scholar]
  39. Gee, A.P. Quality Control of Cellular Therapy Products and Viral Vectors. Cell Ther. 2022, 209–223. [Google Scholar] [CrossRef]
  40. Nikolaidis, Y. Reverse logistics and quality management issues: State-of-the-Art. In Quality Management in Reverse Logistics: A Broad Look on Quality Issues and Their Interaction with Closed-Loop Supply Chains; Springer Science & Business Media: London, UK, 2012; pp. 1–20. [Google Scholar] [CrossRef]
  41. Keist, C.N. Quality control and quality assurance in the apparel industry. In Garment Manufacturing Technology; Woodhead Publishing Series in Textiles; Elsevier: Amsterdam, The Netherlands, 2015; pp. 405–426. [Google Scholar]
  42. Bartel, A.; Ichniowski, C.; Shaw, K. How does information technology affect productivity? Plant-level comparisons of product innovation, process improvement, and worker skills. Q. J. Econ. 2007, 122, 1721–1758. [Google Scholar] [CrossRef] [Green Version]
  43. Fox, M.J. Quality Assurance Management; Springer: New York, NY, USA, 2013. [Google Scholar]
  44. Brandão, R.; Hosseini, M.R.; Macêdo, A.N.; Melo, A.C.; Martek, I. Public administration strategies that stimulate reverse logistics within the construction industry: A conceptual typology. Eng. Constr. Archit. Manag. 2021. [Google Scholar] [CrossRef]
  45. Busetto, L.; Wick, W.; Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2020, 2, 1–10. [Google Scholar] [CrossRef]
  46. Pillay, P.; Mafini, C. Supply chain bottlenecks in the South African construction industry: Qualitative insights. J. Transp. Supply Chain Manag. 2017, 11, 1–12. [Google Scholar] [CrossRef]
  47. Merriam, S.B. Qualitative Research and Case Study Applications in Education; Revised and Expanded from “Case Study Research in Education”; Jossey-Bass Publishers: San Francisco, CA, USA, 1998. [Google Scholar]
  48. AlBahsh, E.R.; Hosseinian-Far, A. The implication of big data analytics on competitive intelligence: A qualitative case study of a real estate developer in the UAE. In Strategy, Leadership, and AI in the Cyber Ecosystem; Academic Press: Cambridge, UK, 2021; pp. 339–360. [Google Scholar]
  49. Ghaljaie, F.; Naderifar, M.; Goli, H. Snowball sampling: A purposeful method of sampling in qualitative research. Strides Dev. Med. Educ. 2017, 14, e67670. [Google Scholar] [CrossRef] [Green Version]
  50. Lin, X.; McKenna, B.; Ho, C.M.; Shen, G.Q. Stakeholders’ influence strategies on social responsibility implementation in construction projects. J. Clean. Prod. 2019, 235, 348–358. [Google Scholar] [CrossRef]
  51. Simms, C.; Rogers, B. The significance of flexibility in improving return on property investment: The UK perspective. Facilities 2006, 24, 106–119. [Google Scholar] [CrossRef]
  52. Smith, B. Generalizability in qualitative research: Misunderstandings, opportunities and recommendations for the sport and exercise sciences. Qual. Res. Sport Exerc. Health 2017, 10, 137–149. [Google Scholar] [CrossRef]
  53. Mason, M. Sample size and saturation in PhD studies using qualitative interviews. Forum: Qual. Soc. Res. Sozi-Alforschung 2010, 11, 8. [Google Scholar] [CrossRef]
  54. Thorne, S. The Great Saturation Debate: What the “S Word” Means and Doesn’t Mean in Qualitative Research Reporting. Can. J. Nurs. Res. 2020, 52, 3–5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Hsieh, H.F.; Shannon, S.E. Three approaches to qualitative content analysis. Qual. Health Res. 2005, 15, 1277–1288. [Google Scholar] [CrossRef] [PubMed]
  56. Creswell, J.W.; Creswell, J.D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th ed.; Sage Publications: Los Angeles, CA, USA, 2018. [Google Scholar]
  57. Morse, J.M.; Barrett, M.; Mayan, M.; Olson, K.; Spiers, J. Verification strategies for establishing reliability and validity in qualitative research. Int. J. Qual. Methods 2002, 1, 13–22. [Google Scholar] [CrossRef]
  58. Guba, E.G. Criteria for assessing the trustworthiness of naturalistic inquiries. Educ. Commun. Technol. J. 1981, 29, 75–91. [Google Scholar] [CrossRef]
  59. McGinley, S.; Wei, W.; Zhang, L.; Zheng, Y. The state of qualitative research in hospitality: A 5-year review 2014 to 2019. Cornell Hosp. Q. 2021, 62, 8–20. [Google Scholar] [CrossRef]
  60. Maxwell, J.A. Why qualitative methods are necessary for generalization. Qual. Psychol. 2021, 8, 111–118. [Google Scholar] [CrossRef]
