Analysis of the impact of Kaizen practices on ESG performance and the mediating role of digital systems

In recent years, several empirical studies have stressed the importance of continuous improvement practices, particularly those rooted in the Kaizen philosophy, for both business and academic environments (Kharub et al., 2023; Franken et al., 2021; Suárez-Barraza et al., 2025). Originally developed within manufacturing contexts, this Japanese management approach is characterized by a strong practice orientation and a commitment to incremental change (Imai, 1986; Fujimoto, 1999). It functions as an umbrella concept encompassing a set of principles and values, supported by tools and routines integrated into Company-Wide Quality Control systems (Suárez-Barraza et al., 2011; Berhe, 2021). Kaizen is operationalized through practices, techniques and routines to maintain and improve standards linked to Kaizen’s guiding principles and values (Suárez-Barraza et al., 2011; Atta-Ankomah et al., 2022).

Rather than focusing solely on technical improvements, the philosophy emphasizes human engagement and collaborative effort in driving change (Jayamaha et al., 2014; Ma et al., 2017; Díaz-Reza et al., 2024). Over time, its scope has expanded beyond operational processes to include broader organizational challenges—notably, those associated with sustainability and ESG (Environmental, Social, and Governance) concerns (Kozhabayev et al., 2023; Antony et al., 2024; Carneiro et al., 2025). ESG, often used interchangeably with sustainability, reflects a company’s performance in minimizing environmental and social harm while promoting sound governance practices (Liu et al., 2022), being the most common framework to measure the sustainability performance of companies (Martiny et al., 2024).

As stakeholders increasingly consider ESG performance in decision-making, organizations have recognized that the costs of remediation often exceed those of prevention (Kharub et al., 2023). In this context, continuous improvement frameworks like Kaizen are being re-evaluated for their potential contribution to long-term sustainability goals (Antony et al., 2024; Carneiro et al., 2025). While prior studies have shown encouraging results regarding the environmental benefits of such approaches (Cherrafi et al., 2019), existing evidence remains fragmented and often embedded within broader methodologies such as Lean (e.g. Figueroa et al., 2024), limiting the clarity of Kaizen’s distinct impact. Despite the growing interest in the impact that Kaizen can have on ESG performance (Carneiro et al., 2025), the relationship between Kaizen practices and environmental sustainability is a topic in the early stages of development with inconclusive results (Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020).Furthermore, many investigations are based on case studies with narrow scopes, lacking generalizability and theoretical depth (Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020). To date, there are no large-scale empirical studies that directly assess the relationship between Kaizen practices and ESG performance in the quality management context (Kozhabayev et al., 2023). This absence of focused, cross-national analysis hampers our understanding of how—and to what extent—Kaizen principles contribute to sustainable value creation.

Furthermore, while relationships between operational improvement and environmental indicators have begun to be explored, the social and governance dimensions remain understudied and yield inconclusive or inconsistent findings (Bortolotti et al., 2018; Sanchez-Ruiz et al., 2020; Díaz-Reza et al., 2024). A recurring limitation is the lack of attention to enabling conditions, such as the role of digital systems in supporting or amplifying these improvement efforts.

Additionally, industry 4.0 technologies can assist and benefit from Kaizen practices (Kumar et al., 2023; Bernard et al., 2025) in obtaining better ESG results. Digital systems can support Kaizen, since data acquisition through intelligent sensors, monitoring systems and Big Data analysis allow organizations to achieve the desired results in Kaizen (Ferreira et al., 2023; Mariappan et al., 2023).

This study aims to explore these relationships systematically and fill the evident gaps in the literature (Bortolotti et al., 2018; Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020; Kozhabayev et al., 2023; Díaz-Reza et al., 2024) expanding the current knowledge through a global empirical investigation, examining the impact of operational Kaizen practices on each ESG pillar. By analyzing not only direct relationships but also the mediating effect of digital systems, the research introduces a novel socio-technical perspective that bridges continuous improvement and digital transformation in the context of sustainability. The study contributes to theory and practice by enabling a discussion and comparison with previous findings on the impact of Kaizen routines on environmental (Jonda et al., 2023) and social (Díaz-Reza et al., 2024) sustainability and advance knowledge about relationship between Kaizen and governance. It also enables advances in the understanding of the impact of digital systems on the relationships between Kaizen practices and the pillars of ESG, which is a topic of current interest for the competitiveness of organizations (Kumar et al., 2023; Bernard et al., 2025). This is even more relevant given the scarcity and need for comprehensive empirical studies with the application of the survey method, which make it possible to confirm or refute hypotheses (Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020) and increase the area covered by the empirical study.

The objective of this research is to examine whether operational Kaizen practices have a positive impact on ESG performances, in addition to verifying whether the use of technologies – digital systems can influence these relationships by conducting a global survey in manufacturing companies. Therefore, the study aims to answer the following research questions: RQ1: Is there a positive impact of Kaizen operational practices on the three pillars of ESG (Environmental, Social and Governance); RQ2: Is there a mediating effect of the use of digital systems on the relationship between Kaizen practices and ESG performance. The study uses a quantitative research approach based on survey method and online questionnaires as research instruments. The primary data collection generated 227 respondents with experience in Kaizen in manufacturing companies from different countries and sectors. The data were analyzed using partial least square structural equation modeling (PLS-SEM). The data obtained provide relevant information regarding the impact generated using Kaizen on the performance of the ESG pillars.

This study makes three key additions to the literature on Kaizen and ESG and manufacturing companies. First, the main theoretical contribution of this study lies in providing robust empirical evidence—based on a diverse, international sample—that clarifies the role of Kaizen in driving ESG performance. The findings reveal that environmental and governance outcomes are positively influenced by Kaizen practices. Second, the present study helps to characterize the role of technology application in the context of continuous improvement and ESG, since the findings show that the integration of Industry 4.0 technologies, particularly digital systems such as real-time monitoring and data analytics is a critical enabler of impact, since the social dimension may require technological mediation to manifest. Thirdly, this study shares new insights into the alignment between Kaizen practices, the ESG concept in a comprehensive way and the application of technology, since previous studies focused only on one of the ESG pillars and the application of technology in isolation. This challenges common assumptions in the literature and suggests new directions for theory development.

The flow of the paper is structured as follows: Section 2 reviews the literature and develops the hypotheses. Section 3 outlines the methodology. Section 4 presents the results, followed by Section 5, which discusses the implications. Finally, Section 6 concludes with limitations and directions for future research.

