The Green Innovation Gateway: How Technological Capabilities and Environmental Strategies Drive Sustainability Performance in Manufacturing Firms

Technological Capabilities and Firms’ Sustainability Performance

Utilizing TC offers significant potential for companies to enhance their SP (Akram et al., 2018; Ferreira et al., 2023). Chesbrough (2003) highlights that leveraging technology to drive innovation, optimize resources, and create sustainable business models can improve economic, social, and environmental outcomes. As technology continues to evolve, its role in promoting sustainable practices is expected to grow. SP is typically measured across three dimensions: environmental, economic, and social (Alsayegh et al., 2020; Omri, 2020). TC refers to a firm’s ability to develop, use, and deploy technologies effectively, making it crucial for improving environmental SP (Y. Zhang et al., 2019). A key component is adopting green technologies, such as renewable energy systems and energy-efficient processes, which have been shown to enhance environmental performance (Fernando & Wah, 2017). These technologies help reduce resource consumption, emissions, and waste, ultimately boosting sustainability. Additionally, TC fosters ongoing innovation and sustainability improvements (Demirel & Kesidou, 2019). Firms with robust TC can create novel solutions to environmental challenges, as demonstrated in studies linking TC to reduced carbon emissions (Xing et al., 2020) and proactive environmental innovation (Ambec et al., 2013).

Consistent with DCT, technological capabilities represent the firm’s ability to sense and seize technological advancements to address sustainability challenges. Prior research suggests that firms with strong TC are better positioned to innovate and improve SP by reducing resource consumption and emissions (Y. Zhang et al., 2019). Furthermore, TC can provide a strategic edge to firms, enhancing their operational efficiency, driving innovation, and improving their economic SP (Helfat & Raubitschek, 2018). Enhanced operational efficiency is one of the significant advantages conferred by TC. By leveraging technology, firms can improve productivity and reduce resource consumption, decreasing operating costs (Volberda et al., 2011). For instance, applying automation technology in the manufacturing sector can minimize resource wastage and optimize production processes. Additionally, TC fosters innovation, a key driver of economic sustainability. Firms with robust TC are better positioned to undertake radical innovation, develop new products, services, or processes, and create new market opportunities (Cohen & Levin, 1989). Empirical evidence corroborates the relationship between firms’ TC and their economic SP. A study conducted by Y. Wu et al. (2020) revealed that firms with superior TC demonstrated better economic SP compared to those with less developed TC.

Moreover, the implementation of TC can serve as a means of enhancing a company’s social SP. According to Jain et al. (2011), technology has the potential to strengthen workplace safety and health, facilitate the creation of solutions for social issues through product and service development, and enhance stakeholder communication. Companies that have good IT capabilities will be better positioned to ensure proper coordination between IT and business planning processes, implement effective and low-cost IT applications that provide them with competitive advantages, anticipate future customer needs, and develop critical new product features ahead of, rather than with, the competition. IT capabilities positively impact the firm’s performance through the development of digital orientation and digital transformation of the organization, state Barba-Sánchez et al. (2024). Technologies ensure supply chain traceability and create transparency to improve sustainability performance (Khan, Parvaiz, Dedahanov, et al., 2022). It can be contended that information technology capability and AI-based customer relationship management, as per Siddik et al., 2023, influence artificial intelligence-based customer relationship management and relationship performance and social sustainability performance. Latest technology adoption can also lead to renewable energy use and minimizing emissions (Rahman & Ferdaous, 2024). Akram et al. (2018) reported that firms with better technological infrastructure and capabilities can attain sustainable competitive advantage and perform comparatively better than their competitors. Similarly, Jum’a et al. (2024) concluded that manufacturing companies with strong TC can enjoy superior sustainability performance. Therefore, firms’ TC can potentially improve their triple bottom line (TBL) sustainability performance. In conclusion, we can develop the following hypothesis:

Technological Capabilities and Firms’ Green Innovation

The role of TC in fostering GI has emerged as a key research theme in recent academic discourse. Numerous scholars concur that TC can efficiently stimulate GI within firms (Y. S. Chen et al., 2012; Ghisetti & Rennings, 2014). TC refers to a firm’s capacity to effectively utilize, augment, and adjust its technical resources (Khan, Parvaiz, Bashir, et al., 2022). This capability plays a pivotal role in propelling the GI process. GI is a multifaceted construct, encompassing two primary components—green product innovation and green process innovation (Y. S. Chen et al., 2006). Green product innovation refers to developing environmentally friendly products or enhancing existing products to minimize their environmental impact. On the other hand, green process innovation entails innovating and improving manufacturing processes to reduce environmental degradation (Y. S. Chen et al., 2006).

