Research on Digital Transformation and the Innovation Model of SMMEs: The Case Study of PAYA

Abstract

The rapid development of digital technology has prompted many enterprises to carry out digital transformation. For SMMEs (small- and medium-sized equipment manufacturing enterprises), digital transformation is not only a necessary measure to deal with changes in the external environment but also a key opportunity to stimulate the vitality of internal innovation. At the same time, the digital transformation of SMMEs and a series of innovation activities caused by it, as well as the various innovation models that have evolved from it, are closely related to managerial cognition. On the premise that digital transformation is regarded as an enterprise innovation activity, this study discusses the ways in which digital transformation drives the innovation model of SMMEs. Taking PAYA as the research object, combined with the theory of resource orchestration, this study adopts the longitudinal single-case study method to explore the connotation and operation logic of the SMME innovation model and constructs a theoretical “digital transformation-innovation-innovation” model. The key findings of the research are as follows: Firstly, innovation models experience a phased evolution. Digital transformation promotes the innovation of SMMEs through three stages: digital technology structuring single-agent innovation, digital technology bundling dual-agent innovation, and digital technology leveraging multi-agent innovation. Secondly, the role of managerial cognition is pivotal. Managerial cognition is the core driving force promoting the digital transformation and innovation of enterprises. Digital transformation, in turn, enables enterprises to continuously accumulate digital technology and digital resources, guiding them from internal single-subject innovation to dual-subject innovation, and ultimately forming a multi-subject innovation model. Thirdly, resource orchestration is a dynamic mechanism. Digital transformation promotes the evolution of enterprise innovation models through resource assembly, resource integration, and resource collaboration. This process not only optimizes the allocation of internal resources within the enterprise but also amplifies the scale effect of innovation through collaboration with external partners. Theoretically, this study aims to enrich the understanding of digital transformation in the context of innovation models for SMMEs. It particularly sheds light on the formation logic of innovation models during the digital transformation process, thereby filling a gap in the existing literature. At the practical level, it will have a certain reference value for the “empowering” and “enabling” processes involved in the digital transformation of SMMEs. It also provides practical guidance for SMMEs on how to achieve innovation during the digital transformation process.

1. Introduction

Since the founding of the People’s Republic of China—and especially since the reform and opening up—the manufacturing industry has developed rapidly, and China has become the world’s largest manufacturing nation. With the continuous increase in domestic labor and raw material costs, the traditional labor-intensive production mode has been difficult to sustain, and the original extensive development mode of China’s manufacturing industry has been impacted.

The rise of digital technology has brought new opportunities for the transformation and upgrading of manufacturing enterprises. On one hand, as a new driving force for enterprises to achieve innovation, digital technology improves the efficiency of information exchange and resource allocation among enterprises [1]. On the other hand, many enterprises invest heavily in digital construction but are faced with the problem of declining innovation performance, and even the slightest mistake may lead to the “digital paradox” [2].

The equipment manufacturing industry is the “backbone” of the manufacturing industry and the “heart” of industry in general. Its digital transformation and innovation play a pivotal role in leading China’s manufacturing industry on the road to high-quality development. Given the booming digital economy, SMMEs are not only key players in this transformation but also a vulnerable segment in urgent need of strengthening. In practice, the digital transformation of SMMEs in the equipment manufacturing industry is not easy. According to research, due to the constraints of scale and capital, SMMEs are faced with the dilemma of being unwilling to transform, hesitant to do so, and ultimately failing to take action [3].

The digital transformation of enterprises provides new opportunities for innovation and high-quality development. There are significant variations in the digital development of different enterprises, showing obvious characteristics of phased development [3,4]. At the same time, most of the existing literature has studied the interaction mechanism between digital transformation and the innovation performance of large enterprises and listed companies [5,6,7]. There are few studies on the impact of manufacturing enterprises’ digital transformation on the innovation model. In response to the limitations of the aforementioned studies, this study proposes the following two key questions: What is the internal mechanism of digitalization promoting the innovation of SMMEs in different stages? And what is the logic driving the innovation model of SMMEs?

Managers drive corporate digital transformation by understanding digitalization, establishing formal environments for digitalization, and leading change. Therefore, whether a company undergoes digital transformation and the extent of the transformation depend on the managers’ perception. According to the resource orchestration theory, enterprises can implement specific “management” of internal and external resources through resource construction, resource transformation, resource coordination, and other actions, causing resources or their combinations to generate value. Emphasizing the interdependence between static resources and dynamic capabilities, resource orchestration theory can describe the dynamic evolution of enterprise digital technology and the innovation model. This is a way of addressing the impact of ignoring resource reconstruction [8].

In line with the research approach mentioned above, this study selects PAYA (a SMME, which leads the niche markets of control panels and distribution cabinets with first-class technology and rich experience and has launched digital transformation consulting services after successfully undergoing its own digital transformation) as the basis of a longitudinal single-case study. Making reference to the resource orchestration theory and carrying out exploratory research on how the whole process of digital transformation is shaping the innovation model of SMMEs, this study thoroughly analyzes the action path and mechanism of digital transformation of SMMEs and reveals its impact on the innovation model. The possible marginal contributions are as follows: Firstly, the research perspective is focused on SMMEs, emphasizing the digital process of SMMEs from “0” to “1”, discussing the innovation model of SMMEs, and identifying the dynamic evolution path of this innovation model, thereby supplementing the research on the digital transformation of enterprises. Secondly, it helps to open up the theoretical “black box” of how digital transformation drives enterprises to achieve innovation and provides empirical evidence and policy enlightenment that will guide enterprises toward successful digital transformation and innovation. Thirdly, this study reinterprets the resource orchestration theory based on the digital context, reveals the action mechanism of digitalization in the enterprise innovation model, and expands the research direction and scope of the resource orchestration theory.

2. Literature Review

2.1. Managerial Cognition

March and Simon [9] believe that the people who make management decisions in an enterprise have a specific cognitive basis. Cognitive processes are involved when people create new knowledge, retrieve old knowledge stored in the memory in the form of knowledge structures, and apply that knowledge [10]. Top executives often make strategic decisions and implement subsequent corporate actions by constructing subjective representations of the environment [11]. Cognition is a set of knowledge structures used by actors when making decisions. The formation of this set of knowledge structures is affected by the environment of the actors [12]. Managerial cognition is an information screening process by which managers refine and transform the cognitive knowledge accumulated from previous work experience into practical action within the framework of bounded rationality [13], which plays an important role in shaping organizational capabilities [14]. Managerial cognition is the ability of managers to match their self-perception to the problem at hand [15] and reflects managers’ abilities in terms of forecasting and rapid adaptation [16].

Managerial cognition refers not only to what managers know, assume, or believe, but also to the cognitive processes involved in acquiring and processing information [17]. From a dynamic perspective, managerial cognition is defined as a series of dynamic processes involving the collection and processing of information: Managers, keenly aware of their changeable environment, actively collect relevant data, thoroughly analyze these data, ascribe unique meanings to them, and then formulate and implement corresponding strategies based on the information [18]. From the perspective of bounded rationality, Hambrick and Mason [13] proposed a strategic choice and performance realization path: strategic situation–manager orientation–selective cognition–organizational results.

Managers’ perception of the environment is the key to understanding its development and making appropriate decisions [19]. Each stage of corporate development requires the generation and selection of action plans through cognitive actions and the implementation of the best alternative through behavioral actions in order to effectively deal with crisis situations [20]. The business model innovation of SMEs (small and medium-sized enterprises) largely depends on the characteristics of managers and their perceptions [21]. Some scholars believe that managerial cognition is influenced by the external environment of enterprises, such as the market, social culture, system, and other factors [12]. Some scholars also pointed out that managerial cognition is affected by internal factors such as resources, capabilities, and organizational practices [22]. Therefore, managerial cognition is influenced by both internal and external factors of the organization.

2.2. Digital Economy and Digital Transformation

The concept of the digital economy was first proposed in the 1990s by Tapscott [23]. The digital economy is defined as an economic model that represents information flow in a digital way.

