Environmental protection through green innovation performance

Abstract

Purpose  Businesses focus on digital transformation (DT) to improve performance; hence, this study aims to examine the relationship between DT and green innovation performance (GIP) within small- and medium-sized enterprises. In line with stakeholder theory, the study investigated the mediating impact of absorptive capacity (AC) and the moderating role of competitive pressure (CP) and environmental regulations (ERs).

Design/methodology/approach  Data were collected from 233 enterprises in China via an online survey and analyzed empirically.

Findings  Outcomes explained the positive and significant impact of DT on GIP and highlighted the mediating role of AC. In addition, CP and ERs were found to be convincing moderators, modeling dynamics of GIP. Conclusions extend enterprises’ understanding of how to strategically control DT and emphasize AC linked with ERs and CP.

Social implications  The study supports cleaner business practices by showing how digital transformation and knowledge absorption can help SMEs reduce environmental harm and contribute to sustainable development.

Originality/value  Theoretically, this study advances the literature on stakeholder theory and DT, shaping GIP. It also offers practical guidance for small- and medium-sized enterprises on aligning digital strategies with regulatory demands and using external knowledge to improve green innovation outcomes.

Business digitalization; Green innovation performance; Knowledge absorption; Market competition; Regulatory environmental standards

Introduction

Digital transformation (DT) has changed industry operations and is crucial for encouraging sustainable practices Qiong et al. (2023). Companies adopt DT to improve performance, adopt eco-friendly solutions (Acciarini et al., 2022) and enhance operational competency (Demartini et al., 2019). DT refers to the incorporation of technologies such as blockchain, the Internet of Things, artificial intelligence, big data analytics and social media platforms into business products and processes (Gong & Ribiere, 2021). These digital technologies become a strategic dominance and an ethical duty to decrease environmental impacts (Rijswijk et al., 2021). Hence, environmental sustainability concerns and digital technology are the main areas of focus for an organization that drive green innovation performance (GIP).

GIP is the application of eco-friendly creative processes and techniques to solve environmental issues (Chang et al., 2024). Many organizations work for GIP through DT (Arshad et al., 2022), but not all achieve success (Zhang et al., 2020). Thus, analyzing the noteworthy impact of DT on GIP is essential. However, an enterprise’s ability to absorb and apply DT for GIP can be inferred from its absorptive capacity (AC). AC is the ability to identify advantages of acquiring, assimilating and applying new external knowledge (Ahmed et al., 2020) in a beneficial way (Aboelmaged & Hashem, 2019). Therefore, AC acts as a bridge between businesses’ GIP and DT.

In the era of technological development, companies struggle to improve GIP to outperform their rivals (Jafari-Sadeghi et al., 2021). Hence, DT dynamics appeared as a significant determinant linking competitive pressure (CP) with GIP (Singh et al., 2022). CP is the internal pressure within businesses used to outperform competitors (Wong et al., 2020) and plays a controlling role in improving GIP following DT (Bilal et al., 2024). Simultaneously, environmental regulations (ERs), responsibilities or restrictions on companies to protect the environment (Nascimento & Fontgalland, 2023), emerged as a fundamental element to enhance GIP (Li et al., 2023). ER describes countries like China that implement laws to promote green innovations (Fang et al., 2021) and ecological processes (Jianming et al., 2020). Consequently, this study aims to examine ER that modify the relationship between DT and GIP.

Numerous studies recognize the importance of DT and GIP (Feng et al., 2022; Li et al, 2022); however, few scholars have discussed combined interaction with CP and ER, despite evidence that external forces matter (Wang & Juo, 2021; Wang, 2023). Hence, this study addressed this gap because China’s evolving regulatory environment and competitive intensity demand implications of DT (Zhang & Zhu, 2019). Moreover, this study examined AC, which offered an integrated explanation of how DT drives GIP under ER and CP. Consequently, this study fills a specific theoretical and practical gap by linking these dynamics within SMEs. The study offers a clearer explanation of how DT becomes a driver of GIP through internal learning capability and external competitive and regulatory conditions.

The other identified gap was that previous studies have focused on large corporations and ignored the difficulties faced by SMEs (Fischer et al., 2020; Llopis-Albert et al., 2021). For example, SMEs face barriers when implementing DT, ER (Jun et al., 2021) and adopting green practices (Tyler et al., 2023). SMEs operate at a different pace than large firms, which makes their innovation dynamics distinct. Hence, this study offered guidelines for coordinating digital plans to improve the SMEs’ green innovation. The “World Economic Forum” aimed to systematically adopt DT with a projection of 23% growth (Quixy, 2023). Moreover, the United Nations emphasizes sustainable technological progress (United Nations, 2023); therefore, this study provides future-oriented solutions compared to other studies that emphasize current solutions. In addition, the study is significant for Chinese enterprises (Slawotsky, 2022), considering “Building a Digital China” and “Accelerating Digital Development” in the 2035 and 14th Five-Year Plan (Ding & Chen, 2022).

Considering the study background, research gaps and significance of relationships, this study aims to examine the influence of DT on GIP, acknowledging sustainability, technological and regulatory contexts; the AC’s mediating role in acquiring and applying external knowledge to support green innovation; and the moderating role of CP and ER, as external forces strengthening innovation in SMEs. The study pursued these objectives through a quantitative survey of Chinese SMEs and applied partial least squares structural equation modeling (SEM).

Theoretical framework and hypotheses development

Theoretical foundation.

The “stakeholder theory” is concerned with the interactions between an organization and its stakeholders (Dmytriyev et al., 2021). Stakeholders are individuals or institutions that are interested in, may affect or are affected by an organization’s decisions (Freeman, 2023). Considering this, organizations should consider stakeholders’ interests to create a positive reputation and proactively address obstacles (Freudenreich et al., 2020). This study focused on stakeholder theory considering the organizations’ concern with competitors, environmental regulatory institutions and their AC of external knowledge. According to Ávila (2022) and Tu and Wu (2021), competitiveness moderates the organizations’ DT strategies regarding green innovation as long as companies feel pressure from rivals.

Environmental regulation (ER) reflects an important stakeholder pressure because regulatory institutions influence how firms respond to environmental expectations and legal requirements (Zarzycka & Krasodomska, 2021; Schaltegger et al., 2019). From the perspective of stakeholder theory, organizations are expected to address such external pressures by aligning their strategies with stakeholders’ environmental concerns (Hörisch et al., 2020). In this study, ER is positioned as a moderating condition that may strengthen the relationship between DT and green innovation by increasing firms’ motivation to meet environmental standards. Similarly, AC represents firms’ ability to acquire and apply external knowledge from stakeholders, including regulators, competitors, customers and industry partners. Thus, AC explains how DT can be translated into green innovation through stakeholder-based knowledge use (Figure 1; Singh et al., 2022; Yu et al., 2021).

Figure 1.
Conceptual framework

Hypotheses development

Digital transformation and green innovation performance.