Y. S. Chen et al. (2006) posit that robust TC plays a significant role in fostering the development of green innovative products.Y.S. Chen et al. (2012) propose that firms possessing advanced TC are more predisposed to invest in and realize success in green product innovation. Similarly, De Marchi (2012) provides empirical evidence that firms endowed with established TC exhibit a higher propensity to launch environmentally friendly products. Adding another dimension to the discourse, Costantini et al. (2013) have found a positive correlation between TC and the probability of developing eco-innovations, emphasizing the role of TC in creating an organizational culture conducive to sustainable innovation.

In parallel, the impact of TC also extends to the realm of green process innovation. This aspect of innovation involves the conception and implementation of novel or considerably improved production processes geared toward environmental sustainability (Rennings, 2000). Tang et al. (2018) argue that companies boasting sophisticated TC can enhance the efficiency of their manufacturing processes, thereby curtailing inefficiencies, reducing energy consumption, and mitigating emissions. Similarly, Florida (1996) maintains that firms with sophisticated TC enjoy a strategic advantage in identifying, developing, and implementing environmentally friendly process innovations. Furthermore, Kesidou and Demirel (2012) argue that TC is essential for firms to mitigate the costs and risks associated with adopting and implementing eco-friendly process innovations. Investments in developing and enhancing TC can lead to environmentally friendly products and processes, thereby supporting environmental sustainability.

As such, the following hypothesis can be formulated:

Environmental Strategies and Firms’ Sustainability Performance

ENS pertains to the methodologies and techniques corporations utilize to mitigate the effects of their operations on the ecological system. In light of mounting expectations for businesses to conform to sustainable practices, ENS has been recognized as a pivotal factor influencing a company’s SP. The ENS can be broadly categorized into two types, namely proactive ENS and reactive ENS (Chan et al., 2022).

The term “Proactive Environmental Strategies (PES),” which consists of voluntary actions that exceed regulatory requirements to integrate environmental considerations into business operations, has been identified as a substantial contributor to a firm’s SP. SP includes a company’s environmental, social, and economic impacts (Hart & Dowell, 2011). This may involve implementing waste minimization techniques, adopting sustainable procurement practices, and incorporating ecologically conscious design principles (Klassen & Vereecke, 2012). Businesses can minimize their environmental impact and promote sustainability by proactively addressing environmental issues. Montabon et al. (2007) revealed that compared to businesses that take a more reactive stance, PES-adopting companies typically exhibit higher levels of environmental performance. Businesses that use PES frequently innovate regarding green technologies, processes, and/or products, increasing environmental sustainability. Companies can cut future ecological risks, penalties, cleanup work, and remediation costs by implementing PES. Additionally, reducing waste and optimizing resources can reduce costs and increase operational efficiency (Ambec & Lanoie, 2012). According to research by Flammer (2013), firms implementing PES were associated with superior financial performance. This effect was attributable to increased operational efficiency and enhanced market recognition, which led to increased sales and profitability. PES can promote social sustainability by positively influencing a firm’s stakeholder relationships. Firms with proactive ENS are often perceived more positively by customers, employees, and the community, increasing customer loyalty, employee engagement, and community support (Muñoz-Pascual et al., 2019).

Conversely, reactive environmental strategies (RES) entail responding to environmental issues as they arise, often to comply with regulatory standards, rather than taking proactive steps. This approach’s influence on a firm’s SP, encompassing environmental, social, and economic dimensions, has been an important research topic (Bansal & DesJardine, 2014). Genç and Benedetto (2019) found that firms that adopted RES to comply with environmental regulations achieved marked improvements in their SP. These strategies include effluent treatment, retrofitting pollution control technologies, and other end-of-pipe solutions (Delmas & Toffel, 2008). Despite being commonly perceived as reactive measures to external pressures, these strategies are instrumental in mitigating environmental risks, safeguarding a company’s image, and upholding its competitive standing. Prior research suggests that reactive strategies can indirectly improve a firm’s SP by reducing the potential for regulatory fines and reputational damage (Jiang & Bansal, 2003).

Furthermore, Chan et al. (2022) observed that companies implementing RES demonstrated improved environmental, social, and governance (ESG) performance. These studies concluded that RES is a critical driver of SP, especially in firms under regulatory or market pressures. ENS is operationalized by implementing specific programs, policies, and procedures, improving product creation. ENS contributes to mitigating energy consumption and waste generation by using sustainable energy sources and effective environmental management systems (Aftab et al., 2023). Based on these findings between the interplay of PES and RES with a firm’s SP, we can hypothesize as below:

