Confirmatory factor analysis for the implementation of Green Lean Six Sigma (GLSS) in Malaysia's wastewater treatment industry

The progression of modernization in manufacturing, driven by the increasing global recognition of environmental risks and the pursuit of enhanced efficiency, has evolved from traditional substitution focus to lean manufacturing, which minimizes waste, and further advanced to green manufacturing guided by the principles of reduce, reuse, and recycle (3Rs) (Sagnak & Kazancoglu, 2016). Enterprises have proactively developed cleaner manufacturing processes and environmentally friendly products. However, numerous industrial activities significantly impact the environment and society assigned to excessive resource consumption, generation of hazardous wastes, and emissions (Parmar & Desai, 2020).

Wastewater, encompassing various types such as sewage, domestic, storm run-off, agricultural, and industrial wastewater (Ishak et al., 2022a), constitutes a notable component of industrial waste. According to the United Nations, approximately 1.6 billion people face economic water shortage, and two-thirds of the world's population experience water scarcity for at least 1 month per year (Christou et al., 2024). This scarcity issue is exacerbated by the fact that around 97% of Malaysia's total raw water supply is derived from freshwater bodies, including lakes, rivers, and tributaries (Kozaki et al., 2016). The problem of water pollution, which has historical roots in urbanization and modernization, continues to escalate in severity (Ismail et al., 2020). Furthermore, industrial effluents pose significant harm to ecosystems. When poorly treated or directly released into sewers, they pollute groundwater and water bodies, adversely affecting animals and aquatic life. Inadequate treatment also leads to air and land pollution, which negatively impacts soil quality. Disposal of industrial wastewater poses risks to crops and potentially disrupts the food chain, contributing to the spread of waterborne diseases (Von Sperling et al., 2020).

Wastewater treatment plants (WWTPs) face multifaceted challenges, including influent fluctuations, reactor dynamics, pollutant origins, process variability, mechanical anomalies, and human proficiency. Equipment malfunction, including damage, breakdowns, and unforeseen downtime, poses a substantial concern in environmental control, disrupting treatment processes and compromising the quality of treated wastewater (Ishak et al., 2022b). Remedial techniques have emerged to identify operational shortcomings and rectify instability and inadequacies in treatment methodologies. Scholars have developed approaches combining green and lean concepts (Siegel et al., 2019) to minimize not only waste production but also ‘green waste’ (Caiado et al., 2018), defined by the United States Environmental Protection Agency (US EPA) as avoidable resource consumption or substance release detrimental to humans and the environment (United States Environmental Protection Agency, 2007). Optimizing the usage of energy, water, chemicals, materials, or transportation can profoundly impact ecosystems.

Recognizing the limitations of green and lean as independent strategies and as an integrated paradigm, Green Lean Six Sigma (GLSS) emerges as a pioneering environmental developmental agenda, transcending these constraints and enhancing the effectiveness of green lean initiatives (Shokri & Li, 2020). This integration draws strength not only from the intrinsic cohesion of lean principles and tools common in both approaches but also from their ostensibly shared attributes (Rahman & James, 2019). Although numerous studies have showcased the effectiveness of this integration, further cutting-edge research, particularly empirical inquiries offering structured guidelines for applying GLSS across diverse domains, is imperative (Gholami et al., 2021). Precisely defining this concept necessitates the systematic consolidation of available knowledge regarding this GLSS initiative.

Despite its recognized potential in the manufacturing industry, practitioners exhibit caution in GLSS adoption (Farrukh et al., 2021). Consequently, there is a research imperative to scrutinize the factors facilitating GLSS implementation in WWTPs. Notably, no prior studies have explicitly and systematically addressed a comprehensive model of GLSS in WWTP operations, indicating a need for a unified model. Critical tasks involve organizing existing GLSS insights and identifying impediments, particularly within Malaysian WWTPs where specific GLSS enablers remain unexplored. This study aims to augment current knowledge and expedite GLSS implementation by (1) delineating key factors enabling GLSS implementation, (2) constructing a structured framework for GLSS integration in the Malaysian WWTP sector, and (3) empirically investigating the GLSS enabler's model in WWTP operations using structural equation modelling (SEM).

