World Class Sustainable Manufacturing: a rational approach for Sustainable Competitive Advantage

The rationale of this empirical study is to identify sustainability assessment constructs in the context of WCSM implementation, which has emerged as a key focus of world class manufacturers striving to achieve Sustainable Competitive Advantage (SCA). This study also intends to identify the critical drivers and barriers toward implementation of WCSM. In order to achieve the study's purpose, the primary data were collected from 143 plant managers and supervisors through structured questionnaires administered at select world class manufacturers in South India based on an area-cum-purposive sampling technique. The survey instrument was exclusively designed to capture sustainability assessment dimensions to examine WCSM implementation and to investigate the impetus of WCSM implementation on the achievement of SCA. The findings of this empirical study revealed that five sustainability assessment constructs are identified with the help of Exploratory Factor Analysis (EFA). They are labeled as “WCSM Practices”, “Environmental Benefit Dimensions”, “Social Benefit Dimensions”, “Economic Benefit Dimensions” and “WCSM Implementation”. These sustainable assessment constructs are regarded as essential by South Indian world class manufacturers for the achievement of sustainable competitive advantage. Moreover, this study also confirmed that the seven key factors which positively influenced the implementation of WCSM are labeled as “WCSM Drivers” and the six key factors which negatively influenced the implementation of WCSM are labeled as “WCSM Barriers”. Finally, the study's findings indicated that the adoption and implementation of WCSM exert a substantial positive influence on achieving sustainable competitive advantage.

1 Introduction

The global competition among manufacturing firms has risen intensely due to the rapid changes in the unique characteristics of manufacturing operations, diffusion of information technology, and shrinkage of global markets (Garetti and Taisch, 2012; Murugesan et al., 2012; Vokurka and Davis, 2014). The notion of “world class manufacturing (WCM)” has received extensive attention in the literature of both academia and industry; however, it has been principally industrialized relative to the prerequisites of the manufacturing context (Dzikriansyah et al., 2023). In today's global business context, manufacturing competition has become more penetrating and the industrial market has changed rapidly (Hariyani and Mishra, 2022). To beat out the cut-throat competition, manufacturing firms must devise WCM as a strategic approach to gain a competitive advantage and edge in their manufacturing operations and to compete especially in the international market (Hayes and Wheelwright, 1984; Joshi et al., 2006; Nordin et al., 2014).

These strategic issues have put pressure on manufacturing operations, and manufacturers are constantly searching for innovative ways to achieve a competitive edge in manufacturing operations (Moldavska and Welo, 2017). Undoubtedly, international competitors functioning in the global manufacturing context always tend to have world class performance in all manufacturing priorities set by the strategic management of the firm (Despeisse et al., 2012). To survive and compete in the global business environment, manufacturing firms need to acquire world class performance in every phase of manufacturing operations (Hariyani and Mishra, 2022).

Nevertheless, although many scholars in the field of operations management have thrown a light on WCM since Schonberger's contributed work in 1986, the notable studies only outlined and highlighted the significant factors describing WCM. Furthermore, it was found that, currently, only a small chunk of research studies on the implementation of WCM in developing countries have appeared (Hariyani and Mishra, 2022; Hegab et al., 2018; Hollensen, 2001). From this standpoint, the main crux of the current study is to explore the critical variables that underpin the rational effect of implementation of WCM practices on the competitive advantage of manufacturing operations of South Indian manufacturing firms. Additionally, the current study is designed as a primary step toward developing manufacturing strategies that identify most of the drivers that motivate the effective implementation of WCM philosophy and eliminate most of the obstacles that impede the effective implementation of WCM philosophy by South Indian manufacturing firms (Hayes and Wheelwright, 1984; Hegab et al., 2018; Hollensen, 2001).

In every manufacturing operation, sustainability attainment is one of the key factors for organizational success. The organization can achieve its growth and development through the judicious mixture of environmental, social, and economic resources (Alodat et al., 2024; Upadhyay et al., 2021; Weingaertner and Moberg, 2014). In this era of global competition, every manufacturing firm strives to produce sustainable products with scarce resources to enhance their competitive edge in the global market (Chege and Wang, 2020).

Every manufacturing concern needs to have an appropriate evaluation model to assess the sustainability of manufacturing operations. Based on past literature, researchers have determined the most five significant factors that are used to evaluate the sustainability aspects of manufacturing operations, as follows: (i) waste management, (ii) manufacturing costs, (iii) environmental concerns, (iv) personal health and safety, and (v) energy consumption (Hegab et al., 2018; Singh et al., 2009). Furthermore, the evaluation of sustainable manufacturing from the perspectives of the product, process, and system can be voiced by three core elements, which are environmental aspects, economic aspects, and societal aspects (Kishawy et al., 2018).

An empirical study by Rehman et al. (2016) measured the impact of green manufacturing practices on organizational performance in the Indian context. This study highlighted that the green manufacturing practices adopted by Indian manufacturing companies have positive impact on the organizational performance by reducing waste, improving resource efficiency, and strengthening environmental compliance. Overall, they concluded that green manufacturing technologies act as a strategic enabler for achieving sustainable organizational performance in the Indian manufacturing sector.

