Digital transformation, innovation capabilities, and servitization as drivers of ESG performance in manufacturing SMEs

Abstract

This study investigates the relationship between digital transformation and Environmental, Social, and Governance (ESG) performance in the context of SMEs. Drawing upon Resource Orchestration Theory, this research proposes a theoretical model that examines the direct effect of digital transformation on ESG performance and the mediating roles of innovation capabilities and servitization level in this relationship. PLS-SEM and fsQCA were employed to analyze survey data from 215 SME executives. The results reveal that digital transformation positively influences ESG performance. Moreover, innovation capabilities and servitization level partially mediate the relationship between digital transformation and ESG performance. This research contributes to the literature by proposing and validating a comprehensive model that integrates these constructs, offering actionable insights for SME managers and policymakers to enhance ESG outcomes through strategic digital initiatives. Notably, fsQCA results highlight three distinct configurations of digital transformation components leading to high ESG performance, providing nuanced pathways for SMEs to achieve sustainable development.

Introduction

The rapid pace of technological change has made digital transformation a critical imperative for businesses seeking to maintain competitiveness and ensure long-term viability^1–5. This is especially crucial for Small and Medium-sized Enterprises (SMEs), which play a vital role in many economies. In the context of digital transformation, companies’ ESG (Environmental, Social, and Governance) performance is encountering new challenges and opportunities⁶. The significance and worth of ESG performance have been further highlighted⁷. ESG is an evaluation system focusing on firms’ environmental protection, social responsibility, and corporate governance performance⁸. Its evaluation results have gradually become one of the important indicators for investors, regulatory agencies, customers, and the public to evaluate firms⁹.

Digital transformation can facilitate organizations in effectively tackling environmental and social concerns, strengthening their commitment to social responsibility and governance, and consequently enhancing their ESG performance^10,11. Digital transformation can significantly impact each dimension of ESG performance. More precisely, digital transformation empowers companies to effectively track and control environmental metrics, such as diminishing energy usage, waste generation, and pollutants, consequently leading to a direct enhancement of their environmental efficacy¹². This includes leveraging technologies such as IoT for real-time environmental monitoring and data analytics to optimize resource usage. Digital transformation enables companies to provide products and services that more effectively address societal needs, thereby fulfilling their social responsibilities¹³. This involves using digital platforms to enhance customer engagement, improve service delivery, and support community initiatives, fostering a positive social impact. Regarding governance, digital transformation improves the monitoring and management of financial and operational activities, enhancing transparency, accountability, and reliability¹⁴. By implementing advanced information systems, firms can ensure better compliance with regulatory standards, improve decision-making processes, and foster a culture of integrity and ethical behavior. By explicitly addressing these mechanisms, the paper highlights how digital transformation enhances ESG performance, providing a clearer linkage between digital initiatives and sustainable outcomes. Digital transformation can also help firms better understand the market and competition¹⁵, adjust their strategies promptly, and improve long-term profitability. SMEs are the primary drivers of employment and innovation in society¹⁶ and hence deserve greater focus and support. Hence, investigating the impact of SMEs’ digital transformation on their ESG performance can greatly enhance enterprises’ social responsibility and sustainable growth¹⁷.

SMEs are the backbone of many economies, accounting for a significant proportion of employment and innovation¹⁶. In the manufacturing sector, SMEs contribute extensively to economic development but often struggle with resource constraints and technological adoption challenges¹⁸. The advent of digital transformation presents both opportunities and challenges for these SMEs. While digital technologies can streamline operations, enhance innovation, and improve service offerings, the implementation requires the investment of limited resources that SMEs may find burdensome^1.19.

ESG performance has emerged as a critical metric for assessing a firm’s commitment to sustainable practices, influencing investor decisions, customer perceptions, and regulatory compliance^8,9. For manufacturing SMEs, improving ESG performance is not just about ethical responsibility but also about ensuring long-term viability and competitiveness in an increasingly sustainability-conscious market¹⁰. However, the direct link between digital transformation and ESG performance in SMEs remains underexplored, with most studies focusing on large, listed companies²⁰. Through digital transformation, SMEs can better understand the market and customer demands²¹, thus serving societal development more effectively. By leveraging digital technology, firms can optimize customer experiences¹³, provide convenient and efficient services¹⁴, and enhance their sense of social responsibility. SMEs’ digital transformation’s impact on ESG performance is complex and requires more research²². The impact of SMEs’ digital transformation on ESG performance is poorly studied and ignores innovative capacity and servitization level¹³.

Investing resources to understand this relationship is worthwhile because it addresses a significant gap in the literature and provides actionable insights for SME managers and policymakers. By elucidating how digital transformation can enhance ESG performance through innovation capabilities and servitization level, SMEs can make informed decisions about allocating scarce resources toward digital initiatives that yield sustainable benefits¹¹. This research not only contributes to academic knowledge but also supports SMEs in their strategic planning, helping them to align their digital transformation efforts with sustainability goals, thus maximizing the return on their limited investments. Thus, pertinent research issues are:

Q1: How do the different components of digital transformation (digital strategy, digital organization, and digital technology adoption) contribute to ESG performance?

Q2: What are the roles of innovation capabilities and servitization level in mediating the relationship between digital transformation and ESG performance in SMEs?

Resource Orchestration Theory (ROT) serves as the theoretical foundation for this study. ROT is particularly suitable for explaining how companies achieve strategic goals by managing and dynamically adjusting their resource portfolio²³. In digital transformation, ROT provides a framework to understand how SMEs can orchestrate their resources to enhance ESG performance. This theory emphasizes the role of managerial actions in orchestrating resources, which includes integrating digital technologies with existing resources to foster innovation capabilities and servitization levels²⁴. This study will examine SMEs’ digital transformation, ESG performance, innovative capabilities, and servitization degree to answer these questions. A scale for SMEs’ digital transformation and ESG performance will be established to validate the ROT-based theoretical model. This model addresses the gap in understanding the nuanced mediating roles of innovation capabilities and servitization level in SMEs, offering fresh insights into how these factors interact within the digital transformation-ESG performance nexus. Survey data from 215 respondents will be analyzed using PLS-SEM, IPMA, and fsQCA. These methodologies let us examine digital transformation, innovation capabilities, servitization level, and ESG performance from many angles. PLS-SEM tests the hypothesized model and examines direct and mediating effects, while IPMA assesses construct relevance and performance. fsQCA uses a configurational method to explore how different conditions contribute to excellent ESG performance.

