A Study of the Influence Mechanism of Digital Technology Affordance on the Disruptive Innovation of Enterprises

Abstract

In the context of increasingly fierce global competition, utilizing digital technology to realize disruptive innovation is an effective way for enterprises to gain access to the mainstream market and form long-term competitive advantages. From the perspective of affordance, this study classifies digital technology affordance into cumulative affordance and variant affordance and explores the influence mechanism of digital technology affordance on enterprise disruptive innovation according to three core innovation phases: “fuzzy front-end–development–commercialization”. Based on 241 data points from different industries and types of enterprises, this empirical study found that (1) cumulative affordance and variant affordance have a significant positive impact on disruptive innovation and its “fuzzy front-end—development—commercialization” phases; (2) dynamic capabilities play a mediating role between digital technology affordance and disruptive innovation; and (3) the three aspects of dynamic capabilities—sensing, seizing, and reconfiguring—are greatly enhanced by cumulative affordance and variable affordance. These findings contribute to the research on the relationship between digital technology affordance and disruptive innovation and provide a micro-level perspective on how firms realize disruptive innovation through digital technology affordance.

1. Introduction

Under the new wave of technological revolution and industrial change, global competition is intensifying. Enterprises are realizing changes and disruptions in the market by continuously generating new technologies, products, or services and gaining sustained competitive advantages through a series of disruptive innovation activities. For instance, BYD Auto has become one of the industry pioneers in the new energy vehicle industry by leveraging disruptive technologies like electric drive systems and battery technology. As a result, disruptive innovation continues to receive attention in both academic settings and practice [1,2].

Since Christensen first proposed the concept of disruptive innovation [3], researchers have studied the connotation [4,5], influencing factors [6,7], and pathways [8,9] of disruptive innovation based on traditional innovation management theories. However, as modern society enters the digital economy era [10], digital technology has subverted the assumptions of many original innovation theories. The traditional technological innovation paradigm has changed, bringing new challenges to research innovation management [11,12]. There are new theoretical foundations for exploring the issue of disruptive innovation driven by digital technology within the framework of the paradigm shift in technological innovation [13,14]. However, after combing through the relevant research, the following gaps in the existing research are found: On the one hand, the process and path of disruptive innovation have changed under the background of digitization. However, there is limited research on the mechanisms behind the link between digital technology and disruptive innovation. On the other hand, most research has used the case study method to investigate the mechanisms of disruptive innovation driven by digital technology, which remains unverified empirically and lacks validation through large-sample data. Therefore, following the innovation phases of “fuzzy front-end–development–commercialization”, this paper examines the micro-generation mechanism of disruptive innovation in digitalization.

Meanwhile, with the continuous development of emerging technologies, scholars continue conducting research and discussions related to digital technology. They summarize the relevant characteristics of digital technology. For example, Nambisan et al. proposed that digital technology is characterized by openness, affordance, and generativity [15]. Among these, the affordance of digital technology refers to the fact that different organizations can use the same digital technology to achieve diverse goals [10]. The subjects who use digital technology vary depending on the setting, and the outcomes and implications also vary [16,17]. This feature allows firms to use digital technology to achieve technological breakthroughs, generate new products, and ultimately disrupt the market. Digital technology affordance can be further separated into cumulative affordance and variant affordance based on the fundamental properties of affordance [18,19]. Therefore, this study takes the affordance of digital technology as an entry point and divides it into cumulative affordance and variant affordance to explore how the affordance of digital technology affects disruptive innovation.

Dynamic capacities support the actualization of disruptive innovation [20]. In a complicated external environment, firms can utilize dynamic abilities to swiftly discover opportunities and integrate resources for technological breakthroughs and advances. In the context of digitization, digital technology can enhance sensing, seizing, and reconfiguring abilities by accurately analyzing large amounts of data and predicting outcomes. Most research has explored the importance of dynamic capabilities in realizing disruptive innovations [21,22,23]. However, there is a lack of empirical data on whether firms can improve dynamic capabilities and realize disruptive innovation through digital technology and its affordance. Further research is still necessary. Therefore, this study will investigate the mediating role of dynamic capabilities between digital technology affordance and disruptive innovation. On this basis, referring to the ideas of Teece [24], Wilhelm [25], Zhou [26], and other scholars, this study categorizes dynamic capabilities into three types: sensing, seizing, and reconfiguring. It then details how digital technology affordance affects each category.