Environmental Strategies and Firms’ Green Innovation

Multiple research strands indicate that companies that adopt ENS practices are more likely to enhance their level of GI (Brunnermeier & Cohen, 2003; Dangelico & Pujari, 2010; Ghisetti & Rennings, 2014; Horbach et al., 2012; Zhu et al., 2013). ENS may encompass a range of sustainability-focused methodologies and undertakings, including but not limited to minimizing waste, implementing energy-efficient measures, managing supply chains in an environmentally conscious manner, and creating sustainable products and services (Dangelico & Pujari, 2010; Horbach et al., 2012). Ghisetti and Rennings (2014) provide empirical evidence that establishes a correlation between ENS and a rise in eco-innovation activities undertaken by firms. Their research findings indicate that using ENS may incite an inventive and forward-thinking reaction toward the environment, ultimately resulting in the creation and execution of environmentally friendly technologies. Consistent with this viewpoint, Triguero et al. (2013) discovered that ENS can promote GI by enabling the integration of eco-friendly technologies. ENS enables firms to meet the evolving market demands for sustainable products and directly shape market expectations for sustainability (Dangelico & Pujari, 2010). By incorporating ENS, firms can design and develop greener products that appeal to an ecologically conscious consumer base and foster GI. Firms with robust ENS are more likely to be proactive in complying with green regulations and standards, encouraging the development of green products (Brunnermeier & Cohen, 2003). Furthermore, engaging stakeholders in ENS plays a crucial role in developing innovative green products by leveraging shared knowledge to identify areas of improvement and drive innovation (Hart & Dowell, 2011). Firms that commit to ENS aim for operational efficiency, which encompasses waste reduction and resource optimization, thereby stimulating green process innovation as firms strive to achieve these objectives (Florida, 1996). To satisfy the environmental demands of stakeholders, corporations must establish connections with their stakeholders as part of their environmental strategy. While pursuing green innovation, businesses rely on the connections they establish with stakeholders as valuable assets to address various issues, such as technological challenges (El-Kassar & Singh, 2019) and a lack of green knowledge (Yang & Jiang, 2023). Additionally, ENS often involve adopting cleaner technologies, which mitigate the environmental impact of a firm’s operations and drive process innovation by prompting the development or implementation of new green technologies (Rennings, 2000). As firms embrace ENS, they foster the establishment of sustainable supply chains, encouraging innovation in sourcing, production, and distribution processes and substantially reducing their environmental footprint (Zhu et al., 2013). Considering these arguments, we hypothesize that:

Green Innovation and Firms’ Sustainability Performance

A widespread consensus exists regarding the significance of GI in enhancing SP. GI pertains to innovative products, processes, or services that substantially reduce environmental impacts (Y. S. Chen et al., 2006; Dangelico & Pujari, 2010). Numerous studies have established the direct effects of GI on SP. Demirel and Kesidou (2011) have suggested that companies adopting GI strategies exhibit improved environmental performance, a key element of SP. The enhancement may materialize in diverse forms, encompassing diminished emissions, decreased energy usage, and waste reduction, all of which are vital for the enduring viability of the enterprises. (Y. S. Chen, 2008) further supports this assertion by illustrating that implementing GI can also enhance economic efficacy, a crucial aspect of sustainability. Adopting GI can yield significant financial advantages, such as reduced expenses resulting from the optimized utilization of resources, increased revenue generated by introducing environmentally sustainable products, and a strategic advantage in progressively more ecologically aware markets. This perspective is further reinforced by Porter and Van Der Linde (1995a), who suggest a mutually beneficial situation, commonly referred to as a “win-win” scenario, in which GI leads to improved operational efficiencies, the discovery of novel market opportunities, and enhanced economic performance, all while simultaneously promoting ENS. Furthermore, the implementation of GI has the potential to impact social sustainability through its contribution to promoting healthier communities and enhancing the overall quality of life (Nidumolu et al., 2013). Green products have the potential to mitigate detrimental emissions and enhance air quality, thereby resulting in improved public health and social welfare. It is worth noting that the implementation of GI has the potential to yield favorable outcomes for a company’s reputation, thereby constituting an additional dimension of SP (Hartmann & Moeller, 2014). Organizations perceived as pioneers in GI are inclined to experience elevated levels of trust from stakeholders and customer loyalty, thereby augmenting their SP from a relational standpoint (Du et al., 2010). Asadi et al. (2020) contended that green innovation directly impacts the SP of Malaysian hotels. Kanan et al. (2023) found that GI substantially affects the SP of manufacturing firms in Palestine. Similarly, Yan et al. (2022) reported a positive linkage between banking firms’ GI and their SP. Building on these observations, we can hypothesize that GI has a direct causal impact on firms’ SP.

Mediating Role of Green Innovation

The association between TC and SP has sparked robust debate in environmental economics and business studies. A range of studies presents contrasting views on this relationship, with some indicating a positive impact, others suggesting a negative effect, and a separate group positing no significant correlation at all (Dangelico & Pujari, 2010; Martín-de Castro, 2015; Omri, 2020). Some studies assert enhanced TC, characterized by cutting-edge equipment, advanced software, and skilled personnel, improves SP. These capabilities, they argue, allow organizations to streamline processes, lower resource usage, and decrease environmental harm, thus bolstering their SP (Cai & Li, 2018). On the other hand, some researchers posit that an indiscriminate investment in TC can inadvertently lead to unsustainable practices. Firms emphasizing technological advancements may ignore the environmental consequences, including increased energy consumption or electronic waste generation (Lin & Zhu, 2019; Song et al., 2012).