This study offers novel contributions to the literature on digital transformation and ESG performance in SMEs. Firstly, while previous research has largely focused on the direct effects of digital transformation on financial outcomes or broad corporate performance measures, our study is among the first to confirm the specific mechanism by which digital transformation affects the ESG performance of SMEs through innovation capabilities and servitization level. This approach provides a more nuanced understanding of how digital initiatives can be leveraged to enhance sustainability outcomes. Secondly, our research reveals some surprising findings that challenge conventional wisdom. Contrary to the common assumption that smaller firms may struggle more with digital transformation due to resource constraints, our results show that firm size does not significantly effect ESG performance. This suggests that SMEs can effectively leverage digital technologies to enhance their ESG performance, regardless of their size. Thirdly, our study presents a counterintuitive insight regarding the role of servitization in the digital transformation-ESG performance relationship. While servitization is often viewed as a key driver of sustainability in manufacturing firms, our findings indicate that its mediating effect is less pronounced than that of innovation capabilities. This challenges the prevailing notion that service-oriented business models are the primary pathway through which digital transformation enhances ESG performance in manufacturing SMEs. By addressing these novel, surprising, and counterintuitive aspects, our research not only fills a significant gap in the literature but also provides actionable insights for SME managers and policymakers seeking to enhance ESG outcomes through strategic digital initiatives. The theoretical backdrop, hypothesis development, theoretical framework, research methods, analytical results, discussion, and conclusion complete this study.

Theoretical background and hypothesis development

Resource Orchestration Theory

ROT is an important theory in corporate strategic management. It is particularly suitable for explaining how companies achieve strategic goals by managing and dynamically adjusting their resource portfolio, especially in digital transformation and improving ESG performance²³.

ROT can show SMEs how digital transformation may boost innovation and service quality, boosting ESG performance. Companies now have big data, cloud computing, and other digital tools thanks to digital transformation. Enterprises need to integrate these digital resources with original human, financial, and other resources, formulate appropriate digital strategies, and adjust organizational structures and processes. Only by optimizing resource allocation can the value of digital transformation be truly unleashed²⁵. ROT also emphasizes the key role of managers in resource integration²⁶. In promoting digital transformation, business managers need to have strategic vision and decision-making capabilities, dynamically adjust resource allocation according to changes in the internal and external environment, and lead organizational change. Managers must also focus on cultivating talent, improving employees’ digital skills, creating an innovation culture, and stimulating organizational vitality²⁴. ROT presents a theoretical framework for studying digital transformation and SME ESG performance. Digital transformation involves hardware, software, and talent integration and management to succeed. Next, we will use this theoretical framework to examine how digital transformation might boost ESG performance through innovation and service quality.

Digital transformation and its components

Digital transformation is a multifaceted organizational change process that leverages digital technologies to create new—or modify existing—business processes, culture, and customer experiences to meet changing business and market requirements¹. This transformation enables firms to reconfigure their resources and capabilities effectively, aligning with the principles of ROT²³. According to ROT, firms must adeptly manage and orchestrate their resource portfolios to achieve sustainable competitive advantages⁹. In the context of digital transformation, SMEs can harness digital resources to enhance critical organizational capabilities, particularly innovation capabilities and servitization level, which play pivotal roles in driving ESG performance²⁶. Innovation capabilities refer to a firm’s ability to develop new products, services, and processes through the integration and reconfiguration of existing resources and the assimilation of new knowledge²⁷. Digital transformation provides SMEs with advanced technologies, data analytics, and collaborative platforms, facilitating innovation by enhancing knowledge sharing and enabling more agile responses to market changes¹. Servitization level denotes the extent to which a firm moves from a traditional product-centric business model to one that integrates products and services, emphasizing value co-creation with customers^28,29. Digital transformation empowers SMEs to elevate their servitization level by adopting technologies such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics, which support advanced service offerings and closer customer relationships³⁰. By integrating innovation capabilities and servitization level into our theoretical framework, we align with ROT’s emphasis on strategic resource management. These mediating variables represent orchestrated capabilities through which digital transformation influences ESG performance, providing a coherent and well-founded basis for our study. This shift improves economic outcomes and supports social and governance aspects by fostering customer loyalty, improving service quality, and ensuring better management of resources and processes³¹. ROT posits that firms need to strategically manage and dynamically integrate their resources to achieve desired outcomes²³. Digital transformation provides an opportunity for SMEs to orchestrate digital resources alongside traditional ones to enhance their innovation capabilities and servitization level, thereby promoting their ESG performance²⁶.

Digital transformation for SMEs refers to the comprehensive transformation of firms’ strategies, organizations, and business processes using digital technology to enhance their innovation capabilities and servitization level¹³, thereby promoting their transformation and upgrading³². In SMEs’ digital transformation, digital strategy³³, digital organization⁶, and digital technology adoption¹² are three crucial components. This division is grounded in the literature and aligns with the principles of Resource Orchestration Theory (ROT), which posits that firms must adeptly manage and orchestrate their resource portfolios to achieve sustainable competitive advantages²³. ROT emphasizes the strategic management and dynamic integration of resources to achieve organizational goals²⁴. In the context of digital transformation, ROT provides a framework to understand how SMEs can orchestrate their digital and traditional resources to enhance innovation capabilities and servitization levels, thereby improving ESG performance²⁶. Digital Strategy refers to the overall planning and direction of firms’ digital transformation, taking into account their strategic goals, business processes, and technological foundations^32,34,35. Firms must examine their characteristics, market environment, organizational structure, and personnel composition when determining the direction and path of digital transformation²¹. Digital organization refers to firms’ organizational structure and personnel configuration in digital transformation^36,37. Firms need to establish an organizational structure and culture compatible with digital transformation, attach importance to cultivating and introducing digital talents, and ensure that the leadership can fully understand and support digital transformation¹⁶. Digital transformation adoption is the various technologies and tools that firms adopt in the process of digital transformation, taking into account their technological foundations, technological talents, and technological costs^37,38. Firms need to choose suitable digital technologies and tools based on their own technological capabilities and business needs, and ensure that applying digital technologies and tools can effectively improve their production efficiency and servitization level³⁹.

