MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY OPEN UNIVERSITY PHAM ANH NGUYEN THE IMPACT OF GREEN INNOVATION AND ENVIRONMENTAL PERFORMANCE ON FIRM PERFORMANCE OF VIETNAMESE MANUFACTURING ENTERPR.
INTRODUCTION
STUDY REASONS
Environmental degradation and resource depletion accompany rapid economic expansion and the broad growth of businesses, with the manufacturing sector often identified as a major contributor to today’s environmental challenges This situation is driven in part by many firms operating at low economic efficiency, limited innovation, and outdated technology Addressing these issues requires focusing on productivity improvements, technological modernization, and innovative practices within the manufacturing industry to reduce ecological impact while sustaining growth.
Many businesses prioritize profits over community, social, and environmental benefits, and they hesitate to invest in environmental protection equipment and production technology because upfront costs can erode profits After a period of rapid economic expansion, Vietnam faced the substantial task of balancing economic development with environmental sustainability Despite increased efforts, research indicates limited success of environmental rules in Vietnam, with low levels of compliance across critical industries such as food, leather, and paper.
Vietnamese consumers are increasingly willing to pay a premium for brands that promise green and clean pledges, offer eco-friendly products, and meet high quality and safety standards This shift has made sustainable and environmentally friendly solutions a core consumer need Consequently, businesses that do not aggressively develop and market eco-friendly offerings will struggle to meet demand, attract customers, and maintain competitiveness and economic efficiency.
Companies are increasingly pursuing cost-reduction strategies in manufacturing, adopting lean production to minimize waste and lower costs, and strengthening competitive positioning to achieve high profitability in global markets (Brigham and Houston, 2012; Tortorella et al., 2018).
Green innovation is a key lever for companies aiming to reduce production costs, achieve product differentiation, and secure a competitive advantage By controlling input resources, energy consumption, and waste in the manufacturing process, firms can improve corporate efficiency (Chen & Liu, 2019) Enterprises that integrate green digital technologies into manufacturing are likely to boost competitive efficiency and profitability while protecting the environment and enhancing quality of life This approach opens substantial opportunities for greener manufacturing while still meeting corporate profit objectives (Rỹòmann et al., 2015; Stock & Seliger, 2016).
Upgrading production technology enables firms to proactively manage the environmental impacts of manufacturing and to implement more effective environmental protection initiatives This proactive approach enhances efficiency, mitigates environmental risk, and supports sustainable growth across the organization.
Practical reasons
of clean technology enterprises Environmental friendliness, lowering greenhouse gas emissions; and contributing to increased competitiveness and green growth are critical needs for our country's businesses today
This study examines the drivers and magnitude of the impact of elements influencing green innovation and analyzes the link between green innovation, environmental performance, and firm performance It provides practical, evidence-based solutions aligned with real-world needs, including steps to encourage firms to engage in environmental protection activities and to design effective, sustainable corporate growth plans that maximize profits while contributing to social well-being By clarifying how green innovation enhances environmental performance and firm performance, the research offers an actionable roadmap for sustainable business success and broader social benefits.
There is growing scholarly interest in examining the relationship between green innovation and firm performance, with researchers increasingly analyzing how eco-friendly capabilities influence outcomes such as productivity, profitability, and competitive position In related industries, studies have yielded findings that reinforce a positive link between green innovation and firm performance, while also identifying context-specific factors that shape the strength and nature of this relationship.
Green innovation's influence on corporate success, including financial and non-financial performance, as well as its environmental effects, has not been adequately investigated in prior research This thesis addresses this gap by examining how sustainability-driven innovation relates to overall firm performance and environmental outcomes, and by identifying the mechanisms that link green practices to value creation for shareholders, employees, and stakeholders The study outlines in-depth the central issues at the intersection of green innovation and corporate performance to provide a clearer understanding of when, where, and how green strategies contribute to competitive advantage and responsible environmental stewardship.
Across global and Vietnamese empirical research, controversies persist over the proper measurement scales for the concepts of factors influencing green innovation, green innovation itself, environmental performance, and business performance Although debate continues, there is no broad consensus on the appropriate scales or the outcomes they produce Accordingly, this thesis clarifies the core concepts and develops and refines the measurement scales to align with the specific research context and objectives.
Green innovation's impact on environmental performance and business performance has not been examined simultaneously, and this study identifies nine component scales across technology, organization, and environment to capture its implementation To clarify these relationships, the research develops a thorough analytical framework that links the factors affecting green innovation to its environmental impact and to firm performance, enabling a test of the proposed research model and hypotheses with the observed variables The aim is to illuminate how technology-, organization-, and environment-related determinants shape green innovation, how this innovation translates into environmental performance, and ultimately how both contribute to overall business performance.
Existing studies on the relationships among green innovation, environmental performance, and enterprise efficiency have yielded inconsistent results To resolve this ambiguity, this study conducts an empirical analysis to examine how green innovation and environmental performance affect enterprise efficiency and to clarify the interrelationships among these three factors.
This study investigates whether environmental performance mediates the relationship between green innovation and corporate performance, including both financial and non-financial outcomes Prior research has not established this mediation, leaving a gap that this thesis aims to fill by testing the environmental-performance pathway and clarifying how green innovation translates into overall firm performance through environmental performance.
RESEARCH OBJECTIVE
This study has the general objective of analyzing the factors affecting green innovation and analyzing the relationship between green innovation and firm performance through the intermediate variable, environmental performance
This study has the following specific objectives:
(1) Determining the factors affecting the green innovation of manufacturing enterprises in Vietnam
(2) Determining the impact of green innovation on the environmental performance of manufacturing enterprises in Vietnam
(3) Determining the impact of environmental performance on firm performance of manufacturing enterprises in Vietnam
(4) Determining the impact of green innovation on firm performance of enterprises through the mediating role of environmental performance
(5) Suggesting management implications to improve firm performance of manufacturing enterprises through green innovation and environmental performance.
RESEARCH QUESTION
(1) Which factors affect green innovation of manufacturing enterprises in Vietnam?
(2) How does green innovation affect the environmental performance of manufacturing enterprises in Vietnam?
(3) How does environmental performance affect the firm performance of manufacturing enterprises in Vietnam?
(4) How does green innovation affect the firm performance of manufacturing enterprises in Vietnam through the mediating role of environmental performance?
(5) What recommendations and solutions can help manufacturing enterprises in Vietnam promote green innovation to develop enterprises effectively and achieve environmental performance?
RESEARCH SUBJECT AND SCOPE
This study explores the impact of factors (technology, organization, environment) impacting green innovation and the relationship between green innovation, environmental performance, and firm performance
The survey subjects are directors of 400 production enterprises in key industrial zones in provinces and cities across the country May–June 2021 is the survey period.
METHODOLOGY
- Qualitative method: Face-to-face interview and group discussion
This study employs a quantitative methodology using a survey questionnaire for scale development and construction across both preliminary and official stages Statistical analysis will be conducted in SmartPLS 3.3.2 and SPSS 22.0, based on characteristic samples and rigorous scale testing, including Cronbach's Alpha reliability, exploratory factor analysis (EFA), and partial least squares structural equation modeling (PLS-SEM) A formal survey will be carried out during the scale development and validation process with a target sample size of 400, recruited via convenience sampling.