According to Imai (1986), Kaizen means: “continuing improvement in personal life, home life, social life, and working life. When applied to the workplace […] [it refers to] continuing improvement involving everyone, managers, and workers alike.” Kaizen is characterized by the Japanese work culture, immersed in the philosophies of Buddhism, Confucianism, Taoism, and Shintoism (Macpherson et al., 2015). According to Imai (1997), it is defined as the derivation of two Japanese ideograms (Kanjis): KAI (改) – change and ZEN (善) – virtuous, benevolent, to improve; that combined we indicated: “change for the better”.

Hence, Kaizen has also been regarded as an ethical force (internally found in oneself) of each worker, who is able to solve problems on a daily basis, fully believing and voluntarily (Styhre, 2001). In this sense, Bessant (2003) indicated that the mobilization and participation of employees generate a channel or medium for them to contribute to the development of the company. In simple terms, “working with your hands but using your brain to think.” This idea compares with and is similar to the early studies of the Human Relations School, in which Mayo, Maslow, McGregor, and Herzberg argued for such an approach to management (Styhre, 2001).

Various authors have explained Kaizen from different perspectives (Berhe, 2021), these perspectives encompass different guiding principles, techniques, and practices for implementing and sustaining Kaizen (Suárez-Barraza et al., 2011; Atta-Ankomah et al., 2022). The principles guide the implementation of Kaizen and encompass, for example, focus on process, people, and standards (maintain and improve) (Berhe, 2021) teamwork, elimination of Muda and Gemba management, education and training, commitment from Top Management and improvement (Suárez-Barraza et al., 2011). The techniques and practices to operationalize Kaizen are used to maintain and improve standards; each of them closely linked to Kaizen’s guiding principles (Suárez-Barraza et al., 2011). These techniques and practices can differ according to the perspective from which Kaizen is understood (Suárez-Barraza et al., 2011; Atta-Ankomah et al., 2022). Imai (1986) presents a perspective of Kaizen as an umbrella term for management philosophy encompassing managerial practices ranging from: total quality control, quality circles, suggestion systems (Kaizen Teian), Kanban, JIT, from Zero Defects to productivity improvement, new product development and Total Productive Maintenance (TPM). Kaizen can be seen as an element of TQM or even as a theoretical principle for improvement methodologies (Suárez-Barraza et al., 2011).

Similar to other authors, this study seeks to observe Kaizen through its main operational practices, that is, those that enable its daily application on the shop floor. Aoki (2008), in a study conducted on the application of Kaizen in Chinese companies, points to standardization as an initial operational practice, supported by discipline and cross-functional communication or management, which helps to eliminate MUDA. The study conducted by Brunet and New (2003) in Japanese companies, identified practices such as small group activities (Kaizen team), suggestion system (Kaizen Teian). Suárez-Barraza and Ramis-Pujol (2010) emphasize Kaizen teams as an important Kaizen practice. Suárez-Barraza et al. (2011) note the importance of problem-solving methodology (A3 or Improvement Kata - problem-solving methodology-format), as a way of directing problem solving. Finally, in recent studies, Berhe (2021) reports the application of Kaizen practices in Ethiopian chemical manufacturing companies, in his findings he reaffirms practices such as Kaizen teams, visual management, the Quality Control (QC) Story problem-solving methodology and the elimination of Muda through the Gemba.

Companies must constantly improve to remain competitive in a globalized and changing world. Kaizen is a work philosophy that gradually improves a company’s competitiveness (Imai, 1997). At the business level, performance is defined as the results obtained by the company after executing its operational processes over a given period. This performance is considered relevant not only to the operation but also to the impact Kaizen has on the expected results in the economic, social, and environmental aspects of the company’s products and services. Rahmanian and Rahmatinejad (2013) mention that continuous performance improvement and ESG are important because operational gains, in the form of cost reduction, can subsequently be used to competitively price products in the market. Dos Santos Bento and Tontini (2018) reinforce this argument by highlighting that the “speed” of Kaizen implementation impacts both the operational and ESG spheres.

When taking a Kaizen approach to business, operational performance is understood as the resulting performance of a company’s daily operations in production and administrative processes. According to Mizuno (1988), operational performance has been linked to the company’s vital competitive priorities, such as quality (Q), cost (C), delivery (D), and volume (V). Validating the impact of Kaizen on a company’s performance in terms of QCDV is essential to link its management to a relationship of fundamental elements of its Opex. Carneiro et al. (2025) argue that not only is it feasible to optimize operational performance, but the ESG dimensions of companies can also be improved using Kaizen.

In that sense, corporate actions that are associated with the concern of the extent to which corporations’ benefit or harm social welfare are often referred to as Environmental, Social and Governance (ESG) (Gillan et al., 2021). Increasing attention has been paid to sustainability issues, which has led to a boom in companies implementing and disclosing information about ESG practices (Li et al., 2018). ESG practices are intended to improve environmental performance, social well-being, and corporate governance (Li et al., 2018). ESG refers to how corporations integrate environmental, social and governance concerns into their business models (Gillan et al., 2021). ESG are recognized as dimensions that affect a firm’s long-term value and sustainability through the minimization of disadvantages that firms may impose on the environment, society, and the maximization of governance utility (Gillan et al., 2021). The terms “ESG”, “Corporate Social Responsibility (CSR)” and “sustainability” are used interchangeably, especially when it comes to benchmarking and disclosing data (Gillan et al., 2021).

The number of firms that employ ESG strategies and disclose information about it continues to increase (Li et al., 2018). The role of ESG has an important significance for today’s organizations, even more so considering the transparency, disclosure and reporting objectives (Kharub et al., 2023). To satisfy the needs of stakeholders such as government and society in relation to sustainability issues and ensure sustained business success, companies aim to enhance their ESG performance (Yuen et al., 2016; Liu et al., 2022). The demand for transparency on sustainable and socially responsible practices is rising (Kharub et al., 2023) and Kaizen can help meet this new organizational need.

Kaizen, as a continuous improvement philosophy, is deeply rooted in both technical rigor and human-centric values. Theoretically, this dual nature makes Kaizen particularly relevant to ESG performance. On the environmental side, the principle of muda elimination (waste reduction) inherently aligns with environmental objectives such as energy efficiency, resource optimization, and pollution control (Cherrafi et al., 2019; Jonda et al., 2023). From a governance standpoint, the emphasis on standardized processes, visual management, and transparency in decision-making supports good governance practices such as auditability, risk control, and ethical conduct (Kumar et al., 2023). In the social dimension, Kaizen fosters employee participation, problem-solving autonomy, and continuous learning—elements that theoretically promote workforce well-being, engagement, and development (Pampanelli et al., 2014; García-Alcaraz et al., 2022).