By dissecting digital transformation into digital strategy, digital organization, and digital technology adoption (Fig. 1), we align our study with established theoretical frameworks¹⁶. This categorization reflects how SMEs strategize for digital initiatives, restructure organizational processes, and adopt new technologies, which is essential for effective resource orchestration²⁵.

Fig. 1.

The composition of digital transformation.

ESG performance in the digital age

ESG performance is the evaluation of firms’ performance in Environmental protection, Social responsibility, and corporate Governance, serving as an important indicator for investors, regulatory agencies, customers, and the public to assess firms^6,8,15. Environmental Performance measures how a firm’s operations impact the natural environment, including resource usage and emission reductions¹⁵. Social Performance assesses the firm’s relationships with employees, customers, and communities, including social responsibility initiatives⁶. Governance Performance evaluates the firm’s internal practices and policies that lead to effective decision-making and compliance with laws¹⁰. ESG performance has become an important responsibility that firms must undertake for sustainable development^3,9. Digital transformation’s impact on ESG performance is gaining attention¹⁹. Digital technology-based transformation can improve organizations’ ESG performance, improving sustainability and social responsibility¹⁷. Digital transformation can boost energy and resource efficiency, reduce environmental impact, and promote sustainable development²¹. Digital transformation can help firms optimize supply chain management, reduce waste and energy consumption⁴⁰. Digital transformation can boost enterprises’ social responsibility by improving employee productivity and welfare¹⁴. Digital transformation can also help firms improve their corporate governance, enhancing transparency and sustainability³³. Digital transformation can assist organizations improve transparency and sustainability by improving data management, auditing, sharing, and openness⁴⁰.

Digital transformation enables organizations to more effectively track and control environmental metrics, enhance social impact, and strengthen governance practices, thereby directly contributing to improved ESG performance. Digital technologies such as IoT sensors and data analytics allow companies to monitor and optimize resource usage in real-time, leading to reduced energy consumption, waste generation, and pollutant emissions¹². For example, smart building management systems can automatically adjust lighting and temperature based on occupancy, significantly reducing energy waste. Digital platforms enhance customer engagement and improve service delivery, allowing companies to better address societal needs¹³. For instance, AI-powered chatbots can provide 24/7 customer support, improving accessibility and responsiveness. Digital tools facilitate more effective community outreach and social responsibility initiatives, such as online volunteering programs or digital fundraising campaigns. Advanced information systems improve the monitoring and management of financial and operational activities, enhancing transparency, accountability, and reliability¹⁴. For example, blockchain technology can be used to create immutable audit trails, reducing the risk of fraud and improving stakeholder trust. Data analytics tools enable more informed and ethical decision-making processes, fostering a culture of integrity within the organization. By leveraging these digital capabilities across all three ESG dimensions, companies can systematically improve their overall ESG performance. This comprehensive approach ensures that digital transformation not only enhances operational efficiency but also drives sustainable and responsible business practices.

In summary, the impact of digital transformation on firms’ ESG performance is multifaceted⁸. However, it can help firms improve their ESG levels, enhancing their sustainability and social responsibility performance³³. In digital transformation, firms should consider technology, society, and the economy comprehensively and develop reasonable digital transformation strategies¹⁶ to achieve their sustainable development goals. Through digital transformation, firms can achieve sustainable development and better ESG performance results¹⁴.

H1: Digital transformation positively affects ESG performance.

Digital Transformation, Innovation Capabilities and ESG performance

Drawing upon ROT, we posit that digital transformation enhances firms’ innovation capabilities by providing access to novel technologies and facilitating the reconfiguration of resources²³. Enhanced innovation capabilities enable firms to develop sustainable products and processes, directly impacting environmental performance by reducing waste and promoting resource efficiency¹². Digital transformation can stimulate employees’ innovation awareness and capabilities, helping firms respond more effectively to market competition¹³. Moreover, digital transformation can enhance various aspects of innovation capabilities, such as innovation speed, quality, and efficiency³³. By strengthening innovation team building and enhancing employees’ innovation awareness and capabilities, digital transformation further bolsters firms’ overall innovation capabilities and sustainability³¹. However, firms should consider improving innovation capabilities as a long-term strategy and continuously innovate to ensure sustainable development¹³. Therefore, applying digital transformation can provide firms with more innovation resources, enhance innovation capabilities, and view innovation as a long-term strategy to promote firms’ sustainable development continuously¹⁰.

Innovation capabilities refer to firms’ ability to continuously innovate during the digital transformation process, including innovation speed, quality, and efficiency^27,28. Enhanced innovation capabilities allow organizations to develop more sustainable and socially responsible products, processes, and services^8,13. For instance, improved innovation capabilities can lead to the design of more eco-friendly products, optimization of supply chains for minimal waste, and development of solutions that address social challenges. These innovations directly contribute to improved environmental performance¹². In terms of social performance, innovative activities can improve employee welfare, enhance their participation and loyalty, and promote firms’ overall ESG performance¹⁴. Furthermore, enhanced innovation capabilities can help firms achieve more efficient management and clearer decision-making processes, thereby enhancing transparency and sustainability in governance²². Additionally, innovation capabilities can contribute to more effective risk management and better compliance practices, further enhancing firms’ ESG performance¹³. Moreover, innovation contributes to social performance by improving employee engagement and developing solutions that address societal needs¹⁴. In terms of governance, innovative practices can lead to improved transparency and decision-making processes, enhancing governance performance¹³. Therefore, we hypothesize:

H2: Digital transformation positively affects innovation capabilities.

H3: Innovation capabilities positively affect ESG performance.