Companies are eagerly transforming their operations for green development and DT is a significant factor concerning environmental protection (Vial, 2021). They have used digital technologies (big data, artificial intelligence, cloud-based services, blockchain and the Internet of Things) as a vital component of their business (Li et al., 2022) and to enhance GIP (Feroz et al.,2021). Therefore, a multifaceted perspective of the effects of DT on an enterprise’s GIP is crucial (Nambisan et al., 2017). Against this backdrop and within the framework of the stakeholder theory, it is possible to say that firms innovating green products are also prioritizing stakeholder’s interests. In addition, the literature has shown that the application of digital technologies and the sustainable growth of businesses are crucial topics. Hence, the study developed the following hypothesis:

H1.

Digital transformation positively and significantly impacts green innovation performance.

Digital transformation and absorptive capacity.

The literature on economics and management emphasizes the value of both internal and external sources of knowledge in the development of innovations as a method of knowledge generation (Ávila, 2022). Therefore, organizations’ greatly benefit from improving their AC, i.e. their ability to use, absorb and assimilate information (Horvat et al., 2019). DT contributes to this process and can increase and encourage learning processes (Kastelli et al., 2022) through implementing artificial intelligence, big data processing and use of digital technology equipment (Rehman Khan et al., 2022). In this sense, AC are crucial to businesses as they need a knowledge base to stay current and incorporate new developments into their processes. Moreover, management needs to innovate and create new methods of delivering value that bridge the knowledge gap between what is already known and what needs to be established. The relationship between DT and AC is inextricably tied to stakeholder theory, which emphasizes organizational responsiveness to varied stakeholder needs (Barrane et al., 2021). Hence, the study proposed that:

H2.

Digital transformation has a positive and significant impact on absorptive capacity.

Absorptive capacity and green innovation performance.

AC refers to a company’s capacity to connect and incorporate new external knowledge with its prior knowledge base (Proeger, 2020). AC is used in two ways: first, “potential AC” includes acquiring and assimilating knowledge. This offers sufficient strategic flexibility to enable adaptation and advancement in a constantly changing environment. The current knowledge base enhances the ability to search for, identify and depict an issue and apply new knowledge for problem-solving (Albort-Morant et al., 2018). The second is “realized AC,” which involves exploiting and transforming knowledge and attaining new intuitions (Kastelli et al., 2022). Organizations transform new knowledge and information into new company abilities. However, both approaches assist the firms in improving GIP. In addition, stakeholder theory emphasizes the importance of comprehending varied stakeholder expectations, highlighting the relationship between AC and the GIP (Hong et al., 2019). By considering this significance, the following hypothesis is developed:

H3.

Absorptive capacity positively and significantly impacts green innovation performance.

Mediating role of absorptive capacity.

Manufacturing sector managers prioritize AC to stimulate the creation of new digital information and advance GIP (Albort-Morant et al., 2018). AC’s function is based on the search for external knowledge, absorption of new technologies and development of new skills for applying technical knowledge (Coronado-Medina et al., 2020). AC could stimulate intellectual capital, creativity, incentives for knowledge sharing and the assimilation of outside information to innovate the products and processes. AC is critical to bring innovation based on externally sourced knowledge (Aljanabi, 2018). Stakeholder theory suggests that, as a mediator, AC facilitates the incorporation of environmental knowledge collected from stakeholders into innovation processes/performance (Aboelmaged & Hashem, 2019). However, there is a lack of studies investigating the mediation of AC on the relationship between DT and GIP. Therefore, this study examines a fourth hypothesis:

H4.

Absorptive capacity mediates the relationship between Digital transformation and green innovation performance.

Moderating role of competitive pressure.

Businesses typically react to their competitors (Cusumano et al., 2015). Businesses in the same sector experience pressure to review their current green innovation status and determine how to enhance the application of environmental practices against competitors (Weng et al., 2015). To ensure that their GIP is comparable with the rest of the industry, businesses should typically be aware of what their competitors deliver (Farida & Setiawan, 2022). For instance, businesses need information on updated equipment and energy-saving strategies to improve digital technology implication and GIP (Trinugroho et al., 2022). Companies must be aware of competitors’ steps to lower expenses while updating production facilities or power sources to outperform rivals (Singh et al., 2021). Therefore, to maintain a competitive edge, businesses can develop new environmental initiatives against rivals (Singh et al., 2022), particularly those at the forefront of their respective industry. According to stakeholder theory, examining DT efforts that affect GIP, a moderator analysis of CP is significant (Soewarno & Tjahjadi, 2020). Consequently, the investigation of CP as a moderator between DT and GPI is still required. Thus, the study proposed the following hypothesis:

H5a.

Competitive pressure moderates the relationship between digital transformation and green innovation performance.

Moderating role of environmental regulations.

ERs are considered the most important external factor affecting GIP (Huang et al., 2016). Several product and process laws have been developed for enterprises to safeguard the environment (Wei et al., 2020). The application of ER examines how companies adapt to manage the environment and their operations to increase the effectiveness of green innovation by abiding by legal requirements (Cai et al., 2020). Businesses’ implementation of ER will vary depending on the strictness of the regulations and the approaches firms use to respond to them (Baah et al., 2020). Stakeholder theory suggests that rather than viewing regulatory compliance as an obligation, businesses should view it as an opportunity to protect the environment (Schaltegger et al., 2019). Organizations can proactively integrate stakeholder values and expectations with ER, serving as a moderator. Hence, it is crucial to examine ER as a moderator to increase the impact of DT on GIP. Therefore, this study proposed that:

H5b.

Environmental regulations moderate the relationship between digital transformation and green innovation performance.

Based on the literature and hypotheses developed considering the relationship and theory, the study developed the following conceptual framework.

Research design

Sampling and data collection

The data was collected from manufacturing enterprises in Jiangsu Province, China. This study follows Kline (2015) sample criterion, which suggests that ten times as many responses as items can indicate good generalizability. Thus, as 22 items were used to measure the variables, a 220-sample size was determined. the research followed China’s SME Promotion Law (2003), which defines small industrial enterprises as employing fewer than 300 workers and medium industrial enterprises as employing 300–2,000 workers (Chen, 2023). Manufacturing firms meeting these official staff headcount criteria were included in the sample. Data collection targeted 400 SMEs to gather data from owners, CEOs, senior managers and supervisors. A total of 278 responses were collected from the SMEs and 233 were completed (58.3% response rate). The respondents were contacted by the business directory (Tanach 天眼查) and professional network (Wecom企业微信).

The survey was developed online and the link was shared with respondents using social media apps (WeChat, QQ, Weiboo). Before disseminating the online survey, a Chinese professor translated it into Chinese, proofread it and shared it with five SME managers to ensure the terminology used was professional and understandable to respondents. The study used a five-point Likert questionnaire, so the responses were easily extracted. The survey mentioned the purpose of the data collection and asked for consent and respondents were informed about data confidentiality. To confirm common method bias, the data were collected in two rounds with a two-month lag time from September to December 2024 (Podsakoff et al., 2012). Furthermore, the collected data is analyzed using Smart PLS (4.0).