As discussed above, in the context of the changing paradigm of traditional technological innovation, we will use empirical research methods to examine how digital technology affects firms’ disruptive innovation. Specifically, we first divide digital technology affordance into cumulative affordance and variant affordance. Then, we examine each type’s influence on enterprises’ disruptive innovation. Secondly, based on the innovation generation process of “fuzzy front-end–development–commercialization”, we examine how digital technology affordance affects disruptive innovation at each level. Finally, this paper emphasizes the mediating role of dynamic capabilities. Based on the segmentation of dynamic capabilities, we further discuss the impact of digital technology affordance on sensing, seizing, and reconfiguring.

The theoretical contribution of this paper lies in three aspects. First, the current literature primarily emphasizes the comprehensive examination of digital technology, with less focus on digital technology’s specific characteristics. This paper examines the influences of the cumulative affordance and variant affordance of digital technology on disruptive innovation, thereby enhancing theoretical research on innovation driven by digital technology and its affordance. Second, the mechanism of the impact of digital technology affordance on disruptive innovation and its process is proposed at the micro level. Enhancing dynamic capabilities is crucial for enterprises to realize disruptive innovation through digital technology affordance, contributing to the literature on disruptive innovation. Finally, the impact of digital technology affordance on sensing, seizing, and reconfiguring capabilities is specifically analyzed to further complement the relevant research on dynamic capabilities in the context of digitization.

2. Literature Review

2.1. Affordance Theory

The theory of affordance, proposed by ecological psychologist Gibson in 1986, refers to the action possibilities that the environment offers to objects. It emphasizes the user’s perception of specific objects’ functions and utilization [27]. Affordance is a notion characterized by both objectivity and subjectivity, relating not only to the inherent features of the object but also to the user’s knowledge or experience and the specific objectives to be attained [28].

After the concept was put forward, Norman extended the concept of affordance to the design domain [29]. Considering that the personality cognition of the actor, such as the growth environment and psychology, would affect affordance itself, he divided affordance into perceived affordance and actual affordance. Subsequently, the affordance theory helps researchers to understand the relationship between technology and actors [30]. Therefore, it has received increasing attention in the fields of “technology” and “information systems” [18,31] from which the theory of technology affordance originates. The theory of technology affordance refers to “the possibility of continuous interaction between actors and technological objects to produce specific results in order to achieve specific goals of actors” [32].

Existing scholars mainly discuss the technology affordance theory from the perspectives of relationship and behavior. The relational perspective highlights the interaction between technological objects and actors. Erofeeva et al. argued that technology affordance shows variety [33]. Diverse actors utilize a specific attribute or feature of the same technology to produce various results. Conversely, the behavioral perspective emphasizes the potential of particular technologies to fulfill the objectives of the actors, focusing on their aims and behavioral outcomes in specific contexts.

The rapid development of digital technology has contributed to a boom in digital innovation activities. Technology affordance can be further extended to digital technology affordance. In order to obtain more innovative performance, enterprises need to rely more on the possibilities brought by digital technology.

2.2. Digital Technology Affordance

The possibility of action brought by digital technology to the subject/object mainly derives from its essential characteristics. Digital technology exemplifies the functional characteristic of affordance through its two fundamental attributes: data homogenization and programmable functionality [10]. The affordance of digital technology highlights an entity’s perception of its functional features and its prior interactions with it. These integrations and alignments enable the entity to take action.