Digital transformation, servitization level and ESG performance

From the perspective of ROT, servitization is a strategic approach wherein firms orchestrate resources to offer integrated products and services, fostering deeper customer relationships and creating additional value^23,29. Digital transformation facilitates this shift by providing digital tools that enhance service delivery, customer engagement, and operational efficiency⁴¹. According to ROT, digital transformation enables firms to integrate digital resources with their existing capabilities to enhance servitization levels²⁶. This integration facilitates efficient and personalized services, improving customer satisfaction and sustainability²¹. Through digital transformation, firms can enhance their capabilities in servitization speed, quality, and efficiency¹⁹, thereby improving their servitization level. Digital transformation can also help organizations strengthen their service team building, improve employee service awareness and capabilities, and thus enhance their servitization level¹³. Digital transformation can increase service quality, efficiency, and ESG performance¹⁶. Digital transformation enables firms to gather and analyze customer data more effectively, leading to better understanding of customer needs and preferences⁴¹. It also provides tools for more efficient and personalized service delivery, such as AI-powered chatbots and IoT-enabled predictive maintenance³⁰. This enhanced capability to offer and manage services facilitates the shift towards a more service-oriented business model, thereby increasing the servitization level. Digital technologies can help firms overcome barriers to servitization by improving their ability to design, deliver, and scale service offerings⁴².

Firm’s servitization level positively impacts its economic, social, and governance benefits¹⁵. In terms of economic benefits, firms can increase their revenue and profit margins by providing more efficient, flexible, and personalized services, thereby enhancing the added value of their products¹⁴. Servitization level can also help firms reduce material and energy consumption¹³, providing a more solid foundation for their social responsibility performance. Regarding social benefits, excellent servitization can help firms better meet customer needs, thereby improving customer satisfaction and loyalty¹⁷. A firm’s servitization level can also help it better understand and respond to social and environmental issues³¹, enhancing its social image and reputation. In terms of governance benefits, improving the servitization level can help firms better manage and coordinate internal resources and processes, thereby enhancing their transparency and sustainability³³. The servitization level can also enhance trust and cooperation between firms and stakeholders, helping firms fulfill their social responsibility¹⁶. A higher servitization level improves customer relationships and loyalty, leading to better social performance⁴³. It also often results in more efficient resource use (e.g., through product-service systems), contributing to environmental performance⁴⁴. For instance, servitization can lead to extended product lifecycles and reduced waste through repair and refurbishment services. Additionally, the shift to a service-oriented model typically requires improved governance structures to manage complex customer relationships and service delivery processes, enhancing the governance aspect of ESG⁴⁵. This improved governance can lead to better risk management and stakeholder engagement, further contributing to overall ESG performance. The hypotheses are as follows:

H4: Digital Transformation positively affects Servitization level.

H5: Servitization level positively affects ESG Performance.

Figure 2 shows the relationships between the hypotheses in the literature and establishes our theoretical framework.

Fig. 2.

Theoretical framework.

Methodology

Our methodology is divided into four processes (Fig. 3).

Fig. 3.

Methodology Flowchart.

Rationale for selecting the Manufacturing SMEs

This study selected the manufacturing industry as the focus of our study for several critical reasons. Manufacturing SMEs are pivotal to economic development, accounting for a significant share of employment and innovation in many economies¹⁶. This sector is particularly influential in driving technological advancements and contributes extensively to GDP¹⁸. However, manufacturing SMEs often face unique challenges, including resource constraints and difficulties in adopting new technologies, which can impede their digital transformation efforts and ESG performance^1,19. Furthermore, the manufacturing industry has substantial environmental impacts due to resource consumption and emissions, making ESG performance especially pertinent¹⁰. By focusing on this sector, our research aims to provide valuable insights into how digital transformation can enhance ESG outcomes in an industry where such improvements are critically needed⁴⁶.

For this research, we define SMEs according to the criteria established by the Ministry of Industry and Information Technology of the People’s Republic of China et al.⁴⁷, SMEs in the manufacturing sector are those with less than 1,000 employees or less than 400 million RMB in operating income. This definition aligns with the prevailing classification of SMEs in the Chinese context and ensures consistency with official statistics and industry benchmarks^19,46. By adhering to this standardized definition, we ensure consistency and comparability with existing research, allowing our findings to be contextualized within the broader literature on SMEs and digital transformation.

Survey instrument development

We developed a survey instrument to measure the model involved in the theoretical framework¹⁷. The scale was adapted from literature and underwent three stages of development⁴⁸. In the first stage, we developed an initial version of the scale based on theory and literature review to ensure content validity. In the second stage, we translated an English questionnaire into Chinese and had a third party back-translate it for correctness. We also sent a test questionnaire to five researchers and business executives and refined it based on their feedback to ensure expert validity. The third stage involved a pilot survey with 24 Chinese business executives and questionnaire revisions based on their comments. The pilot survey results were reliable and valid, allowing for formal data collection. We used a 5-point Likert scale (1 for “strongly disagree” to 5 for “strongly agree” to measure our scale)³¹. For Digital Strategy, sample items include “Digital strategy is integrated into our enterprise’s overall strategy.” and “Our enterprise has formulated a 3–5 year digital transformation strategy.” For Digital Organization, examples are “Management provides strong support for digital transformation.” and “Our enterprise actively promotes digital transformation action plans.” Items measuring Digital Technology Adoption include “Our enterprise uses digital technology to better understand our customers.” and “Our enterprise uses digital technology to make better operational decisions.” For Servitization Level, sample items are “Our enterprise’s service revenue share is steadily increasing.” and “Our enterprise is better at discovering service opportunities than competitors.” Innovation Capabilities are measured with items such as “Our enterprise advocates a risk-taking spirit.” For Environmental Performance, an example item is “Our enterprise strives to reduce the use of materials or energy.” Social Performance items include “Our enterprise focuses on employee satisfaction.” Lastly, for Governance Performance, a sample item is “Our enterprise strives to implement the best corporate governance principles.” Table 1 shows construct sources.

Table 1.

Constructs and adapted sources of survey items.