Meanwhile, a few scholars suggest an insignificant relationship between TC and SP (Omri, 2020). While TC might potentially influence SP, other factors such as organizational commitment, stakeholder pressure, or regulatory frameworks may play a more decisive role. Given these divergent perspectives, the potential exists for the relationship between TC and SP to be mediated by other variables. The concept of GI is one such variable that might mediate this relationship. GI refers to introducing and implementing novel processes, products, or ideas that reconcile economic feasibility with environmental sustainability (Y. S. Chen et al., 2006). Companies with robust TC could apply these toward GI, enhancing their SP. There are three primary arguments supporting GI as a mediator. First, TC forms the basis for any innovation, including GI (Y. S. Chen et al., 2006). Second, GI directly impacts SP by reducing environmental damage and promoting resource conservation (Mongo et al., 2021). Third, GI can help reconcile the conflicting findings from previous studies. If firms leverage their TC for GI, it could lead to a positive impact on SP. Conversely, the lack of GI could account for the adverse or insignificant effects reported in other studies (Dangelico & Pujari, 2010). A few pieces of literature have advocated for the mediating role of green innovation between manufacturing practices, including technological capabilities and sustainability performance (Jum’a et al., 2022, 2024).

Similarly, various studies offer divergent perspectives on the relationship between ENS and SP. While some studies indicate a positive influence, others propose a negative influence, and a distinct group suggests no significant correlation (Porter & Van Der Linde, 1995b; Russo & Fouts, 1997; Schaltegger & Synnestvedt, 2002; Zeng et al., 2010). Proponents of the positive impact perspective argue that implementing ENS, such as renewable energy initiatives or waste reduction programs, can enhance SP (Hartmann & Moeller, 2014). This positive impact is achieved by integrating environmental consciousness directly into business operations, leading to economic and reputational benefits. In contrast, certain studies have suggested that adopting ENS could negatively impact SP (Q. Wu et al., 2012). This argument hinges on the belief that these strategies might divert resources from core business objectives and require a substantial investment and cultural shift, affecting short-term sustainability outcomes. A third stream of research suggests an insignificant relationship between ENS and SP (Zeng et al., 2010). These researchers argue that although ENS are desirable, factors like regulatory compliance, market demand, and stakeholder pressure may profoundly influence SP. Given the divergent viewpoints, we posit that the relationship between ENS and SP is not straightforward but mediated by other factors. One such mediator could be GI. Several arguments could be made to support this proposition. Firstly, ENS could act as the catalyst for GI. Firms with a solid commitment to ENS would naturally seek innovative solutions to environmental challenges (Horbach et al., 2012).

Secondly, GI has been shown to directly impact SP by reducing environmental harm and promoting resource conservation (Mongo et al., 2021). Lastly, GI could help resolve the disparities in existing research on the relationship between ENS and SP. If firms focus their ENS on GI, this could lead to a positive impact on SP. Conversely, the absence of a focus on GI in the ENS could explain the adverse or insignificant effects reported in other studies (Triguero et al., 2013). Further, Rehman et al. (2021) conclude that proactive ENS can strengthen firms’ green innovation and enhance sustainability performance.

Therefore, we posit the hypothesis that:

The conceptual model of this research is illustrated in Figure 1.

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Figure 1.

Conceptual model.

Sampling and Data Collection

We obtained data from knowledgeable managers of China’s medium to large manufacturing firms, focusing on the sector’s crucial role in the country’s economy. The manufacturing sector in China has proved to be instrumental in achieving SDG 9, which encompasses industry, infrastructure, and innovation, with manufacturing value added (MVA) contributing approximately 28.3% to the GDP in 2022 (UNIDO Statistics Data Portal, n.d.; World Bank, 2022). As China embraces the fourth industrial revolution, the country has witnessed a notable adoption of cutting-edge technologies such as intelligent manufacturing systems, big data analytics, the internet of things (IoT), and artificial intelligence (AI) within the Chinese manufacturing industry (Koh et al., 2019). This technology-driven transformation, often called “Industry 4.0,” has enhanced China’s global competitiveness and played a vital role in navigating the economic challenges posed by the COVID-19 pandemic (Dongfang et al., 2022). Many Chinese factories have also embraced environmentally friendly technologies and waste management practices, reducing environmental impact and aligning the country with global climate goals (Beraud et al., 2022). China’s commitment to technological innovation and sustainable practices in the manufacturing sector has solidified its position as a leading global player, with an evident dedication to improving its economy and environment (Morrison, 2019). Hence, assessing how Chinese manufacturing firms can enhance their sustainability performance by employing TC, ENS, and GI is crucial.