  61. Korstjens, I.; Moser, A. Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. Eur. J. Gen. Pract. 2018, 24, 120–124. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Saldaña, J. The Coding Manual for Qualitative Researchers; Sage: London, UK, 2016. [Google Scholar]
  63. Braun, V.; Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 2006, 3, 77–101. [Google Scholar] [CrossRef] [Green Version]
  64. Vaux, J.S.; Kirk, W.M. Relationship conflict in construction management: Performance and productivity problem. J. Constr. Eng. Manag. 2018, 144, 04018032. [Google Scholar] [CrossRef]
  65. Choudhry, R.M.; Hinze, J.W.; Arshad, M.; Gabriel, H.F. Subcontracting practices in the construction industry of Pakistan. J. Constr. Eng. Manag. 2012, 138, 1353–1359. [Google Scholar] [CrossRef]
  66. Banihashemi, T.A.; Fei, J.; Chen, P.S.L. Exploring the relationship between reverse logistics and sustainability performance: A literature review. Mod. Supply Chain Res. Appl. 2019, 1, 2–27. [Google Scholar] [CrossRef]
  67. Namada, J.M. Organizational learning and competitive advantage. In Handbook of Research on Knowledge Management for Contemporary Business Environments; IGI Global: Hershey, PA, USA, 2018; pp. 86–104. [Google Scholar]
  68. Wang, C.; Xu, L.; Peng, W. Conceptual design of remote monitoring and fault diagnosis systems. Inf. Syst. 2007, 32, 996–1004. [Google Scholar] [CrossRef]
  69. Dale, P.; Sporne, I.; Knight, J.; Sheaves, M.; Eslami-Andergoli, L.; Dwyer, P. A conceptual model to improve links between science, policy and practice in coastal management. Mar. Policy 2019, 103, 42–49. [Google Scholar] [CrossRef]
  70. Ågerfalk, P.J. Insufficient theoretical contribution: A conclusive rationale for rejection? Eur. J. Inf. Syst. 2014, 23, 593–599. [Google Scholar] [CrossRef]
Figure 1. Elements of quality assurance system (adopted from [12]).
Figure 1. Elements of quality assurance system (adopted from [12]).
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Figure 2. Research Framework.
Figure 2. Research Framework.
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Figure 3. The multilayered context of the epistemic uncertainties of demolishers (a) and waste processors (b).
Figure 3. The multilayered context of the epistemic uncertainties of demolishers (a) and waste processors (b).
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Figure 4. The multilayered context of the information processing mechanisms.
Figure 4. The multilayered context of the information processing mechanisms.
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Figure 5. Information processing management framework for QA in RLSC of DW based on data and theory.
Figure 5. Information processing management framework for QA in RLSC of DW based on data and theory.
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Table 1. Profiles of interviewees.
Table 1. Profiles of interviewees.
Sector of RepresentationInterviewee (Code)DesignationExperience (Years)
Building dismantling and on-site processingBD1Managing Director19
BD2Managing Director28
BD3Managing Director21
BD4Quality Control and Sales Manager18
BD5Sales and Marketing Manager18
BD6Director14
BD7Director16
BD8Contracts Manager19
BD9Managing Director30
BD10Operations Manager10
BD11Managing Director22
Off-site waste processingWP1Strategic Business Development Manager11
WP2Accounts Manager16
WP3Human Resource and Occupational Health and Safety Manager10
WP4Accounts Manager8
WP5Regional Manager8
WP6Sales and Marketing Manager15
WP7General Manager11
WP8Sales and Marketing Manager10
WP9Principal Sustainability Advisor13
Regulatory sector (state government agencies)SG1Chief Executive28
SG2Regulatory Manager20
SG3Senior Environmental Advisor22
SG4Regulatory Manager11
Regulatory sector (local government agencies)LG1General Manager23
LG2Development Officer24
Forward supply chain—upstream actorsUA1Managing Director23
UA2Design and Construction Manager32
Forward supply chain—downstream actorsDA1Senior Engineer30
DA2Senior Sustainability Advisor15
Table 2. Strategies followed for generalization.
Table 2. Strategies followed for generalization.
Type of GeneralizabilityStrategy EmployedDescription