Digital transformation is inevitable in the digital economy era, but different academic circles define it differently. However, digitization cannot be separated from informatization. Informatization refers to the digitization of production factors based on artificial intelligence, mobile Internet, and the Internet of Things, with the goal of sharing; changing production modes, management forms, and market operation modes; and improving the efficiency of enterprise management.

Some scholars have studied digital transformation from the perspective of digital technology applications. According to them, digital transformation refers to the application of information technology by enterprises in the production process [24]. Enterprises utilize digital technology such as computer information systems and communication connections to alter physical attributes [25] and enhance their main businesses through the application of digital technology and equipment, including social media, mobile Internet, and embedded devices [26,27].

Through in-depth research on digital transformation, academia has begun to view it from the perspective of organizational change. Some scholars believe that digital transformation refers to the process of developing and utilizing digital technology, altering a company’s business model through digital means, promoting the reform and innovation of enterprise production, service, and operation modes [28], and ultimately forming a dynamic digital business model [29].

Some Chinese scholars [30] have also emphasized that enterprise digital transformation is a process whereby enterprises utilize a combination of digital technologies to trigger significant changes in organizational attributes and enhance the organization. Specifically, through the application of digital technology, enterprises achieve intelligent manufacturing, brand communication, internal data fusion and sharing within the industry, and integration of external resources, ultimately accomplishing digital transformation [31]. This is a strategic choice of enterprises [32], involving the integration of all aspects of enterprises with digital technology, the reconstruction of enterprise business models, organizational structures, business processes, and products and services; and a disruptive change in enterprise value creation logic at a deeper level [33]. From the perspective of production factors, Wang and Yang [34] believe that the digital transformation of enterprises entails introducing data elements and digital technologies into the production function and creating new combinations with other production factors and conditions to generate commercial and social value.

In 2018, the German Engineering Academy introduced the Acatech Industry 4.0 Maturity Model, which provided guidance for companies in their respective digital transformation process [35]. Subsequently, Tubis [36] proposed a model framework for assessing corporate digital maturity that considers two dimensions: organization and process. Mick et al. [37] integrated sustainability into the digital transformation maturity model, evaluating the digitalization process of SMEs from six dimensions: digital technology, customer centricity, organizational culture, organizational governance, personnel, and sustainability. With the rise of digital collaboration factories in manufacturing companies, Lee et al. [38] constructed a digital maturity model based on collaborative relationships, covering aspects such as organization, process management, quality control, and logistics operations. Golinska-Dawson et al. [39] attempted to apply the theory of digital transformation maturity models to different partners in the supply chain, such as suppliers, manufacturers, retailers, e-tailers, and logistics service providers, and proposed a universal framework applicable to the logistics industry.

2.3. Enterprise Innovation and Digital Transformation

In 1912, Joseph Alois Schumpeter [40] first proposed the term “innovation” in his book The Theory of Economic Development. Subsequently, scholars around the world put forward different innovation models according to their respective national conditions, such as continuous innovation/disruptive innovation, closed innovation/open innovation, incremental innovation/radical innovation, imitative innovation, and independent innovation [41,42,43,44,45].

In the post-COVID-19 era, the innovation ecosystem has magnified the strengths and weaknesses of companies, managers, and employees [46]. Breakthrough innovation is the main driving force for the continuous growth of the British economy [47]. During the COVID-19 pandemic, SMEs established resilience through digital technology and fundamentally achieved business model innovation [48]. Some scholars believe that SMEs have realized business model innovation in the post-COVID-19 era through their flexible adaptability and a positive entrepreneurial mindset [49]. Other scholars have pointed out that SMEs achieve digital innovation through external pursuing, external browsing, and internalizing. Liu et al. [50] divided the innovation models of SMEs into the following three types based on the dynamic mechanism of enterprises in selecting technological innovation models: market-driven (oriented to meet the actual and potential demand of the market), technology-driven (aimed at realizing high-tech industrialization, which is characterized by high technology, high investment, high risk, and high return), and user experience-driven (providing consumers with a product experience that exceeds their expectations in order to stimulate consumers’ purchasing desire and enhance consumption upgrade). According to the realization model of technological innovation, the innovation model of SMEs has three aspects: independent innovation model, imitation innovation model, and cooperative innovation model [51].

The results of digital transformation mainly include new products, new services, new processes, and innovation performance at the enterprise level [52]. Innovation and digitalization are becoming the main driving forces for the sustainable economic transformation of SMEs in the post-pandemic era [53]. The existing literature has fully discussed whether digital transformation can improve innovation performance, but the results are not consistent. The first type of view believes that digital transformation will bring about cost reduction and improvement of operational efficiency, thus improving enterprise innovation performance [54,55]. Other scholars study digital transformation from more diversified perspectives and put forward the view that the impact of digital transformation on innovation performance is characterized by an inverted U-shaped relationship [56]. Some scholars have further analyzed the mechanism by which digital transformation affects innovation performance by categorizing various types of innovation: Zheng and He [57] empirically found that digital transformation promoted enterprises’ cooperative innovation but did not significantly encourage enterprises to carry out independent innovation. He et al. [58] found a positive relationship between enterprise digital transformation and innovation performance at both the overall and manufacturing process levels. However, from the perspective of the business model, digital transformation promotes corporate innovation in the short term but inhibits corporate innovation in the long term.

2.4. Resource Orchestration Theory

Resource orchestration theory has cross-evolved from resource management theory and asset orchestration theory [59,60], which is the development of the traditional resource-based view. According to this theory, enterprises make dynamic adjustments and achieve an effective allocation of resources based on changes in the internal and external environment [61], revealing how enterprises integrate scattered resources to form overall capabilities and then build their required competitive advantages [62]. It specifically includes three basic subprocesses: resource structuring, the construction of the resource combination required by the enterprise development through the identification, acquisition, and accumulation of valuable resources; resource bundling, the process by which an enterprise transforms the existing resource combination through learning and integration in the early stage to form its own capabilities; and resource leveraging, the process of releasing value resources through resource combination and capability connection to realize value transfer [60,63].

Many scholars have applied resource orchestration theory to the study of digital empowerment. For example, Xu et al. [64] explored the multiple paths of organizational resilience formation in the context of digital transformation based on resource orchestration theory. Zhou and Sun [65] used resource orchestration theory as an analytical tool to analyze the value co-creation mechanism of industrial clusters driven by the industrial Internet. Tang et al. [66] explored the mechanism by which Internet brand enterprises promote the formation and enhancement of digital capabilities through the implementation of resource orchestration and how this subsequently affects the construction of the enterprise innovation ecosystem.

At present, research on how digital resources affect the innovation process of SMMEs remains relatively scarce. The combination of the digital context and resource orchestration theory provides an analytical framework for this study to utilize the resource orchestration theory in exploring the mechanism by which digitalization promotes the innovation model of SMEs. On one hand, the characteristics and internal and external environments of enterprises differ across various stages of digitalization [63]. The resource orchestration theory offers a new perspective and theoretical basis for understanding how enterprise digitalization can drive innovation from a process perspective. On the other hand, by employing the resource orchestration framework, this study effectively reveals the internal mechanism of digitalization at each stage in promoting the innovation process of SMEs and deduces the resource actions driven by key capabilities and the corresponding performance of innovation outcomes.

2.5. Research Thinking

In summary, existing research has provided an important theoretical foundation for understanding the digital transformation of SMMEs in multiple aspects. However, these studies still have some limitations:

• The existing literature primarily discusses the economic consequences of digital transformation on enterprise innovation from a quantitative perspective at the macro and meso levels, while scant literature systematically examines the impact and mechanism of digital transformation on the innovation model of SMMEs at the micro-enterprise level.
• Most existing research focuses on the internals of enterprises, neglecting internal and external coordination in the transformation process, which greatly limits its guiding role in the practice of enterprise digital transformation.
• The research on innovation models of various countries mostly relies on traditional Western innovation theory and rarely combines the practical and developmental needs of Chinese local enterprises, thus neglecting the heterogeneity of Eastern innovation models.