Based on the above theoretical framework, this study addresses the current research gaps in threefold. First, there is a lack of research framework in the literature of world class sustainable manufacturing operations within the context of manufacturing industries in general and South Indian manufacturing industries in particular. Second, a few empirical studies threw a light on the implementation of sustainable manufacturing practices among manufacturing firms in India and abroad and their significant impact on achieving three sustainability benefit aspects of Triple Bottom Line (TBL) (Alodat et al., 2024; Upadhyay et al., 2021; Weingaertner and Moberg, 2014), but this empirical study exclusively attempts to identify sustainability assessment constructs in the context of WCSM implementation, which has emerged as a key focus of world class manufacturers striving to achieve Sustainable Competitive Advantage (SCA). Moreover, this empirical study is first of its kinds to measure the significant impact of sustainability assessment constructs on the achievement of sustainable competitive advantage within the manufacturing context. Third, this study also intends to identify the critical drivers that motivate South Indian world class manufacturers to embrace sustainable practices in their manufacturing operations and barriers faced by them while integrating sustainable practices in their manufacturing operations. In order to meet these current research gaps, this empirical study is carried out within the context of South Indian manufacturing firms. Based upon these research gaps, the following research questions are addressed in this study.

R1: What are the sustainable assessment constructs in the context of implementation of WCSM?

R2: What sustainable competitive advantage have South Indian world class manufacturers currently achieved through the implementation of WCSM?

R3: What are the impetus drivers that strengthen South Indian world class manufacturers to adopt WCSM?

R4: What are the stumbling barriers that can prevent South Indian world class manufacturers from embracing WCSM?

The paper is organized as follows. Section 2 deals with review of imperative literatures, section 3 deals with contribution to current knowledge. Section 4 outlines methodological approach of this study. Section 5 presents empirical analysis, results and implications. Section 6 deals with major discussions and implications of the results. Section 7 highlights conclusions and managerial implications, and Section 8 provides a roadmap for future research.

2 Review of imperative literatures

The term “world class manufacturing” was effectively first coined and introduced in Dzikriansyah et al. (2023). Later, various research scholars embraced the concept of WCM. From their studies, it was clear that WCM describes a set of manufacturing principles, techniques, and tools adopted by manufacturing companies in developing and developed countries to compete globally in the manufacturing context (Chourasiya et al., 2022). Further, WCM also involves a myriad number of factors related to the production of goods apart from other significant resources, such as raw materials, energy, machinery, labor, etc. Furthermore, WCM firms enhance the problem-solving skills of their employees in both modern and traditional manufacturing systems (Salaheldin, 2015).

WCM includes the strategic approach of management, the technical and engineering competencies of manufacturing operations, the skills and capabilities of the workforce, manufacturing strategies, effective participation by the workforce, and incremental improvements in the business process (Gilgeous and Gilgeous, 2018). While comparing the WCM practices adopted by Germany and Japan with those of US manufacturers, Hayes and Wheelwright stated that manufacturing plants in the USA have a clear-cut focus on these six building blocks to achieve their WCM status.

The term WCM is one of the most significant terminologies used in operations management (Schonberger, 1986). Several studies indicated that emerging manufacturing firms focus on competitive manufacturing operations through WCM across the globe (Hegab et al., 2018; Joshi et al., 2006), describing WCM as a set of tools and techniques designed to enable a firm to cope with its best competitors in the same industry. These techniques and tools may include a Flexible Manufacturing System (FMS), Material Requirements Planning (MRP), Manufacturing Resource Planning (MRP-II), Just-In-Time (JIT), Quality Circles (QCs), Kanban System, Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), Total Productive Maintenance (TPM), Total Quality Management (TQM), Enterprise Resource Planning (ERP), Total Quality Control (TQC), Supply Chain Management (SCM), Simultaneous Engineering (SE), Intelligent Manufacturing (IM), Benchmarking, and Business Process Reengineering (BPR) (Fernando et al., 2022; Saxena and Sahay, 2000; Seuring, 2013; Westphal et al., 2017).

2.1 World class sustainable manufacturing

In today's global manufacturing context, the performance of an organization depends upon its concern about the societal environment. As an organization prospers within its societal context, the primary responsibility of the organization is how well it contributes to the well-being and progress of the societal environment as a whole (Hair et al., 1998). As per the definition given by Moldavska and Welo (2017) on sustainable manufacturing, the firm has to integrate sustainable goals into its manufacturing system to minimize natural, material, and energy resources to improve the environmental performance of the firm (Moldavska and Welo, 2017).

There is an extensive acknowledgment that WCSM is one of the significant techniques used by many companies across the world to achieve a competitive advantage in manufacturing operations (Adebambo et al., 2014). WCSM effectively integrates a system of techniques, tools, knowledge, skills, experiences, and organizational characteristics for producing, utilizing, and controlling the output (Awan, 2019). Such a strategic approach is required (after analyzing the rapid changes in manufacturing technology) by the manufacturing firms to face fierce competition globally. Manufacturing firms need to consider the adaptation of modern technologies in alignment with WCSM to achieve a competitive advantage in the business operations of small and medium enterprises (Bocken et al., 2019).

Sustainable growth requires a judicious mixture of environmental, social, and economic development for achieving the improved well-being of the people (Kasul and Motwani, 1995). The main crux of sustainable growth is to realize social advancement while effectively preserving economic resources and protecting the environment from pollution (Rossi et al., 1983). Sustainable manufacturing is one of the key areas considered by many manufacturing firms in developed and emerging countries in the journey of achieving world class status in their manufacturing and business operations (Dogaru, 2020). Manufacturing firms need to focus on manufacturing principles, techniques, and tools in light of sustainability to compete with global leaders in the manufacturing context. Manufacturing firms employ WCM principles and techniques to produce goods from scarce resources such as machinery, labor, energy, and raw materials (Awan, 2019).

The effective application of a sustainable manufacturing system by manufacturing firms is one of the priorities in this modern era of manufacturing contests. The automated factory layout mainly comprises the principles and techniques of Industry 4.0 and Industry 5.0 (Rehman et al., 2016; Skinner, 2021; Upadhyay et al., 2021). This modern manufacturing philosophy highly focuses on sustainable goals when manufacturing final products. Sustainability and well-being are the two essential policy frameworks of any nation (Dornfeld, 2014). In the nation's well-being, the social, economic, and environmental dimensions are significantly considered. The successful implementation of a sustainable manufacturing system is one of the necessities of the Modern Automated Manufacturing System (MAMS). The main objective of MAMS is to achieve flexibility in factory automated manufacturing systems. These automated systems are completely driven by frequent changes in production capacities and conditions (Chege and Wang, 2020).