NEW FINDINGS
1) Clarifying the concepts and finding out the relationship between the factors affecting green innovation, the influence of green innovation, and environmental performance on the efficiency of enterprises
2) Testing models and research hypotheses about the relationship between factors affecting green innovation (factors on technology, organization, and external environment), green innovation, environmental performance, and firm performance (including financial and non-financial performance) have not been studied simultaneously
3) Research on the mediating role of environmental performance on the relationship between green innovation and firm performance including financial and non-financial performance
4) Building and adding a new scale for all the factors in the model based on integrating the previous scales in accordance by the research context in Vietnam
From a governance perspective, the study identifies the key components that drive green innovation and its impact on efficiency in manufacturing companies These insights enable managers to develop high-quality policies and tailored strategies that fit their specific contexts, helping organizations pursue sustainable growth and profit maximization in today’s market environment.
STRUCTURE OF THESIS
Chapter 5 Conclusions and policies implications
LITERATURE REVIEW
CONCEPTS
Relative advantage measures how an innovation is perceived to outperform its alternatives in economic or social terms, such as performance, satisfaction, reputation, and convenience (Rogers, 2003) Compatibility gauges how well the innovation aligns with a company’s current values, experiences, and needs for future change (Rogers, 2003) Complexity describes how difficult the innovation is to understand and use, which can hinder knowledge transfer and the diffusion of the innovation; therefore, simplicity is the degree to which the innovation is easy to understand and use, easy to transfer knowledge, and easy to disseminate.
Organizational support is the degree to which a company backs its employees, including incentives that promote the use of specific technologies or systems, which in turn drives effective innovation The quality of human resources is the foundational element that allows organizations to shape and influence the skills, attitudes, and behaviors of individuals, enabling them to perform their jobs successfully and help achieve organizational goals.
Customer pressure from consumers and business customers—the key stakeholder groups—drives firms to improve environmental and social performance (Ateş et al., 2011; Ehrgott et al., 2011) Government pressure represents the perceived regulatory push for green innovation through rules and enforcement (Huang et al., 2016), while government support—via incentives, financial resources, technical assistance, pilot projects, and training—helps advance green innovation (Scupola, 2003; Tornatzky & Fleischer, 1990) Market uncertainty, caused by external events, reflects the difficulty in predicting future conditions and states (Wong et al., 2011; Pfeffer & Salancik, 1978).
Green innovation comprises new or enhanced processes, techniques, practices, systems, and products that help businesses continually improve production and operational efficiency while pursuing sustainable development It also aims to reduce raw-material costs, boost production capacity, and uphold social responsibility to prevent or minimize harm to the environment, as described by Kemp et al (2001) and Beise & Rennings (2005).
Environmental performance can be basically defined as the extent to which companies meet their stakeholders' expectations for environmental responsibility (Ruf et al., 1998; Carroll, 2000)
Financial efficiency is a company’s ability to manage and control its resources, reflecting the financial condition over a given period and evaluated using indicators such as the capital adequacy ratio, liquidity, leverage, solvency, and profitability In contrast, non-financial performance measures aspects of enterprise performance that cannot be expressed in monetary terms, often gauged by factors like customer satisfaction, product quality, supplier reliability, company reputation, and competitive advantage, which together assess and promote effective management performance.
MAIN THEORIES
With comprehensive policy changes, a nation may implement a green growth plan for its economic, social, technical, environmental, and development elements (Dinda,
The green growth hypothesis argues that GDP growth can be decoupled from resource use and carbon emissions, enabling rapid progress that helps avert climate change and ecological disruption UNEP (2011) defines green growth as development that improves human health and social equity while substantially reducing environmental risks and the depletion of scarce resources, characterized by being low‑carbon, resource‑efficient, and socially inclusive When green growth and environmental protection are effectively implemented, they support sustainable business development through adherence to environmental laws, pollution control, waste reduction, and resource savings Additionally, investment in clean technology can create competitive advantages and open up new business opportunities, illustrating how environmental action can drive economic innovation, a perspective echoed by OECD (2012).
Therefore, this study uses the green growth theory of UNEP (2011) to propose hypotheses about the impact of green innovation on environmental performance and firm performance
Sustainable Development Theory emerged in the 1980s, focusing on the synchronous development of the economy, society, and environment, and has entered the high-level political agenda (Shi et al., 2019) The United Nations' World Commission on Environment and Development (WCED, 1987) defines sustainable development as "development that meets the needs of the present but does not interfere with meeting the needs of future generations," emphasizing the effective use of natural resources and ensuring the living environment for people in the development process Criteria for an overall assessment of sustainable development may include stable economic growth; making good progress and social justice; rational exploitation, economical use of natural resources, and protection and improvement of living environment quality In this study, green innovation was used to develop green goods with the intention of conserving
Seven resources should be used optimally, with emissions reduced to protect the environment, propelling sustainable development and economic efficiency This approach demonstrates how responsible resource use supports long-term growth while environmental protection preserves natural assets for the future Businesses play a vital role in driving the nation’s economy toward sustainable development, balancing profitability with ecological stewardship.
As a result, this study employs WCED's (1987) theory of sustainable development to propose hypotheses regarding how the performance of businesses is impacted by green innovation
Stakeholder theory, introduced by Freeman (1984), argues that businesses should consider the interests of all stakeholders—customers, workers, owners, communities, shareholders, and others—when making strategic decisions that affect the organization To ensure long-term success, the theory posits that firms must treat stakeholders equitably and seek mutually beneficial compromises in conflicts of interest Building on this view, Weng and Lin (2011) show that firms pursue strategic actions to appease major stakeholders and actively monitor both internal and external issues The drivers of green innovation are the means by which a company can address stakeholder concerns to achieve commercial objectives, with customer expectations and government regulations serving as key forces (Weng et al., 2015) Consequently, success in the modern marketplace depends on understanding each stakeholder's impact on the adoption of green technologies.
Therefore, the stakeholder theory of Freeman (1999) was applied in this study to propose hypotheses about the impact of organizational and environmental factors on green innovation
According to Cameron (1978), an organization's ability to efficiently harness its most valuable resources is central to organizational performance When a firm operates effectively, it minimizes waste, delivers cost-efficient products, and generates revenue by reducing costs or maximizing earnings The theoretical lenses used to measure performance include the goal model, the resource system model, the process model, the strategy model, and the value-competition technique (Chelladurai et al., 1987; Yuchtman & Seashore, 1967) Researchers have established criteria to evaluate organizational performance from both financial and non-financial angles, with efficiency widely used to compare the benefits produced by an operating process over a period to the capital invested Enterprises can gauge the outcomes of current resource use by comparing them against the organization’s full potential.
8 capacity and learn about the factors that influence output efficiency by calculating the efficiency of resource utilization
Grounded in the organizational effectiveness theory of Price (1968) and drawing on the foundational work of Yuchtman and Seashore (1967) as well as Chelladurai and colleagues (1987), this study proposes hypotheses about the impact of green innovation on environmental performance and firm performance.