However, the literature presents inconsistent empirical findings across ESG pillars, especially in the social domain. This inconsistency may be attributed to differences in research context, focus, or the presence of enablers (e.g. digital systems) that mediate or amplify Kaizen’s impact. Thus, a disaggregated analysis of each ESG pillar becomes necessary, not only to understand the differential effects of Kaizen practices but also to uncover hidden mechanisms or contingencies affecting these relationships. This rationale underpins the structure of the following hypotheses development, in which we explore the effect of Kaizen on each ESG pillar individually.

From a theoretical standpoint, Kaizen offers mechanisms that may generate ESG benefits through both socio-technical and operational pathways. Its foundation in Total Quality Management (TQM), Just-in-Time (JIT), and employee-driven problem solving provides a systemic platform for organizations to align efficiency with responsibility (Suárez-Barraza et al., 2011; Berhe, 2021). In environmental terms, the practice of waste elimination (muda), energy efficiency, and process control contributes directly to sustainable production (Cherrafi et al., 2019). In governance, Kaizen fosters transparent workflows, auditability, and participative decision-making, all aligned with good governance indicators (Kumar et al., 2023). In the social domain, continuous improvement circles and training systems contribute to employee empowerment, safety, and well-being, consistent with the tenets of social sustainability (Pampanelli et al., 2014; García-Alcaraz et al., 2022).

These mechanisms justify the need for empirical testing of Kaizen’s effects across all three ESG pillars and suggest that its adoption may result in broader sustainability advantages for organizations.

There is limited research in the literature addressing the correlation between Kaizen practices and environmental considerations (Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020). Organizations implementing the improvement of environmental aspects built their development models based on Kaizen to identify environmental waste (Jonda et al., 2023). For example, Pampanelli et al. (2014) adopted a Kaizen approach to integrate environmental sustainability into pure Lean thinking, obtaining positive results in improving mass and energy flows in manufacturing cells. The manufacturing operations were improved by combining Lean Kaizen, and green activities, reducing green waste and emissions (Kurdve and Bellgran, 2021). The Kaizen approach was applied to improve mass and energy flows in manufacturing environments (Jonda et al., 2023). The empirical results obtained from the studies conducted by Garza-Reyes et al. (2018) and Cherrafi et al. (2019) confirm the positive effects of Kaizen on the reduction of material consumption and environmental impacts. Kaizen was positively related to recycling waste products, decreasing waste resulting from pollution and reducing waste resulting from operations in terms of environmental performance (Sanchez-Ruiz et al., 2020).

However, more research is needed on the application of Kaizen practices as results obtained so far seem not to be conclusive (Sanchez-Ruiz et al., 2020). Additionally, previous studies do not specifically focus on daily Kaizen practices and analyze the impacts of Kaizen inserted in other operational excellence approaches, such as Lean (e.g. Pampanelli et al., 2014; Jonda et al., 2023). Another aspect is that studies on this topic address case studies covering one or a small number of companies (e.g. Pampanelli et al., 2014; Kurdve and Bellgran, 2021; Jonda et al., 2023). Differently, this study is global involving different countries, rather than data collected in a single company or country. Therefore, the following hypothesis is proposed.

The demand for transparency on sustainable and socially responsible practices is rising (Kharub et al., 2023). Kaizen, due to its socio-technical nature, can be a power approach for obtaining better results in the organization’s culture and team effectiveness (Kumar et al., 2023) and governance. However, there are no empirical studies on the relationship between governance and continuous improvement. In other words, there is a lack of empirical evidence on how Kaizen practices influence governance performance in organizations (Kozhabayev et al., 2023).

Kaizen practices are grounded in structured routines of continuous improvement that promote process standardization, transparency, and traceability of decisions (Suárez-Barraza et al., 2011; Cheng, 2018). Tools such as A3 problem-solving, Kaizen events, visual management, and suggestion systems foster collaborative review cycles and employee engagement (Brunet and New, 2003; Suárez-Barraza and Miguel-Dávila, 2014; Berhe, 2021), which in turn strengthen compliance and governance mechanisms (Kumar et al., 2024). Moreover, Kaizen’s emphasis on eliminating variability and ensuring consistency across operations enhances predictability and internal control—both critical to sound governance structures (Kharub et al., 2023; Kumar et al., 2023). As such, Kaizen contributes not only to operational excellence but also to improved organizational accountability, ethical behavior, and decision-making frameworks. Based on this rationale, we propose the following hypothesis.

Social sustainability is related to the well-being of their workers, the relationships between them, an adequate work environment, safety for employees and morale by showing that companies care about them (García-Alcaraz et al., 2022). The literature highlights the importance of social factors within the Kaizen concept, such as an adequate reward system for new ideas and personnel development through training (Kozhabayev et al., 2023). For Kaizen to be directed towards sustainability, it is important that it is concerned with the social character, Kaizen team event should ensure that all team members were fully involved and had the opportunity to contribute their ideas (Pampanelli et al., 2014). Kaizen has a significant correlation with the social issue of the sociotechnical system in which it is encompassed, being statistically related to the organization’s culture and team effectiveness (Kumar et al., 2023). Implementing CI approaches such as Kaizen and Gemba in production lines, companies get better safety and morale in employees, who observe that the company has a social responsibility, care about quality of life, skills development, and employee’ well-being (García-Alcaraz et al., 2022). Despite the relevance of social outcomes for Kaizen initiatives, studies fail to provide a clear picture of what and whether Kaizen practices affect social outcomes (Bortolotti et al., 2018). Therefore, the following hypothesis is proposed.

Industry 4.0 (I4.0) technologies act to contribute to continuous improvement/Kaizen methodologies, such as Big Data and Analytics, Cloud, IoT and Augmented Reality (Vinodh et al., 2021). There are several potential relationships between Kaizen and I4.0 technologies (Vinodh et al., 2021). Several digital systems can support Kaizen practices (Vinodh et al., 2021; Sanchez-Ruiz et al., 2020). Data from production processes, smart devices, stakeholders, are collected through sensors and shared in a cloud computing environment with speed and variability. This data is subsequently analyzed by Big Data Analytics, contributing to the solution of problems that corroborate the Kaizen approach to continuous improvement (Sanchez-Ruiz et al., 2020). Wireless industrial network technology of I4.0 can help in timely delivery of products (Vinodh et al., 2021). Intelligent predictive maintenance systems can reduce breakdowns and productivity losses (Vinodh et al., 2021; Sanchez-Ruiz et al., 2020). Intelligent, real-time, and in-process quality control/monitoring system can enhance the product quality as well as process improvement (Vinodh et al., 2021; Sanchez-Ruiz et al., 2020).