Constructs				No. of items	References
Second-order	Type	First-order	Type
Digital transformation	Formative	Digital strategy	Reflective	4	32, 35
		Digital organization	Reflective	4	36, 37
		Digitalized technology adoption	Reflective	5	37, 38
Innovation capabilities			Reflective	4	27, 38
Servitization level			Reflective	5	28, 43
ESG performance	Formative	Environmental	Formative	3	49, 50, 51
		Social	Formative	4
		Governance	Formative	3

Second-order formative construct

By linking the lower-order constructs, first-order constructs can form second-order variables, which help researchers simplify complex models and obtain more intuitive research results⁵². Digital transformation is composed of three parts: digital strategy, digital organization, and digital technology adoption³⁷. Therefore, we constructed digital transformation as a second-order variable. In addition, ESG itself is a construct formed by the three dimensions, so ESG is also constructed as a second-order variable^31,50. This study’s two second-order constructs are formative variables (Table 1).

Data collection and sample

Original data came from a large-scale online survey, which helped to obtain more generalizable findings¹³. We used the validated scale, which had undergone three rounds of validation, to create an electronic questionnaire through the survey platform (wenjuan.com), and generated access links and QR codes. The survey clearly explained the purpose and data usage of the survey, and promised data would be kept confidential. None of the questions contained personally identifiable information, and respondents could withdraw from the survey at any time. We used convenience sampling to improve the theoretical validity^53,54. Finally, we distributed survey invitations to social platforms where SME executives gather, such as Maimai, QQ, and WeChat. The data collection process lasted about two months in 2023.

These excluding invalid questionnaire criteria included incomplete responses, consistent patterns of responses (such as selecting the same option for all items), and unusually quick completion times that suggested a lack of thoughtful engagement. Additionally, responses containing contradictory information or showing signs of random answering patterns were discarded. After applying these criteria, we obtained 215 valid questionnaires for our analysis (Table 2).

Table 2.

Distribution of the sample.

Demographic description	Characteristics	%
Ownership	State-owned	17.2
	Private	50.7
	Foreign investment	10.7
	Other	21.4
Firm age (years)	≤ 5	4.2
	6–10	28.9
	11–20	40.9
	21–30	22.3
	≥ 31	3.7
Firm size (employee)	≤ 100	6.5
	101 ~ 300	21.9
	301–1000	71.6
Industry	Manufacturing	100

Multiple analysis techniques

PLS-SEM is widely used in digital transformation, including studies related to ESG^55,56. PLS-SEM can test models that include high-order and low-order variables and is recommended for developing new theoretical frameworks or predicting ESG performance⁵⁰. In addition, PLS-SEM is also adept at analyzing mediating effects¹⁴, which can help us find answers to research question 3. Therefore, this study will use SmartPLS 3.3.9 for the next data analysis stage⁵². This includes measurement, structural model, mediating effects, and IPMA.

We broaden the application of fsQCA to augment the insights obtained from PLS-SEM and IPMA¹⁹. The purpose of employing fsQCA is to capture the complexity and configurational nature of the relationships among digital transformation, innovation capabilities, servitization level, and ESG performance. While PLS-SEM helps understand the direct and indirect effects linearly and symmetrically, fsQCA enables us to explore how different combinations of conditions (e.g., high digital transformation and high innovation capabilities) can jointly influence ESG performance. This method allows us to identify multiple pathways to high ESG performance, reflecting the real-world complexity where different firms may achieve high ESG performance through different routes. Integrating fsQCA with PLS-SEM and IPMA provides a holistic view of the data, combining variance-based analysis with configurational analysis, thereby enhancing the robustness and comprehensiveness of our findings.

Robustness checks

We conducted various robustness checks on the sample data to obtain more robust results. Specifically, we conducted tests for common method, non-response bias, and multicollinearity¹². For common method bias, we used an anonymous survey to reduce social desirability bias⁵⁷. Harman one-factor test results show that the first factor explained only 35.1% (less than the threshold of 50%). In addition, we used the marker variable technique⁴⁶, and we found that the constructed marker variable didn’t have significant relationships with any other variables in the model. Therefore, there is no common method bias in this study.

We utilized independent sample t-tests to examine for significant variations in sample characteristics between early and late responders⁴⁸ and found no non-response bias (P > 0.05).

PLS-SEM and IPMA results

Assessment of measurement model

Tables 3 and 4 provide measurement model evaluation findings. The reflected variables’ factor loadings, Cronbach’s alpha, composite reliability, and discriminant validity were examined first. Table 3 shows that most of factor loadings above 0.7 demonstrate reliability. Convergent validity is indicated by each construct’s composite reliability exceeding 0.7 and the average variance extracted above 0.5. We tested discriminant validity (Table 4) using the Fornell-Larcker, HTMT, and cross-loadings tests. Fornell-Larcker tests show that the square root of each construct’s AVE is greater than the correlation coefficients between construct pairs. The HTMT test shows less than 0.9 between each pair of constructs. The cross-loadings test shows that each construct has higher factor loadings than others. The results show our model has strong discriminant validity⁵⁵.

Table 3.

Measurement model results.