We collected data on TC, ENS, GI, and SP through a self-administered questionnaire targeting company managers. A stratified sampling technique was employed, considering industry type, firm size, and geographic location to ensure representative results. The questionnaire was pre-tested by four subject experts and seven manufacturing managers to validate its clarity and accuracy. Based on their feedback, several items were revised for better comprehension. After these adjustments, the final questionnaire was distributed to 380 manufacturing firms, accompanied by a cover letter explaining the study’s purpose and assuring respondents of anonymity and voluntary participation. A follow-up effort yielded 272 valid responses, achieving a response rate of 71.58%. Data collection occurred from March to August 2022.

The statistical power required for the PLS-SEM technique utilized in our analysis is adequate, as indicated by the sample size of 272. PLS-SEM is known for handling smaller sample sizes effectively; however, our sample size surpasses the bare minimum necessary to attain reliable outcomes. Hair et al. (2019) state that a rule of thumb for PLS-SEM is 10 times the number of structural paths directed toward a specific construct in the model. Considering the intricacy of our model, the sample size we have utilized is considerably larger than the threshold generally advised to guarantee robust and dependable findings. The respondents’ group reflects the wider population, comprising managers employed in manufacturing companies located in China. By selecting respondents from a broad spectrum of manufacturing companies, we ensured the diversity of our sample and captured a wealth of perspectives and experiences about TC, ENS, GI, and SP.

Measurement Items

Survey items for TC, ENS, GI, and SP were adapted from previous studies and rated using a 5-point Likert scale, where 1 represented “strongly disagree” and 5 represented “strongly agree.” Reliability analysis confirmed that all scales were robust, with Cronbach’s alpha exceeding .70 (Hair et al., 2019). For instance, we measured the predictor TC using six Bhatia (2021) items. The participating firms were asked about their capabilities regarding technology adoption and application. We adopted three items from Tan et al. (2022) to measure the ENS construct. The mediating variable—GI, was measured using six items from Singh et al. (2020). We asked the managers whether they introduced green products and process innovation in their supply chain. They were asked whether their production process reduces environmental consequences and improves resource efficiency. Finally, we measured the SP of the respondent firms with six items adopted from Siddik, Yong, & Rahman. (2023) and Yan et al. (2022). The SP items incorporated performance measures of the companies in terms of environmental, economic, and social sustainability (Table 1).

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Table 1.

Measurement Items.

Variables	Codes	Items	Sources
Technological capabilities	TC1	We have heavily invested in R&D projects.	Bhatia (2021)
	TC2	Key knowledge for end products is developed internally within our company.
	TC3	Our firm remains at the forefront of technological advancements.
	TC4	Our firm excels in establishing programs for internal development or technology absorption.
	TC5	Our production department operates with high efficiency.
	TC6	Our equipment uses the latest technologies.
Environmental strategy	ENS1	Environmental considerations are integrated into our strategic planning.
	ENS2	Environmental issues are considered in all new product or service developments.	Tan et al. (2022)
	ENS3	Our company invests in environmental R&D.
Green innovation	GI1	We use materials that produce minimal pollution.
	GI2	We utilize materials that reduce energy and resource consumption.
	GI3	Our products are designed to be environmentally friendly.
	GI4	Our manufacturing processes reduce hazardous substances and waste.	Singh et al. (2020)
	GI5	Our manufacturing processes lower consumption of coal, oil, electricity, and water.
	GI6	Our manufacturing processes minimize the use of raw materials.
Sustainability performance	SP1	TC, ENS, and GI significantly reduce paper usage and energy consumption in our company.	Siddik et al. (2023) and Yan et al. (2022)
	SP2	TC, ENS, and GI significantly improve compliance with environmental standards.
	SP3	TC, ENS, and GI significantly enhance revenue and market share.
	SP4	TC, ENS, and GI significantly improve our company’s reputation and image.

Demographic Information

Our final dataset comprised responses from 272 manufacturing firm managers in China, who were selected as the unit of analysis due to their direct involvement in their firms’ TC, ENS, GI, and SP. Most respondents (84%) held managerial positions for at least 1 year, with 67% aged between 41 and 50, and 72% having completed post-secondary education. Regarding business models, 43% of firms were B2C, 26% were B2B, and 31% operated as hybrid. The firms were categorized by age: 11% were under 3 years, 26% were 3 to 5 years, 35% were 6 to 10 years, and 28% were over 10 years old. Additionally, 45% of the firms had more than 50 employees, 38% had between 21 and 50, and 17% had over 100 employees (Table 2).

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Table 2.

Respondents’ Profile and Firm Characteristics.