Industry 4.0 technologies can help achieve the transparency necessary for ESG (Narwane et al., 2023; Vitto et al., 2023). However, Industry 4.0 technologies alone may not be sufficient to address issues related to social and environmental sustainability (Sahoo and Upadhyay, 2024), for this, different continuous improvement/Kaizen practices are required to make holistic improvements coupled with a set of digital technologies (Kumar et al., 2023). Kaizen practices in conjunction with Industry 4.0 technologies lead to the incorporation of the human being and cognitive thinking, which leads to improved work safety in the workplace, which could impact social sustainability (Kumar et al., 2023). Kaizen initiatives can build the right culture for I4.0 through team autonomy and collaborations, since creativity and involvement are needed in industry 4.0, enabled by Kaizen. Therefore, the Kaizen mindset in I4.0 can lead to better results (Kumar et al., 2023). Therefore, considering that I4.0 technologies and especially digital systems can interfere in the relationships between Kaizen and ESG performance, the following hypotheses are proposed.

The hypotheses were tested using data surveyed from respondents who work with Kaizen in manufacturing companies. The data was analyzed using partial least square structural equation modeling (PLS-SEM), which seeks to explain the relationships among multiple constructs (Hair et al., 2019a, b, c; Suárez-Barraza and Miguel-Dávila, 2014). This study employed a survey research design to answer research questions with an exploratory perspective, which is applied in the early stages of research into a phenomenon, providing the basis for more in-depth studies (Forza, 2002; Rahi, 2017; Hair et al., 2019a, b, c). For the research design survey, a series of sub-steps were applied (Flynn et al., 1990; Forza, 2002). These steps include analysis of theoretical bases and development of the research model (Literature review and development of hypotheses), select data collection instrument and develop measurement model (Research instrument and variables), definition of both the target population and sample and collect data (Sampling and Data Collection) and data analysis process (Flynn et al., 1990; Forza, 2002; Hair et al., 2017, 2019a, b, c). For data analysis, PLS_SEM was used, which has steps for its conduction, which include identification and analysis of sampling size and characteristics, Common method bias measurement and structural model evaluation (Hair et al., 2017, 2020; Legate et al., 2023).

PLS-SEM was chosen over covariance-based SEM (CB-SEM) due to several methodological and contextual factors aligned with the goals of this study. First, the research has a predictive and exploratory nature (Ringle et al., 2020; Manley et al., 2022), focusing on uncovering the relationships between Kaizen practices, ESG performance dimensions, and the mediating effect of digital systems. As such, PLS-SEM is more appropriate in early-stage theory development, particularly when the theoretical background is not yet well established (Hair et al., 2019a, b, c, 2020). Second, PLS-SEM is robust in handling non-normal data distributions and suitable for complex models involving many constructs both formative and reflective constructs, as well as mediation analysis (Manley et al., 2021). Third, PLS-SEM offers solutions when models have many constructs and a large number of items by computing measurement and structural model relationships separately instead of simultaneously (Hair et al., 2019a, b, c). Considering the sample size and the multidimensionality of the model, PLS-SEM provides greater statistical power (even with small samples) and flexibility compared to CB-SEM (Sarstedt et al., 2020). Therefore, the choice of PLS-SEM is methodologically justified and coherent with both the study’s conceptual framework and data characteristics.

Recent studies have increasingly adopted PLS-SEM to investigate the Kaizen methodology and its impact. For example, Mui et al. (2022) examined the antecedents of Kaizen culture and its effect on operational performance, while Alsmairat et al. (2024) analyzed the influence of Kaizen on quality management practices. Additionally, Kamal et al. (2024) explored the implementation of bottom-up Kaizen.

The survey research used an online questionnaire developed to be self-administered and composed of four sections. The first of them has questions related to the respondent’ position and years of experience with Kaizen. The second section encompasses questions about the characterization of the company, such as size, location, sector of activity, Kaizen implementation duration and use of other quality programs adopted by respondents’ organizations. The third section addresses questions about the use of Kaizen practices and digital technologies, while the last section addresses questions about the organization’s performance in the ESG pillars.

Measurement scales for daily Kaizen practices, ESG performance and use of digital systems are designed and developed based on previous literature (Table 1).

The questions in the first and second sections of the questionnaire were translated into qualitative variables (nominal) and the items related to the constructs were translated into Likert-type scale questions from 1 (“strongly disagree”) to 5 (“strongly agree”) (Hair et al., 2017). In order to guarantee the validity and suitability of the items for the context, the questionnaire was pretested (Forza et al., 2002) with 8 Kaizen and quality management experts, 3 academic experts, 2 experts in Kaizen implementation in manufacturing companies and 3 Operational Excellence professionals with background on Kaizen practices. As a result, the questionnaire was revised based on experts’ opinion to guarantee the scales’ reliability and validity.

The first step in applying PLS-SEM is the evaluation of the reliability and validity of the measurement model, conducted through the Confirmatory Composite Analysis (CCA) (Hair et al., 2020; Manley et al., 2021; Legate et al., 2023). In CB-SEM, this step refers to a Confirmatory Factor Analysis (CFA) to test and validate measurement theory (Legate et al., 2023). The CCA in the recommended procedure to confirm measurement models in PLS-SEM through a structured and systematic process that allows researchers to assess the reliability and validity of reflective and formative measurement models by sequentially analyzing key PLS-SEM metrics (Hair et al., 2020; Manley et al., 2021; Legate et al., 2023). Thus, CCA serves as an essential methodological procedure for validating the measurement scales used, offering an alternative to CFA (Legate et al., 2023).

The study used a cross-sectional mail survey of manufacturing companies from different parts of the world, since the study is global. For the sample, professionals with experience in Kaizen in manufacturing companies were invited. To increase the representativeness and diversity of the sample, professionals with experience in Kaizen were sought globally, with each researcher focusing on a region of the globe and listing potential respondents and sending the invitation. The professionals were located through social media with corporate information, with explicit indication of knowledge and experience in Kaizen. Social media has been increasingly used as a source of potential respondents to investigate organizational phenomena, due to its ease, speed, and accurate information about experience in management practices (Potter, 2021; Lizarelli et al., 2023). An invitation explaining the research objectives was sent to potential respondents, as well as the Google Forms link to the questionnaire. A sample of 227 complete questionnaires was obtained.