First-order constructs	Type	Indicators	Factor loadings /Weights	α	CR /VIF	AVE /T value
Digital strategy	Reflective	DS1	0.830	0.839	0.892	0.674
		DS2	0.840
		DS3	0.837
		DS4	0.776
Digital organization	Reflective	DO1	0.787	0.823	0.883	0.654
		DO2	0.849
		DO3	0.826
		DO4	0.770
Digitalized technology adoption	Reflective	DTA1	0.713	0.863	0.902	0.648
		DTA2	0.703
		DTA3	0.751
		DTA4	0.678
		DTA5	0.652
Innovation capabilities	Reflective	IC1	0.846	0.866	0.909	0.714
		IC2	0.829
		IC3	0.863
		IC4	0.841
Servitization level	Reflective	SL1	0.800	0.862	0.901	0.644
		SL2	0.795
		SL3	0.811
		SL4	0.776
		SL5	0.831
Environmental	Formative	Environment1	-/0.433	-	-/1.686	-/5.125
		Environment2	-/0.401		-/1.737	-/4.669
		Environment3	-/0.346		-/1.768	-/4.062
Social	Formative	Social1	-/0.349	-	-/1.900	-/3.722
		Social2	-/0.320		-/2.174	-/3.207
		Social3	-/0.238		-/2.030	-/2.404
		Social4	-/0.290		-/1.842	-/3.264
Governance	Formative	Governance1	-/0.426	-	-/1.732	-/5.847
		Governance2	-/0.396		-/1.986	-/5.394
		Governance3	-/0.347		-/1.755	-/5.030
Second-order constructs	Type	First-order constructs	Path coefficient	t value	P value	VIF
Digital transformation	Formative	Digital strategy	0.853	42.431	0.000	1.000
		Digital organization	0.891	44.738	0.000	1.000
		Digitalized technology adoption	0.870	43.350	0.000	1.000
ESG performance	Formative	Environmental	0.885	56.618	0.000	1.000
		Social	0.890	47.891	0.000	1.000
		Governance	0.873	57.644	0.000	1.000
Note: α = Cronbach’s Alpha; CR = Composite Reliability; AVE = Average Variance Extracted; VIF = Variance Inflation Factor.

Table 4.

Discriminant validity.

Constructs	DO	DS	DTA	IC	SL
DO	0.809	0.784	0.773	0.488	0.486
DS	0.653	0.821	0.741	0.426	0.406
DTA	0.654	0.630	0.805	0.502	0.367
IC	0.414	0.363	0.432	0.845	0.536
SL	0.412	0.346	0.316	0.463	0.803
Note: The value on the diagonal is the square root of AVE, the values at the bottom left of the diagonal line are related coefficients, and the value of the upper right is HTMT results.

We assessed formative constructs using path coefficients, VIF, and T values⁵³. Table 3 indicates that all formative constructions have VIF values below 3, indicating no multicollinearity. The T values of each formative component are more than 1.96, indicating weight relevance. All second-order formative construct path coefficients are greater than 0.8, showing convergent validity. These results show that our formative conceptions and model are appropriate⁴⁶.

Assessment of structural model

Using Bootstrapping with a sample size of 5000^{52, 56}, we obtained the results shown in Fig. 4. All five hypotheses proposed in our study were supported. The overall explanatory power of the model reached 42.1% (R² = 0.421), explaining 22.2% (R² = 0.222) of innovation capabilities and 17.5% (R² = 0.175) of servitization level. To enhance the reliability of the model’s findings, we included firm ownership, firm age, and firm size as control variables⁵⁸. Figure 4 shows no significant control variable-ESG performance association.

Fig. 4.

Structural Model Analysis.

Blindfolding was used to test model prediction capacity⁴⁸. The calculation indicated that all model Q² values were larger than 0, suggesting good predictive power. The model fit well since its standardized root mean square residual (SRMR) was < 0.08⁵².

Mediation effect analysis

Our PLS-SEM model was used to undertake a mediation effect analysis to determine how innovation capabilities and servitization level affect digital transformation and ESG performance^31,52. The data analysis results are in Table 5. Digital transformation can positively impact ESG performance both directly (β = 0.181**) and indirectly (β = 0.165***) when compared to innovative capabilities. Digital transformation positively impacts ESG performance both directly (β = 0.181**) and indirectly (β = 0.117***) in the link between it and servitization level. This suggests that innovation capabilities and servitization level partially mediate digital transformation’s effect on ESG performance.

Table 5.

Mediation effect analysis.

Path		Path coefficient	T-value	95% Confidence Interval	Type
Digital transformation→ Innovation capabilities → ESG performance	Direct effects	0.181	2.650	[0.054,0.324]	Partial mediation
	Indirect effects	0.165	4.273	[0.098,0.250]
Digital transformation→ Servitization Level → ESG performance	Direct effects	0.181	2.650	[0.054,0.324]	Partial mediation
	Indirect effects	0.117	3.556	[0.060,0.190]

PLS_predict

To further evaluate the predictive power of our proposed digital transformation-ESG performance model⁵², we also conducted additional testing using PLS_predict. As shown in Table 6, the values of root mean squared error (RMSE), Mean absolute percentage error (MAPE), and mean absolute error (MAE) in PLS-SEM were all lower than those in the linear regression model (LM). In addition, the Q²_predict values of each item in PLS-SEM were higher than those in LM. These results confirm that our model has excellent predictive power.

Table 6.

PLS_predict.

Items	PLS-SEM - Linear Model
	RMSE	MAE	MAPE	Q²_predict
Environmental2	-0.030	-0.026	-1.204	0.060
Environmental1	-0.017	-0.021	-0.081	0.029
Environmental3	-0.013	0.001	0.787	0.023
Governance3	-0.034	-0.034	-0.763	0.065
Governance2	-0.048	-0.036	-0.986	0.085
Governance1	-0.052	-0.054	-1.950	0.092
IC4	-0.059	-0.040	-1.825	0.097
IC2	-0.042	-0.037	-0.965	0.074
IC3	-0.067	-0.054	-1.592	0.107
IC1	-0.053	-0.007	-0.163	0.080
SL2	-0.050	-0.034	-0.940	0.106
SL4	-0.042	-0.041	-0.970	0.088
SL3	-0.042	-0.033	-0.723	0.082
SL5	-0.037	-0.027	-0.692	0.084
SL1	-0.033	-0.021	-0.417	0.074
Social2	-0.052	-0.032	-1.016	0.092
Social4	-0.055	-0.047	-1.309	0.102
Social3	-0.053	-0.038	-1.361	0.098
Social1	-0.043	-0.037	-1.410	0.073

Importance-performance map analysis

IPMA can help researchers discover valuable potential factors from the perspectives of importance and performance⁵². Researchers and managers can also identify which factors are more worthy of attention and improvement based on the analysis results. The results in Fig. 5 confirm that digital transformation (0.451) has the highest impact on ESG performance, followed by servitization level (0.305) and innovation capabilities (0.280). Currently, the highest ESG performance is attributed to the servitization level (78.876), followed by innovation capabilities (70.091) and digital transformation (66.166).