Resources—assets, processes, firm capabilities, information, and knowledge under the organization’s control—form the basis for developing and executing strategic plans The resource-based view (RBV) offers an inside-out explanation of why firms succeed or fail in the market by focusing on unique resources and capabilities According to Barney (1986), a resource is valuable when it enables a company to act in ways that increase sales, reduce costs, boost profits, or create other financial gains RBV helps managers recognize why resources can be the most valuable assets a firm owns and how to deploy them to improve performance and achieve superior results The theory also links green innovation to better environmental performance, competitive advantage, and sustainable development, treating green innovations as significant intangible resources that are difficult for competitors to imitate.
Therefore, the resource-based view theory of Barney (1991) was applied in this study to propose hypotheses about the impact of green innovation on environmental performance and firm performance
Rogers’ innovation diffusion theory (as used since Parisot, 1995) is a widely adopted framework for examining how new technology spreads across disciplines It treats diffusion as a process that reduces uncertainty about an innovation by focusing on five key characteristics: relative advantage, compatibility, complexity, testability, and observability Innovations that offer greater relative advantages, better compatibility, lower complexity, easier testability, and clearer observability tend to be adopted more rapidly Yet Rogers also cautions that implementing an innovative idea is never easy, even when benefits are evident, and diffusion is accelerated when these attributes are available and demonstrable.
Therefore, the theory of innovation diffusion of Rogers (2003) was applied in this study to propose hypotheses about the impact of technology factors on green innovation
Grounded in prior research, this study examines how technology, organizational factors, and environmental factors influence green innovation, recognizing that these dimensions shape the adoption and implementation of eco-friendly innovations Building on the findings of Lin and Ho (2011) and Ruslan (2014), the paper investigates the linkages among these three elements and their impact on the application of green innovation.
Earlier studies (Sia et al., 2004; Lin & Ho, 2011; Kousar et al., 2017) found that relative advantage and perceived compatibility influence green-innovation adoption Complexity is detrimental to adoption, while the relative advantage—encompassing price, quality, ease of use, durability, and the satisfaction gained after implementation—drives firms to pursue greater economic benefits (Lin & Ho, 2011; Rogers, 2003) Adoption of green innovation is frequently linked to relative advantage (Rogers et al., 2012; Tornatzky et al., 2008) Compatibility reflects an organization’s perception of technology’s consistency with its values, experiences, and needs; if technology is seen as compatible with existing knowledge, organizations are more easily persuaded to adopt it (Chau & Tam, 1997) Prior work shows compatibility positively affects green-innovation adoption (Lin & Ho, 2011; Kousar et al., 2017) The degree of technology complexity—the difficulty of understanding and applying the technology—leads researchers to hypothesize that rising complexity lowers adoption likelihood Diffusion and knowledge-sharing challenges (Tornatzky & Klein, 1982) raise technical complexity; however, this complexity can be overcome if knowledge is easily shared within the organization, enabling adoption Knowledge transfer and diffusion challenges are often viewed negatively for adoption (Rogers, 2003; Tornatzky & Klein, 1982; Lin & Ho, 2011; Weng & Lin, 2011), suggesting technological factors are expected to have a positive effect on green innovation.
Based on the findings of the previous research and Roger's (2003) innovation diffusion theory, the research hypothesizes are proposed as follows:
H 1 : Relative advantage has a positive impact on green innovation of Vietnamese manufacturing enterprises
H 2 : Compatibility has a positive impact on green innovation of Vietnamese manufacturing enterprises
H 3 : Simplicity has a positive impact on green innovation of Vietnamese manufacturing enterprises
This study categorizes two main stakeholder groups as internal (workers, firm executives) or external influences using Freeman's (1999) Stakeholder Theory Customers
External factors, including government influence, positively affect how businesses use and adopt green innovations, as shown by Lin & Ho (2011), Wang et al (2016), and Ibrahim et al (2018) Because organizational resources have been extensively studied in technology innovation and the management environment, this study focuses on the quality of human resources and the level of organizational support (Weng et al., 2015; Lin & Ho, 2011; Lee, 2008) Adopting green innovations benefits firms with competent human resources, a link supported by Lin & Ho (2011) and Weng et al (2011) Prior research (Soliman & Janz, 2004; Teo et al., 2009) also shows that organizational support, particularly from senior management, positively affects the adoption of green innovation, implying that organizational characteristics are connected to higher green innovation uptake.
Based on the findings of the previous research and Freeman’s(1999) stakeholder theory, the research hypothesizes are proposed as follows:
H 4 : Organizational support has a positive impact on the green innovation of Vietnamese manufacturing enterprises
H 5 : Quality of human resources has a positive impact on green innovation of Vietnamese manufacturing enterprises
This study highlights stakeholder pressure—primarily from customers and regulatory authorities—along with government support and environmental uncertainty as key drivers of green innovation adoption in enterprises, a link confirmed by prior studies (Lin & Ho, 2011; Weng et al., 2015; Guo et al., 2018) Grounded in stakeholder theory, firms undertake activities to satisfy their major stakeholders, with customers and regulators consistently identified as among the most influential to business strategy (Christmann, 2004; Etzion).
2007) According to the findings of earlier studies, there is a relationship between business activities and customer and regulatory pressure (Christmann, 2004; Lee, 2008; Wong & Fryxell, 2004) Thus, it is anticipated that government and consumer pressure will have a positive effect on green innovation According to studies by Aragon-Correa, Sharma, Lee, and Ai from 2003, 2008, and 2010, the authors demonstrate how government support policies may help enterprises make up for a lack of technical innovation Companies are also more likely to implement green technologies to generate the possibility of improving firm performance and environmental performance in environmental uncertainty (Aragon- Correa & Sharma, 2003; Rothenberg & Zyglidopoulos, 2007) This suggests that environmental factors could influence green innovation in a helpful manner
Based on the findings of the previous research and Freeman’s (1999) stakeholder theory, the research hypothesizes are proposed as follows:
H 6 : Customer pressure has a positive impact on green innovation of Vietnamese manufacturing enterprises
H 7 : Government pressure has a positive impact on green innovation of Vietnamese manufacturing enterprises
H 8 : Government support has a positive impact on green innovation of Vietnamese manufacturing enterprises
H 9 : Environmental uncertainty has a positive impact on green innovation of Vietnamese manufacturing enterprises
2.4 IMPACT OF GREEN INNOVATION AND ENVIRONMENTAL PERFORMANCE OF VIETNAMESE MANUFACTURING ENTERPRISES
For businesses to compete in today's market, they should develop and implement environmentally friendly products or manufacturing processes that support energy efficiency This is an effective strategy that is frequently employed by businesses to reduce waste during production, minimize their negative impact on the environment, and achieve exceptional economic efficiency (Dangelico & Pujari 2010; Triguero et al., 2013) Environmental performance, or the general objective of green innovation, is to minimize pollution, energy consumption, and waste, and to substitute precious resources with sustainable or recycled ones (Kemp & Arundel, 1998) Researchers frequently evaluate environmental performance by looking at factors including emissions, wastewater, solid waste, effective input material utilization, frequency of environmental incidents, and the environmental state of businesses Improvements in manufacturing techniques and green productivity will increase the likelihood of improving environmental performance, according to Montabon et al (2007) and Seman et al (2019), and research by Zhang et al
A 2017 study indicates that the majority of green innovations adopted by enterprises reduce carbon emissions, signaling that green innovation drives meaningful environmental improvements Accordingly, these findings lead to the proposed research hypothesis: higher levels of green innovation within firms are associated with greater reductions in carbon emissions and improved sustainability performance.