Naturalistic generalizationThick descriptionThe study provided comprehensive explanations and interpretations of the research setting, participants and data collection procedure, enabling the researcher to understand the proximal similarity of the context of the study and the participants.
Know the dataThe study followed a robust data preparation and analysis process that allowed the reader to immerse iteratively and grab strong insights from the collected data. Furthermore, the study incorporated excerpts from interviewees advocating readers to match their personal and interviewees’ experiences.
Analytical generalizationReplication in samplingThe study employed a combination of purposive sampling and snowball sampling techniques to enable analytical generalization. With this approach, the most appropriate interviewees were able to be incorporated to increase the rigor of the study’s findings.
Table 3. Summary of IPMs.
Table 3. Summary of IPMs.
Internal Stakeholder CategoryInformation Processing MechanismDescription
Demolishers and waste processorsRules and programsThe rules and programs encompass internal policies, procedures, guidelines and frameworks that provide a fixed, objective knowledge base from which the internal stakeholders could learn how to respond to epistemic uncertainties that they may encounter recurrently.
Planning and goal settingEstablish plans and set goals leaving the internal stakeholders to decide which behaviors to enact.
Business partneringDevelop strategic relationships with business partners to achieve competitive advantage while sharing the lack of resources and competencies.
Forming contractsForm a legitimate formal document between parties mentioning what parties agreed to do and not agreed to do within the scope of the job.
Group meetingsConduct face-to-face group meetings to coordinate work, discuss issues and share opinions, perceptions and judgements.
Hierarchical referralRefer the infrequent problems up to the hierarchy in finding solutions.
Incorporation of self-contained groupsForm independent groups with all the resources and capabilities to perform the tasks.
Investment in technologiesAugment the information-processing capacity by embracing various man-machine combined assistants.
Creation of slack resourcesIncrease the planning targets or reduce the performance level to reduce the number of exceptions.
DemolisherCollection of building documents and asbestos registersCollect as-built drawings and asbestos registers to understand the details of the as-is condition of the building.
Site visits/Pre-demolition auditsSite visits/pre-demolition audits allow the demolishers to assess the as-is condition of the building while evaluating the non-hazardous and hazardous waste required to be removed from the building.
Performance evaluation of subcontractorsA mechanism that enables an understanding of what subcontractors are performing to produce quality output.
Waste processorCreation of integrator rolesWaste processors assign an organizational role known as ‘accounts manager’ to a boundary-spanning activity, i.e., monitoring and building relations with demolishers. This role ensures that the waste processors are not receiving mixed waste from demolishers.
Waste acceptance criteria/gate feeThe criteria used to accept the waste into the MRF, and their corresponding gate fees advocate the waste processors avoid receiving mixed waste from demolishers.
Creation of special reportsGather feedback from end users about the quality of reprocessed products, synthesize them and take appropriate measures for any identified issue.
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MDPI and ACS Style

Wijewickrama, M.K.C.S.; Chileshe, N.; Rameezdeen, R.; Ochoa, J.J. Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective. Sustainability 2022, 14, 5493. https://doi.org/10.3390/su14095493

AMA Style

Wijewickrama MKCS, Chileshe N, Rameezdeen R, Ochoa JJ. Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective. Sustainability. 2022; 14(9):5493. https://doi.org/10.3390/su14095493

Chicago/Turabian Style

Wijewickrama, Madduma Kaluge Chamitha Sanjani, Nicholas Chileshe, Raufdeen Rameezdeen, and Jose Jorge Ochoa. 2022. "Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective" Sustainability 14, no. 9: 5493. https://doi.org/10.3390/su14095493

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