Fig. 5.

Importance-performance map analysis for Digital Transformation and ESG Performance.

fsQCA results

The preliminary stage of fsQCA entails the calibration of causal conditions and outcomes. We converted the Likert scale values of variables into fuzzy membership scores to signify the extent to which a variable is part of a specific set. Scores range from 0.05 (complete non-membership) to 0.95 (complete membership), with 0.5 acting as the crossover point¹⁹. In the Analysis of Necessary Conditions, we did not identify a crucial condition that must be present. The Consistency and Coverage of each condition are below 0.9. We constructed a truth table to show all causal situations and selected configurations for analysis based on frequency and consistency. We set minimum frequency at 3, original consistency criterion at 0.80, and PRI threshold at 0.6¹⁹.

The fsQCA yielded complex, parsimonious, and intermediate solutions. We interpreted the intermediate solution since it was comprehensive and interpretable¹⁹. We identified 3 configurations leading to high ESG performance. Table 7 shows high consistency (0.870 > 0.85) for each configuration and the overall solution, indicating consistent outcomes. The coverage of the overall solution (0.576 > 0.55) implies significant empirical relevance.

Table 7.

Configurations of high-level Environmental, Social and Governance Performance.

	Configurations
	1	2	3
SL	○	●	◉
IC	◉	◉
DO		●	●
DTA	●		◉
DS	◉	◉	●
DT	◉	◉	●
Raw coverage	0.302	0.467	0.477
Unique coverage	0.074	0.025	0.035
Consistency	0.886	0.923	0.898
Solution coverage	0.576
Solution consistency	0.870
Note: Symbols “◉” and “●” are employed to represent the existence of central or peripheral conditions, respectively. The symbols “◎” or “○” are used to denote the nonexistence of these central or peripheral conditions. Any cells that are left without a mark suggest a scenario where the condition does not play a decisive role, a situation commonly known as a “do not care” situation.

The fsQCA results reveal three distinct configurations leading to high ESG performance, each with high consistency (> 0.870). Configuration 1 highlights the importance of adopting digital technology and strategy, even without high servitization levels. Configuration 2 emphasizes the role of digital organization and strategy, coupled with high innovation capabilities. Configuration 3 underscores the synergy between all aspects of digital transformation, high servitization levels, and digital organization. These nuanced findings complement and extend our PLS-SEM results. While PLS-SEM demonstrated the overall positive impact of digital transformation on ESG performance (β = 0.181, p < 0.01), fsQCA reveals the specific combinations of factors that lead to high ESG performance. The fsQCA results provide deeper insights into the roles of innovation capabilities and servitization level. While PLS-SEM showed their mediating effects (β Indirect effects = 0.165 and 0.117 respectively, p < 0.001), fsQCA demonstrates that high innovation capabilities are crucial in two out of three configurations, and high servitization levels are central in one configuration. This suggests that these factors, while important, may not be universally necessary for high ESG performance, depending on the specific combination of other factors.

To ensure the robustness of our fsQCA results, we conducted additional sensitivity analyses by varying the frequency and consistency thresholds in our truth table analysis and slightly altering the calibration points for our fuzzy sets¹⁹. We have included a robustness check of the fsQCA results: (1) re-running the truth table analysis with different consistency thresholds (ranging from 0.75 to 0.85) to verify the stability of the identified configurations, (2) re-running the truth table analysis with different PRI consistency thresholds (ranging from 0.60 to 0.65) to further ensure the stability of the configurations, and (3) comparing fsQCA results with those from PLS-SEM to ensure the consistency of paths and effects across methods. These robustness checks, combined with additional sensitivity analyses, demonstrate that the identified configurations remain robust under varying analytical conditions. The results remained consistent across these variations, supporting the robustness of our findings.

Discussion

Theoretical contribution

This study proposed a new model, the “Digital Transformation-ESG Performance model,” which details the relationship between digital transformation and ESG performance. Previous studies have examined how digital transformation affects corporate performance, including financial outcomes⁸ and market and ESG performance¹⁹. Our study developed a comprehensive theoretical model elucidating the nuanced mediating roles of innovation capabilities and servitization level in the digital transformation-ESG performance relationship among SMEs. Unlike existing studies, our model builds on this foundation by incorporating the nuanced mediating roles of innovation capabilities and servitization levels, leveraging the Resource Orchestration Theory²⁴ to elucidate how firms can strategically manage and reconfigure digital and traditional resources to foster ESG performance. This approach addresses a vacuum in the research on how digital transformation affects ESG performance and gives practical advice for SMEs looking to improve their ESG results through digital efforts²⁰.

Secondly, this study developed and validated measurement tools (Tables 1 and 3). We constructed a multidimensional measurement framework, validated it using PLS-SEM, and demonstrated good reliability and validity (Sect. 4.1). Confirming the second-order formative constructs provides a more accurate understanding of their nature and characteristics¹⁹. This study expands the measurement tools and methods available for studying digital transformation and ESG performance, providing researchers in related fields with effective tools for empirical research. Furthermore, by validating the measurement tools and path analysis (Sect. 4.2), we examined the methods by which digital transformation affects ESG performance to better understand the link.

Thirdly, our study contributes to the literature by uncovering new influencing factors—specifically, innovation capabilities and servitization level—as key determinants of ESG performance in manufacturing SMEs. While prior research has primarily focused on the direct effects of digital transformation on financial outcomes or broad corporate performance measures, our findings highlight the significance of these two factors as critical mechanisms through which digital transformation impacts ESG outcomes. By integrating innovation capabilities and servitization level into our theoretical framework and empirical analysis (Figs. 4 and 5), we address a gap in the existing literature that has overlooked their roles in the context of SMEs’ ESG performance. This contribution advances understanding by providing a more nuanced view of how digital transformation drives ESG performance, offering scholars and practitioners deeper insights into the factors that can enhance sustainability efforts in SMEs.