Table 1 provides the demographic characteristics of the respondents based on gender, age and position within the firm. Most respondents were male (82.40%) while females comprised 17.60%. The largest age group among the respondents was 20–30 years. The distribution of respondents by position in the firm was senior managers (43.35%), CEOs (35.62%), owners (11.59%) and supervisors (9.44%).

Table 1.
Demographic statistics

Respondents	Categories	Frequency	%
Gender	Male	192	82.40
	Female	41	17.60
Age (years)	20–30	143	61.37
	31–40	51	21.89
	41–50	24	10.30
	>50	15	6.44
Position in the firm	Owner	27	11.59
	CEO	83	35.62
	Senior manager	101	43.35
	Supervisor	22	9.44
Total		233	100.00

Variables description

The dependent variable.

GIP was measured by considering the four aspects, “raw materials, energy-saving, waste disposal and recyclability.” Hence, the items used, followed by the study of Qiong et al. (2023), were “Our company has introduced new products or services that employ environmentally friendly materials.” “Our company has introduced new products or services to reduce energy consumption.” “Our company has developed a new product or business that promotes pest control or engages proper disposal of waste materials.” “Our company has developed new products or businesses that involve the recycling and reuse of all sold goods.”

The independent variable: DT was measured based on the aspects of “using digital technology in business process and use of big data, analytics, cloud, mobile & social media.” Hence, the items used for the measurement followed the study of Fachrunnisa et al. (2020) and were “Our firm focuses the use of digital technology in its business activities.” “Our firm summarizes few business processes because it switches to the use of digital technology.” “Our firm increases the mastery of digital technology in its business processes.” “Our firm is using new business processes, built on technologies such as big data, analytics, cloud, mobile & social media platform.” “Our firm processes are shifting toward making use of digital technologies.”

The mediating variable: AC was measured on the aspect of “novel ideas and use of external knowledge.” Hence, the items used followed by the study of (Aboelmaged & Hashem, 2019) were “My firm recognizes valuable novel knowledge.” “My firm absorbs useful external knowledge.” “My firm uses novel knowledge to enhance performance.” “My firm identifies new and useful ideas”. “My firm encourages novel and useful ideas.”

The moderating variables: ERs were measured on the aspects of “government rules applied to control the pollution and maintain standard thresholds.” Hence, the items used followed by the study of Ramanathan et al. (2018) and were “Company faces environmental regulations that offer economic incentives.” “Company faces environmental regulations which offer economic disincentives/penalties.” “Company faces environmental regulations which encourage integration of pollution control into production processes.” “Company faces environmental regulations which set standards/absolute thresholds.” “Company faces environmental regulations which stipule specification standards.”

The CP was measured on the aspect of “rival competition,” and the items used followed by the study of Dai et al. (2015) and were “Our main competitors that have implemented environmental management benefited greatly.” “Our main competitors that have implemented environmental management are perceived favorably by customers.” “Our main competitors that have implemented environmental management became more competitive.”

Data analysis

Data was analyzed using Smart PLS to infer partial least squares structural equation modeling (PLS-SEM) for direct, moderation and mediation paths because of its widespread application in the fields of management research (Borsatto & Amui, 2019; Shela et al., 2023). The PLS-SEM approach concurrently establishes the relationship among variables and predicts the developed model’s computations (Davari & Rezazadeh, 2013). Thus, this method was used to evaluate the study’s hypotheses as applied by previous scholars (Chan et al., 2016; Shahzad et al., 2023) (Hojnik & Ruzzier, 2016).

In the measurement model, Cronbach’s alpha, the heterotrait–monotrait (HTMT) ratio, composite reliability (CR) and average variance extracted (AVE) were tested. In the theoretical model, this study examined discriminant validity and correlations. The structural model is evaluated using the standardized root mean square residual (SRMR), predictive relevance (Q²), coefficient of determination (R²), variance inflation factor (VIF) and collinearity/common method bias. Structural equation modeling was used to evaluate the study’s hypotheses.

In the mediation and moderation assessment, this study measured the direct impacts of DT on AC and GIP. Moreover, the mediating impact of AC was measured in the relationship between DT and GIP. The moderating impact of CP and ER was assessed on the relationship between DT and GIP. The conceptual framework was based on these relationships (Figure 1) and below were the corresponding regression equations where ε shows the “term of error.”

1) The direct impacts of DT, AC and GIP:

(1) $${GIP}_1={\alpha }_1+{\beta }_1\left(DT\right)\pm {\text{ε}}_1$$

(2) $${GIP}_2={\alpha }_2+{\beta }_2\left(AC\right)\pm {\text{ε}}_2$$

(3) $${AC}_1={\alpha }_3+{\beta }_3\left(DT\right)\pm {\text{ε}}_3$$

2) The mediation effect of AC:

$$i.e.\mathrm{ }a={\beta }_3\left(DT\right),\mathrm{ }b={\beta }_2\left(AC\right)$$

(4) $$G{IP}_3={\alpha }_4+{\beta }_4\left(a\mathrm{*}b\right)\pm {\text{ε}}_4$$

3) The moderation effects of CP and ER:

(5) $${GIP}_4={\alpha }_5+{\beta }_5\left(DT\mathrm{*}CP\right)\pm {\text{ε}}_5$$

(6) $${GIP}_5={\alpha }_6+{\beta }_6\left(DT\mathrm{*}ER\right)\pm {\text{ε}}_6$$

Results

Measurement model analysis

The measuring model analysis is shown in Table 2. Composite reliability (CA), an internal consistency reliability metric, measures the degree of correlation between the items within a construct. As stated, all CA values fall between 0.834 and 0.938, showing strong internal consistency reliability (Shahzad et al., 2020), as the value is above 0.7. Another indicator of internal consistency dependability is construct reliability (CR), whose values were greater than 0.7, indicating good reliability as recommended by Jun et al. (2021). The AVE was obtained by comparing the variables to the measurement error variance. Since every AVE value was over 0.5, it accounts for a more significant proportion of variance than measurement error (Zhang et al., 2020). The VIF estimates the degree to which a correlation between the predictors increases the variance of the predicted regression coefficient. VIF values in the analysis were less than 3, suggesting that multicollinearity is not significant (Singh et al., 2022).