H 10 : Green innovation has a positive impact on the environmental performance of Vietnamese manufacturing enterprises
2.5 IMPACT OF GREEN INNOVATION, ENVIRONMENTAL PERFORMANCE, AND FIRM PERFORMANCE OF VIETNAMESE MANUFACTURING ENTERPRISES
IMPACT OF GREEN INNOVATION AND ENVIRONMENTAL PERFORMANCE
To stay competitive, businesses should develop and implement environmentally friendly products or manufacturing processes that promote energy efficiency, a strategy that reduces production waste, minimizes environmental impact, and delivers strong economic efficiency (Dangelico & Pujari 2010; Triguero et al., 2013) Environmental performance, the core aim of green innovation, seeks to minimize pollution, energy consumption, and waste while substituting finite resources with sustainable or recycled alternatives (Kemp & Arundel, 1998) Researchers commonly evaluate environmental performance through metrics such as emissions, wastewater, solid waste, efficient input material utilization, frequency of environmental incidents, and the overall environmental state of the business Improvements in manufacturing techniques and green productivity increase the likelihood of boosting environmental performance (Montabon et al., 2007; Seman et al., 2019; Zhang et al.).
A 2017 study shows that the majority of green innovations in enterprises reduce carbon emissions, suggesting that green innovation is predicted to have a positive effect on green innovation Based on these findings, the following research hypothesis is proposed:
H 10 : Green innovation has a positive impact on the environmental performance of Vietnamese manufacturing enterprises
IMPACT OF GREEN INNOVATION, ENVIRONMENTAL PERFORMANCE
Green innovation activities, including green product design and environmental stewardship, can help firms reduce energy use, prevent pollution, and improve recycling rates However, evidence on the financial impact of implementing green innovation and maintaining a sustainable environment is mixed: some studies report positive effects on profitability, while others find negative effects on financial performance In particular, the costs associated with social responsibility and environmental compliance can be higher and may be passed on to customers, potentially dampening profitability (Lin et al., 2019; Walley & Whitehead, 1994; Palmer et al., 1995).
Although some studies link higher product costs to a competitive disadvantage and reduced profitability (Aupperle et al., 1985; Guerard, 1997), most research indicates that complying with environmental standards can spur green innovation that boosts productivity Being first to develop green, eco-friendly solutions can improve organizational performance, lower environmental compliance costs, and enable more efficient use of raw materials, thereby reducing profit losses from regulations and enhancing financial efficiency (Altman, 2001; McWilliams & Siegel, 2001; Chang, 2011; Singh et al., 2020; Asadi et al., 2020) In addition, green innovation has been shown to affect non-financial performance—impacting customer satisfaction, product quality, supplier reliability, brand recognition, and competitive advantage—which supports overall effectiveness in the green space (Porter & Van Der Linde, 1995; Ahmad & Zabri, 2016; Gurlek & Tuna, 2017) These findings lead to the following research hypotheses:
H 11 : Green innovation has a positive impact on the firml performance of Vietnamese manufacturing enterprises
While earlier studies argued that green innovation is difficult to achieve and that environmental responsibility costs businesses capital (Aupperle et al., 1985; Walley & Whitehead, 1994), Prace (2005) contends that efficient environmental performance can create value through stronger perceptions of green products, lower pollution-control costs, and higher productivity by reducing energy and resource waste and pollution The literature generally supports a positive link between environmental performance and financial and non-financial performance, treating environmental performance as a performance-enhancing innovation (Aguilera-Caracuel & Ortiz-de-Mandojana, 2013; Porter & Van der Linde, 1995), and noting it can boost corporate legitimacy (Hart, 1995), sustainable competitive advantage (Hart, 1995; Russo & Fouts, 1997), as well as environmental reputation and employee engagement.
Strong environmental performance reflects solid managerial capabilities, a link highlighted in Aragón-Correa (1998) and Aschehoug et al (2012) By enhancing productivity and profitability while reducing costs tied to environmental compliance, environmental performance directly contributes to stronger firm performance In short, firms investing in effective environmental management can expect better efficiency, higher profits, and lower compliance expenses, creating a positive impact on overall performance.
The findings of the investigations mentioned above led to the following research hypotheses:
H 12 : Environmental performance has a positive impact on the firm performance of Vietnamese manufacturing enterprises
PROPOSED RESEARCH MODEL
H1 Relative advantage has a positive impact on green innovation
H2 Compatibility has a positive impact on green innovation
H3 Simplicity has a positive impact on green innovation
H4 Organizational support has a positive impact on green innovation
H5 Quality of human resources has a positive impact on green innovation
H6 Customer pressure has a positive impact on green innovation
H7 Government pressure has a positive impact on green innovation
H8 Government support has a positive impact on green innovation
H9 Environmental uncertainty has a positive impact on green innovation
H10 Green innovation has a positive impact on firm performance H11 Green innovation has a positive impact on environmental performance H12 Environmental performance has a positive impact on firm performance
Based on the above hypotheses, the research model is proposed as follows:
- Independent variables: Relative advantage, Compatibility, Simplicity, Organizational support, Quality of human resources, Customer pressure, Government pressure, Government support, and Environmental uncertainty
- Intermediate variables: Green innovation, Environmental performance
METHODOLOGY
RESEARCH DESIGN
The research process unfolds in a clear, ordered sequence: first, the problem statement is defined; second, the research objectives are established; and third, prior theories and empirical studies are reviewed to examine the factors that influence green innovation and the links between green innovation, environmental performance, and firm performance.
This section explains how to identify research gaps and formulate hypotheses and models, select and adjust scales, carry out sampling and data collection, and conduct data analysis that includes descriptive statistics, scale validity assessment, and the estimation of both the measurement model and the structural model.
In particular, this thesis uses a mixed method Quantitative methods are mainly used to analyze all data and explain the results Qualitative methods were used to develop the scale
Employing a qualitative method, the study developed and refined a scale by drawing on theoretical foundations and scales from prior research, producing a preliminary instrument that was then adjusted and finalized through qualitative techniques A group discussion with 10 participants—including staff from the Management Economic Zone, the Department of Natural Resources and Environment, the management boards of the zones, and representatives of typical production enterprises in industrial zones and clusters—provided initial insights Six subject-matter experts in business and environmental topics were subsequently interviewed in depth The research model will be further enhanced through additional in-depth interviews and focus groups, with ongoing adjustments to the scale and its observed variables.
Table 3.1 Summary of qualitative method results
Constructs Expected scale Qualitative methods
This study employs quantitative methods—descriptive statistics analysis, scale reliability analysis, exploratory factor analysis (EFA), and structural equation modeling (SEM)—to estimate relationships between concepts The quantitative analysis activities include Descriptive statistics, Composite Reliability, Convergence validity, Discriminant validity, Multicollinearity statistics, Path coefficient, R square, and f square.