Therefore, the authors wished to explore the innovation model of SMMEs in the digital era under China’s specific conditions.

3. Research Data Collection and Design

3.1. Research Methods and the Selection of Research Subjects

3.1.1. Research Methods

The case study method has always been one of the most important research methods for the creation of management theories [67,68]. The case study method mainly states and explains phenomena in reality and constructs an overall picture through a description of the situation, focusing on the exploration of how, why, and other basic questions [69].

Although single-case studies primarily focus on one enterprise, the reliability and validity of the research can be enhanced through the use of multiple data sources. Moreover, the single-case study can allow for a comprehensive analysis of the process of digitalization promoting innovation, which is conducive to discovering and explaining the evolutionary process and mechanism [68,70]. This level of in-depth analysis is difficult to achieve through multiple-case studies. Considering that digitalization promoting the innovation of manufacturing enterprises is a dynamic evolution process, this study intends to use the exploratory single-case study to conduct research, based on the following three points: first, the innovativeness of the research question—there is currently a relative scarcity of research on the innovation model of SMMEs in the digital economy [69]; second, the longitudinal case study approach is particularly suitable for dynamic research questions—through long-term tracking and in-depth analysis, it can confirm the sequence of key events, identify causal relationships [71] and, thus, more accurately capture the evolutionary trajectory of innovation models in the process of digital transformation; third, the applicability for theory building—the exploratory single-case study method is more suitable for the extraction of rules and theoretical induction behind a specific phenomenon [72], allowing one to fully explore the research phenomenon in order to provide a foundation for theory-building [73].

3.1.2. Case Selection

Case studies prioritize typicality over representativeness [74,75]. Consequently, the method of theoretical sampling is employed in case studies, meaning that the selection of cases is based on theoretical needs rather than statistical sampling considerations [76].

This study takes Guangzhou PAYA Electromechanical Equipment Co., Ltd. (Guangzhou, China, hereinafter referred to as “PAYA”) as the research object, as the practices of this enterprise embody two key principles in sample selection: typicality and theoretical relevance. Firstly, as a small- to medium-sized equipment manufacturing enterprise, PAYA has a long history in the automation field. It is a leading player in domestic market segments related to control cabinet production, electrical goods, and program standardization. Additionally, it serves as the digital value-added partner of Siemens DVP (Digital Value Partner) and a strategic partner of EPLAN. Hence, PAYA can be considered representative of enterprises within this industry, fulfilling the principle of typicality. Secondly, there is a strong alignment between the theoretical goals of the research and the case object. PAYA boasts 7 patents and 22 software copyright registrations, encompassing areas such as visual recognition, five-axis control, and automatic control systems. This intellectual property portfolio ensures that the case study can provide data that are congruent with the theoretical objectives of the research, adhering to the principle of consistency between theoretical goals and case objects.

Although single-case studies may have certain limitations in terms of generalizability, the typicality and representativeness of PAYA enable it to provide important references and insights for other SMMEs. Through analyzing the evolutionary path of PAYA’s innovation model, this study not only offers theoretical support for the digital transformation of SMMEs but also provides valuable references for subsequent broader research.

3.2. Case Introduction

PAYA, founded in 2004 and located in Guangzhou, Guangdong Province, is a small- to medium-sized high-tech enterprise that boasts first-class technology and rich experience, pioneering the domestic market segments for control cabinets and distribution cabinets.

Initially, the company specialized as a technical service provider, offering services such as control cabinet installation, field upgrades, and maintenance. As customer needs evolved, PAYA expanded its horizons, dedicating itself to the design, development, and production of control cabinet products. Throughout this process, it amassed a wealth of unique expertise. Addressing practical challenges such as time-consuming consumables and low production efficiency, the company proactively embraced automation and digital technology, successfully establishing a digital production workshop. This endeavor significantly enhanced production efficiency and product quality, garnering widespread attention and imitation from peers in the industry. Leveraging this foundation, PAYA further established its own production testing base and ventured into offering digital transformation technical consulting services for electrical and procedural standardization. It now provides tailored solutions to equipment enterprises seeking to digitize their production processes. Figure 1 illustrates the company’s development trajectory.

3.3. Data Sources

In order to ensure the conclusions were authentic and rigorous [77], the author’s research team employed a triangulation method during the data collection process. Through cross-verifying data from multiple sources such as interviews, observations, and documents, the accuracy and reliability of the data were ensured. Specifically, the author and team members utilized multiple channels and diverse methods to obtain case information, including open and semi-structured in-depth interviews, secondary data, and internal data from interviewees, in order to ensure that the data sources strictly adhered to the strategy of “triangular verification” [68].

In terms of secondary data collection, the authors reviewed the official website of PAYA to obtain publicly available information on the company’s basic profile, development history, and technological achievements. Meanwhile, through text mining and web crawling techniques, information related to digital transformation and innovation models was extracted from the company’s WeChat official account and the WeChat video account.

In terms of field research, the author and the research team visited the digital production workshop set up by PAYA in Foshan, Guangdong Province, to gain an in-depth understanding of the application of digital twin technology and production line virtual simulation technology. During the visit, technical personnel from PAYA provided detailed demonstrations and explanations of the relevant technologies, further enhancing the research team’s understanding of the company’s digital transformation practices. Through on-site observation, the authors gained an intuitive understanding of the company’s production processes and technological innovations, which provided important references for subsequent data analysis.

In terms of semi-structured interviews, the research team visited Shenzhen, Dongguan, and Foshan multiple times in 2023 and 2024. The interviewees included PAYA’s leaders, technical directors, and the heads of its partners, such as EPLAN and L-MARK, focusing on key issues such as the company’s digital transformation journey, the evolution of innovation models, the effectiveness of technology application, and future development directions. The total duration of the interviews exceeded 14 h, yielding approximately 130,000 words of interview transcripts. Interviewing internal senior managers and technical experts as well as external partners, the team gathered information from diverse perspectives to avoid the bias that could result from a single viewpoint, thereby enhancing the comprehensiveness and objectivity of the research findings.

To corroborate the data, after each interview, the research team transcribed the interview recordings, cross-checked the consistency of answers to the same question among different interviewees, noted the differences in perspectives, and sought to verify and supplement these insights in subsequent interviews. The specific data are presented in

Table 1. Case data sources and descriptions.

Data Type	Data Source	Target	Duration	Manuscript
First-hand data	Interview	PAYA leader	4 h	3.1 w
		Technical Director	4 h	2.69 w
		Partner, EPLAN	2 h	2.98 w
		Partner, L-MARK	3 h	2.1 w
	Site visit	Digital manufacturing shop	3 h	1.56 w
		Digital twin technology	1 h	0.5 w
		Virtual simulation technology	1 h	0.8 w
	Online conference	PAYA leader	3 h	2.25 w
Second-hand data	PAYA official website	www.paya.cn (accessed on 1 March 2025)	/	/
	WeChat official account	PAYA	/	Crawler, 5 w
	WeChat video account	PAYA	/	55 Videos

.

3.4. Data Encoding

3.4.1. Coding Process

A single case study requires systematic conceptual coding grounded in observed phenomena. To this end, this study employs the structured data analysis methodology proposed by Gioia et al. [72] to code case data, yielding rigorous qualitative analysis results. This methodology adheres to the principle that every significant finding stems from thorough data analysis. The coding process unfolds in three distinct stages: in the first stage, the coding process remains faithful to the interviewees’ language, directly extracting concepts from the raw data. Through inductive reasoning, first-order concepts are constructed, reflecting the immediate insights derived from the data. In the second stage, the coding shifts to a more theoretical perspective centered on the research topic’s dimensions. The first-order concepts are then classified and abstracted into second-order themes, guided by the researchers’ theoretical framework and research focus. Finally, the third-stage coding delves deeper into the relationships between the data, newly induced concepts, and existing theories. It involves extracting and summarizing second-order themes with similar attributes, ultimately forming aggregated dimensions [78,79].