Table 2.
Measurement model

Constructs	Items	Loadings	CA	CR	AVE	VIF
Green innovation performance	GIP1	0.861	0.892	0.925	0.756	1.651
	GIP2	0.840
	GIP3	0.890
	GIP4	0.885
Absorptive capacity	AC1	0.886	0.937	0.952	0.799	1.650
	AC2	0.863
	AC3	0.93
	AC4	0.893
	AC5	0.896
Digital transformation	DT1	0.889	0.938	0.953	0.801	1.640
	DT2	0.909
	DT3	0.912
	DT4	0.885
	DT5	0.880
Competitive pressure	CP1	0.833	0.834	0.9	0.751	1.666
	CP2	0.884
	CP3	0.881
Environmental regulation	ER1	0.887	0.908	0.933	0.736	2.067
	ER2	0.882
	ER3	0.906
	ER4	0.882
	ER5	0.721

Structural model analysis

The Kaiser–Meyer–Olkin (KMO) statistic is used in factor analysis to measure the adequacy of the collected data. The KMO value evaluates if the variables in a data set are suitable for factor analysis and explains patterns of correlations among variables. The KMO value ranges from 0 to 1, where the higher values indicate better appropriateness for factor analysis. A KMO value above 0.6 is usually considered acceptable, suggesting that the variables are sufficiently interrelated for meaningful factor analysis [Shrestha (2021)]. This study reported a KMO value of 0.934, above the threshold measure. The statistics specified that the data set is highly suitable for factor analysis. Moreover, factor analysis requires that the factor variance exceed 50% and the study’s factor variance is 77.658%. This presents the proportion of total variance in the variables and exceeds the threshold value. The outcomes suggested that the identified factors account for a significant amount of the variability in the original variables.

Harmon’s single-factor test is a statistical technique used in SEM to measure whether a data set’s common method bias (CMB) is present. Common method bias is the potential bias generated when the same method is applied to measure multiple variables, leading to magnified correlations. The test value must be less than 50%; otherwise, that data may explain a significant presence of common method bias (Wang & Juo, 2021). In this study, the value was 42.7%, which was below the threshold of 50%, suggesting the impact of common method bias was relatively lower in the data set.

Exploratory Factor Analysis (EFA) is a statistical method that identifies underlying factors or dimensions to explain patterns of correlations among observed variables. The loading value, which must be greater than 0.5, reveals the direction and intensity of the association between each variable and each component (Zhang et al., 2020). Thus, the study outcomes showed that all values are according to the threshold and that all the items used in the study were reliable and authentic. The SRMR value shows the discrepancy between the expected and actual covariance matrices. A lower value of SRMR, at 0.044, indicated a better fit. Baeshen et al. (2021). Moreover, the value of R² for both GIP and AC shows a good and moderate change due to the independent variable DT, as shown in Table 3.

Table 3.
Structured model

Constructs	R 2	Adjusted R2	Q 2	VIF	SRMR
GIP	0.419	0.410	0.306	2.327	0.044
AC	0.285	0.283	0.222	1.650

Discriminant validity is an essential measurement in SEM, which is required to evaluate whether a model’s constructs are distinct. Discriminant validity is calculated through the HTMT ratio and the Fornell–Larcker criteria (Table 4). HTMT ratio compares the correlations between constructs (heterotrait correlations) with the correlations among items measuring the same construct. Studies indicate that HTMT values should be less than 0.85 and this study meets this criterion (Rehman et al., 2021). The Fornell–Larcker criteria include comparing the square root of the AVE for every variable with the correlations between that variable and other variables (Albort-Morant et al., 2018). Hence, the square root of the AVE for a variable was greater than the correlation between that variable and any other variable.

Table 4.
Discriminant validity

Constructs	AC	CP	DT	ER	GIP	ER × DT
Heterotrait–monotrait ratio (HTMT)
CP	0.348
DT	0.566	0.415
ER	0.586	0.552	0.543
GIP	0.623	0.422	0.451	0.56
ER × DT	0.347	0.551	0.47	0.63	0.453
CP × DT	0.228	0.63	0.374	0.48	0.407	0.67
Fornell–Larcker criteria
	1	2	3	4	5
AC	0.894
CP	0.309	0.866
DT	0.533	0.366	0.895
ER	0.541	0.479	0.501	0.858
GIP	0.571	0.365	0.413	0.505	0.869

Hypothesis testing

Partial least squares structure equation modeling (PLS-SEM) was used to test the hypothesis. The study found the following results.

Direct effect.

The path from DT to GIP, representing H1, has a significant positive effect (β = 0.409, p = 0.000), suggesting that an increase in DT is associated with an increase in GIP. The path from DT to AC representing H2 has a significant positive effect (β = 0.533, p = 0.000). The result indicated that an increase in DT is linked with an increase in AC. Moreover, H3, the path from AC to GIP, showed values (β = 0.415, p = 0.000), indicating a significant positive effect.

Indirect effect.

The sequential path from DT to AC to GIP has a significant positive effect (β = 0.222, p = 0.000), suggesting that the combined influence of DT and AC on GIP is significant (H4). The interaction term between DT and CP has a significant positive effect on GIP (H5a) with values of (β = 0.104, p = 0.009). The interaction term between DT and ER has a significant positive effect on GIP (H5b) with values of (β = 0.102, p = 0.002), suggesting that the joint effect of DT and ER has a positive impact on GIP. All the hypotheses proposed in the study are accepted (Table 5; Figure 2).

Table 5.
Hypothesis testing

Hypothesis		β	Mean	STDEV	T-Statistics	p-values
H1	DT → GIP	0.409	0.406	0.056	6.336	0.000
H2	DT → AC	0.533	0.535	0.038	14.106	0.000
H3	AC → GIP	0.415	0.415	0.063	6.571	0.000
H4	DT → AC → GIP	0.222	0.221	0.034	6.568	0.000
H5a	DT*CP → GIP	0.104	0.103	0.039	2.621	0.009
H5b	DT*ER → GIP	0.102	0.109	0.029	2.270	0.002

Figure 2.
Bootstrapping results explaining hypotheses testing

Discussion

This study explored the complex relationships among DT, AC, CP, ERs and GIP in SMEs. The study applied the stakeholder theory to understand how these factors interact and affect the GIP of SMEs. The findings established a positive and significant relationship between DT and GIP. The findings explained that businesses adopting digital technologies enhance their GIP by integrating AI, the Internet of Things and big data analytics, contributing to eco-friendly solutions and sustainable development.

In addition, the study showed that DT has a favorable and noteworthy effect on AC, demonstrating the role of digital technology in helping an organization to acquire, assimilate and use new external knowledge. DT makes learning more accessible and broadens enterprises’ knowledge base, which is essential for competitiveness in a quickly transforming world. The significant influence of AC on GIP underscores the significance of firms’ capability to incorporate outside expertise in promoting environmentally conscious innovation. Serving as a mediator, AC helped close the gap between advancing GIP and modern digital technologies. The study’s results supported the idea that AC the relationship between DT and the success of green innovations. The outcomes implied that AC is required to translate the advantages of DT into enhanced GIP.