The continuous integration of sustainable goals into automated manufacturing systems is one of the essential practices of world class sustainable manufacturing (Greene, 1991). These systems are essential for achieving flexibility in factory manufacturing systems, conserving natural resources and energy, and minimizing environmental, social, and health concerns. The authors have identified three levels in the sustainable manufacturing approach. They are: (i) product level, (ii) process level, and (iii) system level.

Moreover, the integration of sustainable goals into the automated manufacturing process is completely analyzed and identified through three key levels, specifically: the product level, the process level, and the system level. The effective integration of these three levels helps manufacturing firms to achieve a competitive advantage in production processes (Joshi et al., 2006; Garetti and Taisch, 2012). From the benefit perspective of the product level, the sustainable manufacturing system mainly focuses on the innovative 6R approach (i.e., reduce, redesign, reuse, recover, remanufacture, and recycle) instead of the 3R approach (i.e., reduce, reuse, and recycle).

The empirical studies in the past clearly indicated that customer satisfaction, profitability, customer loyalty were the critical parameters for judging the competitive performance of any organization. In the omnipresence of sustainability, these parameters were being replaced rapidly by environmental, social and economical sustainable goals, which are popularly known as “Triple Bottom Line (TBL) framework (Alodat et al., 2024; Upadhyay et al., 2021; Weingaertner and Moberg, 2014). Therefore, this empirical study aims to measure adoption and implementation of WCSM in the light of TBL framework and their effect on sustainable competitive advantage.

Rehman et al. (2016) identified several critical success factors that shape the impact of green manufacturing practices on organizational performance. These key factors such as top management commitment, adoption of clean technologies, employee training, integration of environmental standards into production systems, effective supplier collaboration and regulatory pressure also emerge as important drivers for successful implementation of green manufacturing technologies. The authors also emphasize the role of supplier collaboration and regulatory pressure as key external drivers that strengthen green initiatives. According to the study, these factors collectively enhance operational efficiency, reduce environmental impact, and improve competitiveness.

3 Contributions to current knowledge

The contribution of this empirical study to the current knowledge is fourfold. First, the empirical outcomes of this study effectively contribute to manufacturing literature in general and to sustainable manufacturing literature in particular. The current study provides present researchers with a new dimensional scale to identify the key sustainability assessment constructs in the context of WCSM implementation. The effective implementation of WCSM is generally considered a key focus by world class manufactures in achieving SCA in their manufacturing operations. Second, a diminutive research study was found in the field of implementation of WCSM by manufacturers in emerging countries in general, and in India in particular. Thus, this study primarily intends to reflect sustainability assessment dimensions to examine the impetus of WCSM implementation on the SCA of world class manufactures in South India. Finally, the meaningful contribution of this empirical study is to provide policy makers with the managerial implications for integrating emergent sustainable practices and Triple Bottom Line (TBL) benefits in world class manufacturing operations, which can be used as a road map for other manufacturing companies in India and abroad.

4 Methodological approach

This study is empirical in nature and the methodological approach of this study follows a sequence of three phases. In the first phase, an extensive review of existing literatures was conducted to establish strong theoretical background and identify the key sustainability assessment constructs in the context of WCSM implementation world class manufacturers striving to achieve sustainable competitive advantage. In the second phase, expert opinions were gathered through a pilot study involving industrial practitioners (i.e., plant managers/plant supervisors), who had integrated at least any one of the sustainable practices in world class manufacturing operations to achieve sustainable competitive advantage for more than 1 year. The six indicators for measuring sustainable competitive advantage adopted extensively from previous literatures and were confirmed in the pilot study through expert validation. In the final phase, a research instrument was developed, drawing primarily on newly designed scales, and subsequently validated through expert reviews as well as standard statistical validity and reliability measures like Principal Component Analysis and Cronbach's alpha tests. The researchers have employed Exploratory Factor Analysis on 45 sustainability assessment statements in light of WCSM and extracted 34 sustainability assessment statements under five latent constructs, such as “WCSM Practices”, “Environmental Benefit Dimensions”, “Social Benefit Dimensions”, “Economic Benefit Dimensions” and “WCSM Implementation”.

Moreover, this study employed six simple regression models to measure the significant impact of these latent constructs on the achievement of Sustainable Competitive Advantage (SCA). The outcome of this analysis indicated that these key latent constructs have significant positive influence on the achievement of SCA. Besides, this empirical study is the first of its kinds to explore the key factors, which encourage world class manufacturers in South India to embrace WCSM, are regarded as “WCSM Drivers” and the key factors, which hinder them from adopting sustainable practices in the world class manufacturing operations, are regarded as “WCSM Barriers”. This study uses the primary data collected from 143 plant managers and supervisors through structured questionnaires administered at select world class manufacturers in South India based on an area-cum-purposive sampling technique. The survey instrument was exclusively designed to capture sustainability assessment constructs and dimensions in the context of implementation of WCSM. The companies for this study were selected on the basis of area-cum-purposive sampling technique that the manufacturing companies located in South India, which have successfully adopted any form of sustainable practices, technologies, and initiatives in their world class manufacturing operations in the pursuit of achieving sustainable competitive advantage for a period of more than 1 year.