In the literature, enablers are recognized as key factors that significantly influence the alignment of quality management with organizational goals and performance (Yadav & Desai, 2017). Various researchers have explored enablers within the realm of GLSS. For instance, Kumar et al. (2015) conducted a study identifying the 44 GLSS enablers that impact the sustainability performance of Indian enterprises. Singh et al. (2021) employed a hybrid Best-Worst method (BWM), analytic hierarchy process (AHP), and analytic network process (ANP) approach to categorize five types of GLSS enablers, including strategic, environmental, cultural, resource, and linkage-based enablers.

Strategic enablers, propelled by top management's commitment, serve as catalysts for GLSS by promoting innovation (Kaswan & Rathi, 2020a), resource allocation, and employee motivation (Kaswan & Rathi, 2019). Their adept decision-making skills contribute to sustainability and organizational improvements (Hariyani & Mishra, 2022; Shokri et al., 2022). Additionally, effective project leadership, encompassing diverse roles, fosters transparency, cooperation, and alignment with business objectives, leading to significant outcomes (Ershadi et al., 2021; Mishra, 2022). Furthermore, integrating rewards for employees encourages heightened engagement with human resources, enhancing eco-friendly results, thus reinforcing fairness and loyalty (Parmar & Desai, 2020; Letchumanan et al., 2022).

Organizational readiness necessitates competent individuals and supportive structures (Kaswan & Rathi, 2020b), which are important for sustainable GLSS adoption (Letchumanan et al., 2022; Mishra, 2022). Moreover, robust performance measurement and reliable results tracking are crucial (Pandey et al., 2018), especially in dynamic WWTP operations, guiding decision-making and error-proofing through feedback mechanisms (Singh et al., 2021). Additionally, a resilient data collection system facilitates structured information retrieval, enabling comparisons across WWTP stages and the supply chain. Monitoring and controlling using information tools are central for effective management in GLSS contexts (Hariyani & Mishra, 2022).

Transitioning to green practices in manufacturing (Farrukh et al., 2021), including WWTP operations, facilitates the reduction of energy use, CO₂ emissions, and waste generation, thereby positively impacting environmental performance (Abdul-Rashid et al., 2017; Kaswan & Rathi, 2019; Farrukh et al., 2022). Environmental-based enablers, such as emphasizing biodegradable packaging and supplier adherence (Dieste et al., 2019), for instance, utilizing materials like biodegradable options and lightweight, flexible packaging, help minimize costs and environmental emissions (Farrukh et al., 2023a; Rathi et al., 2023). In many cases, environmental initiatives rely on government regulations to support GLSS through incentives like subsidies, influencing top management, and enhancing organizational capabilities (Hariyani & Mishra, 2022).

Additionally, eco-design prioritizes minimizing environmental footprints (Parmar & Desai, 2020), aligning with WWTP goals (Ishak et al., 2022b), which not only focus on treating wastewater to acceptable standards but also optimize energy recovery and nutrient recovery sources. Furthermore, the impact of logistics and transportation on emissions underscores the importance of green methods (Pandey et al., 2018). Similarly, WWTP practices optimize chemical use and effluent transport, promoting sustainability (Rimantho & Nugraha, 2020). Moreover, the environmental management system (EMS) integrates various environmental activities (Shokri et al., 2022), enhancing facility sustainability (Singh & Rathi, 2022, 2023). Additionally, stakeholder pressure emphasizes demands for eco-conscious practices (Gandhi et al., 2018; Parmar & Desai, 2019), driven by regulations and consumer preferences for green initiatives (Nagadi, 2022) and sustainable performance (Yadav et al., 2023a, b).

In culture-based enablers, team selection holds paramount importance (Kumar et al., 2015), leveraging diverse skills and experiences as valuable assets (Singh et al., 2021). For example, talented WWTP employees enhance treatment efficiency (Letchumanan et al., 2022; Mishra, 2022), emphasizing unified effort and effective communication channels (Pandey et al., 2018). Similarly, GLSS emphasizes teamwork for sustainable improvements (Kaswan & Rathi, 2020a), supported by effective communication (Hussain, et al., 2023; Hariyani & Mishra, 2024) and inter-departmental exchanges (Singh et al., 2021; Hariyani et al., 2023; Hussain et al., 2023).