This study explained how ER and CP in SMEs affect the relationship between DT and GIP. Where ER are facilitators, forcing firms toward environmentally friendly processes and products, CP pushes to improve GIP. The study’s outcomes were consistent with previous study findings (Aboelmaged & Hashem, 2019; Acciarini et al., 2022; Singh et al., 2022; Wei et al., 2020), which collectively show that DT, knowledge assimilation and external pressures enhance firms’ GIP. These studies suggest similar theoretical mechanisms, particularly the role of learning capabilities and stakeholder-driven pressures, which our results confirm. However, this study differs by examining these mechanisms concurrently within SMEs, a context often overlooked and by empirically demonstrating AC as the primary channel through which DT affects GIP. Therefore, the study moved beyond prior work by explaining both the internal mechanism and external conditions through which DT supports GIP. Internally, AC transforms digital resources into usable environmental knowledge. Externally, CP and ER strengthen the value of DT by creating stronger incentives for SMEs to pursue green innovation. These integrated perspectives extended existing evidence and clarified how these relationships operate in practice.

Conclusion

The study investigated how DT affects GIP in Chinese manufacturing SMEs and whether AC, CP and ERs influence this relationship. Analyzing survey data through PLS-SEM, the study found that DT directly enhances GIP and strengthens firms’ ability to acquire and apply external knowledge. AC emerged as a significant mediator and explained the significance of digital capabilities required for sustainable innovations. CP and ER demonstrated a moderating impact between DT and GIP and highlighted the importance of external forces. Comprehensively, it is found that if DT is supported by strong learning mechanisms and a demanding competitive and regulatory environment, it provides SMEs with a meaningful pathway to achieve high GIP.

Theoretical implications

The study’s findings complemented and broadened the understanding of the connections among innovation, sustainability and DT. The study has uniquely contributed to the moderating effects of CP and ER, focusing on AC as a mediator. Organizations that understand DT and ecological accountability can develop strategies that support stakeholder expectations and green innovation goals. As a result, the study has significant theoretical implications for SMEs, mainly because of the developing digital economy in China. Moreover, this study advanced the stakeholder theory in two specific ways, considering the interaction between organizations and stakeholders. First, it showed that the influence of stakeholder to improve GIP is not only direct but also functions through internal knowledge-processing capability of SMEs. It highlighted that stakeholder influence is multi-layered, dynamic and embedded in digital and sustainability-driven organizational contexts. Hence, in this study, AC demonstrated that SMEs identify, absorb and implement external knowledge obtained from stakeholders and improve GIP. Second, this study extended the stakeholder theory by explaining that the influence of stakeholder is conditional on external pressure. CP and ER represented two significant stakeholder-related forces that advanced the effect of DT on GIP. Consequently, this study moved stakeholder theory beyond a broad explanation of stakeholder responsiveness and showed that internal capability and external pressures also shape GIP in digitally transforming SMEs.

Moreover, this study also clarified two distinct mechanisms linking DT and GIP. First, AC explained the internal learning approaches where DT works to improve GIP as digital technologies strengthen the capability of SMEs to acquire, assimilate and apply new environmental knowledge. Second, CP and ER explained external conditions through which DT becomes more effective for GIP. Therefore, the findings showed that DT produces higher GIP when SMEs combine internal and external knowledge capabilities.

Practical and managerial implications

SMEs can use the study’s findings to enhance their sustainability strategies. SMEs can make strategic investments in technology that supports environmentally friendly solutions and comply with ERs. Managers should use digital technologies to increase the effectiveness of green innovation. SMEs can adapt to the pressures of a competitive market by staying current with industry benchmarks and competitors’ environmental activities. Moreover, SMEs must view ER as opportunities to synchronize with stakeholder values and take them as morally and legally compliance responsibilities. Managers can adopt a holistic strategy to effectively handle the connected effects of DT, AC, CP and ER on GIP. For example, aligning business objectives with sustainability targets, utilizing digital technologies in a morally responsible way and adapting strategies to internal and external circumstances.

Future research directions

Future studies can improve the effectiveness of GIP by examining different countries’ contexts with different regulatory systems affected by DT. SMEs across regions can be compared to gain important insights and develop dynamic models to investigate shifts in industry rivalry and market conditions. This study is limited to Chinese SMEs and does not fully account for China’s institutional, cultural and economic characteristics that may shape the observed relationships. Cross-sectional design also restricts causal interpretation and may compromise the stability of mediating and moderating effects. In addition, data collected through social networks may introduce sampling bias and limit representativeness. Future research should address these contextual and methodological constraints.

Funding:

This work was supported by National Social Science Fund of China (No. 21BGL069).

Data Availability

The entire dataset supporting the findings of this study is included in the article.