QUALITATIVE METHODS
In this thesis, qualitative research is carried out through two specific steps as follows:
Step 1: Conduct a group discussion outline This step's goal is to better explain the scale's theory, formation, construction, and development Therefore, this qualitative study conducts group discussion with a sample size of 10 people including experts staffs of the Management Economic Zone, Department of Natural Resources and Environment, the Management Boards of the zones, and representatives of typical production enterprises located in industrial zones and clusters
Step 2: The next phase is to conduct in-depth interviews with 06 experts in the field of business - environment after receiving a preliminary scale from expert group talks in order to continue adjusting and developing the scale to provide the best value content and form for quantitative research In this step, interviews are conducted with the survey subjects according to the interview outline in order to adjust and develop the scales As a consequence of the in-depth interview, additional remarks were also recorded, and the scale was adapted to reflect the circumstances at the production sites There were no novel insights on the scope of the study's components found at the conclusion of the in-depth interviews.
MEASUREMENT SCALE
After being modified and expanded, the scale for the concept of Relative advantage (LTD), which was derived from Lin and Ho's (2011) study, now comprises 13 observable variables
After being modified and expanded, the scale for the concept of Compatibility (KTT), which was derived from Lin and Ho's (2011) study, now comprises 9 observable variables
After being modified and expanded, the scale for the concept of Simplicity (SDD), which was derived from Lin and Ho's (2011) study, now comprises 7 observable variables
After being modified and expanded, the scale for the concept of Organizational support (HTC), which was derived from Lin and Ho's (2011) study, now comprises 12 observable variables
3.4.5 Quality of human resources (CNL)
After being modified and expanded, the scale for the concept of Quality of human resource (CNL), which was derived from Lin and Ho's (2011) study, now comprises 11 observable variables
Following modification and expansion, the Customer Pressure (AKH) scale, originally derived from Lin and Ho (2011) and Ehrgott et al (2011), has been broadened to include additional dimensions of customer influence The revised scale now comprises multiple indicators that capture how customer expectations, demand signals, and relationship dynamics shape supplier decisions, the perceived pressure exerted by customers, and the responsiveness of suppliers to market demands This expanded structure enhances the scale’s reliability and validity across diverse contexts, enabling more nuanced analyses of customer-driven pressure in supply chains.
After being modified and expanded, the scale for the concept of Government pressure (ACP), which was derived from the study of Lin and Ho (2011), Lopez-Gamero et al., (2010), Liu
(2009), and Zhao et al., (2015), now comprises 11 observable variables
After being modified and expanded, the scale for the concept of Government support (HCP), which was derived from the study of Lin and Ho (2011) and Lee (2008), now comprises
After being modified and expanded, the scale for the concept of Environmental uncertainty (DTT), which was derived from the study of Lin and Ho (2011), now comprises 11 observable variables
After being modified and expanded, the scale for the concept of Green product innovation (DSP), which was derived from the study of Chen et al., (2006) and Chen (2008), now comprises
After being modified and expanded, the scale for the concept of Green process innovation (DQT), which was derived from the study of Chen et al., (2006) and Chen (2008), now comprises
After being modified and expanded, the scale for the concept of Environmental performance (SMT), which was derived from the study of Zhu and Sarkis (2004), now comprises
After being modified and expanded, the scale for the concept of Financial performance (HQC), which was derived from the study of Cooper et al., (1994), Avlonitis et al., (2001) and
Zhu and Sarkis (2004), now comprises 11 observable variables
The non-financial performance (HQP) scale has been revised and expanded from its original derivation in the studies by Cooper et al (1994) and Avlonitis et al (2001), and it now comprises 11 observable variables.
QUANTITATIVE METHODS
Because the study population is readily accessible and located near the research sites, convenience sampling is the most appropriate data-collection method for this study This approach leverages the investigator’s ability to reach subjects where they are most likely to be encountered, which is especially important given the evolving and widespread developments of the COVID-19 pandemic in recent years By prioritizing accessibility and proximity, this method enables efficient data gathering while reflecting real-world conditions of the pandemic.
Direct survey questionnaires were distributed to directors of manufacturing firms located in Vietnam’s major industrial zones to collect the data for this study These directors, who are deeply familiar with the firms currently participating in decision-making processes, were selected as respondents for data collection Following data gathering and screening for valid responses, 400 observations were included in this analysis, and the sample size satisfies the study’s requirements.
Quantitative data from the official survey were analyzed with SPSS 22.0 and PLS-Smart 3.3.2 SPSS was employed to perform descriptive statistics, scale reliability analysis, exploratory factor analysis of the independent and dependent factors, and hypothesis testing, while PLS-Smart was used to examine the study model.
RESULTS ANALYSIS
DESCRIPTIVE STATISTICS
100% Time of start of business
100% Frequency of using a green product
100% Total revenue of the enterprises
SCALE VALIDITY ASSESSMENT
Cronbach's Alpha for the Relative Advantage scale is 0.887, exceeding the 0.6 reliability threshold and indicating strong internal consistency (Hair et al., 2006) After removing LTD1, LTD4, LTD7, LTD9, LTD10, LTD12, and LTD13, the corrected item-total correlations remain above 0.3, showing that LTD2, LTD3, LTD5, LTD6, LTD8, and LTD11 are reliable and adequately reflect the Relative Advantage construct in the study.
Cronbach's Alpha for the Compatibility scale (KTT) was 0.857, exceeding the 0.6 threshold, indicating good reliability (Hair et al., 2006) After removing observed variables KTT6, KTT7, and KTT8, the Corrected Item-Total Correlation remained above 0.3, confirming acceptable item discrimination (Hair et al., 2006) Consequently, the remaining observed variables KTT1, KTT2, KTT3, KTT4, KTT5, and KTT9 meet the reliability criteria and accurately reflect the study's Compatibility scale (KTT).
Cronbach's Alpha for the Simplicity (SDD) scale is 0.896, exceeding the 0.6 threshold and indicating strong reliability (Hair et al., 2006) All items meet reliability criteria, as the corrected item-total correlations for SDD1–SDD7 are greater than 0.3, confirming that these observed variables reliably measure the Simplicity (SDD) construct Together, these results demonstrate that SDD1 through SDD7 satisfy reliability conditions and effectively represent the SDD scale.
By removing HTC3, HTC4, HTC7, HTC9, and HTC10, the corrected item-total correlation stays above 0.3 and Cronbach's Alpha is 0.870, surpassing Hair et al (2006) reliability criteria for the scale These results show that HC1, HTC2, HTC5, HTC6, HTC8, HTC11, and HTC12 meet reliability requirements and collectively represent the Organizational Support (HTC) scale.
4.3.1.5 Quality of human resources(CNL)
Cronbach's alpha is 0.853, exceeding the 0.6 reliability threshold (Hair et al., 2006), indicating strong internal consistency of the Quality of Human Resources (CNL) scale After removing CNL3, CNL5, CNL8, CNL10, and CNL11, the corrected item-total correlations exceed 0.3, demonstrating that the remaining items contribute reliably to the construct Accordingly, CNL1, CNL2, CNL4, CNL6, CNL7, and CNL9 meet the criteria, are reliable, and properly represent the Quality of Human Resources (CNL) scale.