The database was cleaned by removing professionals who did not work in manufacturing or Kaizen consultancy in manufacturing companies (46 responses excluded) and who did not have experience in Kaizen (5 responses excluded). To ensure data quality (Hair et al., 2017), questionnaires with suspicious response patterns such as straight line or inconsistent responses (13 questionnaires excluded) and outliers performed using Mahalanobis distance (2 questionnaires excluded) were removed. One of the most used tests for detecting outliers in multivariate data (Dai, 2020). The procedures resulted in a final sample of 161 complete responses. The main information about the respondents and companies is presented in Table 2. The sample is made up of respondents who, for the most part (56%), hold the position of continuous improvement manager (mid-level and senior). This demonstrates that the respondents had knowledge about the company’s management practices. The majority of the sample (56%) has more than 5 years of experience with Kaizen, with a small minority with little experience (10% with less than one year of experience). These characteristics demonstrate that the respondents of the proposed study can answer the survey questionnaire. The companies in the sample (Table 2) belong mainly to different sectors, the most representative of which are Automotive (10%), Food/Beverage (6%), Agroindustry (5%), and Textile (4%). There is also a portion of respondents who are consultants in manufacturing companies (9%) with relevant experience with Kaizen practices. Most of the respondents who participated in the study have implemented Lean for at least for 3 years (54%). The respondents of the survey are located in different parts of the world (refer to Table 2). In the sample, there is a greater concentration of companies located in Mexico (30%), India (13%), Portugal (13%), Brazil (9%) and China (9%). Most organizations (54%) have more than 500 employees and are considered large companies. Only 10% of the companies in the sample have up to 49 employees.

Common method bias is a problem attributable to the measurement method used in a SEM study (e.g. questionnaire instructions, response format, implicit social desirability) rather than to the constructs of interest or to the network of causes and effects in the model (Podsakoff et al., 2012; Kock, 2015, 2023). Some approaches are suggested to test the common method bias, two of them were adopted. Harman’s single factor test for all items (Podsakoff et al., 2012) and full collinearity variance inflation factor (FCVIFs), which are VIFs that are calculated considering all latent variables in a model VIF to test the collinearity in the dataset (Kock, 2023). The Harman’s single factor test showed that the variance of the single factor is 39.3%, which is less than 50% (standard value widely used in scientific research) indicating that there is no substantial common method variance. Although widely used, the Harman’s single-factor technique presents limitations, such as low statistical power, the assumption of single source of method bias across all items, and reliance on arbitrary thresholds, which limits its robustness (Podsakoff et al., 2024).The VIF test for collinearity revealed that the values are below 1.06, less than the threshold value 3.3, which indicates that the data does not have collinearity problems (Kock, 2015). FCVIFs are increasingly used for diagnosing common method bias in PLS-SEM, one limitation is that they may produce inflated values in factor-based models and thresholds may need to be adjusted accordingly (Kock, 2015). Due to limitations, the two tests were applied together in this study. Additionally, to test for non-response bias, we compared the responses of early (49.7%) and late respondents (50.3%) to test for possible non-response bias. Levene’s test and a t-test (to check equality of variance and means) were applied and no differences were found in any variable (p-value < 0.05). The lack of significant differences between the responses of early and late responders supports the contention that non-response bias is not significant (Armstrong and Overton, 1977). Non-response bias was also assessed using the same test comparing respondents and non-respondents (professionals invited to respond) based on their declared time of experience with Kaizen. The results indicated no significant differences between the two groups, implying that non-response bias was not an issue (Barclay et al., 2002; Sax et al., 2003).

PLS-SEM was adopted to analyze the results, due to its advantages over similar methods (Hair et al., 2017,2020; Manley et al., 2022). PLS-SEM has been a widely used method in business and operations management (Hair et al., 2019a, b, c). Although PLS-SEM is suitable for small sample sizes, methods indicating the required sample sizes considering PLS-SEM measurement and structural model characteristics should be used (Hair et al., 2017, 2019a, b, c). Two methods for minimum suitable size were observed to verify the adequacy of the sample size: (1) the minimum R-squared method was used (Hair et al., 2017) and calculated with power of 0.90 (minimum sample size of 88); (2) use of the inverse square root method for minimum sample size estimation, and a retrospective estimation was used observing the path coefficient with the minimum magnitude in the model and statistically significant (p-value <0.05) (Kock and Hadaya, 2018) (minimum sample size of 160). Observing the methods used, the sample of 161 respondents used in this study is suitable for the subsequent analysis of data collected for this research. The research model is presented in Figure 1.

The application of the CCA procedure and its main metrics, as well as the analysis and evaluation of the model through PLS-SEM, is composed of two main steps, evaluation of the measurement model and assessment of the structural model (Hair et al., 2020; Legate et al., 2023; Manley, 2021). The steps, criteria and threshold values ​​will be presented in the Analysis and Results section.

The research model is composed of reflective constructs and proposes mediation of the digital systems construct between Kaizen operational practices and ESG performance. Therefore, the measurement model is reflective and has specific evaluation criteria for its analysis (Hair et al., 2017, 2020). The assessment stages and criteria of the reflective measurement model for CCA/PLS-SEM include (Henseler et al., 2015; Hair et al., 2017, 2020; Manley et al., 2021): indicator outer loadings (≥0.708), and statistically significant (p-value<0.05); Internal consistency reliability measured by Composite Reliability (CR) and Cronbach’s Alpha (CA) (CA and CR > 0.7, <0.95); Convergent validity assessed through the Average Variance Extracted (AVE) (AVE ≥0.5); Discriminant validity verified via the Heterotrait–Monotrait Ratio (HTMT), (HTMT <0.9 and one-sided 95% confidence intervals). Table 3 shows that all constructs and items meet the criteria of internal consistency and convergent validity. Table 4 shows that the constructs also meet the discriminant validity criteria. Therefore, the measurement model meets the evaluation criteria.

The first step to assess the structural model for CCA/PLS-SEM is to check collinearity between constructs via the inner VIF values (VIF<3.0), the second one is to assess the statistical significance between the constructs through a Bootstrapping procedure to verify whether the proposed hypotheses are supported by empirical data through the size (β) and significance (p-value <0.05) of the path coefficients (Hair et al., 2017, 2020; Manley et al., 2021). Standardized coefficients (β) with values closer to |1| indicate stronger relationships (Hair et al., 2020). The model’s in-sample predictive ability is assessed by the R² of endogenous variables, with thresholds of ≥0.25 (weak), ≥0.50 (moderate), and ≥0.75 (strong). The effect size (f²) should be interpreted as small (0.02–0.15), medium (0.15–0.35), or large (>0.35). For out-of-sample predictive ability, the Q² predict metric and RMSE should indicate prediction errors lower than those of a linear model benchmark (Hair et al., 2017, 2020; Manley et al., 2021). Table 5 shows the path coefficients obtained by the Bootstrapping procedure.