References

• Aboelmaged, M. & Hashem, G. (2019). Absorptive capacity and green innovation adoption in SMEs: The mediating effects of sustainable organisational capabilities. Journal of Cleaner Production, 220, 853-863, 10.1016/j.jclepro.2019.02.150.
  » https://doi.org/10.1016/j.jclepro.2019.02.150
• Acciarini, C., Borelli, F., Capo, F., Cappa, F., & Sarrocco, C. (2022). Can digitalization favour the emergence of innovative and sustainable business models? A qualitative exploration in the automotive sector. Journal of Strategy and Management, 15(3), 335-352, 10.1108/JSMA-02-2021-0033.
  » https://doi.org/10.1108/JSMA-02-2021-0033
• Ahmed, S. S., Guozhu, J., Mubarik, S., Khan, M., & Khan, E. (2020). Intellectual capital and business performance: The role of dimensions of absorptive capacity. Journal of Intellectual Capital, 21(1), 23-39, 10.1108/JIC-11-2018-0199.
  » https://doi.org/10.1108/JIC-11-2018-0199
• Albort-Morant, G., Leal-Rodríguez, A. L., & De Marchi, V. (2018). Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance. Journal of Knowledge Management, 22(2), 432-452, 10.1108/JKM-07-2017-0310.
  » https://doi.org/10.1108/JKM-07-2017-0310
• Aljanabi, A. R. A. (2018). The mediating role of absorptive capacity on the relationship between entrepreneurial orientation and technological innovation capabilities. International Journal of Entrepreneurial Behavior & Research, 24(4), 818-841, 10.1108/IJEBR-07-2017-0233.
  » https://doi.org/10.1108/IJEBR-07-2017-0233
• Arshad, M., Kun Yu, C., Qadir, A., Ahmad, W., & Rafique, M. (2022). The impact of big data analytics on organizational sustainability: The influencing factors of autonomous research and development and absorptive capacity. International Journal of Engineering Business Management, 14, 18479790221141537, 10.1177/18479790221141537.
  » https://doi.org/10.1177/18479790221141537
• Ávila, M. M. (2022). Competitive advantage and knowledge absorptive capacity: The mediating role of innovative capability. Journal of the Knowledge Economy, 13(1), 185-210, 10.1007/s13132-020-00708-3.
  » https://doi.org/10.1007/s13132-020-00708-3
• Baah, C., Jin, Z., & Tang, L. (2020). Organizational and regulatory stakeholder pressures friends or foes to green logistics practices and financial performance: Investigating corporate reputation as a missing link. Journal of Cleaner Production, 247, 119125, 10.1016/j.jclepro.2019.119125.
  » https://doi.org/10.1016/j.jclepro.2019.119125
• Baeshen, Y., Soomro, Y. A., & Bhutto, M. Y. (2021). Determinants of green innovation to achieve sustainable business performance: Evidence from SMEs. Frontiers in Psychology, 12, 767968, 10.3389/fpsyg.2021.767968.
  » https://doi.org/10.3389/fpsyg.2021.767968
• Barrane, F. Z., Ndubisi, N. O., Kamble, S., Karuranga, G. E., & Poulin, D. (2021). Building trust in multi-stakeholder collaborations for new product development in the digital transformation era. Benchmarking: An International Journal, 28(1), 205-228, 10.1108/BIJ-04-2020-0164.
  » https://doi.org/10.1108/BIJ-04-2020-0164
• Bilal, M., Xicang, Z., Jiying, W., Sohu, J. M., & Akhta, S. (2024). Navigating the manufacturing revolution: Identifying the digital transformation antecedents. Management Decision, 62(6), 1775-1805, 10.1108/MD-09-2023-1722.
  » https://doi.org/10.1108/MD-09-2023-1722
• Borsatto, J. M. L. S., & Amui, L. B. L. (2019). Green innovation: Unfolding the relation with environmental regulations and competitiveness. Resources, Conservation and Recycling, 149, 445-454, 10.1016/j.resconrec.2019.06.005.
  » https://doi.org/10.1016/j.resconrec.2019.06.005
• Cai, X., Zhu, B., Zhang, H., Li, L., & Xie, M. (2020). Can direct environmental regulation promote green technology innovation in heavily polluting industries? Evidence from Chinese listed companies. Science of The Total Environment, 746, 140810.
• Chan, H. K., Yee, R. W., Dai, J., & Lim, M. K. (2016). The moderating effect of environmental dynamism on green product innovation and performance. International Journal of Production Economics, 181, 384-391, 10.1016/j.ijpe.2015.12.006.
  » https://doi.org/10.1016/j.ijpe.2015.12.006
• Chang, K., Luo, D., Dong, Y., & Xiong, C. (2024). The impact of green finance policy on green innovation performance: Evidence from Chinese heavily polluting enterprises. Journal of Environmental Management, 352, 119961, 10.1016/j.jenvman.2023.119961.
  » https://doi.org/10.1016/j.jenvman.2023.119961
• Chen, R. (2023). The role of SMEs in China’s circular economy transition: A circular economy vision, Retrieved from Link to a pdf of the cited article
  » Link to a pdf of the cited article
• Coronado-Medina, A., Arias-Pérez, J., & Perdomo-Charry, G. (2020). Fostering product innovation through digital transformation and absorptive capacity. International Journal of Innovation and Technology Management, 17(06), 2050040, 10.1142/S0219877020500406.
  » https://doi.org/10.1142/S0219877020500406
• Cusumano, M. A., Kahl, S. J., & Suarez, F. F. (2015). Services, industry evolution, and the competitive strategies of product firms. Strategic Management Journal, 36(4), 559-575, 10.1002/smj.2235.
  » https://doi.org/10.1002/smj.2235
• Dai, J., Cantor, D. E., & Montabon, F. L. (2015). How environmental management competitive pressure affects a focal firm’s environmental innovation activities: A green supply chain perspective. Journal of Business Logistics, 36(3), 242-259, 10.1111/jbl.12094.
  » https://doi.org/10.1111/jbl.12094
• Davari, A., & Rezazadeh, A. (2013). Structural equation modeling with PLS. Tehran, Jahad University, 215(2), 224.
• Demartini, M., Evans, S., & Tonelli, F. (2019). Digitalization technologies for industrial sustainability. Procedia Manufacturing, 33, 264-271, 10.1016/j.promfg.2019.04.032.
  » https://doi.org/10.1016/j.promfg.2019.04.032
• Ding, W., & Chen, L. (2022). Regulating the use of big data: Justifications, perspectives and the Chinese way forward. Computer Law & Security Review, 46, 105729, 10.1016/j.clsr.2022.105729.
  » https://doi.org/10.1016/j.clsr.2022.105729
• Dmytriyev, S. D., Freeman, R. E., & Hörisch, J. (2021). The relationship between stakeholder theory and corporate social responsibility: Differences, similarities, and implications for social issues in management. Journal of Management Studies, 58(6), 1441-1470, 10.1111/joms.12684.
  » https://doi.org/10.1111/joms.12684
• Fachrunnisa, O., Adhiatma, A., Lukman, N., & Ab Majid, M. N. (2020). Towards SMEs’ digital transformation: The role of agile leadership and strategic flexibility. Journal of Small Business Strategy, 30(3), 65-85.
• Fang, Z., Kong, X., Sensoy, A., Cui, X., & Cheng, F. (2021). Government’s awareness of environmental protection and corporate green innovation: A natural experiment from the new environmental protection law in China. Economic Analysis and Policy, 70, 294-312, 10.1016/j.eap.2021.03.003.
  » https://doi.org/10.1016/j.eap.2021.03.003