Cronbach's Alpha for the AKH scale is 0.894, exceeding the 0.6 threshold, and the Corrected Item-Total Correlation values are above 0.3, indicating reliable scale performance after removing observed variables AKH8, AKH10, and AKH11 (Hair et al., 2006) The remaining items AKH1, AKH2, AKH3, AKH4, AKH5, AKH6, AKH7, and AKH9 meet the reliability criteria and together satisfy the reliability requirements of the Customer Pressure scale (AKH).
After removing ACP4, ACP5, ACP10, and ACP11, the Government Pressure (ACP) scale shows strong reliability, with Cronbach's Alpha = 0.884, well above the 0.6 threshold, and corrected item-total correlations exceeding 0.3 for all items (Hair et al., 2006) Accordingly, the observed variables ACP1, ACP2, ACP3, ACP6, ACP7, ACP8, and ACP9 meet the reliability criteria and represent the Government Pressure (ACP) construct.
After removing observed variables HCP2, HCP5, and HCP9, the corrected item-total correlation exceeds 0.3 and Cronbach's alpha reaches 0.884, well above the 0.6 threshold, confirming the reliability of the Government Pressure (ACP) scale (Hair et al., 2006) The remaining items HCP1, HCP3, HCP4, HCP6, HCP7, HCP8, and HCP10 meet reliability requirements, satisfy the constraints, and collectively represent the Government Pressure (ACP) construct.
The Cronbach's Alpha = 0.878>0.6 fulfills the reliability of the scale (Hair et al., 2006) and the Corrected Item-Total Correlation >0.3 after the variable DTT6 has been removed (Hair et al.,
2006) This demonstrates that the observed variables DTT1, DTT2, DTT3, DTT4, DTT5, DTT7, and DTT8 all satisfy the requirements, are reliable, and properly represent the Environmental uncertainty scale (DTT)
Cronbach's Alpha for the Green Product Innovation (DSP) scale was 0.858, surpassing the 0.6 threshold and indicating reliable measurement consistent with Hair et al (2006) After removing the significant variables DSP1, DSP2, DSP4, DSP5, and DSP8, the Corrected Item-Total Correlations exceeded 0.3, confirming that the remaining items—DSP3, DSP6, DSP7, and DSP9—are reliable, meet the criteria, and adequately represent the study's Green Product Innovation (DSP) construct.
Cronbach's Alpha = 0.829, well above the 0.6 reliability threshold (Hair et al., 2006), indicating strong reliability of the Green Product Innovation (DSP) scale After removing the significant variables DQT1, DQT2, DQT4, DQT5, DQT7, DQT8, DQT10, and DQT11, the Corrected Item-Total Correlation remains >0.3, demonstrating that the observed items DQT3, DQT6, and DQT9 are reliable, meet the requirements, and accurately reflect the DSP construct for Green Product Innovation.
The Environmental Performance Scale (SMT) demonstrates strong reliability, with a Cronbach's Alpha of 0.906, exceeding the 0.6 threshold, indicating solid scale reliability per Hair et al (2006) After removing SMT4, SMT5, SMT7, SMT9, SMT10, SMT11, SMT12, and SMT16, the Corrected Item-Total Correlation for the remaining items exceeds 0.3, confirming their internal consistency Consequently, SMT1, SMT2, SMT3, SMT6, SMT8, and SMT13 meet the reliability criteria and accurately reflect the study's Environmental Performance Scale (SMT).
Cronbach's Alpha index = 0.862>0.6 meets the reliability of the scale (Hair et al al., 2006) and Corrected Item-Total Correlation >0.3 after the relevant variables HQC2, HQC5, HQC7,
In line with Hair et al (2006), HQC8, HQC10, and HQC11 were removed, indicating that the remaining observed indicators—HQC1, HQC4, HQC6, and HQC9—satisfy the criteria, define the financial performance scale (HQC) used in this study, and meet reliability standards.
The reliability analysis shows a Cronbach's Alpha of 0.892, well above the 0.6 threshold, indicating strong reliability for the scale (Hair et al., 2006) After removing HQP4, HQP5, HQP6, HQP7, HQP8, HQP9, HQP10, HQP11, HQP13, and HQP14, the corrected item-total correlations remain above 0.3, confirming the reliability of the retained items Consequently, HQP1, HQP2, HQP3, and HQP12 meet reliability criteria and define the financial performance scale (HQC) used in the research.
EXPLORE FACTOR ANALYSIS (EFA)
4.4.1 EFA analysis of the Independent variables
With a KMO value of 0.907, the data meet the criteria for factor analysis (0.5 < KMO < 1), indicating that the factor solution is acceptable after excluding the variables LTD6, KTT9, CNL6, and HTC12 Bartlett's test of sphericity is significant (Sig 0.000, p < 0.05), supporting a linear correlation among the observed variables and the underlying factor structure (Hair et al., 2016) The Extraction Sums of Squared Loadings (Cumulative Percent) is 54.33%, above 50%, showing that observable variables account for 54.33% of the variance of the nine components, satisfying the criteria (Gerbing & Anderson, 1988) Overall, the exploratory factor analysis (EFA) for the independent variables is compatible with the data as a whole.
4.4.2 EFA analysis of the Dependent Variables
A KMO value of 0.957 indicates that factor analysis is highly suitable for examining the exploratory factors of the dependent variables The inter-variable correlations across the population are significant (Sig = 0.000, p < 0.05), and these results meet the study’s requirements The Extraction Sums of Squared Loadings show a Cumulative Percent of 61.66% when the total extraction variance is considered, exceeding the 50% benchmark This means the three identified factor categories account for 61.66% of the data variance Overall, the exploratory factor analysis of the dependent variables aligns with the study data.
MEASUREMENT MODEL VALIDITY ASSESSMENT
All observed variables show outer loadings above 0.7 (Hulland, 1999), indicating that each observed variable is significant in the model Regarding reliability and convergent validity, Cronbach's Alpha values for all scales exceed 0.7, confirming good reliability across the constructs.
All Composite Reliability scales assure convergence because they are all more than 0.7 (>0.7) and the Average Variance Extracted is greater than 0.5 (>0.5)
Results show that the square root of AVE for every latent variable exceeds its correlations with other latent variables, confirming discriminant validity Additionally, all HTMT values are below 0.85, indicating that the observed variables have sufficient discriminating power across groups.
STRUCTURAL MODEL ASSESSMENT
In this study, the adjusted R^2 and R^2 values for the intermediate variable DMX are 0.423, for the dependent variable HQD is 0.393, and for the intermediate variable SMT is 0.324 R^2 (the same as the adjusted R^2 in this context) ranges from 0 to 1 and indicates how well the independent factors explain the dependent variable The R^2 results demonstrate the structural model's high quality and a consistent explanatory power for the relationship between green innovation and the related factor.