Moreover, efficient scheduling (Yadav et al., 2021; Shokri et al., 2022) aids environmental sustainability (Letchumanan et al., 2022). Similarly, motivating employees (Singh et al., 2021; Mishra, 2022) and cross-departmental sharing enhances efficiency (Hussain et al., 2023). Likewise, sharing success stories facilitates learning (Mishra, 2022), emphasizing factors such as management commitment and training (Singh et al., 2021). Ultimately, GLSS culture values sustainability (Gandhi et al., 2018), ethics (Kaswan & Rathi, 2019), and profitability (Kaswan & Rathi, 2020a; Hariyani & Mishra, 2023; Rathi et al., 2023). Therefore, cooperative WWTP environments focus on quality, efficiency, and participative cultures (Letchumanan et al., 2022; Mishra, 2022) for successful GLSS implementation (Hussain et al., 2023).

The GLSS methodology, integrating Lean Six Sigma (LSS; Yadav et al., 2021) and flow cost accounting, addresses inefficiencies (Kaswan & Rathi, 2019). Emphasis is placed on understanding for successful adoption (Hussain et al., 2023). Project selection aligns with sustainability (Parmar & Desai, 2019), utilizing Lean tools such as fishbone diagrams for prioritizing improvements and resource allocation to enhance continuous improvement projects in organizations (Letchumanan et al., 2022; Hussain et al., 2023). Mastery of project selection and prioritization skills (Singh et al., 2021; Rathi et al., 2023), along with effective training, is crucial (Hariyani & Mishra, 2024) for GLSS success (Mishra, 2022).

Careful financial planning (Hussain et al., 2023) ensures effective resource allocation for technology upgrading (Kumar et al., 2015; Singh et al., 2021), particularly in WWTP operations, which require capacity and efficiency commensurate with process enlargement. Periodical staff training improves skill sets, employability, team spirit, and organizational cohesiveness (Shokri et al., 2022). Continual assessment of financial benefits (Pandey et al., 2018; Yadav et al., 2021) and early involvement of finance departments are essential (Letchumanan et al., 2022; Shokri et al., 2022; Hussain et al., 2023) for effective resource management. In WWTP operations, selecting eco-friendly polymers, exemplified by involving financial perspectives for cost-effective measures, helps reduce long-term treatment expenses and environmental impact (Singh et al., 2021; Mohan et al., 2022).

Supplier engagement in GLSS promotes innovation and quality improvement (Kaswan & Rathi, 2020b; Parmar & Desai, 2020). Ensuring reliable suppliers is crucial for the timely delivery of chemicals, nutrient additives, and mechanical equipment, which are vital for WWTP operations' performance and waste management (Digalwar et al., 2020; Singh et al., 2021), including bio-sludge and solid waste disposal. Moreover, customer satisfaction in WWTP operations depends on adaptable processes, legal compliance, cost control, and pollution prevention (Kaswan & Rathi, 2020b). Accurate predictions and digital advancements enhance customer satisfaction and engagement (Singh et al., 2021). Customer involvement is integral to GLSS success (Pandey et al., 2018), prioritizing satisfaction through feedback and database utilization (Ershadi et al., 2021).

Meeting customer demand entails maintaining stable treated wastewater quality and cost-effective plant operations (Singh et al., 2021). Strong customer–supplier relationships (Pandey et al., 2018; Letchumanan et al., 2022) focusing on sustainability, optimizing outcomes, and minimizing waste (Hariyani & Mishra, 2024) contribute to efficient treatment processes. Integrating GLSS into strategy enhances sustainability (Kaswan & Rathi, 2020b; Hussain et al., 2023), fostering staff responsibility (Farrukh et al., 2019) and collaboration (Farrukh et al., 2021) in high-risk operations such as WWTP.

In conclusion, this literature review explores essential GLSS enablers for sustainable operations, aiming to underscore the importance of holistic assessments of these enablers to optimize their impact on operational sustainability and to further enhance Malaysia's wastewater treatment practices. Understanding the interplay among these enablers will facilitate the development of improved strategies and implementation methodologies for sustainable wastewater management.

This study constitutes exploratory research, focusing on an unexplored area within Malaysian WWTPs. It adopts a descriptive and analytical approach towards its exploratory aim, comprising two distinct phases. The first phase entails a comprehensive review of existing literature on GLSS enablers. Following this, an analytical method is formulated to conclude the research process. These steps are further elaborated upon in the subsequent sections.