• Farida, I., & Setiawan, D. (2022). Business strategies and competitive advantage: The role of performance and innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 163, 10.3390/joitmc8030163.
  » https://doi.org/10.3390/joitmc8030163
• Feng, H., Wang, F., Song, G., & Liu, L. (2022). Digital transformation on enterprise green innovation: Effect and transmission mechanism. International Journal of Environmental Research and Public Health, 19(17), 10614, 10.3390/ijerph191710614.
  » https://doi.org/10.3390/ijerph191710614
• Feroz, A. K., Zo, H., & Chiravuri, A. (2021). Digital transformation and environmental sustainability: A review and research agenda. Sustainability, 13(3), 1530, 10.3390/su13031530.
  » https://doi.org/10.3390/su13031530
• Fischer, M., Imgrund, F., Janiesch, C., & Winkelmann, A. (2020). Strategy archetypes for digital transformation: Defining meta objectives using business process management. Information & Management, 57(5), 103262, 10.1016/j.im.2019.103262.
  » https://doi.org/10.1016/j.im.2019.103262
• Freeman, R. E. (2023). Managing for stakeholders: Trade-offs or value creation. In Dmytriyev S. D. & Freeman R. E. (Eds), R. Edward freeman’s selected works on stakeholder theory and business ethics (pp. 295-299). Cham: Springer International Publishing.
• Freudenreich, B., Lüdeke-Freund, F., & Schaltegger, S. (2020). A stakeholder theory perspective on business models: Value creation for sustainability. Journal of Business Ethics, 166(1), 3-18, 10.1007/s10551-019-04112-z.
  » https://doi.org/10.1007/s10551-019-04112-z
• Gong, C., & Ribiere, V. (2021). Developing a unified definition of digital transformation. Technovation, 102, 102217, 10.1016/j.technovation.2020.102217.
  » https://doi.org/10.1016/j.technovation.2020.102217
• Hojnik, J., & Ruzzier, M. (2016). The driving forces of process eco-innovation and its impact on performance: Insights from Slovenia. Journal of Cleaner Production, 133, 812-825, 10.1016/j.jclepro.2016.06.002.
  » https://doi.org/10.1016/j.jclepro.2016.06.002
• Hong, J., Zheng, R., Deng, H., & Zhou, Y. (2019). Green supply chain collaborative innovation, absorptive capacity and innovation performance: Evidence from China. Journal of Cleaner Production, 241, 118377, 10.1016/j.jclepro.2019.118377.
  » https://doi.org/10.1016/j.jclepro.2019.118377
• Hörisch, J., Schaltegger, S., & Freeman, R. E. (2020). Integrating stakeholder theory and sustainability accounting: A conceptual synthesis. Journal of Cleaner Production, 275, 124097, 10.1016/j.jclepro.2020.124097.
  » https://doi.org/10.1016/j.jclepro.2020.124097
• Horvat, D., Dreher, C., & Som, O. (2019). How firms absorb external knowledge—Modelling and managing the absorptive capacity process. International Journal of Innovation Management, 23(01), 1950041, 10.1142/S1363919619500415.
  » https://doi.org/10.1142/S1363919619500415
• Huang, X.-X., Hu, Z.-P., Liu, C.-S., Yu, D.-J., & Yu, L.-F. (2016). The relationships between regulatory and customer pressure, green organizational responses, and green innovation performance. Journal of Cleaner Production, 112, 3423-3433, 10.1016/j.jclepro.2015.10.106.
  » https://doi.org/10.1016/j.jclepro.2015.10.106
• Jafari-Sadeghi, V., Garcia-Perez, A., Candelo, E., & Couturier, J. (2021). Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation. Journal of Business Research, 124, 100-111, 10.1016/j.jbusres.2020.11.020.
  » https://doi.org/10.1016/j.jbusres.2020.11.020
• Jianming, Z., Liang, G., Feng, T., Yuan, C., & Jiang, W. (2020). Green innovation to respond to environmental regulation: How external knowledge adoption and green absorptive capacity matter? Business Strategy and the Environment, 29(1), 39-53, 10.1002/bse.2349.
  » https://doi.org/10.1002/bse.2349
• Jun, W., Ali, W., Bhutto, M. Y., Hussain, H., & Khan, N. A. (2021). Examining the determinants of green innovation adoption in SMEs: A PLS-SEM approach. European Journal of Innovation Management, 24(1), 67-87, 10.1108/EJIM-05-2019-0113.
  » https://doi.org/10.1108/EJIM-05-2019-0113
• Kastelli, I., Dimas, P., Stamopoulos, D., & Tsakanikas, A. (2022). Linking digital capacity to innovation performance: The mediating role of absorptive capacity. Journal of the Knowledge Economy, 15(1), 1-35, 10.1007/s13132-022-01092-w.
  » https://doi.org/10.1007/s13132-022-01092-w
• Kline, R. (2015). Principles and practice of structural equation modeling (4th ed.). (Methodology in the social sciences, New York, NY: The Guilford Press.
• Li, H., Tang, H., Zhou, W., & Wan, X. (2022). Impact of enterprise digitalization on green innovation performance under the perspective of production and operation. Frontiers in Public Health, 10, 971971, 10.3389/fpubh.2022.971971.
  » https://doi.org/10.3389/fpubh.2022.971971
• Li, L., Wang, Y., Tan, M., Sun, H., & Zhu, B. (2023). Effect of environmental regulation on energy-intensive enterprises’ green innovation performance. Sustainability, 15(13), 10108, 10.3390/su151310108.
  » https://doi.org/10.3390/su151310108
• Llopis-Albert, C., Rubio, F., & Valero, F. (2021). Impact of digital transformation on the automotive industry. Technological Forecasting and Social Change, 162, 120343, 10.1016/j.techfore.2020.120343.
  » https://doi.org/10.1016/j.techfore.2020.120343
• Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management. MIS Quarterly, 41(1), 223-238, 10.25300/MISQ/2017/41:1.03.
  » https://doi.org/10.25300/MISQ/2017/41:1.03
• Nascimento, M. C., & Fontgalland, I. L. (2023). Mitigation of environmental liabilities in Brazilian companies: What does this have to do with compliance? Revista Contemporânea, 3(2), 855-899, 10.56083/RCV3N2-014.
  » https://doi.org/10.56083/RCV3N2-014
• Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539-569, 10.1146/annurev-psych-120710-100452.
  » https://doi.org/10.1146/annurev-psych-120710-100452
• Proeger, T. (2020). Knowledge spillovers and absorptive capacity—Institutional evidence from the “German Mittelstand”. Journal of the Knowledge Economy, 11(1), 211-238, 10.1007/s13132-018-0539-8.
  » https://doi.org/10.1007/s13132-018-0539-8
• Qiong, W., Wang, S., Zhou, A., Xia, B., Abruquah, L. A., & Chen, Z. (2023). Effects of digital transformation and environmental resource integration capability on medical equipment suppliers’ green innovation performance. Scientific Reports, 13(1), 17559, 10.1038/s41598-023-44274-5.
  » https://doi.org/10.1038/s41598-023-44274-5
• Quixy (2023). 80+ Digital transformation statistics and trends to help you navigate it in 2024. Retrieved from Link to a pdf of the cited article
  » Link to a pdf of the cited article
• Ramanathan, R., Ramanathan, U., & Bentley, Y. (2018). The debate on flexibility of environmental regulations, innovation capabilities and financial performance–A novel use of DEA. Omega, 75, 131-138, 10.1016/j.omega.2017.02.006.
  » https://doi.org/10.1016/j.omega.2017.02.006