To avoid one-sided conclusions caused by personal subjectivity and to ensure the reliability and validity of the research findings, we independently conducted a coding analysis of the collected data. After we jointly determined the preliminary coding scheme, we each separately coded the data materials in a back-to-back manner. During the data coding process, for items with inconsistent coding, we discussed them collectively and verified the coding results to ensure the accuracy of the coding. This team-based approach not only reduced the impact of individual subjective biases on the research results but also enhanced the accuracy and scientific nature of the data processing through collective wisdom.

During the coding process, we paid continuous attention to the consistency and completeness of the data and promptly conducted supplementary research and verification for any issues discovered. For example, certain technical details or innovative achievements mentioned in the interviews were confirmed by consulting relevant technical documents or conducting follow-up interviews with the relevant technical personnel to ensure the authenticity and reliability of the research data.

Through the above coding analysis, we obtained preliminary coding results for how PAYA drives innovation in SMMEs through digitalization. The next step involved conducting a theoretical saturation test. Consequently, in accordance with the established research design, approximately one-third of the texts from the initial data samples were selected to undergo sequential three-stage coding, with the aim of assessing the theoretical saturation of the research findings.

After iterative and repetitive comparisons among the data, constructs, and relevant literature, no new concepts or categories emerged during the secondary coding process. This indicated that the model construction achieved theoretical saturation, ensuring a satisfactory alignment between the data and theory and justifying no further sampling.

3.4.2. Coding Results

Through the above coding process, this study identified 24 first-order concepts, including “software defines the future”, “lean production”, “to help SMMEs transform digitally”, and “PC-based technology”, among others. Subsequently, these classified first-order concepts were grouped under 15 second-order themes, such as “perceptive cognition”, “reframing cognition”, “matching cognition”, and so on. Finally, the coherent second-order themes were consolidated and refined into five overarching constructs: “managerial cognition”, “digitalization”, “innovation”, “resource activities”, and “innovation model”. The specific data structure is depicted in Figure 2.

3.5. Research Framework

Enterprise digital transformation, innovation, and innovation models are interconnected, with managerial cognition serving as the pivotal driving force. As managers gain a deeper understanding of the potential of digital technology, enterprises are motivated to undertake continuous iteration and upgrading of digital technologies. This not only optimizes production processes but also reshapes business models, fostering diverse types of innovation. Notably, these various types of innovation frequently involve the participation and collaboration of diverse innovation actors, ultimately resulting in distinct patterns. Consequently, grounded in the resource orchestration theory, this study delves into the construction process of innovation models centered on resource orchestration. Furthermore, it examines the underlying mechanism whereby resource orchestration and resource integration spark innovation, subsequently influencing enterprise innovation models, as illustrated in Figure 3.

4. Case Analysis

Based on the theory of resource orchestration and the practical experience of the enterprise chosen for the case study, this study explored three distinct stages: the initial digital exploration stage, the digital growth phase, and the digital development period.

4.1. Digital Exploration Period

During the digital exploration phase, the managers of PAYA, based on their initial understanding of digital technologies, began to introduce basic digital technologies such as PC-Based solutions and virtual simulation technology to improve production efficiency and product quality. Innovation during this stage was primarily focused within the enterprise, characterized mainly by single-subject innovation.

4.1.1. Perceptive Cognition

In 2004, China’s economic development environment exhibited a positive trend, and the national economy of the Guangdong region sustained stable and rapid growth. Against this backdrop, Mr. Zhang, a technician with six years of experience at Siemens, who had amassed a solid technical foundation and a keen market sense, decided to leave Siemens and establish Guangzhou PAYA Electromechanical Equipment Co., Ltd. His business background gave him unique insight into future trends.

In pursuing professional technology and positive and enthusiastic service, the company has been improving since its establishment. However, Mr. Zhang became acutely aware of the trend that “software defines the future”. He recognized that, with the rapid advancements in information technology, iterative technological upgrades necessitate not just hardware updates but also the integration and mutual reinforcement of software and hardware. This realization set the stage for PAYA to keep abreast of advanced technology, continually carry out technological iterations and upgrades, and adapt to—and even lead—new market demands.

Perceptive managerial cognition refers to the actions taken by managers with bounded rationality to discern changes in the external environment and subsequently introduce new technologies within their organizations. Embracing the overarching concept of “software defines the future”, the enterprise proactively implemented automation and digitalization. This period marks the first stage of PAYA’s digital transformation journey: the exploration stage.

4.1.2. Digital Technology Structuring, Process Innovation, and the Single-Agent Innovation Model

At its establishment, PAYA focused on the installation and maintenance of control cabinets. Through close interaction with customers, the company’s management has keen insight into a major pain point in the market: many customers are troubled by the uneven quality of the control cabinets they purchase, which not only hampers production efficiency but also hampers repairs, ultimately leading to additional losses.

At the same time, PAYA has gathered a highly skilled team of electricians through its work in the field of technical services. They not only have excellent component quality identification ability but have also established a solid and trusted cooperative relationship with a number of component suppliers. Based on the external environment and internal resources of the company, PAYA made a strategic adjustment after independent research to meet the market’s urgent demand for high-quality and easy-to-maintain control cabinets.

After taking up the production of control cabinets, managers found that the internal structure of non-standardized control cabinets was complex, requiring a lot of manpower and time, and further pushing up the cost of the enterprise. Therefore, the company began to carry out a comprehensive process improvement and digital transformation: firstly, the automatic wire cutting machine developed by L-Mark Co., Ltd. (Dongguan, China), was used to solve the longest and most expensive wiring link in the production of non-standardized control cabinets, achieving the automation of the control cabinet wiring process. Secondly, in view of the gap between the efficiency in the laboratory environment and the wear and loss in the actual environment, virtual simulation technology was introduced to carry out an all-round digital simulation of the entire production line, ensuring that all possible wear and loss factors are fully predicted and optimized before actual deployment. Thirdly, digital technology was utilized to automate the cutting and assembly processes, and the internal structure of the control cabinets was modularized.

PAYA utilized automation and digital technologies to conduct an in-depth analysis and transformation of the production process, achieving intelligence in production workflows. This significantly enhanced production efficiency and product quality, leading to process innovation, as embodied in the following aspects: First, through the integration of printing technology and control technology, the automatic wire cutting machine automatically completes the cutting, casing, and terminal processing of the wire, greatly improving the processing efficiency of the wire and realizing the automation of the wire link. Second, through virtual simulation technology, production personnel can remotely observe the status of the production line, enabling real-time monitoring and feedback of production data, timely adjustment, and optimization of the production process, thereby improving production efficiency and product quality. Third, modular assembly and automatic cutting technology simplify assembly for workers, greatly improving assembly efficiency.

In this process, both automatic cutting capabilities and virtual simulation rely on external resources, which can be seen as a form of resource assembly. Therefore, the innovation model at this stage can be summarized as single-agent innovation.

4.1.3. Resource Assembly

Resource assembly refers to the process by which enterprises reasonably tap existing resources to develop new products for themselves under conditions of limited resources [80,81]. In the early stage of entrepreneurship, PAYA faced the severe challenge of a lack of resources. After an in-depth analysis of the external environment, managers became well aware that effective use of resources and innovation drive are the keys to the survival and development of enterprises. During this stage, managers engaged in resource construction by identifying and acquiring new digital technology resources, such as PC-Based solutions and virtual simulation technologies. The introduction of these technologies not only improved production efficiency but also laid the foundation for subsequent digital transformation. Managers implemented a resource assembly strategy with internal resources, focusing on the R&D and application of automation and digital technology, and achieved single-agent innovation (as shown in Figure 4). Typical evidence of innovation patterns during this period is cited in

Table 2. Typical evidence of the innovation model in the digital exploration period.