Profile category	Subcategory	Percentage
Managerial position tenure	At least 1 year	84
Age group	41–50 years	67
Education level	Post-secondary education	72
Customer type	B2C	43
	B2B	26
	Hybrid	31
Firm age	3 years	11
	3–5 years	26
	6–10 years	35
	Over 10 years	28
Firm size	More than 50 staff	45
	21–50 staff	38
	More than 100 staff	17

Data Analysis Techniques

This study examined the relationships between TC, ENS, GI, and SP using the partial least squares structural equation modeling (PLS-SEM) approach. The analysis was conducted with SmartPLS software version 4.0, which facilitates the PLS-SEM method. Structural equation modeling (SEM) allows researchers to test theoretical frameworks by estimating statistical relationships (Ringle et al., 2015). This method helps uncover and verify relationships among multiple latent variables and their constructs, minimizing errors in the model. It is preferred for its capability to explore numerous latent constructs (Hair et al., 2021). PLS-SEM, in particular, offers advantages over other techniques, particularly in mediation analysis, as it provides more accurate estimates (Preacher & Hayes, 2008). According to Chin (1998), PLS-SEM accounts for measurement error, making it well-suited for experimental research. Additionally, it removes the need for normality assumption, making it applicable to simple and complex studies (Hair et al., 2019). SmartPLS offers various statistical tools for understanding intricate interactions between independent and dependent variables. We employed the bootstrapping technique with 5,000 subsamples to evaluate our hypotheses. The SEM produced a measurement model that defined the relationship between observed and latent variables and a structural model that tested the links among latent variables. Random errors were calculated and removed, leaving only general variance. We applied multiple convergent and discriminant validity criteria to ensure the structural model’s accuracy. The methods of this study are outlined in Figure 2.

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Figure 2.

Flowchart of research methods.

To assess potential non-response bias, we compared early and late respondents based on the assumption that late respondents are more similar to non-respondents. The sample was divided into two groups: early respondents (the first 25% of responses) and late respondents (the last 25%). These groups were compared using key demographic variables, such as firm age and size. A t-test was conducted to detect significant differences between the groups. The absence of statistically significant differences suggests that non-response bias does not pose a significant limitation (Armstrong & Overton, 1977). Additionally, Harman’s single-factor test was used to evaluate common method variance. The test showed that a single factor explained 48.73% of the total variance, indicating that common method bias is unlikely to be a concern in this study (Podsakoff et al., 2003).

Measurement Model

Before constructing PLS-SEM, it is necessary to assess the reliability and validity of the measurement to guarantee that the techniques and data are appropriate. The vital stage is establishing the model’s internal consistency; the correlation between the variables is acceptable for further investigation. The common approaches include two requirements to be met: the initial is a model performance utilizing individual indicator reliability and convergent validity (CV), and the second is the assessment of discriminant validity (DV) once the first criterion has been met. This requires several assessment iterations by removing low-loading components (Henseler et al., 2016).

To analyze the measurement model, we computed indicator loadings, Cronbach’s alpha, composite reliability, average variance extracted, and the heterotrait–monotrait ratio of correlations (HTMT) (Hair et al., 2021). As shown in Table 3, the CA and CR for each construct were more than .70. Therefore, the construct’s reliability was established (Tian et al., 2023). Fornell and Larcker (1981) propose that AVE values must be larger than 0.5 to confirm convergent validity (CV). The AVEs for all components were within the range of 0.564 to 0.713, verifying the CV of variables. Each indicator has a significant and high standard loading (>0.05) on its intended construct (Hair et al., 2021), as revealed by our analysis.

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Table 3.

Summary Results of the Measurement Model.

Constructs	Items	Mean	SD	Factor loadings	CA	CR	AVE
Technological capabilities	TC1	3.404	0.865	0.726	.845	.846	0.564
	TC2	3.471	0.931	0.756
	TC3	3.493	0.931	0.772
	TC4	3.507	0.895	0.757
	TC5	3.518	0.895	0.785
	TC6	3.533	0.931	0.706
Environmental strategy	ENS1	3.324	0.882	0.683	.709	.743	0.631
	ENS2	3.529	0.954	0.854
	ENS3	3.632	0.91	0.836
Green innovation	GI1	3.599	0.961	0.790	.879	.881	0.624
	GI2	3.706	0.96	0.821
	GI3	3.662	1.034	0.758
	GI4	3.588	1.003	0.812
	GI5	3.691	0.985	0.801
	GI6	3.647	0.959	0.756
Sustainability performance	SP1	3.654	1.032	0.769	.865	.864	0.713
	SP2	3.82	1.051	0.860
	SP3	3.82	1.085	0.882
	SP4	3.868	1.042	0.861

Note. SD = standard deviation; CA = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

After confirming the model performance and meeting Hair et al.’s (2021) criteria, the next stage in the evaluation is to examine the discriminant validity (DV). DV is the extent to which factors discriminate among or gauge distinct concepts by examining the correlation between measurements of possibly overlapping factors (Hair et al., 2021). We estimated the DV for every factor using the HTMT criterion. As shown in Table 4, all HTMT scores fell below the conservative threshold of 0.85 (Kline, 2011). The maximum value on the HTMT was 0.83, thus validating the DV of the factors (Islam et al., 2019). Consequently, our model’s components exhibited high reliability and validity levels.