• Rehman Khan, S. A., Ahmad, Z., Sheikh, A. A., & Yu, Z. (2022). Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance. Science Progress, 105(4), 368504221145648.
• Rehman, S. U., Kraus, S., Shah, S. A., Khanin, D., & Mahto, R. V. (2021). Analyzing the relationship between green innovation and environmental performance in large manufacturing firms. Technological Forecasting and Social Change, 163, 120481, 10.1016/j.techfore.2020.120481.
  » https://doi.org/10.1016/j.techfore.2020.120481
• Rijswijk, K., Klerkx, L., Bacco, M., Bartolini, F., Bulten, E., Debruyne, L., … Brunori, G. (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation. Journal of Rural Studies, 85, 79-90, 10.1016/j.jrurstud.2021.05.003.
  » https://doi.org/10.1016/j.jrurstud.2021.05.003
• Schaltegger, S., Hörisch, J., & Freeman, R. E. (2019). Business cases for sustainability: A stakeholder theory perspective. Organization & Environment, 32(3), 191-212, 10.1177/1086026617722882.
  » https://doi.org/10.1177/1086026617722882
• Shahzad, M. A., Jianguo, D., & Junaid, M. (2023). Impact of green HRM practices on sustainable performance: Mediating role of green innovation, green culture, and green employees’ behavior. Environmental Science and Pollution Research, 30(38), 88524-88547, 10.1007/s11356-023-28498-6.
  » https://doi.org/10.1007/s11356-023-28498-6
• Shahzad, M., Qu, Y., Javed, S. A., Zafar, A. U., & Rehman, S. U. (2020). Relation of environment sustainability to CSR and green innovation: A case of Pakistani manufacturing industry. Journal of Cleaner Production, 253, 119938, 10.1016/j.jclepro.2019.119938.
  » https://doi.org/10.1016/j.jclepro.2019.119938
• Shela, V., Ramayah, T., Aravindan, K. L., Ahmad, N. H., & Alzahrani, A. I. (2023). Run! this road has no ending! A systematic review of PLS-SEM application in strategic management research among developing nations. Heliyon, 9(12), 10.1016/j.heliyon.2023.e22476.
  » https://doi.org/10.1016/j.heliyon.2023.e22476
• Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11, 10.12691/ajams-9-1-2.
  » https://doi.org/10.12691/ajams-9-1-2
• Singh, S. K., Del Giudice, M., Chiappetta Jabbour, C. J., Latan, H., & Sohal, A. S. (2022). Stakeholder pressure, green innovation, and performance in small- and medium‐sized enterprises: The role of green dynamic capabilities. Business Strategy and the Environment, 31(1), 500-514, 10.1002/bse.2906.
  » https://doi.org/10.1002/bse.2906
• Singh, S., Sharma, M., & Dhir, S. (2021). Modeling the effects of digital transformation in Indian manufacturing industry. Technology in Society, 67, 101763, 10.1016/j.techsoc.2021.101763.
  » https://doi.org/10.1016/j.techsoc.2021.101763
• Slawotsky, J. (2022). Digital currencies and great power rivalry: China as a disseminator in the digital age. Asia Pacific Law Review, 30(2), 242-264, 10.1080/10192557.2022.2085412.
  » https://doi.org/10.1080/10192557.2022.2085412
• Soewarno, N., & Tjahjadi, B. (2020). Mediating effect of strategy on competitive pressure, stakeholder pressure and strategic performance management (SPM): Evidence from HEIs in Indonesia. Benchmarking: An International Journal, 27(6), 1743-1764, 10.1108/BIJ-06-2019-0292.
  » https://doi.org/10.1108/BIJ-06-2019-0292
• Trinugroho, I., Pamungkas, P., Wiwoho, J., Damayanti, S. M., & Pramono, T. (2022). Adoption of digital technologies for micro and small business in Indonesia. Finance Research Letters, 45, 102156, 10.1016/j.frl.2021.102156.
  » https://doi.org/10.1016/j.frl.2021.102156
• Tu, Y., & Wu, W. (2021). How does green innovation improve enterprises’ competitive advantage? The role of organizational learning. Sustainable Production and Consumption, 26, 504-516, 10.1016/j.spc.2020.12.031.
  » https://doi.org/10.1016/j.spc.2020.12.031
• Tyler, B. B., Lahneman, B., Cerrato, D., Cruz, A. D., Beukel, K., Spielmann, N., & Minciullo, M. (2023). Environmental practice adoption in SMEs: The effects of firm proactive orientation and regulatory pressure. Journal of Small Business Management, 62(5), 1-36, 10.1080/00472778.2023.2218435.
  » https://doi.org/10.1080/00472778.2023.2218435
• United Nations. (2023). The sustainable development agenda. Retrieved from Link to the cited article
  » Link to the cited article
• Vial, G. (2021). Understanding digital transformation: A review and a research agenda. Managing Digital Transformation, 13-66, 10.4324/9781003008637.
  » https://doi.org/10.4324/9781003008637
• Wang, C. H., & Juo, W. J. (2021). An environmental policy of green intellectual capital: Green innovation strategy for performance sustainability. Business Strategy and the Environment, 30(7), 3241-3254, 10.1002/bse.2800.
  » https://doi.org/10.1002/bse.2800
• Wang, F. (2023). The intermediary and threshold effect of green innovation in the impact of environmental regulation on economic growth: Evidence from China. Ecological Indicators, 153, 110371, 10.1016/j.ecolind.2023.110371.
  » https://doi.org/10.1016/j.ecolind.2023.110371
• Wei, Z., Li, G., Uddin, M. K., & Guo, S. (2020). Environmental regulation, foreign investment behavior, and carbon emissions for 30 provinces in China. Journal of Cleaner Production, 248, 119208, 10.1016/j.jclepro.2019.119208.
  » https://doi.org/10.1016/j.jclepro.2019.119208
• Weng, H.-H. R., Chen, J.-S., & Chen, P.-C. (2015). Effects of green innovation on environmental and corporate performance: A stakeholder perspective. Sustainability, 7(5), 4997-5026, 10.3390/su7054997.
  » https://doi.org/10.3390/su7054997
• Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, 101997, 10.1016/j.ijinfomgt.2019.08.005.
  » https://doi.org/10.1016/j.ijinfomgt.2019.08.005
• Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, 120999, 10.1016/j.techfore.2021.120999.
  » https://doi.org/10.1016/j.techfore.2021.120999
• Zarzycka, E., & Krasodomska, J. (2021). Environmental key performance indicators: The role of regulations and stakeholder influence. Environment Systems and Decisions, 41(4), 651-666, 10.1007/s10669-021-09825-z.
  » https://doi.org/10.1007/s10669-021-09825-z
• Zhang, F., & Zhu, L. (2019). Enhancing corporate sustainable development: Stakeholder pressures, organizational learning, and green innovation. Business Strategy and the Environment, 28(6), 1012-1026, 10.1002/bse.2298.
  » https://doi.org/10.1002/bse.2298
• Zhang, Y., Sun, J., Yang, Z., & Wang, Y. (2020). Critical success factors of green innovation: Technology, organization and environment readiness. Journal of Cleaner Production, 264, 121701, 10.1016/j.jclepro.2020.121701.
  » https://doi.org/10.1016/j.jclepro.2020.121701

*

Shamim Akhtar can be contacted at: E-mail: [e-mail]

ASSOCIATE EDITOR:

Muddassar Sarfraz

Publication Dates

• Publication in this collection
  12 Oct 2026
• Date of issue
  2026

History

• Received
  20 June 2025
• Reviewed
  09 Dec 2025
• Reviewed
  16 Mar 2026
• Reviewed
  04 May 2026
• Accepted
  08 May 2026