Aggregation Dimensions	Second-Order Themes	First-Order Concepts	Typical Evidence Cited
Managerial cognition	Perceptive cognition	Software defines the future	At that time, I believed that software was the future. Many things that require hardware to solve can be solved with just one piece of software.
Digitization	Digital technology structuring	PC-Based technology	The previous PLC is a dedicated controller, and the PC-Based controller can be fully integrated into the information system of the network era.
		Introducing automatic wire cutting machine	We purchased an automatic wire cutting machine to improve the chaos of wiring harnesses at the production site and reduce waste.
		Virtual simulation	When the production in the laboratory environment changes to the real environment, it is necessary to consider some wear and tear in all aspects of the field environment. In addition to the virtual simulation used in the research and development stage, it can also be used in the actual production line operation process.
Innovation	Process innovation	The wire automatic cutting	About 70% of the man-hours of a control cabinet are spent on assembly and wiring, often requiring skilled electricians. With the automatic offline machine, a novice with little training can be employed and still maintain high quality and efficiency.
		Assembly of modules	We divide the area in the control cabinet, and each part corresponds to different functions. It becomes very convenient for workers to install the drawings.
		Cabinet splicing	The multi-function workbench can move, tilt, and lift freely. With the assistance of the workbench, the mounting plate can also tilt and slide into the electric cabinet from the side, which is convenient for the installation of the electric cabinet.
Resource activities	Resource assembly	Internal resource portfolio	In the beginning, we had nothing but our accumulated technical service experience and accumulated component manufacturers to explore the transformation to automation and digitalization.
Innovation model	Single-agent innovation	Raising the digital level	We rely on our own continuous research and attempt to achieve automatic production and digital production upgrades.

.

4.2. Digital Growth Period

After multiple attempts during the digital exploration phase, PAYA successfully achieved the digitalization of the control cabinet production process. Managers further deepened their understanding of digital technology and began to apply digital technology to the entire design and production process. They also established in-depth cooperative relationships with external partners (such as EPLAN), realizing the transition from single-subject innovation to dual-subject innovation.

4.2.1. Reframing Cognition

With the advancement of the company’s digital transformation, Zhang’s management understanding deepened. He is no longer satisfied with innovation at the technical level but has instead turned his focus to the optimization of production processes. An important principle in lean production is not to pass on mistakes to the next step. Inspired by this principle, managers believe that to truly optimize the entire production process of control cabinets, PAYA must innovate from the source—that is, the design stage.

Reframing managerial cognition refers to a forward-looking management concept and action strategy. Its core lies in actively introducing innovative changes at the root level of the organization, such as in product design and process planning. This is achieved through a deep understanding of and active response to industry changes, thoroughly optimizing the production process, improving efficiency, reducing waste, and ensuring product quality. The innovative practice of introducing digital technology from the design stage embodies managers’ insight into the future development trends of the manufacturing industry.

4.2.2. Digital Technology Bundling, Total Innovation, and the Dual-Agent Innovation Model

Industrial design is essentially the fusion of art and science in three-dimensional space, encompassing the form, structure, function, material, technology, and user experience of products. However, in the past, industrial design has relied heavily on two-dimensional representations, such as hand-drawn sketches, two-dimensional drawings, and two-dimensional views from CAD (computer-aided design) software. With advancements in science and technology and the evolution of industrial design concepts, people are increasingly recognizing the significance of three-dimensional design.

Therefore, after establishing a virtual production line, PAYA undertakes 3D modeling design for the control cabinet. This involves using EPLAN software to create a digital twin model of the control cabinet and subsequently exporting assembly, wiring, and cabling data. First, the number of circuits, power, and other parameters are entered into the EPLAN software system, which automatically generates a circuit schematic diagram and exports an accurate and clear BOM (bill of materials) table. Then, on the EPLAN software platform, the color, length, and diameter of the wiring; the polarity of the number tube; the source and target wire numbers; and the model of the wire lug are set. These data are imported into the automatic wire cutting machine. Finally, utilizing digital twin technology, the physical model of the control cabinet can be precisely replicated on the computer platform.

After connecting the design and production links through digital twin technology, PAYA upgraded from the virtual simulation of a single-link production line to a digital workshop, becoming the first fully digital control cabinet complete set manufacturing workshop in China. With the accomplishments of digital workshop construction, PAYA has not only achieved a leap in production efficiency and quality but has also successfully forged a partnership with EPLAN, an internationally renowned software supplier.

Due to the intangible nature of software, its performance cannot be directly demonstrated in isolation. Therefore, EPLAN frequently leads potential customers to PAYA’s advanced digital workshop, allowing them to witness how EPLAN software is seamlessly integrated into PAYA’s production process. This enables them to realize the smooth transition from control cabinet drawing design and data processing to actual production. In recognition of PAYA’s achievements, EPLAN officially authorized PAYA to be its software joint training and certification center. Subsequently, PAYA planned and launched several offline training courses on EPLAN software to cultivate more professionals for the industry.

The introduction of digital twin technology in the design stage not only facilitates the digital upgrading of design and production but also enables PAYA to innovate in product design, production, delivery, and other aspects based on customer demand, achieving total innovation. On this foundation, PAYA is no longer content with simply iterating and upgrading digital technology but actively expands its reach, establishing a close cooperative relationship with EPLAN and forming a dual-agent innovation model whereby PAYA and EPLAN jointly participate in the entire process of product research and development, design, production, and more.

4.2.3. Resource Integration

The innovation and development of enterprises rely on the competitive advantage created by the integration of externally acquired and internally developed resources [82]. While continuously advancing the iteration and upgrading of internal technology, PAYA recognizes that technological advancements alone are no longer sufficient to address the complex and ever-changing market environment. Therefore, PAYA proactively adjusts its strategic direction, bundling its internal resources with those of external partners to digitally reshape the value chain. This has led to an integration of digital technology with the design and production processes. Through cooperation with EPLAN, PAYA has not only enhanced its own technological capabilities but also formed a dual-agent innovation model centered on PAYA and EPLAN (as depicted in Figure 5). This resource integration strategy has significantly enhanced the company’s production efficiency and market competitiveness. Typical examples of innovation patterns during this period are listed in

Table 3. Typical evidence of the innovation model in the digital growth period.

Aggregation Dimensions	Second-Order Themes	First-Order Concepts	Typical Evidence Cited
Managerial cognition	Reframing cognition	Lean production	We would go to lean management experts to advise us, and their philosophy was not to pass on mistakes to the next step.
Digitization	Digital technology bundling	Digital twin	Digital twin is used not simply to input the 3D model but also to input the internal chemical properties, physical properties, and materials into the model.
		Digital manufacturing workshop	A complete control cabinet production workshop from design to module assembly, line down, and then to cabinet installation was born.
Innovation	Total innovation	Connecting the production end of the design	We will extend the digital approach to the front end of the design so that the whole content will be more complete.
		Digitization of the whole process	Based on the EPLAN software design platform, we integrate digital technology and lean production so that the whole process of control cabinet integration is digitized and standardized.
Resource activities	Resource integration	External cooperation search	We can export the opening diagram of the electrical cabinet through EPLAN software, use CAM software to identify the opening diagram and automatically generate a machining program, and import it into the board box processing center so as to complete the opening of the installation plate, door plate, side plate, and other plates.
Innovation model	Dual-agent innovation	Expanding the digital boundaries	We are also a strategic partner of EPLAN, who recently presented our company as a success case at the 6th Intelligent Manufacturing Conference held in Suzhou.

.

4.3. Digital Development Period

After the iteration and update of digital technology, PAYA’s digital transformation has achieved initial success, attracting many peers to visit and learn. On this basis, the managers of PAYA have further expanded the boundaries of digital transformation, sharing their digital experience with more customers and peers, and thus forming a multi-subject innovation model.

4.3.1. Matching Cognition

After successfully establishing a digitalized production workshop, the managers’ understanding further expanded to external collaboration. Mr. Zhang realized that digitalization encompasses not only the optimization of internal enterprise resources but also the seamless coordination with external customers. Managers acknowledge that the digital transformation of SMMEs can be challenging; thus, leveraging external expertise becomes paramount. As an industry leader, PAYA recognizes its unwavering responsibility to assist SMMEs in achieving digital transformation.

Matching managerial cognition entails having a holistic vision that encompasses both internal and external coordination. This involves accurately aligning internal resources with external market demands and actively partnering with relevant enterprises to leverage their key resources, advanced technologies, and standardized processes. By doing so, PAYA can facilitate the acceleration of digital transformation for these enterprises through external forces, proactively guiding and supporting their successful digital transformation endeavors.