Given that all of the VIF coefficients in the above SEM model structure are less than 5, it can be concluded that the model, as shown in the table below, does not demonstrate multicollinearity
The results of the analysis of the study were as follows:
These factors have coefficient of impact of 0.168, 0.147, 0.142, 0.132, 0.130, 0.128, 0.127, and 0.116 respectively Therefore, the impact of these eight factors should affect DMX in decreasing order of strength: HTC, LTD, CNL, KTT, ACP, DTT, SDD, and lastly, HCP has minimal influence on DMX Particularly for AKH with P-value = 0.098 > 0.05, the AKH variable has no influence on DMX at 95% confidence, while at 90% confidence it has a tiny effect with low significance
* Impact of green innovation on environmental performance and firm performance
DMX exerts a strong influence on the intermediate variable SMT, with a standardized coefficient of 0.571, indicating a substantial positive relationship It also has a strong impact on the dependent variable HQD, demonstrated by a standardized coefficient of 0.566, underscoring DMX's significant predictive power for HQD.
* The impact of the intermediate variable (SMT) on the dependent variable (HQD)
In this analysis, the SMT variable has no effect on DMX at the 95% confidence level, but shows a marginal influence at the 90% level; the estimated SMT impact coefficient is 0.102—the smallest among the factors—with a P-value of 0.090, which exceeds 0.05, indicating a lack of statistical significance at conventional thresholds.
The study shows that DMX is only slightly influenced by factors affecting its f-square, with values ranging from 0.006 to 0.036; however, DMX has a significant impact on factors driving SMT and HQD, as reflected in f-square coefficients of 0.483 and 0.357, respectively, both above 0.35.
Table 4.3: The results of the research hypothesis
Hypothesis Hypothetical path P Values Result Similar to prior research results
H1 LTD -> DMX 0.001 Accepted Rogers, 2003; Lin & Ho, 2011; Kousar et al., 2017
H2 KTT -> DMX 0.003 Accepted Rogers, 2003; Lin & Ho, 2011; Kousar et al., 2017
H3 SDD -> DMX 0.001 Accepted Rogers, 2003; Lin & Ho, 2011; Kousar et al., 2017
H4 HTC -> DMX 0.000 Accepted Zhe et al., 2008; Lin, 2014; Namagemba
H5 CNL -> DMX 0.004 Accepted Tornaky & Fleischer, 1990; Lin & Ho,
H6 AKH -> DMX 0.098 Accepted Etzion, 2007; Guoyou et al., 2011; Chu et al., 2018
H7 ACP -> DMX 0.000 Accepted Lee, 2008; Weng et al., 2015; Borsatto,
H8 HCP -> DMX 0.000 Accepted Scupola 2003; Guo et al., 2018; Bai et al., 2019
H9 DTT -> DMX 0.006 Accepted Rothenberg & Zyglidopoulos, 2007;
H10 DMX -> SMT 0.000 Accepted Chiou et al., 2011; Fernando & Wah,
H11 DMX -> HQD 0.000 Accepted Porter & Van Der Linde, 1995; Yan &
H12 SMT -> HQD 0.090 Accepted Russo & Fouts, 1997; Schultze et al.,
2011; Haninun & Lindrianasari, 2018 All of the hypotheses are statistically significant, according to Table 4.3's results In particular, the P-Values for hypotheses H7 (P Value = 0.098) and H12 (P Value = 0.090) are both
In Vietnam's manufacturing sector, the adoption of green initiatives remains modest, with around 10% being an acceptable but not particularly impactful level In practice, Vietnamese buyers rarely pressure firms to go green, unlike customers in export markets, and purchasing decisions center mainly on cost, quality, and product advantages Consequently, customer pressure exerts only a weak influence on green innovation Environmental performance does affect firm performance, though at a low level, because environmental investments are costly; nonetheless they enhance environmental quality, meet customer expectations, create a competitive edge, strengthen the company’s brand, and improve efficiency The study's findings support all hypotheses, indicating that each factor contributes to and improves the overall performance of manufacturing firms.
RESULT DISCUSSION
4.7.1 Result of the Green Innovation scale (DMX)
Research shows that green innovation is shaped by nine factors—government pressure, customer pressure, the quality of human resources, environmental uncertainty, government support, organizational support, compatibility, simplicity, and relative advantage Among these, organizational support exerts the strongest impact (standardized coefficient 0.168), followed by relative advantage (0.147) and customer pressure (0.072) The quality of human resources is the third most influential factor (impact coefficient 0.142), then compatibility (0.132), government pressure (0.130), market changes (0.128), ease (0.127), and government support (0.116) Although prior research has explored these effects, findings across studies have not always agreed.
Manufacturing firms are increasingly under government and policy pressure to reduce environmental impact by boosting energy efficiency, cutting emissions, and promoting reuse and recycling In this context, the DMX framework’s management innovation components (DQT3, DQT6, DQT9) and product innovation components (DSP3, DSP6, DSP7, DSP9) align with regulatory expectations and the practical needs of DNSXs during DMX deployment Consequently, adopting the green innovation scale helps businesses raise overall operational quality and profitability, while expanding the market for green products among environmentally conscious consumers and improving corporate efficiency.
4.7.2 Result of the Environmental scale (SMT)
The study shows that the observed variables within and across the construct’s components display unidirectional relationships, achieve convergent validity and reliability, exhibit discriminant validity, and are conceptually aligned with value theory.
Research shows that DMX exerts a strong direct influence on SMT, with a path coefficient of 0.571, and it also significantly affects HQD at 0.566, while SMT has only a small impact on HQD with a path coefficient of 0.102 Taken together, these findings indicate that DMX is a major driver for both SMT and HQD, whereas the influence of SMT on HQD is comparatively limited.
Environmental performance has little to no effect on firm performance in Vietnam, where outdated machinery and technologies drive production costs while creating emissions, waste, noise, and the exceedingly expensive processes required for waste and wastewater treatment This study’s findings are reinforced by Nham et al (2012) Moreover, the results suggest that SMT may mediate the impact of DMX on HQDN, but this mediation appears at a low level of significance with 90% confidence.
Figure 4.3: Result of regression model
Note: H(+) has a Sig value 0.05: Low level of statistical significance
4.7.4 The results of the research hypothesis
The research results have concluded that the hypotheses H1, H2, H3, H4, H5, H6, H7, H8, H9,
H10, H11, and H12 are accepted and have statistical significance
4.7.4.1 Relationship between Relative advantage (LTD) and Green innovation (DMX), hypothesis H 1 :
With an impact level of 0.147 and a Sig value ranging from 0.000 to 0.05, the results show that H1 is acceptable and that relative advantage positively influences the adoption of green innovation in manufacturing firms This supports the original hypothesis that relative advantage drives eco-friendly technology adoption, and confirms that advantages are a key technical characteristic shaping green innovation The findings align with earlier studies (Damanpour, 1991; Afuah, 1998; Le et al., 2006; Lin & Ho, 2011; Kousar et al., 2017) and corroborate Rogers' (2003) diffusion theory that relative advantage is the central predictor of rapid renewal uptake To differentiate themselves and win customer adoption, firms can leverage green innovation by introducing novel forms and products; this inventiveness and comparative edge can help firms outperform rivals within the same industry and region Finally, the study suggests that firms should consider the advantages of green technology adoption and even retire older technologies when such innovations help close gaps in financial performance, a motivation echoed by Tornatzky & Klein (1982).