To begin, following Yadav et al. (2023a, b) scholarly papers were gathered from reputable databases including Elsevier, Springer, Science Direct, Taylor & Francis, Emerald, Sage, and among others. A comprehensive literature review and on-site visits to actual WWTPs enabled the compilation of a list of key factors that impact the implementation of GLSS in Malaysian WWTP scenarios. Articles focusing on enablers, drivers, and critical success factors (CSFs) related to GLSS were meticulously examined, both theoretically and empirically, to inform this compilation.

Next, in the classification of GLSS enablers, experts' insights and exploratory factor analysis (EFA) have been utilized. EFA serves to gauge the identified variables and unveil the underlying relationships among them. This method yields two interconnected outcomes: data summarization and reduction. Through data summarization, EFA identifies core dimensions that succinctly encapsulate the data, condensing numerous individual variables into a smaller set of concepts. Data reduction builds on this by assigning a numerical value (factor score) to each dimension (factor), replacing the original values. Many researchers find EFA valuable for uncovering patterns among variables or as a means of streamlining data. This analytical approach encompasses three primary components, elaborated upon in the following sections.

The EFA design focused on two critical queries: identifying key GLSS enablers and determining an optimal sample size. A comprehensive literature review and collaboration with certified LSS Belts, National Registration of Certified Environmental Professional experts in WWTP operations, and a GLSS academician led to refining 30 enablers. Hair et al. (2019) recommends at least 50 observations for EFA, while Habidin & Yusof (2013) used 161 observations to identify the LSS CSFs model in the Malaysian automotive sector. Employing non-probabilistic convenience sampling, 296 responses were collected from local WWTP professionals between February and May 2023. A five-point Likert scale measured perceptions of agreement (strongly disagree – 1 to strongly agree – 5) regarding GLSS enabler importance, validated by another five WWTP experts. A pilot study ensured questionnaire clarity and relevance. Data collection targeted diverse viewpoints from technicians, engineers, executives, and managers involved in various aspects of WWTP in industrial operations. Subsequent analysis choices, like factor extraction methods and matrices, will critically shape the understanding of the identified enablers' underlying structure. These decisions are crucial for interpreting the study's outcomes effectively.

Again, Hair et al. (2019) advocates using principal component analysis (PCA) with varimax rotation due to its ability to consider total variance and highlight factors with less unique variance. This method maximizes variance in factor loadings, simplifies fundamental structures, and aids in factor division (Hashemi et al., 2022). Employing IBM SPSS version 27, this study used Bartlett's test of sphericity (BTS) and Kaiser–Meyer–Olkin (KMO) measure to assess data suitability. Acceptable EFA standards include BTS at 0.05 significance and KMO between 0 and 1, with 0.5 as minimal adequacy (de Freitas et al., 2017). Criteria for determining factor numbers include variance contribution (>20%), eigenvalues (>1), and the Scree test (Hair et al., 2019), while Letchumanan et al. (2021) suggest considering factor loadings >0.5. Internal consistency, evaluated by Cronbach's α (>0.6), ensures reliability in the exploratory survey.

Finally, SEM, increasingly popular in operations management empirical studies (Habidin & Yusof, 2013), combines regression and factor analysis to explore relationships between observed and latent variables. It comprises two stages: EFA, exploring links between observed and unobserved variables, and confirmatory factor analysis (CFA), confirming and validating models as explained previously by EFA. While CFA, conducted in Analysis of Moment Structure (AMOS) version 24, validates the measured and structural models. This method enables a comprehensive evaluation of complex relationships between variables in this study.

The study assessed the reliability and validity of GLSS enablers through an EFA. Initially, 30 enablers were identified from an extensive literature review and industrial visit. These enablers were distinct in their characteristics and application within WWTP organizations to ensure the successful execution of GLSS. They were categorized based on their traits, employing both fundamental and statistical methods. Building on prior work by Singh et al. (2021), EFA was conducted to unveil the structure of GLSS using these 30 enablers, as outlined in Table 1.