4.1 Conceptual research framework

The objective of this empirical research is to identify sustainable assessment constructs in the context of WCSM implementation and its effect on achieving sustainable competitive advantage of South Indian world class manufacturers in a manufacturing context. This study is also designed to examine the impetus drivers and resisting obstacles of implementing WCSM. To comprehend the purpose of this study, the research framework was designed by the researchers, as shown in Figure 1. This framework is a self-effacing linear model of the relationship between dependent and independent research variables. In Figure 1, the SCA, WCSM implementation, and adoption of WCSM practices are the dependent variables and Triple Bottom Line (TBL) aspects (i.e., environmental benefit aspects, social benefit aspects, and economic benefit aspects) considered as essential by world class manufactures in South India are independent variables. The arrows in Figure 1 represent the logical relationships among the key research variables and are statistically tested to achieve the objectives of this research study.

Figure 1

Figure 1. Adoption and implementation of WCSM in light of Triple Bottom Line (TBL) and its impact on achievement of SCA.

4.2 Hypotheses development

Figure 1 portrays the theorized relationships between key sustainable assessment constructs of WCSM implementation and the achievement of SCA by world class manufacturers. In view of conceptual research framework developed in Figure 1, the following hypotheses are investigated in this empirical study:

H1 TBL benefit dimensions have significant influence on adoption of sustainable manufacturing practices.

H2 Adoption of WCSM practices have significant influence on implementation of WCSM H3 Basic drivers of WCSM positively affect the implementation of WCSM in the South Indian manufacturing context.

H4 Basic barriers to WCSM negatively affect the implementation of WCSM in the South Indian manufacturing context.

H5 There is an impact of WCSM implementation on the achievement of SCA in manufacturing operations.

H6 Environmental benefit dimensions have a significant influence on the achievement of SCA.

H7 Social benefit dimensions have a significant influence on the achievement of SCA. H8 Economic benefit dimensions have a significant influence on the achievement of SCA.

4.3 Research instrument development

The design of the research instrument was largely supported by new scales because no earlier empirical research could be found directly or indirectly concerning all of the issues discussed in this empirical study. Where possible, researchers instead relied on validated measures that have been used previously. The constructs, scale items, and factor loadings obtained from confirmatory factor analysis are presented in the data analysis section.

This study completed the pre-testing of this instrument in two consecutive rounds to ensure that the respondents could follow all of the measurement scales used in this empirical study. First, the author and co-authors of this study reviewed the research instrument and then piloted it with the three senior production executives from three manufacturing industries. The pilot study consisted of an interview format involving the research instrument being distributed to all senior production executives, who were asked to provide responses and any comments regarding the measurement scales used in the research instrument. The pre-test process resulted in making little change to the existing scales based on the scale context of the new scales under study. The attributed importance of each element, as is customary in academic practice, was measured on a five-point Likert scale (Hair et al., 1998; Rossi et al., 1983), ranging from “strongly considered” to “not considered”. Each concept variable was on a five-point Likert scale-−1 to 5—where 5 was highly considered and 1 was low. The reliability and construct validity of the constructs were assessed for their measurement properties that were used in the statistical analysis using the Cronbach's alpha and principal components analysis (PCA) methods (Alodat et al., 2024; Dick and Hagerty, 1971).

4.4 Reliability and validity

Before surveying developers, the questionnaire underwent a thorough validation, assessment, and rationality review. This process involved practitioners, industrialists, and academic researchers from prominent educational institutions. Their task was to review the questionnaire to eliminate any ambiguous expressions and ensure that the scale items used were appropriate for this empirical study. Based on the feedback received, the questionnaire was refined and finalized. A reliability test on the surveyed data by applying the Cronbach's alpha test produced a Cronbach's alpha value of 0.799 for the 26 identified underlying dimensions in light of WCSM (Hair et al., 1998; Rossi et al., 1983). This value exceeded the threshold of 0.70, indicating that the data collected using the five-point measurement scale were highly reliable for subsequent statistical analysis (Dick and Hagerty, 1971).

4.5 Sampling frame and characteristics

4.5.1 Area of study: South India panorama

South India is considered to be one of the most industrially developed states of India. This region hosts a resilient manufacturing culture that has greatly assisted attracting investments in manufacturing sectors such as automobiles, electronics, and textiles. Thus, it is quite plausible that South India has become one of the areas that is becoming the manufacturing capital of South Asia. One of the largest automobile component industries of India is located in South India. In South India, many international auto giants have established their manufacturing plants and it has become the largest manufacturing hub, which is also a remarkable point for the study.

4.5.2 The sample characteristics

The survey reported here was carried out at production plants, within and around South Indian locations. This study involved 200 manufacturing firms after validating the questionnaire based on expert validation and pilot testing. The study population of the survey is defined as the manufacturing companies in and around South India. In each company, the target respondent was the senior production executive or plant supervisor. All of the manufacturing firms surveyed here had implemented at least one of the WCSM practices more than 1 year ago.

The research compendium included a closed e-mail and an anonymous questionnaire (self-administered questionnaire) that was electronically mailed to the senior production managers or plant supervisors of 200 manufacturing industries located in and around South India. This approach yielded 143 relevant responses, which provided a 71.5% overall response rate, and was judged by the researchers as satisfactory for the nature of the study. Hence, the study is limited only to 143 manufacturing companies and the sample size of the study is confined to 143 target respondents. Each manufacturing firm is treated as one sample unit in this study.

The sample is explained as: 28.7 per cent of the respondents in the manufacturing sector were in the automobile sector, 25.4 per cent were in the engineering and electronic sector, 19.8 per cent were in chemical companies, 11.5 per cent were in textile companies, 9.4 per cent were in the mining industry, and only 5.2 per cent produced other manufacturing products. Regarding firm age, nearly one-third of the sample firms (30.4 per cent) were less than 10 years old, one-quarter (24.8 per cent) had 10–15 years of experience, less than one-fifth (16.8 per cent) had 16–20 years of experience, and nearly 30 per cent (28.0 per cent) had more than 20 years of experience.