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Table 4.

HTMT Criterion.

Constructs	Environmental strategy	Technological capabilities	Green innovation
Technological capabilities	0.754
Green innovation	0.703	0.796
Sustainability performance	0.735	0.830	0.720

Source. Authors’ calculation.

Structural Model

Before testing the hypotheses, we conducted a collinearity assessment to rule out multicollinearity issues (Hair et al., 2019). The VIF values for all constructs ranged from 1.91 to 2.32, well below the cutoff of 5.00, confirming that multicollinearity was not a concern. With this confirmation, the PLS-SEM analysis proceeded. Next, we evaluated the R² values of the endogenous constructs. R² values of 0.75, 0.50, and 0.25 are considered strong, moderate, and weak, respectively (Hair et al., 2011). The R² values for GI and SP were 0.569 and 0.597, indicating strong predictive power. Additionally, the Q² values of 0.413 for SP and 0.350 for GI confirmed the model’s predictive relevance (Hair et al., 2019). Finally, the model fit was assessed using the SRMR value of 0.064, below the threshold of 0.08, indicating a good fit (Hu & Bentler, 1999) (Table 5).

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Table 5.

R² and Q² of the Model.

Constructs	R 2	Q 2
Green innovation	0.569	0.350
Sustainability performance	0.597	0.413

Source. Authors’ calculation.

The next stage is to evaluate the significance and relevance of the path coefficients, having established the model’s explanatory and predictive capacity. Hypothesis testing aims to verify the significance values empirically and examine the hypothesized link between constructs in which the antecedents substantially impact the outcome variables. Both t-values and p-values are used as thresholds to assess whether a significant relationship exists. The t-value must be more than 1.96, and the p-value must be less than .05 (Hair et al., 2021). Table 6 and Figure 3 represent each hypothesis’s path coefficients, standard errors, and t-values.

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Table 6.

Results of Hypothesis Testing.

Hypotheses	Coefficients	SE	t Statistics	Remarks
Direct effects
H1: TC → SP	0.148***	0.056	2.624	Supported
H2: TC → GI	0.441***	0.045	9.910	Supported
H3: ENS → SP	0.239***	0.054	4.408	Supported
H4: ENS → GI	0.405***	0.046	8.876	Supported
H5: GI → SP	0.479***	0.053	8.971	Supported
Indirect effects
H6: TC → GI → SP	0.211***	0.032	6.602	Supported
H7: ENS → GI → SP	0.194***	0.031	6.261	Supported

Note.SE = standard error.

***

p < .01.

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Figure 3.

Structural model.

Table 6 shows that technological capabilities significantly influence manufacturing firms’ sustainability performance (β = .148, p = .009) and green innovation (β = .441, p = .000), which supports Hypotheses 1 and 2. As expected, the findings revealed that environmental strategy significantly drives firms’ sustainability performance (β = .239, p = .000), thus confirming Hypothesis 3. Additionally, Hypothesis 4 was supported, indicating that ENS strongly impacts GI (β = .405, p = .000). Next, we found that firms’ sustainability is influenced by green innovation (β = .479, p = .000). Thus, H5 was confirmed. Furthermore, the mediation analysis results confirmed Hypothesis 6 of green innovation’s mediating (partial) effect on the relationship between technological capabilities and sustainability performance (β = .211, p = .000). Finally, we observed that GI successfully mediates the linkage between ENS and SP (β = .194, p = .000), supporting H7.

We conducted the Sobel test to check the robustness of the mediation results. The Sobel test output demonstrates that green innovation successfully mediates the associations between technological capabilities and sustainability performance and environmental strategies and sustainability performance, supporting hypotheses H6 and H7. Hence, the Sobel test results are consistent with the PLS-SEM results, establishing the robustness of our findings (Table 7).

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Table 7.

Sobel Test for Mediation.

Constructs	Test statistic	Std error	p-Value
Technological capabilities
Sobel test	6.644	0.031	.000
Aroian test	6.625	0.032	.000
Goodman test	6.663	0.032	.000
Environmental strategies
Sobel test	6.307	0.031	.000
Aroian test	6.287	0.031	.000
Goodman test	6.326	0.030	.000

Source. Authors’ calculation.