Fourthly, we extend the theoretical discourse by empirically establishing the mediating roles of innovation capabilities and servitization level between digital transformation and ESG performance (Table 5). Previous studies have not sufficiently explored the mechanisms underlying this relationship, particularly in the SME context. By demonstrating that innovation capabilities and servitization level partially mediate this relationship, our research fills a critical gap in the literature. This finding advances the field by elucidating the processes through which digital transformation influences ESG outcomes, thereby providing a theoretical basis for future research to build upon and offering practical guidance for SMEs to strategically focus on enhancing these mediators to improve their ESG performance.

Finally, we introduced three control variables, namely firm ownership, age, and size, into the model (Fig. 4) to mitigate their potential confounding effects on the results. This study found that these control variables do not significantly influence ESG performance, suggesting that digital transformation and ESG performance are independent of firm factors. This research emphasizes the importance of digital transformation on ESG performance and shows that SMEs should not worry about their size-related digital divide.

Practical implications

This research established a model to describe and predict digital transformation and ESG performance⁹. It helps firms assess and improve digital transformation and ESG performance. Companies may better understand how digital transformation affects ESG performance and take focused steps to improve it by identifying mediating and control variables. The approach helps investors assess a company’s ESG performance and make smarter investments. ESG rating agencies can also use the approach to assess a company’s ESG performance.

Second, discovering digital transformation and ESG performance as second-order formative constructs can help companies understand the complex relationship between the two and provide a richer reference for investing in digital transformation and improving ESG performance. Better quantitative and data analysis can help companies monitor and evaluate digital transformation and ESG performance. Companies can use the complicated relationship between digital transformation and ESG performance to inform decision-making and enhance sustainable growth⁴⁶.

Third, after controlling for company ownership, age, and size, digital transformation, servitization degree, and innovation skills affect ESG performance. Digital transformation affects ESG performance most, followed by servitization and innovation. Digital transformation is a major corporate transformation and upgrade trend, which can boost ESG performance¹⁹. Managers can use digital transformation to boost a company’s competitiveness and social responsibility. Company ESG success also depends on servitization. Service levels can boost social responsibility and consumer loyalty, making a company more competitive. To increase ESG performance, social responsibility, market competitiveness, and sustainable development, companies should focus on digital transformation, servitization, and innovation³.

Finally, policymakers and managers can promote the improvement of innovation capabilities and servitization levels by formulating and promoting relevant policies and regulations, thereby promoting the improvement of digital transformation and ESG performance⁵⁹. The government can provide tax incentives and increase investment in technological innovation to encourage companies to participate actively in digital transformation. The government can promote innovation capability improvement by formulating technological innovation policies and increasing intellectual property protection. The government can encourage companies to provide better services by providing high-quality after-sales services and promoting online customer service to improve the company’s servitization level. The government can formulate policies to encourage companies to combine digital transformation, innovation capabilities, and servitization levels to improve their comprehensive competitiveness and ESG performance. Policymakers should actively formulate relevant policies to support corporate development, create a favorable business environment, and promote economic development and social progress¹⁰.

Limitation and future direction

Some limitations in our current study may inspire future research directions. Firstly, our data was collected from a questionnaire survey of SMEs in China. Future research could consider replicating our study in different geographical contexts to examine the generalizability of our findings. Secondly, we used self-reported data to measure digital transformation and ESG performance, which may be subject to social desirability and recall biases. Future research could try to integrate qualitative and quantitative data to obtain more interesting research conclusions. Thirdly, to focus on our research question, we mainly focused on the relationship between digital transformation and ESG performance. However, digital transformation for SMEs is a complex and long-term process. Therefore, future research can explore factors such as R&D intensity, financial subsidiaries, and technology leadership to develop our findings further. In summary, the limitations of our study provide important insights and directions for future research, which are of great significance for promoting research in digital transformation, ESG performance, and sustainable development.

Conclusion

This study examines the linkages between digital transformation, ESG performance, innovation capabilities, and servitization level, providing valuable theoretical and practical insights into SME development. By using PLS-SEM and fsQCA to analyze questionnaire survey data from 215 valid samples, we propose and verify a theoretical model of digital transformation and ESG performance, develop and validate the measurement scale, and find that digital transformation improves ESG performance. Innovation and servitization also mediate ESG performance. This study provides theoretical insights for policymakers and academic researchers, enriches the literature on digital transformation and ESG performance in SMEs, and offers recommendations for SME managers to improve ESG performance and sustainable development through digital transformation. This study should improve understanding and practice of digital transformation, ESG performance, innovative capabilities, and servitization in diverse domains.

Author contributions

Authors’ contributions: Conceptualization, S.W., and D.C.; methodology, S.W.; writing-original draft preparation, S.W., and D.C.; writing-review and editing, S.W., and D.C.; visualization, S.W.; project administration, S.W., and D.C.; funding acquisition, D.C., and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Zhejiang Province Philosophy and Social Science Planning Project [24NDJC201YB]; Zhejiang Soft Science Project [2022C35047]; Major Humanities and Social Sciences Research Youth Key Projects in Zhejiang Colleges and Universities under Grant [2021QN014]; the Key Research Center of Philosophy and Social Science of Zhejiang Province – Modern Port Service Industry and Creative Culture Research Center; Youth Fund for Humanities and Social Science Research by the Ministry of Education [24YJC630213]; Zhejiang Provincial Philosophy and Social Sciences Planning Project [45]; National Statistical Science Research Project of China [2024LY060].

Data availability

The datasets used or analysed during the current study are available from the corresponding author (Shaofeng Wang: vipwhsl@hotmail.com) on reasonable request.

Declarations

Conflict of interest

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

Human Ethics and Consent to Participate declarations, and the ethics declaration

This study, akin to other surveys that preserve organizational anonymity and refrain from revealing sensitive data, has been exempted from requiring informed consent by the university (Reviewed by the Ethics Review Committee of the School of Logistics and E-commerce, Zhejiang Wanli University). Additionally, as the collected data pertains to Innovation Capabilities and Servitization, there was no requisite for a written ethical endorsement from the academic institution. Nonetheless, the confidentiality of individual respondents and their respective organizations was diligently upheld, and the study was conducted following the Declaration of Helsinki.