In terms of the number of employees in the companies: 28.0 percent of the total sample had 500+ employees, 26.4 percent had 100–300 employees, 24.0 percent had 301– 500 employees, and only 21.6 percent had less than 100 employees. The distribution of the respondents according to the company size showed that, collectively, companies operated 36.8 per cent on a medium scale, 33.6 per cent on a small scale, and 29.6 per cent on a large scale.

5 Empirical analysis, results, and implications

5.1 Descriptive statistics of sustainability assessment constructs

The crux of this empirical research is to identify key Sustainability Assessment Constructs (SAC) in the context of WCSM implementation as a key focus of South Indian world class manufactures in the achievement of SCA in their manufacturing operations. According to Table 1, the descriptive statistics of key research variables are significant and different from the mid-value (MV = 3.0). In the SAC1 of WCSMP, “Sustainable product design & development” was found to be the most significant WCSM practice, with the highest mean score of 4.256.

Table 1

Table 1. Descriptive statistics of SACs and their indicative parameters.

World class manufacturers in South India are more inclined to adopt emergent WCSM practices in light of environmental, social, and economic benefit dimensions for effective implementation of WCSM. In the SAC2 of EBDs, “Well-defined environmental policy” was found to be the most critical dimension, with the highest mean score of 3.856. In the SAC3 of SBDs, “Employee involvement and participation in eco-initiatives” was considered to be the critical construct, with the highest mean score of 4.236. In the SAC of EBDs, “cost–benefit analysis” was observed as significant economic benefit (mean score = 3.685). Finally, “integrating the supply chain with the WCSM” is one of the significant reasons for implementing WCSM in South Indian manufacturing context. These results highly substantiate the purpose of this empirical study that South Indian world class manufacturers have implemented at least one of the social, environmental, and economic dimensions in the context of WCSM implementation and the achievement of sustainable completive advantage in the manufacturing operations.

5.2 Sustainable competitive advantage

One of the key objectives of this empirical study is to examine the effect of WCSM implementation on the achievement of Sustainable Competitive Advantage (SCA) among world class manufacturers in South India. Based on insights from prior literature reviews, six SCA parameters were adopted for this empirical study. As presented in Table 2, the mean scores of all six parameters exceeded the test value (TV = 3.0), indicating a favorable response from the sample companies toward achieving SCA. Among these, “Environmental Performance” emerged as the most significant sustainable competitive priority, recording the highest mean score of 3.678.

Table 2

Table 2. Results of regression coefficients for six proposed models.

5.3 Principal component analysis and main findings

In this research paper, principal component analysis was employed to identify sustainability assessment constructs in the context of WCSM implementation and their influence in achieving sustainable completive advantage in manufacturing operations. A total of 45 sustainability assessment statements in light of WCSM were subjected to principal component analysis to reduce them into a few uncorrelated component factors. Initially, all 45 assessment items were used for the principal component analysis, which efficiently extracted seven component factors. It was observed that some assessment statements were not properly loaded on any of the component constructs and some assessment statements were completely duplicated. Therefore, 11 sustainability assessment statements were deleted from the original list. Further, principal component analysis was also employed with the remaining 34 sustainability assessment statements, and five component constructs were extracted with eigenvalues greater than 1 (Dick and Hagerty, 1971).

KMO and BTS tests were performed to examine the sampling adequacy of the field data with the help of principal component analysis. The outcome of the KMO and BTS tests revealed that there were sufficient correlations among the sustainability assessment statements to advance with the field data, and the anti-image correlation for each assessment statement was also computed in light of WCSM. These outcomes gave a clear picture of the partial correlations among sustainability assessment statements and indicated that true component constructs existed in the field data. The KMO-MSA for the underlying sustainability assessment statements was computed from the original diagonals of the partial correlation matrix. The value of KMO-MSA was deemed to be highly satisfactory for the sustainability assessment statements. The overall KMO-MSA was computed to show whether the samples selected for this study were adequate or not (Rossi et al., 1983).

Bartlett's test of sphericity (BTS) provided the statistic to test the null hypothesis that the number of statistically significant correlations among sustainability assessment statements was no greater than would be expected by chance. The total KMO measure of sampling adequacy was 0.837 and Bartlett's test of sphericity (BTS) was significant at a 5% level (approx. Chi-square = 6,803.509, df = 276, p-value = 0.000), showing that the survey data were highly suitable and sufficient for principal component analysis. Consequently, all of these studies established that the sample and field data were appropriate for principal component analysis. The application of the latent root criterion enabled the meaningful extraction of the proposed number of sustainability assessment constructs. The factor loadings of sustainability assessment constructs were all > 0.70 (ignoring signs). All results have been fully discussed in this study. For the threshold limit to be considered for large factor loading, a cut-off point of 0.30 is advised when N > 350 (Dick and Hagerty, 1971).

As the first column of Table 3 indicates, the eigenvalues between sustainability assessment constructs 1 to 5 were 5.789, 5.071, 4.443, 3.375, and 3.214, respectively. The second column of Table 3 denotes the fraction variances, i.e., the proportion of the variance to be clarified by the individual factor. For example, factors 1 to 5 explained 24.057 per cent, 21.129 per cent, 18.515 per cent, 14.061 per cent, and 12.567 per cent of the variance, respectively. Cronbach's alpha reliability coefficient was used to investigate the reliability of the research variables. Cronbach's alpha was calculated to test the internal consistency of the constructs in the scale of measurement for each research variable and for the overall construct. This analysis was performed to check the convergent and divergent validity of the measurements. Results were reported for Cronbach's alpha, which was used to measure the internal consistency and reliability of the construct (Table 3).