4.7.4.2 Relationship between Compatibility (KTT) and Green innovation (DMX), hypothesis
Compatibility has a positive effect on the Green Innovation of manufacturing enterprises, with a standardized coefficient β = 0.132 and a significance value Sig = 0.003 (p < 0.05), supporting hypothesis H2 These results align with prior studies (Kemp et al., 2001; Beise et al., 2005; Le et al., 2006; Weng et al., 2011; Kousar et al., 2017) and Rogers' diffusion of innovations theory, highlighting that compatibility is a dynamic determinant of innovation acceptability Green innovation that is more compatible with current technology disseminates more readily within the organization, enabling faster adoption and alignment with contemporary business practices In the long run, technology-driven green development benefits businesses by advancing economic efficiency and facilitating a transition to a green economy; therefore, manufacturing companies seeking sustainable growth should incorporate developed-nation technology in their production processes in a way that remains compatible with their existing capabilities, helping them quickly meet customer and community needs for green innovation.
4.7.4.3 Relationship between Simplicity (SDD) and Green innovation (DMX), hypothesis H 3 :
The study finds that simplicity significantly influences the adoption of green innovations (β = 0.127, p = 0.001), supporting H3 and indicating that easier-to-use technologies boost SMEs’ uptake of sustainable solutions This aligns with prior research showing that technological complexity hinders green-innovation adoption (Tornatzky & Klein, 1982; Rogers, 2003; Lin & Ho, 2011; Kousar et al., 2017), suggesting that firms with limited technical knowledge or human resources are less likely to adopt Conversely, when innovations are simple, user-friendly, and easy to learn, businesses are more likely to adopt and implement them The findings corroborate Rogers’ diffusion theory, where complexity acts as a major barrier to innovation spread Consequently, especially for small and medium-sized enterprises, investing in environmental knowledge and training to simplify technology is essential for effectively deploying innovations toward sustainable development (Al Qirim).
4.7.4.4 Relationship between Organizational support (HTC) and Green innovation (DMX), hypothesis H 4 :
Study results indicate that with β = 0.168 and Sig = 0.000 < 0.05, hypothesis H4 is supported, showing that organizational support has a favorable impact on green innovation in manufacturing enterprises The findings align with the proposed hypothesis that organizational support promotes green innovation and are consistent with Zhu et al (2008), as well as with Gonzalez-Benito & Gonzalez-Benito (2006), Lin & Ho (2011), Namagembe & Sridharan (2019), and Awan (2020), who report that organizational support—especially top management backing—is critical to successfully adopting green innovation and gaining a competitive edge When environmental challenges are made salient to firms, the likelihood of adopting green innovation increases (Qi et al., 2010) These results echo Freeman's (1999) stakeholder theory, which posits that satisfying stakeholders helps secure a competitive advantage; by providing organizational support as a resource, employees are encouraged to apply green innovation, enabling manufacturing businesses, particularly SMEs, to foster an environment where all available resources help boost performance.
4.7.4.5 Relationship between Quality of human resource (CNL) and Green innovation (DMX), hypothesis H 5 :
With an impact level β = 0.142 and a significance value Sig = 0.004 (< 0.05), human resource quality positively influences green innovation in DNSX, supporting H5 and indicating that stronger HR capabilities drive sustainable innovation These findings align with Zhu et al (2008) and echo the conclusions of Christmann (2000), Lin and Ho (2011), and Rehman et al (2021) that high-quality human resources are crucial for advancing green processes Firms that embrace green innovation and cultivate creative capacity are more likely to successfully implement environmental policies, though green innovation can increase industrial process complexity and raise the demand for training and education, making employee education and training essential for improved management.
Murphy and Poist (2003) identify 32 environmental challenges, yet the central finding is that a company's production and commercial success hinges on the quality of its human resources Implementing technological innovation requires competent, trainable personnel who can learn and apply new skills, and research shows that human resource quality is a crucial driver of technical innovation (Weng & Lin, 2011; Tornatzky & Fleischer, 1990) Consequently, investing in high-quality human resources—combining technical expertise with soft skills and proficient use of new technologies and procedures—should be a top priority, because firms cannot innovate without capable people.
4.7.4.6 Relationship between Customer pressure and Green innovation (DMX), hypothesis
The findings show that H6 is supported at a low level, indicating that customer pressure exerts only a tiny influence on firms' green innovation, with β = 0.072 (below 0.1) and a p-value of 0.098 at the 10% significance level; at the 95% confidence level, customer pressure has no effect on green innovation Findings from Weng et al (2015) and Qi et al (2010) likewise confirm no effect This suggests that managers actively promoting green innovation may not be primarily driven by customer pressure, though context and consumer knowledge can alter this Simple environmental regulations or employee incentives can address customer concerns, and customers may not have much impact on domestic firms' adoption of green innovation Yet these findings diverge from many manufacturing-focused studies that treat consumers as key stakeholders whose pressure significantly shapes green-innovation initiatives (Etzion, 2007; Gonzalez-Benito and Gonzalez-Benito, 2006a; Saunila et al., 2017; Chu et al., 2018) The findings imply firms must satisfy stakeholders to achieve objectives and maintain profitability, reflecting a limited interpretation of Freeman's stakeholder theory, and that other stakeholder influences may offset the effect of customer demand Consequently, manufacturers should consider customer and partner preferences to maximize success in green-innovation implementation, which can diversify product lines and business models, raise profits, and ensure compliance with environmental regulations.
4.7.4.7 Relationship between Government pressure and Green innovation (DMX), hypothesis H 7 :
Hypothesis H7 is supported by the data, with β = 0.130 and P-Value = 0.003 (p < 0.05), indicating that government pressure positively influences business green innovation These findings corroborate the proposed hypothesis and diverge from earlier work by Naila (2013), Nham (2012), Ho (2015), and Chen (2021) While Naila (2013) and Nham (2012) argued that SMEs invest in environmental measures mainly when required by regulation due to higher manufacturing costs and may resort to temporary waste-treatment investments because of funding constraints, the current results suggest that government-imposed environmental regulations compel manufacturing firms to engage in and comply with the environmental regulation market in order to survive and grow.
Vietnam's environmental standards are tightening, making environmental compliance a predictor of future profitability and sustainable development As compliance becomes more stringent, failing to meet environmental requirements can hurt production, operations, reputation, and product quality, even if it appears to trade off manufacturing efficiency The findings align with Yan and Lin (2021), Qi et al (2020), and Freeman's stakeholder theory, supporting the strategic use of green innovation to satisfy stakeholders and gain a competitive advantage (Christmann, 2004; Lee, 2008; Weng et al., 2015) Sustainable growth now hinges on integrating green innovation into production and business processes, a path that is feasible only when firms are socially responsible and adhere to environmental protection legislation in Vietnam.
4.7.4.8 Relationship between Government support (HCP) and Green innovation (DMX), hypothesis H 8 :
Statistical analysis shows that Government Support positively influences Green Innovation in DNSX, with a standardized effect size β = 0.116 and a significance level p = 0.008 (p < 0.05), leading to the acceptance of hypothesis H9 This evidence aligns with prior work by Scupola (2003) and Tornatzky et al., underscoring the positive role of government interventions in driving eco-friendly innovation.