Theoretical Implications

This study aims to provide a new perspective on dynamic capability theory (DCT) by examining its applicability and relevance in TC, ENS, GI, and SP. The article presents persuasive arguments that offer a new perspective on the relationship between TC, ENS, and the SP of firms. Additionally, this study presents a crucial theoretical argument highlighting the essential nature of TC in driving the progress of GI. This challenges the prevailing perspective that narrowly focuses on the significance of TC in conventional business settings and broadens it to encompass its crucial role in the realm of sustainability and GI. Furthermore, a significant theoretical implication that can be drawn from this study pertains to the vital influence of ENS on firms’ SPs. The study posits that implementing a robust ENS can be a potent driver that accelerates the development of eco-friendly innovations, directly influencing the overall SP. This study enhances our comprehension of dynamic capabilities theory (DCT) and offers a fresh outlook on how ENS can be contextualized within the broader dynamic capabilities’ framework.

The study also contends that GI is a mediating variable between firms’ TC and SP and their ENS and SP. Companies can increase their SP by utilizing their TC and ENS following the roadmap outlined in this study. Furthermore, this research presents an innovative viewpoint on the relationship between innovation and sustainability, positing that a firm’s SP can be substantially enhanced through TC, ENS, and GI. This perspective advocates for a comprehensive approach incorporating these concepts to foster a cohesive understanding.

Managerial Implications

Our research questions are designed to address the practical challenge of how manufacturing firms can leverage technological capabilities (TC) and environmental strategies (ENS) to enhance their sustainability performance (SP). This is a pressing issue for practitioners seeking actionable insights into effective sustainability strategies. Our study’s findings are pertinent for businesses seeking to enhance their SP. The collected data supports the hypothesis that a company’s TC and ENS significantly affect its SP, with GI playing a crucial mediating function. Management must prioritize the comprehensive development of TC while concurrently acknowledging the interconnected impact of TC on both GI and SP. This implies that it actively supports sustainability principles in tandem with fostering innovation. This could involve designating resources to cutting-edge technologies, integrating technology seamlessly into business operations, and fostering a culture that encourages technological advancement.

Our research also emphasizes the significance of employing ENS to boost GI and enhance SP. Managers are presented with the opportunity to integrate effective environmental and sustainability practices into the strategic objectives of their organizations. Organizations may effectively expand GI initiatives and improve their SP by implementing these strategies. This may entail integrating environmental goals into strategic planning, adopting business models prioritizing sustainability, and fostering ecological consciousness.

Moreover, our research underscores the significance of GI in enhancing SP. Managers should strategically utilize GI to convert TC and ENS into measurable sustainability results. It is imperative for managers to proactively endorse and facilitate the development of inventive, environmentally friendly solutions to guarantee that these competencies and approaches result in significant enhancements in sustainability.

In addition, the findings propose adopting a comprehensive framework for decision-making guided by sustainability principles. Understanding the complex interaction between TC, ENS, and GI can improve resource allocation and strategic planning for managers. This may involve prioritizing investments in eco-friendly technologies, developing strategic plans aligned with ecological principles, and nurturing an organizational culture that places a premium on sustainability. This entails advocating for GI, allocating resources toward TC, and formulating comprehensive ENS. In this respect, the optimization and readjustment of the industrial structure by industry experts and policymakers to explore the development potential of manufacturing businesses through technological innovation will lead to the sustainable development of the manufacturing industry (Bagh et al., 2023).

Limitations and Future Research Directions

While this study provides valuable insights into the roles of TC, ENS, and GI in improving firms’ SP, several limitations must be acknowledged, along with suggestions for future research. One limitation is the focus on a specific industry, which may restrict the generalizability of the findings to other sectors or organizational types. Future studies should explore these relationships across broader industries, such as the service, technology, and non-profit sectors, to enhance understanding of these dynamics in different contexts. Additionally, the use of cross-sectional data limits the ability to capture the evolution of these relationships over time. Future research should employ longitudinal data to understand better how TC, ENS, GI, and SP interact over extended periods, potentially revealing new causal patterns and time-dependent effects.

Further investigation is also needed to explore potential feedback loops and time-delay effects in these relationships, which could provide deeper insights into the complexities of these dynamics. Exploring contextual factors like organizational culture, external stakeholder pressures, and regulatory environments may also offer new perspectives on how these factors influence the relationships between TC, ENS, GI, and SP. Finally, while this study focused on positive correlations, future research could examine possible trade-offs and challenges firms face when enhancing SP through TC and ENS. Such exploration could provide a more comprehensive understanding of the complexities of fostering GI and improving sustainability performance.

Future research could finally be directed toward the question: “How do specific technological capabilities and environmental strategies interact over time to influence firms’ sustainability performance across different industry sectors?” It would provide much earlier warnings of the dynamism and contingency of such relationships and assist firms in checks and balances to pull off a particular strategy toward sustainable outcomes. By addressing these limitations and expanding upon the directions suggested for future research, scholars can significantly deepen our understanding of the complex interconnections between TC, ENS, GI, and firms’ SP. This knowledge will empower managers with the insights needed to effectively integrate sustainability principles into their strategic decision-making processes, allowing firms to navigate the intricate landscape of sustainable business and establish a strong and resilient position in their respective markets.