Table 3

Table 3. Factor loadings, initial eigenvalues, and % of variance of factor constructs.

Cronbach's alpha coefficient of 0.70 was applied to test the reliability and validity of each factor. Table 3 shows the results, and they mostly indicated a Cronbach's alpha ranging between 0.70 and 1 for all research variables, which meant success for the reliability test (Table 3). The alpha values were determined for the extracted sustainability assessment constructs, including WCSM practices (Cronbach's α = 0.986), environmental benefit dimensions (Cronbach's α = 0.928), social benefit dimensions (Cronbach's α = 0.964), economic benefit dimensions (Cronbach's α = 0.864), and WCSM implementation (Cronbach's α = 0.849).

5.3.1 Naming of extracted factor constructs and labeling

The five Sustainability Assessment Constructs (SAC) were appropriately named based on the variables they represent. Table 3 summarizes the names of these factor constructs, the labels, and the factor loadings. Below, we discuss the factor constructs that highlight the importance and significance of sustainability assessment dimensions in the context of WCSM implementation and achievement of SCA.

5.3.2 SAC 1: WCSM practices (Cronbach's α = 0.986)

The first extracted SAC “WCSM Practices” emerged as the most significant factor explaining 24.057% of the total variance. This factor had an eigenvalue of 5.789 and Cronbach's alpha of 0.986. In total, seven WCSM practices were loaded into this factor. The highest loading was found for the practice of “Green manufacturing (0.937), followed by “Operational efficiency (i.e., energy, resources, water, etc.) (0.934)”, “Circular economy practices (3R or 6R practices) (0.927)”, “Integration of sustainability goals (0.921)”, “Sustainable product design & development (0.913)”, “Integration of sustainability goals (i.e., TBL, SDGs, etc.) (0.921)“, “Eco-initiatives for safety & well-being of employees (0.902)”, and “Integration of 4.0/5.0 in manufacturing operations (i.e., IoT, AI, Big Data, ML, Robotics, etc) (0.824)” (Table 3).

5.3.3 SAC 2: Environmental benefit dimensions (Cronbach's α = 0.928)

The second extracted SAC “Environmental Benefit Dimensions” explained 21.129% of the total variance. This factor had an eigenvalue of 5.071 and Cronbach's alpha of 0.928. It was made up of four environmental benefit dimensions in light of WCSM. The highest loading was for the dimension “Support of environmental programs (0.835)”. Linked to this were “Awareness of environmental policy (0.801)”, “Well-defined environmental policy (0.781)”, and “Allocation of funds for environmental goals (0.770)” (Table 3).

5.3.4 SAC 3: social benefit dimensions (Cronbach's α = 0.964)

The third extracted SAC, “Social Benefit Dimensions,” explained 18.515% of the total variance. This factor had an eigenvalue of 4.443 and Cronbach's alpha of 0.964. It was made up of five social benefit dimensions in light of WCSM. The highest loading was for the dimension “Recognition and reward for employees (0.755)”. Followed by “Customer satisfaction (0.740)”, “Employee development (0.735)”, “Employee involvement and participation (0.731)”, and “Customer attitude toward eco-friendly products” (0.721) (Table 3).

5.3.5 SAC 4: economic benefit dimensions (Cronbach's α = 0.864)

Four highly economic benefit dimensions in light of WCSM were loaded into this extracted SAC and explained 14.061% of the total variance. This factor had an eigenvalue of 3.375 and Cronbach's alpha of 0.864. The highest loading in this factor was for the dimension “Operational efficiency (0.805)”. Linked to this, “Cost–benefit analysis (0.738)”, “Human and material resources (0.715)”, and “Natural and financial resources (0.712)” (Table 3).

5.3.6 SAC 5: WCSM implementation (Cronbach's α = 0.849)

Six dimensions of WCSM implementation were loaded into this extracted SAC and explained 12.567% of the total variance. This factor had an eigenvalue of 3.214 and Cronbach's alpha of 0.849. The highest loading in this factor was for the dimension “Integrated supply chain (0.807)”. Linked to this, “Energy efficient and cleaner production technology” (0.803), “Agility (0.802)”, “Factory automated systems (Industry 4.0/5.0) (0.789)”, “Lean manufacturing (0.788)”, and “Leadership and management (0.769)” (Table 3).

5.4 WCSM drivers and barriers

The third and fourth (H3 and H4) hypotheses of this empirical study focused on the relational effect of “WCSM Drivers” and “WCSM Barriers” on the “WCSM Implementation”. A one-sample t-test was performed to test whether the observed means of “WCSM Drivers” and “WCSM Barriers” were statistically significant from the mid-value of 3.0. The resultant outcomes are presented in Table 4. As per Table 4, the results were observed to be highly significant and statistically different from the mid-value of 3.0 (p-value < 0.01). The one sample t-test results empirically confirmed that the seven key factors, which positively influenced the implementation of WCSM, are regarded as “WCSM Drivers” and the six key factors, which negatively influenced the implementation of WCSM, are regarded as “WCSM Barriers”. These empirical results greatly supported third and fourth (H3 and H4) hypotheses of this study.

Table 4

Table 4. One-sample t-test of WCSM drivers and barriers for effective implementation of WCSM.