The results in Table 5 show that the two hypotheses (H1, H2) proposed in the research model are statistically supported. The Kaizen operational practices have a positive and statistically significant impact on environmental performance (β = 0.200; p-value = 0.014) and on governance (β = 0.207; p-value = 0.017). In addition, there is no statistical evidence to support that Kaizen operational practices directly impact social performance (β = 0.166; p-value = 0.082). The path coefficients and effect size f2 allow, respectively, to compare the intensities of relationships and the importance of a construct to explain a given endogenous latent variable (Hair et al., 2017, 2019a, b, c). The results show that the impact of Kaizen operational practices has a similar effect on environmental performance and governance, both are significant, but have a small effect on explaining the variables.

An important analysis for the measurement model is the predictive power of the model and the analysis of generalizable results (Shmueli et al., 2019; Hair et al., 2020). For this, there is a need to evaluate whether the results apply to data sets within and out-of-sample (Shmueli et al., 2019; Hair et al., 2020). The coefficient of determination (R2) measures the in-sample predictive power (Table 3), while assessing the statistical model’s out-of-sample predictive power, the PLS predict procedure was used (Hair et al., 2017; Shmueli et al., 2019). The PLS predict procedure evaluates if Q2 predict values are higher than 0 for all items; and the mean absolute error (MAE) values from the PLS-SEM analysis was smaller than the linear regression model (LM) results (Shmueli et al., 2019; Hair et al., 2020). The results showed that Q2 values were greater than 0 and PLS-SEM < LM (MAE values) for a majority of the items, which indicates a medium predictive out-of-sample power of the model (Shmueli et al., 2019). The out-of-sample predictive power indicates the ability of the model to predict the values of new cases not included in the estimation process (Shmueli et al., 2019). In this case, the explanatory and generalization power of the model to other samples is medium.

Given its predictive nature, similar to multiple regression analysis, model fit indices were not established for PLS-SEM, which are central to confirmatory techniques like CB-SEM and CFA (Hair and Alamer, 2022). Instead, PLS-SEM focuses on assessing the predictive validity of the structural model, with the explained variance (R²) in the dependent variables (in-sample predictive power) and out-of-sample predictive power serving as evaluation criteria (Hair et al., 2017; Manley et al., 2021; Hair and Alamer, 2022). The application of GoF is not inherently aligned with the goals of CCA within PLS-SEM, which accommodates not only confirmation, but also explanation and prediction (Hair et al., 2020). GoF can be useful only for those investigations that follow a purely confirmatory objective within PLS context (Hair and Alamer, 2022).

Mediation is when a third variable, referred to as mediator, intervenes between two related constructs (Sarstedt et al., 2020). One of the objectives of the research is to investigate the mediating role of digital systems in the relationships between Kaizen operational practices and ESG performance. To verify the existence of the effect and the type of mediation (partial, full or no mediation), the first step is to address the significance of the indirect effect via the mediator variable (Hair et al., 2017). If the indirect effect exists, it is necessary to verify the direct effect between the constructs to classify the type of mediation (Hair et al., 2017). Table 6 shows the specific indirect effects. If the direct effect (direct relationship) is nonsignificant and the indirect effect exists (significant), we have the situation of indirect-only mediation (Hair et al., 2017). This occurs to the relationship between Kaizen operational practices and social performance mediated by digital systems (H4c). The type of mediation that occurs in this case is full mediation. The other two relationships (H4a and H4b) present complementary mediation since the product of the direct and indirect effect is positive (Hair et al., 2017).

The control variable “|size” was added to the single-item constructs in the PLS path model (Manley et al., 2022). However, the path coefficients and effect sizes were not significant or representative (Manley et al., 2022), therefore, there is no effect of company size in the investigated relationships and model.

The study discusses the importance of Kaizen practices and their influence on ESG performance. Since Imai (1986; 1997), the literature indicates that Kaizen has an impact on operational performance in the Quality (Q), Cost (C), Delivery (D) and Volume (V) variables of work processes (Mizuno, 1988; Brunet and New, 2003; Suárez-Barraza and Ramis-Pujol, 2010; Berhe et al., 2023). Our research goes much further, showing a theoretical contribution to other competitive dimensions of the organization such as ESG. The results obtained advance existing knowledge and literature, since the findings demonstrate that a global sample of companies (considering different types of manufacturing organizations and countries) confirms the positive relationship between Kaizen practices and environmental and governance performance. In relation to environmental sustainability, results that had been identified through case studies (e.g. Pampanelli et al., 2014), or by observing Kaizen inserted as a Lean tool (e.g. Garza-Reyes et al., 2018) were confirmed in a larger sample and only by observing Kaizen practices, allowing greater generalization of the results. The findings highlight that manufacturing firms implementing structured Kaizen practices, such as standardized work, waste reduction, and team-based problem solving, can achieve significant improvements in environmental and governance indicators. Some examples of application of the results include Kaizen teams may systematically work to reduce CO₂ emissions and resource waste by optimizing production flows using real-time data analytics.

Regarding the findings on the positive relationship between Kaizen and governance, this allows for an advancement in knowledge about the impacts of Kaizen practices. In which structured routines that promote standardization, transparency, and traceability of decisions (Suárez-Barraza et al., 2011; Cheng, 2018), also contribute to this transparency reaching strategic levels. The findings confirm that Kaizen’s emphasis on predictability and internal control through variability elimination, standardization and consistency across operations (Kharub et al., 2023; Kumar et al., 2023) enhances governance structures. For example, visual management and Gemba walks supported by sensor-based monitoring can enhance, for example, health and safety, regulatory compliance, and reporting transparency, improving governance. In addition, the culture of improvement enabled by Kaizen teams, Kaizen events and Suggestion Systems encourage an environment of employee empowerment and participation that is disseminated throughout the company also through adequate remuneration policies, respect of shareholders’ rights and transparency and reporting.

However, there is no statistical evidence to support a direct impact of Kaizen operational practices on social performance. Although Kaizen depends on the continuous efforts, collaboration and engagement of people (Pampanelli et al., 2014), empirical evidence has not demonstrated that organizations are directing Kaizen efforts towards achieving better performance results in the social pillar. Some potential factors in the conduct of the study and the methodological approach may have influenced the results obtained about the relationship between Kaizen e social performance. The first of these is related to the measures used to measure social performance, since this is a multifaceted concept, and measuring its impact can be challenging (Distelhorst et al., 2017). The measurement method may not have been able to capture the more traditional aspects of Kaizen’s influence, such as the well-being of people in companies, as it also includes community and social issues. The study uses a specific statistical analysis technique (PLS-SEM), which may yield different results compared to studies that use different methods. Organizations may need time to fully integrate and optimize Kaizen practices (Suárez-Barraza and Miguel-Dávila, 2014). In the study, 39% of the sample investigated have been implementing Kaizen for up to two years, which may mean that they are in the early stages of implementing these practices, and their full impact on social performance may become evident over time. Additionally, the use of a cross-sectional study instead of a longitudinal study may have an influence on the absence of statistical evidence, since the impact of Kaizen practices on social performance may take time to materialize. Changes in operational practices may need time to cascade through the organization and influence social aspects, leading to a delayed statistical association. Short-term analyses may not capture the full picture of their interconnectedness.