PCA was employed to evaluate responses gathered from 296 experts working in various Malaysian WWTPs. This insight from experts is crucial as it provides decision-makers with a comprehensive understanding of a significant stakeholder group. The analysis revealed a high overall reliability coefficient (α) of 0.969, which is considered suitable (Hair et al., 2019). Moreover, the KMO measure surpassed the threshold of 0.7 at 0.968, indicating the adequacy of the data for PCA. Similarly, the BTS was significant (p < 0.001), affirming sufficient correlation among the items to proceed with the analysis.

Furthermore, construct validity was evaluated through assessments of convergent and discriminant validity. Convergent validity ensures consistent measurement outcomes across various variables and methods (O'Leary-Kelly & Vokurka, 1998). It was confirmed by examining composite reliability (CR) and average variance extracted (AVE) values. CR > 0.6, CR > AVE, and AVE > 0.5 criteria were met by all factors in the model (Hundleby & Nunnally, 1968; Table 4), affirming convergent validity. Discriminant validity was assessed using the Heterotrait-Monotrait (HTMT) approach, which compares inter-construct correlations (heterotrait) to intra-construct correlations (monotrait). HTMT values ranging from 0.674 to 0.815, all below the 0.9 threshold, indicated satisfactory discriminant validity (Henseler et al., 2015). This conclusion was further supported by factor loadings, AVE, and cross-loadings from previous tables (Table 2), strengthening the evidence for discriminant validity.

Table 4 outlines the results of GLSS enablers in the Malaysian WWTP sector, presenting various means reflecting respondent perceptions of agreement. The overall mean for each factor was computed to gauge the perceived level of importance of GLSS enablers. These mean values range from 4.310 to 3.949, signifying a good level of agreement on the importance of GLSS enablers. The two highest-rated enablers are Strategic (4.310) and Resource (4.289), followed by Culture (4.269) and Environment (4.051). Conversely, Linkage (3.949) is perceived as the GLSS enabler with the least agreement among respondents.

Interestingly, the ranking of the top three enablers identified in this study aligns with the analytic hierarchy process (AHP) ranking in Pandey et al.'s (2018) research. However, a contrast emerges in this study, as the linkage-based enablers are considered the least significant, with Elnk5, Elnk2, and Elnk4 hitting the lowest mean values. This finding contradicts the outcomes of studies by Kaswan & Rathi (2019), and Rathi et al. (2023), where the Interpretive Structural Modeling (ISM)-Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) model and BWM analysis highlighted the integration of GLSS in core business (Elnk5) as among the top enablers in their research.

Respondents emphasize the critical need for a strategic approach to sustainable WWTP operations. Key components include top management commitment (EStra1), effective project leadership (EStra2), and precise data collection (EStra6) within Malaysian contexts. Active support and resource provision by management are important for successful implementation (Kaswan & Rathi, 2019). According to Pandey et al. (2018), this strategy enhances profits by streamlining operations. Top management must ensure compliance with current pollution laws, develop plans for future regulations, and integrate technological advancements (Gandhi et al., 2018). Their commitment significantly influences sustainable practices such as utilizing alternative energy and waste reduction (Kaswan & Rathi, 2020a). Consistent effective leadership is crucial in implementing manufacturing philosophies across the organization. Accurate data collection and assessment of Lean and Green waste are crucial for thorough system analysis (Kaswan & Rathi, 2020a), assessing eco-efficiency using specific tools (Farrukh et al., 2021).

The key facilitators for successful GLSS implementation revolve around essential resources, playing the second most crucial role in the sustainable process. This includes securing resource allocation (ERes4) like funding (Kumar et al., 2015; Hussain et al., 2023), providing necessary awareness training (ERes3) for employee skills (Singh et al., 2021), and external support, such as consultant expertise (Pandey et al., 2018). Thoughtful financial planning by management ensures adequate allocation of resources to meet project goals (Yadav et al., 2021). This planning involves initial investments in technology, estimating tools, and staff training, emphasizing the efficient use of finances for comprehensive GLSS adoption (Letchumanan et al., 2022; Shokri et al., 2022). Mastery through understanding and practical application of GLSS methodology (ERes1) is also important for successful adoption (Hussain et al., 2023).