4.3.2. Digital Technology Leveraging, Collaborative Innovation, and the Multi-Agent Innovation Model

Driven by the understanding of the importance of managerial cognition, PAYA decided to share its digital practices and experiences with a wider audience of customers and peers. Consequently, it initiated digital consulting services focused on electrical standardization and procedure standardization, aimed at empowering peers to tackle the challenges stemming from the diminishing demographic dividend through digital technology.

Electrical standardization is a specialized technical service offered by PAYA, tailored specifically for the design of control cabinets. Leveraging IEC (International Electrotechnical Commission)/GB (National Standard of the People’s Republic of China) and other international and domestic standards, PAYA customizes production standards and process specifications for control cabinets, taking into account the unique characteristics of each customer’s industry. This optimization of the control cabinet’s internal structure ensures that it meets the personalized needs of its clients. Utilizing the EPLAN software design platform, PAYA generates standardized drawings for customers, with a commitment to transforming customer information into reusable data. Furthermore, EPLAN Electric P8 2024 software simplifies production processes by enabling the one-click generation of various BOM tables and facilitating the swift conversion of languages and international standards to meet the requirements of export projects. Through PAYA’s electrical standardization service, customers can retain and reuse data and models, transforming their originally non-standardized products into a portfolio primarily composed of standardized products, supplemented by a limited number of customized options. This approach reduces enterprises’ reliance on repetitive employee tasks and fosters information sharing and collaboration between PAYA, its customers, and industry peers.

Unlike electrical standardization, procedure standardization services cover the design phase of control cabinets through to the virtual simulation of entire production lines, effectively creating a digital twin. PAYA empowers customers to utilize advanced digital twin and virtual simulation technologies, enabling them to gain a comprehensive understanding of a production line’s operations right from the negotiation stage. By offering procedure standardization services, PAYA helps customers establish a structured, modular, and reusable program development framework, thereby significantly enhancing their efficiency in developing control cabinets.

While deepening the cooperation and exchange between enterprises, PAYA also attaches great importance to strategic cooperation with universities and research institutions. At present, PAYA is cooperating with South China University of Technology to carry out industry–university–research cooperation. Through advanced visual recognition technology, PAYA conducts fine scanning of the control cabinet so as to automatically create an accurate virtual model of the control cabinet. This innovative technology not only can quickly identify potential problems with control cabinets but also quickly proposes targeted maintenance programs. In addition, PAYA and Guangdong Polytechnic of Water Resources and Electric Engineering jointly held training activities for teachers at vocational colleges in Guangdong Province, aiming to enhance the professional skills and quality of students in digital fields and thereby contribute to the development of more outstanding digital talent.

In the process of providing digital transformation consulting services, PAYA not only makes personalized adjustments on the basis of standard products but also provides tailor-made solutions for customers. Furthermore, PAYA is closely integrated with universities and research institutions, achieving collaborative innovation. This kind of collaborative innovation ultimately leads to an innovation model with the participation of multiple actors—a small innovation ecosystem composed of PAYA, customers, universities, and research institutions.

4.3.3. Resource Collaboration

Resource collaboration refers to the effective management and utilization of enterprise resources achieved through the implementation of dynamic resource management and integration strategies [83]. In the process of expanding its digital transformation consulting services, PAYA leveraged resources to integrate its internal resources with those of external customers, suppliers, universities, and research institutions. This not only enhanced its own core competitiveness but also propelled the digital upgrade of the entire industry. This resource collaboration strategy further strengthened the company’s market influence and industry leadership, ultimately leading to the formation of a multi-subject innovation model (as depicted in Figure 6). Typical evidence of innovation patterns during this period is cited in

Table 4. Typical evidence of the innovation model in the digital development period.

Aggregation Dimensions	Second-Order Themes	First-Order Concepts	Typical Evidence Cited
Managerial cognition	Matching cognition	To help SMMEs transform digitally	We have a responsibility to disseminate our digital transformation experience and develop a new technology service for it.
Digitization	Digital technology leveraging	Electrical standardization	The design and production of many enterprises are completely separated and become an island. The error from the design end to the production end will waste production, so we first do lean production from the design end.
		Procedure standardization	Take out the things common to the whole process of the control cabinet and make them modular and standardized so that it is possible to reuse them.
Innovation	Collaborative innovation	Collaborating with customers	Many small businesses may not be able to afford equipment, but we can create shared workshops. The customer’s data are transmitted, and, as long as they are accurate, my side can produce.
		Coordinating with upstream and downstream	By integrating upstream and downstream resources and jointly upgrading technology, we can have a perfect supply chain and thus better competitiveness.
		Industry–university–research cooperation	We cooperated with the South China University of Technology on a horizontal project, trying to apply visual recognition technology to control cabinet maintenance.
Resource activities	Resource collaboration	Internal resource transfer	We hope to say that after the successful internal transformation of the enterprise, there are a small number of customers to try. After success, we hope to extend this methodology to other manufacturing industries, especially SMMEs.
Innovation model	Multi-agent innovation	Broadening network externalities	I can carry out systematic coordination with my upstream and downstream peers and also carry out horizontal research with universities such as the South China University of Technology.

.

Table 5. Comparison of various stages of the digital transformation of PAYA.

Digital Transformation Stage	Managerial Cognition	Critical Events	Resource Orchestration	Innovation Model
Digital exploration period	Perceptive cognition	Realizing the digitalization of production links	Resource assembly	Single-agent innovation model
Digital growth period	Reframing cognition	Realizing the whole chain of digitalization and building a digital workshop	Resource integration	Dual-agent innovation model
Digital development period	Matching cognition	Developing digital transformation consulting services	Resource collaboration	Multi-agent innovation model

provides a comparison to more intuitively illustrate the characteristics and achievements of PAYA’s digital transformation across three stages.

5. Discussion

The successful experience of PAYA’s digital transformation provides valuable references for SMMEs. However, digital transformation is not always successful. A report from McKinsey indicated that the failure rate of corporate digital transformation ranges from 70% to 80%. Driven by the digital economy, manufacturing companies have successively embarked on the path of transformation, but this process is fraught with challenges. Companies such as Kodak and Nokia, which once dominated the market, gradually declined due to their inability to adapt to the changes of the digital age and thus have become typical cautionary examples [84]. During the interview process, the management of PAYA also mentioned the challenges encountered in the transformation process, such as the complexity of technology implementation, employees’ adaptation to new technologies, and the pressure of initial cost investment.

Based on the theory of resource orchestration and centering on the core question of “how does digital technology affect the innovation mode?” this study examines how SMMEs allocate resources within the digital wave and ultimately shape the evolution of their innovation model. Based on this analysis, the study refines the theoretical model of digital technology fostering the enterprise innovation model, as depicted in Figure 7.

Through careful analysis of resource structures and enterprise contextual factors, managers internalize them and develop unique cognitions. This cognition assists enterprises in accurately iterating and upgrading digital technology, which then guides enterprises to adopt matching resource action modes. Through this process, enterprise innovation is nurtured and promoted, ultimately resulting in the formation of diversified innovation models. These innovative models not only adapt to rapid market changes but also confer sustained competitive advantages on enterprises.

5.1. The Antecedents of Digitalization

Porfirio et al. [85] employed the fsQCA (fuzzy-set Qualitative Comparative Analysis) method to analyze questionnaire data from 47 Portuguese companies and concluded that enterprise characteristics (such as scale) and management characteristics (such as leadership style) are important prerequisites for digital transformation. Managerial cognition refers to the process by which bounded rational managers, based on their understanding of external situation changes, transform their knowledge into behaviors during strategic selection and decision-making through an information screening process [86]. The Upper Echelons Theory emphasizes that the personal characteristics of top executives have a significant impact on corporate strategic decision-making. In the field of digital transformation and resource strategy, the cognitive level and decision-making ability of managers play a decisive role [87,88]. Wrede et al. [89] clarified the role of top managers in promoting digital transformation, finding that top managers deal with digital transformation in three ways: understanding digitalization, setting up a formal environment for digitalization, and leading change. As managers, they should not only have keen insight into environmental changes and make appropriate interpretations and responses but also possess the ability to formulate, implement, and even innovate resource allocation strategies to ensure that the organization can flexibly adapt to environmental changes [90,91]. Therefore, managerial cognition is constantly upgraded, and changes are continually generated in response to environmental changes.