5.5 Sustainable competitive advantage analysis

To examine the influence of various predictors, six simple regression models are proposed in this study. In Model 1, the dependent variable “Adoption of WCSM Practices” is explained by overall Triple Bottom Line (TBL). Benefit Dimensions as independent variable. In Model 2, the dependent variable “Implementation of WCSM” is determined by the “Adoption of WCSM Practices”. In models 3, 4, 5, and 6, SCA was employed as the regressand to determine the extent to which SCA was influenced by the four regressors, such as WCSM Implementation in Model 3, Environmental Benefit Dimensions in Model 4, Social Benefit Dimensions in Model 5, and Economic Benefit Dimensions in Model 6. Furthermore, these six proposed hypotheses (H1, H2, H5, H6, H7, and H8) of this empirical study can also be expressed in a simple linear regression equation, as described below:

Model 1:

WCSMPs Adoption = Constant + β 1 Overall TBL Benefit Dimensions + ε

Model 2:

WCSM Implementation = Constant + β 1 WCSM Practices + ε

Model 3:

SCA = Constant + β 1 WCSM Implementation + ε

Model 4:

SCA = Constant + β 1 Environmental Benefit Dimensions + ε

Model 5:

SCA = Constant + β 1 Social Benefit Dimensions + ε

Model 6:

SCA = Constant + β 1 Economic Benefit Dimensions + ε

To investigate the proposed hypotheses of this study, entering key sustainability assessment constructs into a simple regression model, we found that the proposed model of simple regression analysis clearly explained a substantial percentage of the variance in the “WCSMPs Adoption”, “WCSM Implementation” and “Sustainable Competitive Advantage (SCA)” explained by six regressors, such as “Overall TBL Benefit Dimensions”, “WCSM Practices”, “WCSM Implementation”, “Environmental Benefit Dimensions”, “Social Benefit Dimensions, and “Economic Benefit Dimensions”. Table 2 clearly shows that the R2 value of 0.683 in Model 1 indicated that “WCSMPs Adoption” together explained 68.3% of the variation in the “Overall TBL Benefit Dimensions”. In addition to this, the R2 value of 0.792 in Model 2 indicated that “WCSM Implementation” together explained 79.2% of the variation in “WCSM Practices”, the R2 value of 0.768 in Model 3 indicated that sustainable competitive advantage (SCA) together explained 76.8% of the variation in “WCSM Implementation”, the R2 value of 0.726 in Model 4 indicated that the SCA together explained 72.6% of the variation in “Environmental Benefit Dimensions”, the R2 value of 0.604 in Model 5 indicated that SCA together explained 60.4% of the variation in “Social Benefit Dimensions” and finally the R2 value of 0.634 in Model 6 indicated that SCA together explained 63.4% of the variation in “Economic Benefit Dimensions”.

A closer look at the unstandardized coefficients of these six models indicated that there was a significant relationship between the dependent variables, such as WCSMPs Adoption, WCSM Implementation and SCA in Models 1, 2, 3, 4, 5, and 6 and all six independent variables, such as “Overall TBL Benefit Dimensions”, “WCSM Practices”, “WCSM Implementation”, “Environmental Benefit Dimensions”, “Social Benefit Dimensions”, and “Economic Benefit Dimensions”. In Model 1, “Overall TBL Benefit Dimensions” was significant in influencing “WCSMPs Adoption” (β = 0.409, t = 4.080, p < 0.001). In Model 2, “WCSM Practices” were significant in influencing “WCSM Implementation” (β = 0.494, t = 4.830, p < 0.01). In Model 3, “WCSM Implementation” was statistically significant in influencing SCA (β = 0.468, t = 4.652, p < 0.01). In Model 4, “Environmental Benefit Dimensions” were highly significant in influencing SCA (β = 0.423, t = 4.236, p < 0.01). In Model 5, “Social Benefit Dimensions” were highly significant in influencing SCA (β = 0.362, t = 3.688, p < 0.01). In Model 6, “Economic Benefit Dimensions” were highly significant in influencing SCA (β = 0.384, t = 3.745, p < 0.01).

The results from Table 2 showed that the ratio of the mean squares (F) was highly significant at a 1% level (p < 0.01). The F-value of all six models, which was significant at a 1% level, denoted that there was a significant relationship between dependent and independent variables. Since the p-value was less than 0.01 in models 3,4, 5, and 6, all the key sustainability assessment constructs influenced world class manufacturers' attitudes toward achieving sustainable competitive advantage. To test the null hypothesis that the population partial regression coefficient for a variable was 0, the t-statistic and its observed significance level were used. The results are shown in Table 2. Multicollinearity between the independent variables was minimal, as shown by the values of tolerance (0.395 for “Overall TBL Benefit Dimensions” in Model 1, 0.321 for “WCSM Practices” in Model 2, 0.345 for social dimensions, 0.345 for “WCSM Implementation” in Model 3, 0.386 for “Environmental Benefit Dimensions” in Model 4, 0.438 for “Social Benefit Dimensions” in Model 5 and 0.412 for “Economic Benefit Dimensions” in Model 6) and VIF (variance inflation factor), which was less than ten for all six independent variables, indicated that the results were reliable. These results supported six proposed hypotheses (H1, H2, H5, H6, H7, and H8) of this study.

6 Discussions and research implications

In the ubiquity of sustainable manufacturing, this empirical study is designed to identify sustainability assessment constructs in the context of WCSM implementation and achievement of SCA. In this empirical study, the key sustainability assessment constructs that drive South Indian world class manufacturers to implement WCSM and to achieve SCA are identified as: (i) WCSM Practices, (ii) Environmental Benefit Dimensions, (iii) Social Benefit Dimensions, (iv) Economic Benefit Dimensions, and (v) WCSM Implementation. These empirical findings of this study are highly consistent and corroborate with the major findings from previous studies (Adebambo et al., 2014; Bocken et al., 2019; Dick and Hagerty, 1971; Dornfeld, 2014). The findings from recent empirical studies clearly revealed that the adoption of sustainable manufacturing practices and technologies have significant positive impacts on environmental benefits, economical benefits and social benefits of the manufacturing firms. These findings are consistent with our empirical findings that the TBL benefit dimensions (i.e., the environmental benefit dimensions, social benefit dimensions and economic benefit dimensions) have significant influence on the implementation of WCSM and the achievement of sustainable competitive advantage (Alodat et al., 2024; Awan, 2019; Dzikriansyah et al., 2023; Bocken et al., 2019).