In the context of Malaysian WWTPs, culture-based elements rank third, while environmental factors follow as the fourth most critical enablers. Respondents stress teamwork (ECul2) (Kaswan & Rathi, 2019), effective communication (ECul3) (Pandey et al., 2018), and strategic team selection (ECul1) in GLSS as important (Letchumanan et al., 2022; Mishra, 2022). Embracing diverse employee skills is key to enhancing organizational culture (Singh et al., 2021). Strong teamwork cultivates adaptability, confidence in new approaches, and solid employee relations amidst business changes emphasized by Singh et al. (2021). Employee involvement ensures cooperative cultures, key to successful GLSS adoption (Rathi et al., 2023). Transparent communication, facilitated by an efficient organizational structure, supports positive work environments (Hariyani et al., 2023). In addition, companies respond to eco-friendly demands by adopting strategies like efficient manufacturing, green procurement, and waste reduction (Pandey et al., 2018). This approach mitigates costs amid market unpredictability (Kumar et al., 2015; Hariyani & Mishra, 2023).

The lowest agreement was observed regarding linkage-based enablers, as seen in Habidin & Yusof (2013), specifically in supplier relationships. Connecting GLSS with customers and suppliers (ELnk4) and prioritizing customer satisfaction (ELnk2) scored lowest in this study. A notable decrease in consumer complaints reflects a customer-focused social performance (Pandey et al., 2018), emphasizing product responsibility (Farrukh et al., 2020). Organizations supporting suppliers' environmental shifts through training, workshops, and financial aid (Hussain et al., 2023) foster long-term customer relationships by responding to their expectations and concerns (Farrukh et al., 2021).

This study aims to assess the enablers influencing GLSS implementation in Malaysia's wastewater treatment industry, which are crucial for ensuring effective implementation and reaping associated benefits. Understanding the complex and diverse elements affecting GLSS implementation is essential. Data from 296 certified professionals in Malaysian WWTPs were utilized, and SEM validated the research model through EFA, CFA, reliability, and model fit tests, confirming the factors' validity and reliability. The study reveals five significant enablers for GLSS implementation in Malaysian WWTPs. Overall, the majority of Malaysian WWTPs demonstrated a moderate to high level of agreement in GLSS adoption, signalling positive progress in enhancing sustainable performance.

Theoretical implications of this research enrich specialized literature through the originality of the CFA model, allowing examination of GLSS enabler dimensions in the context of WWTP operation. Particularly, the ‘strategic’ and ‘resource’ enablers emerged as highly crucial for GLSS implementation in Malaysian WWTPs. Key components include top management commitment, effective project leadership, and precise data collection within Malaysian contexts. Active support and resource provision by management are important for successful implementation (Kaswan & Rathi, 2019). Moreover, securing resource allocation and funding (Kumar et al., 2015; Hussain et al., 2023), providing necessary awareness training for employee skills (Singh et al., 2021), and external support, such as consultant expertise, are essential (Pandey et al., 2018). Thoughtful financial planning by management ensures adequate allocation of resources to meet project goals (Yadav et al., 2021). The research findings are robust and support the stability of the proposed conceptual CFA model.

Besides theoretical implications, this study also reveals practical implications. It provides a clear picture for WWTP organizations to adopt GLSS enablers for sustainable operations, prioritizing GLSS strategies to improve wastewater treatment processes. WWTP top management should support their teams in fostering effective teamwork, communication, and scheduling through initiatives like employee retention, empowerment, and knowledge sharing, creating a more conducive environment for sustainability-focused organizational culture and ethics. Integrating GLSS into core business strategies aligns with environmental, social, and corporate governance principles to support Sustainable Development Goals.

This study has limitations worth noting and suggests future research directions. First, the sample comprises WWTP professionals only from Malaysia, limiting generalizations attributable to specific country characteristics, culture, and the degree of GLSS implementation. Future research should conduct cross-country comparative analyses to ascertain the universality of the proposed model. Second, exploring the structural relationship between these enablers and sustainable performance in future research is planned. Developing sustainable performance metrics considering economic, environmental, and social aspects will aid the WWTP industry in evaluating its sustainable performance. Future research aims to address these limitations by combining the use of quantitative methods.

This research received crucial support from the Public Service Department of Malaysia and Universiti Teknikal Malaysia Melaka, greatly facilitating the successful execution of this impactful study. Additionally, heartfelt appreciation is extended to the experts and industry stakeholders whose invaluable insights and suggestions significantly enhanced the research design.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.