The digital transformation process of PAYA perfectly illustrates the point that managerial cognition is the driving force behind digital transformation. Firstly, in the initial stages of digital exploration, managers demonstrated remarkable insight, identifying the potential benefits of digital technology and leading enterprises towards internal automation and the digital transformation of production. Subsequently, as digitalization progressed, managers exhibited a reframing cognition and embarked on rebuilding the value chain, successfully bridging the gap between the production and design ends of the control cabinet and achieving digital management across the entire control cabinet chain. Finally, given the current rapid pace of digitalization, managers further demonstrate matching cognition. They are not merely content with their own digital transformation achievements but also commit to sharing this experience with partners and fostering collective progress across the entire industry.

5.2. Digital Action Process

5.2.1. Digitalization, Innovation and Innovation Model

Digital transformation has a positive impact on innovation [92]. Ferreira et al. [93] pointed out that integrating digital technology into the production process significantly enhances a company’s competitiveness in product and service innovation. At the same time, digital transformation can significantly strengthen a company’s technological innovation capabilities in resource integration [94], profoundly change the interaction mechanisms between enterprises and consumers as well as among enterprises and reshape the intrinsic characteristics of corporate innovation activities [95,96], thereby forming different innovation models.

In the early stage of digital exploration, PAYA recognized the potential of digital technology to enhance production efficiency and optimize processes. As a result, it promptly introduced PC-based technology, automatic wire cutting machines, and virtual simulation technology. With the in-depth application of digitalization in production processes, companies can not only significantly reduce operating costs but also greatly enhance management and production efficiency [97]. This has led to process innovation and laid a solid foundation for the company in the digital realm. During this stage, PAYA primarily concentrated on internal technological innovation, elevating its own digital capabilities, and establishing a single-agent innovation model.

With the continuous changes in the market and the ongoing advancements in technology, PAYA has entered the digital growth phase. At this stage, the company has not only continued to deepen its application of digital technology but also introduced digital twin technology to bridge the gap between the design and production ends. Through conducting simulation experiments on innovation projects using technologies such as virtual simulation and digital twins, companies can promptly reduce later-stage investment in a project if the experiment fails, thereby lowering the innovation costs for the enterprise [98]. Simultaneously, the company has achieved full-chain digitalization and established a digital production workshop, enhancing the efficiency and flexibility of the entire production process. Through incorporating customer demand into consideration, PAYA applies digital technology across all links of the value chain, fostering total innovation. Subsequently, PAYA expands the boundaries of digitalization, establishing the dual-agent innovative model with customers and PAYA as the dual entity.

The digital economy is characterized by digitalization, networking, and intelligence, as well as sharing and inclusiveness [99]. Industrial digitalization emphasizes the application of digital technology at the industrial level [100], while the digitalization of industries focuses on providing digital technology services and solutions to support industrial digitalization [101]. In the era of digital development, PAYA, leveraging its extensive experience in digital transformation, has provided consulting services for electrical and procedural standardization while actively collaborating with customers and universities. This mode of cooperation not only fosters the exchange of knowledge and technology but also collaborative innovation, enabling a company to better address market demands and drive the upgrading and development of the entire industrial chain. At this stage, PAYA has further leveraged network externalities to establish a multi-agent innovation model that encompasses PAYA, customers, and universities.

5.2.2. Resource Activities

Resource activity is a complex and fine work carried out by enterprise managers in the field of resource management with the core goal of creating consumer value and enterprise competitive advantage. It involves the structured arrangement, bundled use, and reuse of resources, which covers not only the effective deployment of internal resources but also the ingenious integration of external resources.

Resource assembly has a significantly positive impact on the overall innovation outcomes of startups and young firms [102]. The behavior of resource bricolage can help enterprises identify their strengths and weaknesses in market competition [103,104]. By leveraging strengths and compensating for weaknesses, enterprises acquire operational and knowledge resources that can form their core competitive advantages. In the era of digital exploration, PAYA is dedicated to mining and comprehensively assembling the company’s internal resources. Through the adoption of accurate and effective strategies, it combines these resources to drive technological innovation and business expansion.

Resource integration refers to the reorganization of both internal and external resources of an enterprise and involves two stages: identifying resources and attracting resources [105,106]. During the digital growth stage, PAYA is committed to fostering close cooperative relationships with customers while relentlessly pursuing technology iteration and upgrading. It integrates resources from both sides to maintain a leading position in the fiercely competitive market.

The stronger an enterprise’s capability for resource collaboration, the more it helps the enterprise identify, integrate, and utilize internal and external resources in a highly competitive market environment, reduce the costs and risks associated with transformation and upgrading, and make all necessary preparations for rapid transformation and upgrading [107]. In the period of digital development, PAYA has successfully achieved the precise matching and efficient coordination of internal and external resources through close industry collaboration and innovation. Additionally, it has established a collaboration model with universities and research institutions, further solidifying its industry-leading position.

6. Research Implications and Prospects

6.1. Research Conclusions

Against the backdrop of the thriving digital economy, this study carried out a longitudinal single-case study with PAYA as the research subject. It delves into how digital technology has reshaped the innovation model of SMMEs, discovering that the transformation in their innovation model primarily manifests in the following aspects:

Firstly, in the era of the digital economy, the innovative models of SMMEs have shown significant transformation characteristics. The process can be summarized as a progressive evolution from initial single-agent innovation to dual-agent innovation and ultimately to multi-agent innovation. This transformation not only demonstrates the diversified expansion of innovation subjects but also reflects profound changes in the complexity and synergy of innovation activities.

Secondly, from the perspective of resource allocation, digital technology has reshaped the pattern of innovation models through resource allocation. Specifically, digital technology has built single-agent innovation through resource assembly and bundling, helping enterprises improve production efficiency. With the continuous evolution of digital technology, the dual subject-agent model has emerged through resource integration and the bundling of partners. Furthermore, digital technology has leveraged multi-agent innovation models through resource coordination, forming a strong collaborative force for innovation.

Thirdly, the innovation driven by digital transformation in SMMEs has shown diverse forms of expression at different stages of development. These forms not only reflect the Chinese characteristics of total innovation and collaborative innovation but also integrate the unique advantages of digital technology.

Fourthly, the evolution of digital technology empowerment innovation models for SMMEs in the digital economy era presents clear and systematic characteristics: managers’ cognition is a key factor in promoting digital transformation and innovation model evolution for enterprises. Subsequently, enterprises continuously accumulate and deepen digital technology, gradually build digital infrastructure and platforms, and achieve innovation. With different resource activities in play, enterprises have formed innovative models with unique characteristics.

Fifthly, traditional SMMEs first need to obtain the “empowerment” of digital technology to achieve their own digital transformation, thereby forming “spillover” and “empowering” other enterprises. By leveraging digital technology, PAYA has not only gained direct benefits from digital transformation but also indirect benefits from sharing the transformation experience and “empowering” other enterprises. In the process of “empowering” other enterprises, enterprises promote and accelerate the pace of industry digital transformation while sharing external economies of scale benefits. Due to the unlimited use, repetition, and copying of digital resources, their marginal cost is very low. Therefore, the renewable and reusable nature of digital resources amplifies the scale and scope of enterprise benefits, promoting the collaborative progress of the entire ecosystem.

6.2. Theoretical Contribution

Combined with the above research conclusions, this study delves deeply into the factors underlying the innovation model of SMMEs in the digital economy era, as well as their motivations. Specifically, it examines the operational mechanism that drives the innovation model of SMMEs within this context.