7 Conclusions and managerial implications

This study is empirical in nature aims to identify the sustainability assessment constructs in the context of WCSM implementation, which has emerged as a key focus for world class manufactures striving to achieve sustainable competitive advantage. In today's landscape of sustainability, the manufacturing sector is required to integrate economic, environmental, and social sustainable dimensions into their manufacturing activities to become sustainable organization. The successful manufacturers must increase productivity, maximize profitability, lead industry best trends, and drive competitive advantages in the global markets through adopting and implementing emerging sustainable practices in their manufacturing operations.

In the ubiquity of sustainability, significant changes have occurred in management styles and approaches, process and product technologies, customer expectations, supplier relationships and attitudes, as well as the market's competitive behaviors in the manufacturing industry around the world. In the era of fierce global competition and rapidly changing environmental scenarios, there is enormous pressure on manufacturing firms to integrate sustainability practices and technologies into their manufacturing operations to meet global Sustainable Development Goals (SDGs).

The outcomes of this empirical study clearly indicate that world-class manufacturers in South India are actively adopting and implementing at least one form of sustainable practice, technology, or initiative within their manufacturing operations, thereby enhancing their competitiveness in today's dynamic business environment.

When asked about the extent of adopting and implementing WCSM practices in manufacturing operations, “Sustainable product design & development (mean = 4.256)”, “Circular economy practices (3R or 6R practices) (mean = 4.126)”, “Eco-initiatives for safety & well-being of employees (mean = 3.654)”, “Integration of sustainability goals like TBL and SDGs (mean = 3.523)”, “Green manufacturing (mean = 3.425)”, “Integration of 4.0/5.0 in manufacturing operations like IoT, AI, Big Data, ML & Robotics (mean = 3.345)” and “Operational efficiency in utilizing energy, resources, & water (mean = 3.145)” are considered as key WCSM practices by South Indian world class manufacturers in the journey of achieving sustainable competitive advantage. The empirical results of this study are highly consistent with the outcomes of relevant research conducted by Hartini and Ciptomulyono (2015).

Well-defined environmental policy (mean = 3.856), employee involvement and participation in eco-initiatives (mean score = 4.236), and cost–benefit analysis (mean score = 3.685) are considered as key environmental, social, and economic benefit dimensions, respectively. Moreover, the key practices, techniques and technologies for implementing WCSM by South Indian world class manufactures are lean manufacturing (mean = 4.321), Agility (mean = 4.211), leadership and management (mean = 4.025), integrated supply chain (mean = 3.845), Energy efficient and cleaner production technology (mean = 3.684), and Factory automated systems (Industry 4.0/5.0) (3.245). The study's empirical results also confirmed that the “WCSM Drivers” positively influenced the implementation of WCSM and “WCSM Barriers” negatively influenced the implementation of WCSM.

The study also found that the implementation of WCSM have a significant influence on sustainable competitive advantage (for instance, cost efficiency, operational efficiency, resource utilization, eco-innovation, sustainable supply chain, and customer trust and loyalty). These findings of this study effectively confirm and support the research results of previous research conducted by Nordin et al. (2014). They clearly stated that the implementation of sustainable manufacturing practices in light of environmental, social, and economic benefits by Malaysian manufacturing firms has a significant influence on their competitive performance in manufacturing operations.

World class manufacturers should acknowledge that the aim of being competitive is not merely a matter of simply reducing operating costs, improving quality, enhancing productivity, designing efficient factory layouts, or establishing state-of-the-art manufacturing systems. It is, in fact, the ability of the manufacturers to link sustainability goals with their manufacturing capabilities to produce products with sustainable market requirements and enhance the firm's competitive advantage to continually meet and delight the expectations of customers.

8 Scope for future research

Despite its significant contributions to current knowledge in the sustainable manufacturing context, this empirical study has certain limitations. Thus, it is highly necessary to look at this empirical study in the context of its limitations. First, the research schema on WCSM was developed by the researchers with the help of the limited literature available on world class manufacturing and sustainability. Future researchers need to reconnoiter all aspects of world class manufacturing and sustainability while integrating sustainability aspects into world class manufacturing operations to evaluate sustainable competitive advantage from the implementation of WCSM. Second, this empirical survey was confined to 143 plant managers and supervisors of the industrial zones in South India. There is a need to replicate the results of the study with manufacturing firms located in other parts of India and abroad.

Finally, this empirical study seeks to identify sustainability assessment constructs in the context of WCSM implementation from the general perspectives of plant managers and supervisors in selected manufacturing industries in South India. The empirical results, however, are not intended to be generalized to any specific manufacturing sector and may not exhibit statistical consistency across the different manufacturing sectors. To address this limitation, future researchers are encouraged to conduct a few comparative studies for exploring the unique sets of sustainability assessment constructs of an individual manufacturing sector to measure the achievement of sustainable competitive advantage. Consequently, the scope of this study remains restricted to the data and insights derived from the structured questionnaire administered among manufacturers in South India. Nevertheless, the findings offer a strong foundation for future research aimed at developing new measurement scales to examine WCSM implementation in relation to environmental, social, and economic dimensions and their combined influence on the sustainable competitive advantage of manufacturing industries globally.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Inti International University IRB. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

PB: Data curation, Funding acquisition, Methodology, Validation, Writing – original draft, Writing – review & editing. TM: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing, Visualization. KM: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing, Supervision. NS: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was financially supported by Inti International University, Persiaran Perdana BBN, Nilai, Negeri Sembilan, Malaysia. The authors gratefully acknowledge the institution's funding contribution, which enabled the successful completion of this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

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