The results may also be influenced by internal and external factors, for example, the effectiveness of Kaizen practices in influencing social performance could vary based on the industry and organizational culture (Bortolotti et al., 2018). The impact of Kaizen practices on social performance may be indirect, factors such organizational structure and culture may be responsible for enabling Kaizen practices to impact social performance, acting as full mediators just like digital systems. External factors, such as economic conditions (Wood, 2010), government regulations lack of skilled and properly trained manpower and political instability (Kharub et al., 2023), can influence social performance independently of Kaizen practices.

The use of digital systems fully mediates the relationship between Kaizen practices and social performance. Chavez et al. (2024) studied the moderating effect of digitalization on the relationship between Lean and social performance and identified that digitalization strengthens this relationship. The authors point out that digital tools, in the context of the Chinese companies studied, have been implemented with a focus on people management, employee wellbeing, health and safety. Mesquita et al. (2023) also points out that the integrated relationship between Big Data Analytics Capability and the Lean socio-technical system improves social performance, since the information allows for faster actions, facilitating worker performance and well-being. The presence of digital systems and technologies allows Kaizen practices to reach new heights, going beyond issues related to well-being and impacting ethical and corporate social responsibility issues.

Empirical results demonstrated that the use of digital systems partially moderates the relationship between Kaizen operational practices and operational performance. These results corroborate the literature’s assertion that digital systems and technologies help identify waste in the production system (Amjad et al., 2021), enhancing the effectiveness of improvement methodologies (Castiglione et al., 2024). Real time data from shop floor strengthens Kaizen practices such as Go-To-Gemba-Walk, which helps in the drafting of current state map and identify waste that impacts the environment (Amjad et al., 2021). The systems coming from I4.0 make it possible to combine improvement practices and green practices in the supply chain, improving cost efficiency, flexibility, and environmental sustainability (Yu and Ye, 2023). The data driven approach and the real-time monitoring systems improve the ability to identify the causes of the deterioration of technical-economic-environmental performance (Mesquita et al., 2023; Castiglione et al., 2024). The presence of digital technologies and systems strengthens the results of Kaizen practices on environmental results.

The use of digital systems also partially moderates the relationship between Kaizen operational practices and governance performance I4.0 technologies and systems enable applications in improving governance and making transparent decisions, they are particularly useful in enhancing ESG disclosure authenticity, real-time reporting, customizing ESG reports and extending the scope of reporting to multi-tier supply chains (Asif et al., 2023). These actions help prevent corruption, audit and control systems, risk and crisis management, transparency and reporting and perform continuous disclosures. At the same time, it allows Kaizen practices to support governance more effectively in organizations.

The originality of the research lies in extending the application of Kaizen beyond operational efficiency to a broader sustainability context, thereby bridging continuous improvement and ESG frameworks. In this way, this study contributes to theory by being the first empirical investigation to examine the relationship between Kaizen operational practices and ESG performance, particularly in the governance dimension—a relationship not previously explored in the literature (Kozhabayev et al., 2023). The findings show that organizations implementing Kaizen practices more strongly tend to also adopt stronger governance structures, including transparent remuneration policies, protection of shareholders' rights, and board diversity (Sancha et al., 2022; Kumar et al., 2024). This highlights the socio-technical role of Kaizen in fostering organizational culture and team effectiveness (Kumar et al., 2023), and offers a theoretical basis for linking Kaizen to governance outcomes—an area previously overlooked in the literature.

Secondly, the research contributes to the literature by demonstrating evidence of the positive impact of Kaizen practices on environmental performance in a global empirical study, absent in the literature (Garza-Reyes et al., 2018; Sanchez-Ruiz et al., 2020). The study also confirms, in a larger and global sample, evidence found by other studies (e.g. Sanchez-Ruiz et al., 2020; Kurdve and Bellgran, 2021; Jonda et al., 2023), such as the impact of Kaizen practices on the reduction of air emissions, use of energy, soil contamination and increased use of renewable energy and recyclable and reused materials.

Thirdly, the study advances by studying the impact of Kaizen practices on social performance, a relationship little explored in the literature, although extremely relevant to understanding the impacts of Kaizen (Bortolotti et al., 2018; García-Alcaraz et al., 2022). By not confirming the relationship, the results differ from previous studies (e.g. Díaz-Reza et al., 2024) allowing us to identify that other factors or conditions may be necessary for Kaizen to impact such performance. The absence of a direct effect on social performance raises theoretical questions regarding the complexity of this dimension. It invites future research to explore contextual moderators, time-lagged effects, and other mediating mechanisms—such as organizational culture, leadership commitment, or workforce engagement—to better understand how and when Kaizen contributes to social sustainability. In this way, the study opens new avenues for developing integrated, system-based theoretical models that connect operational excellence with sustainable development goals.

In addition, the study introduces digital systems as both a full and partial mediator, depending on the ESG dimension analyzed. The full mediation observed between Kaizen and social performance suggests that digital tools are essential for unlocking the social benefits of continuous improvement efforts. This expands socio-technical theory by showing how technology enables the translation of Kaizen principles into social outcomes. Similarly, the partial mediation effects on environmental and governance performance reinforce the relevance of Industry 4.0 systems as theoretical enablers of modern Kaizen strategies (Asif et al., 2023; Mesquita et al., 2023).

The findings of the study have several practical implications for a wide range of stakeholders, from operational managers to policy-makers. For practitioners and decision-makers in manufacturing organization settings, the Kaizen operational practices should be applied systematically and ESG-oriented. This highlights the strategic value of Kaizen, as practitioners and decision-makers can prioritize the integration of Kaizen principles into their operations to reduce waste, enhance resource efficiency, and minimize environmental impact, enabling organizations to enhance their environmental sustainability. They can also prioritize the implementation of Kaizen practices to improve transparency, accountability, and ethical behavior within the organization, promoting employee involvement and empowerment, and fostering a culture of continuous improvement. Another implication is that managers should leverage digital technologies to enhance the effectiveness and efficiency of Kaizen implementation, thereby improving social performance. Although Kaizen practices alone did not show direct statistical significance in improving social outcomes, their combination with digital technologies, such as real-time monitoring, predictive analytics, and intelligent quality systems, unlocks indirect benefits related to employee well-being, workplace safety, and corporate social responsibility. Managers should therefore consider the co-deployment of digital enablers as essential for achieving holistic sustainability performance.