Firstly, most existing studies use empirical methods to quantitatively analyze the impact of digital technology on innovation performance, with less attention paid to the theoretical construction of the impact of digitalization on enterprise innovation in the digital economy era. Traditionally, the innovation model of SMMEs focuses on a single dimension such as product iteration, process optimization, or market expansion and is limited by resources and the technological level, resulting in a relatively slow pace of innovation. In the context of the rapid development of digital technology, this article opens up the black box of innovation models for SMMEs in the digital age, revealing their trend of diversification, integration, and intelligence. This micro-level analysis helps to fill the gap in existing research and provides more specific theoretical support for the digital transformation of SMMEs.

Secondly, existing research mostly follows the paradigm of Schumpeter’s innovation theory, neglecting the uniqueness of innovation models for SMMEs in the Chinese context. This article follows the traditional innovation research paradigm dominated by the West and analyzes the actual operational logic of innovation models for SMMEs in the Chinese context. It not only focuses on the digital transformation within enterprises but also emphasizes collaborative innovation with external partners, demonstrating how the integration of internal and external resources can drive corporate innovation and transformation, thereby forming a theoretical deepening based on the Chinese context: China’s SMMEs rely on digital technology to construct, bundle, and leverage different forms of innovation at different stages of digital transformation, gradually realizing the evolution from single-agent to dual-agent and then to multi-agent innovation models.

Thirdly, existing resource orchestration theory has played an important role in revealing the utilization of enterprise resources and the construction of competitive advantages, but there is little in-depth exploration of the internal process of value formation and utilization in a single time period [108]. This article analyzes the development process of digitally driven innovation models at different stages. It also proposes an internal driving logic framework that runs through each stage, integrating the current fragmented research on digitalization and innovation models and revealing the underlying logic of the evolution of digitally driven innovation models.

Fourthly, the novelty of the theoretical model constructed in this study lies in its dynamism and phased nature; it clearly illustrates the evolutionary path of corporate innovation models. The model not only emphasizes the process through which enterprises achieve innovation through resource structuring, bundling, and leveraging at different stages, but also reveals the importance of integrating internal and external resources and engaging in collaborative innovation. Compared with traditional exploitative and exploratory innovation and ambidextrous innovation, this model pays more attention to the uniqueness of SMMEs and provides a new perspective for understanding the innovation models of SMMEs in the context of digital transformation.

6.3. Practical Implications

To deepen the understanding of the digital transformation trajectory of PAYA, we conducted a comparative analysis with SMMEs from other countries. The results indicate that PAYA’s transformation path is consistent with the practices of international SMMEs in certain aspects, while also exhibiting significant differences.

In terms of commonalities, the universality of technology application is particularly notable. During its digital transformation, PAYA introduced PC-based solutions, virtual simulation technology, and digital twin technology, which aligns with the practices of SMMEs from other countries in their digital transformation processes. This demonstrates that, despite differences in national and industry contexts, digital technologies have a broad applicability in enhancing production efficiency and innovation capabilities. Moreover, the crucial role of internal and external collaboration is also verified. Through in-depth collaboration with external partners, PAYA achieved resource integration and collaborative innovation, a strategy that is equally important in the digital transformation of SMMEs in other countries.

However, the digital transformation trajectory of PAYA also shows specific heterogeneity, mainly reflected in differences in resource base and innovation models. With limited resources, PAYA achieved innovation and transformation through resource orchestration, demonstrating the adaptive development model of SMMEs in resource-constrained environments and providing a reference for other similar enterprises. Secondly, the innovation model evolution framework constructed by PAYA reveals its unique innovation path in digital transformation, namely, the logic of evolving from single-agent to multi-agent collaborative innovation.

Based on the above comparison, the research conclusions of this study have practical guiding significance for the digital transformation of SMMEs. Firstly, with the rapid development and popularization of the digital economy, entrepreneurs are facing unprecedented opportunities and challenges. In this rapidly changing era, relying solely on changes in traditional thinking patterns and business models is not enough to cope with the rapid changes in the market. Similarly, relying solely on single, purely technical changes and innovations is also insufficient to achieve the ultimate development of enterprises. Taking PAYA as an example, the key to its success lies in the management’s profound insight into digital technologies and their continuous pursuit of innovation. Entrepreneurs must possess an innovative spirit and cognition and constantly pursue the coordinated development of new ideas, technologies, and business models in order to respond to the opportunities and challenges brought by the digital economy. Specifically, entrepreneurs should regularly attend industry seminars and technical training sessions, communicate with technical experts and peers, and get to grips with the latest technological information and innovative concepts. Meanwhile, companies can establish special innovation funds to encourage employees to propose innovative ideas and collaborate with universities and research institutions to accelerate the development and application of new technologies.

Secondly, in the pursuit of sustainable development and competitive advantage, enterprises must prioritize the cultivation and accumulation of innovation. Through years of technological accumulation and innovative practice, PAYA has gradually developed its own core competitive strengths. Enterprises need to foster a team with both professional skills and an innovative mindset, establish a robust internal management system, continually refine the production process, and optimize resource allocation. Simultaneously, enterprises must maintain flexibility to adapt to external environmental changes, effectively integrate both internal and external resources, and foster both technological and organizational innovation in harmony. Specifically, companies can establish internal innovation labs to encourage employees to conduct small-scale innovation experiments and reward successful projects. In addition, companies should use digital tools to optimize production processes and improve the efficiency of resource allocation. For example, by introducing advanced ERP (Enterprise Resource Planning) systems, companies can achieve efficient collaboration among production, sales, and procurement.

Finally, companies should recognize that success is not the result of solitary striving but is closely related to that of numerous stakeholders. In the digital economy era, competition between enterprises is no longer a simple zero-sum game but rather an ecosystem of common cooperation and mutual benefit. PAYA has significantly enhanced its innovation capabilities through cooperation with partners such as Siemens and EPLAN, achieving resource sharing and complementary technologies. Therefore, enterprises should actively establish close cooperative relationships with peers and upstream and downstream partners in the supply chain, jointly develop new technologies and products, and share resources, information, and market opportunities. Through collaborative progress, companies can achieve resource sharing and complementary advantages, reduce research and development costs and market risks, and enhance overall competitiveness. Specifically, companies can initiate or join industry alliances to jointly develop technical standards and market regulations. For example, as a Siemens DVP for digital value-added services, PAYA has continuously enhanced its digital capabilities by participating in Siemens’ technical training and project collaborations. Meanwhile, companies can also promote cooperation with partners by organizing technical exchange meetings and industry forums.

6.4. Research Limitations and Future Prospects

To keep up with the digital economy, PAYA actively engages in resource mobilization through digitization, fostering diverse innovation types, and ultimately establishing distinct innovation models. Conducting a longitudinal single-case study on PAYA, a representative SMME, provides a useful example. However, verification of the research findings is necessary due to the potential limitations on data comprehensiveness. To address this, future research should broaden the case sample, incorporating more representative cases for analysis and aiming to derive more universally applicable conclusions and provide comprehensive guidance for SMMEs’ innovation models.

Secondly, although the theory of resource orchestration is of great significance in explaining how enterprises can achieve innovation and competitive advantage through the integration of resources, it falls short in considering the role of organizational culture and human resources. On one hand, organizational culture has a significant impact on the utilization and coordination of resources. A positive organizational culture can promote the effective use of resources, while a negative one may lead to resource wastage and poor coordination. On the other hand, the skills, capabilities, and innovative spirit of employees are crucial for the effective use of resources. However, the theory focuses more on the physical attributes of resources and pays insufficient attention to the development and management of human resources.

Finally, this study posits that the innovation model formation path for SMMEs involves managerial cognition driving the accumulation of digital resources, thereby catalyzing innovation and ultimately yielding varied innovation models. The operational logic of this innovation model, derived conceptually from the PAYA case, is still in the nascent stages of SMMEs’ innovation model theory development. Hence, a crucial next step for research is to empirically analyze or simulate the theoretical framework presented herein, leveraging quantitative data and model validation to deepen our understanding of SMMEs’ innovation models in the digital era. This endeavor will enable a more precise grasp of SMMEs’ innovation pathways and key factors during digital transformation, ultimately offering more scientifically grounded guidance for practical applications.