The study also presents implications for investors, financiers, and market participants, since it offers empirical evidence that Kaizen-driven organizations tend to exhibit stronger governance structures and environmental accountability. This relationship strengthens the argument that operational excellence frameworks can serve as informal ESG signals, especially where formal ESG reporting is limited or under development. As information about ESG is important for funders' decisions and obtaining resources (Kharub et al., 2023), the practice of Kaizen can be an additional indicator for positive signaling.

Regulators and Policy Makers can also benefit from the research results, as the results indicate that incentivizing the adoption of socio-technical systems like Kaizen integrated with Industry 4.0 technologies can contribute to ESG goals without necessarily requiring separate compliance structures and can accelerate sustainable manufacturing practices. This research calls for a review of traditional quality management and continuous improvement frameworks to incorporate ESG-relevant performance dimensions. Standards bodies and professional associations that drive industry standards (e.g. ISO, ASQ) could integrate sustainability and digital maturity criteria into continuous improvement standards and support the development of new maturity models that align operational excellence with ESG goals.

This study provides empirical evidence that operational Kaizen practices contribute significantly to two key ESG dimensions: environmental and governance performance in manufacturing organizations worldwide. Using PLS-SEM and a global sample of experienced Kaizen professionals, the findings demonstrate that structured Kaizen routines such as waste reduction, standardization, and problem-solving are positively associated with improved environmental outcomes and stronger governance structures. Although no direct effect was found on social performance, the study reveals that digital systems fully mediate this relationship, suggesting that technology plays a crucial role in enabling Kaizen to generate social sustainability benefits. These insights position Kaizen not only as a productivity-enhancing philosophy but also as a strategic enabler of sustainable value creation across ESG domains.

The results reinforce that there are synergies between the improvement methodology and the achievement of better environmental results, such as a reduction in the use of resources and waste emissions, as well as a greater commitment to transparency, ethics and corporate responsibility associated with governance. The research did not identify positive and statistically significant relationships between the adoption of operational practices and social performance; however, this may be due to a series of factors, including methodological aspects, such as the measurement scale for social performance, cross-section approach and the sample of companies with recent implementation of Kaizen, as well as internal and external factors that may influence and explain the results obtained. The research findings also made it possible to highlight the importance of adopting digital systems for monitoring, predictive maintenance, and data analysis, to boost the relationships between Kaizen operational practices and ESG performance. Digital real-time data monitoring and data analysis technologies enable Kaizen practices to support ESG dimensions by providing information support and guidance to Kaizen practices and can support the three ESG dimensions.

The results found made it possible to point out managerial and academic implications. Among the academics are the advancement in theory by proposing and empirically investigating relationships between a continuous improvement methodology and results in ESG dimensions. Additionally, it also identified the importance of digital technologies and systems in these relationships. This investigation allowed for managerial implications by indicating greater alignment of Kaizen practices and the desired results, as well as greater investment in digital systems.

However, the study has limitations, since it is a global study, and the implementation of Kaizen can be influenced by variables in the context of the country in which the company is located. The failure to investigate differences from this perspective is a limitation of the study. Another limitation is related to the use of a cross-sectional approach, in which longitudinal studies can enable a deeper investigation into the topic. Explore contextual moderators (e.g. national culture, labor regulations, unionization) and alternative mediators (e.g. leadership style, organizational learning) are possible paths to a greater understanding of the topic.

The study provides avenues for future research, since a greater understanding of how Kaizen can assist in environmental sustainability and governance is still needed, especially considering the influence of the maturity of Kaizen implementation on these results. These questions are even more relevant, since the study does not provide specific findings or empirical evidence regarding the impact of Kaizen on social performance, therefore, further investigations, including case and longitudinal studies, are necessary for deeper empirical research aiming to understand why these relationships are or are not occurring in companies or examine delayed effects of Kaizen on social outcomes.

Other possibilities for future studies are related to a greater understanding of how technologies are supporting Kaizen practices for environmental performance. Perform comparative case studies across industries or regions to explore how digital system maturity influences Kaizen’s contribution to ESG dimensions. Empirical studies on the relationship between governance and quality management systems within the concept of continuous improvement are also necessary and opportunities for future research.

All items were measured using a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree).

• KZ1. Our company promotes the use of small group activities or quality circles (Kaizen teams).

• KZ2. Standard Operating Procedures (SOPs) and other standardization tools are consistently implemented.

• KZ3. Kaizen practices are focused on eliminating waste and inefficiencies.

• KZ4. Visual management tools (e.g. performance boards, process flow maps) are widely used.

• KZ5. Suggestion systems are actively used to collect employee improvement ideas.

• KZ6. There is strong support from leadership for continuous improvement initiatives.

• KZ7. Employees receive regular training in Kaizen or continuous improvement techniques.

• KZ8. Cross-functional collaboration is encouraged to solve problems.

• KZ9. Continuous improvement efforts are aligned with strategic objectives.

• EP1. The company has established policies and roles for environmental management.

• EP2. Eco-friendly products or services are developed and promoted.

• EP3. Environmental risk management is embedded in our operations.

• EP4. The company implements energy-efficient processes and equipment.

• EP5. Waste reduction and recycling practices are in place.

• EP6. Environmental performance indicators are tracked and reviewed.

• EP7. Suppliers are evaluated based on environmental criteria.

• EP8. Customers are informed about the environmental benefits of our offerings.

• SP1. The company invests in employee training and development.

• SP2. Workplace safety and health conditions are regularly monitored.

• SP3. Diversity and equal opportunity are promoted within the organization.

• SP4. Employees are engaged in decision-making processes.

• SP5. The organization supports local community initiatives.

• SP6. Social performance is monitored using clear indicators.

• SP7. There is transparent communication with stakeholders about social initiatives.

• SP8. The company maintains stable and secure employment conditions.

• SP9. Ethical conduct is actively encouraged at all levels.

• GP1. Policies against corruption and bribery are effectively enforced.

• GP2. Executive compensation is based on performance and transparency.

• GP3. Governance structures are clear and well-defined.

• GP4. Risk management is systematically implemented across departments.

• GP5. There is a strong culture of accountability and compliance.

• GP6. Stakeholder rights are respected and protected.

• GP7. Decision-making processes are inclusive and transparent.

• GP8. Internal audits and controls are regularly conducted.

• GP9. ESG (Environmental, Social, and Governance) performance is reported publicly.

• DS1. Big Data or Big Data Analytics systems are used to support operations.

• DS2. Real-time monitoring and control systems are in place.

• DS3. Digital tools are used to improve transparency and traceability.

• DS4. Information systems are integrated to support ESG decision-making.