In prior relevant research, researchers examined the affordance of digital technology from many perspectives. For example, Hennebert et al. proposed four types of digital affordance—visibility, enhancement, aggregation, and addressability—in examining digital communication technology applications [34]. Autio et al. believed that digitalization produced robust digital affordance, categorizing it into “decoupled affordance, dismediated affordance, and generated affordance” [35]. Crosby et al. identified three complex layers of digital affordance in their study of educational technology platforms: functional affordance, perceived affordance, and adaptability [36]. Referring to Yoo and Verstegen et al.’s research on the characteristics of digital technology—data homogenization and programmable functionality [18,19]—Cheng et al. divided the affordance of digital technology into cumulative affordance and variant affordance [37]. Cumulative affordance denotes the homogenization of data and information in enterprises’ production and operations through digital technology, while variant affordance pertains to digital technology’s ability to alter the underlying digital programming of devices, reconfigure technological combinations, and facilitate the diverse transformations resulting from these new combinations [16,18,19]. This paper examines how digital technology affordance affects innovation using this division approach.

The current literature categorizes the affordance of digital technology into cumulative affordance and variant affordance, examining their association with organizations’ value for digital innovation, business model innovation, and innovation performance [37,38,39]. It finds that two types of digital technology affordance positively affect the scope and intensity of enterprises’ digital innovation value and that enterprises’ search mechanism moderates the process. Cumulative and variant affordances significantly influence companies’ incremental and disruptive business model innovation by augmenting digital capabilities and improving innovation performance. Existing research has demonstrated that the affordance of digital technology enhances the innovation and development of enterprises by augmenting digital capabilities and decreasing expenses. This research provides theoretical support for this paper to explore the impact of digital technology affordance on disruptive innovation.

Upon reviewing the literature, we found that few studies have investigated how digital technology affordance affects enterprises’ innovation, including disruptive innovation. The affordance theory offers an appropriate perspective to formulate and address the research question about using digital technology for innovation. Therefore, this paper integrates digital technology affordance into innovation research to clarify digital technology’s influence on innovation and is grounded on the theoretical framework of existing studies.

2.3. Dynamic Capabilities

The dynamic capabilities theory explains how enterprises dynamically match with the environment to quickly respond to changes in the external market [40]. In 1997, Teece first defined dynamic capabilities as an enterprise’s ability to assimilate, organize, and reconfigure internal and external resources, along with its ability to adapt to swiftly changing environments [20]. The definitions and dimensions of dynamic capabilities vary with the advancement of the dynamic capabilities theory. For example, Teece divided dynamic capabilities into sensing, seizing, and transforming [24], while Wilhelm et al. divided them into sensing, learning, and reconfiguring [25]. After reviewing the current research, it was found that the dimensional categorization of dynamic capabilities by scholars almost includes the response to the external environment, such as identifying opportunities and acquiring resources. Meanwhile, due to complex and unforeseen market improvements, the ability to identify and seize opportunities is crucial for enterprises. Therefore, the three dimensions of dynamic capabilities—sensing, seizing, and reconfiguring—are the most extensively, profoundly, and maturely examined elements by scholars [41]. This research utilizes this classification method to categorize dynamic capabilities into sensing, seizing, and reconfiguring.

Scholars have mostly agreed that dynamic abilities enhance innovation performance [40] and foster long-term competitive advantage [20]. The existing literature has examined the influence of dynamic capabilities on disruptive innovation using several study methods, concluding that dynamic capabilities facilitate disruptive innovation [42,43,44]. For example, Zhang et al. conducted empirical research demonstrating that the capability for meta-knowledge development benefits disruptive innovation through the mediating effects of knowledge transfer and dynamic capabilities [42]. Zhang et al. used a case study approach to explore the impact of dynamic capabilities and business model innovation on disruptive innovation [43]. These studies provide theoretical support for this paper to explain the impact of dynamic capabilities on disruptive innovation. In the context of digitalization, digital technology can strengthen the dynamic capabilities of enterprises. Midea, for example, used digital software and platforms to find that 48.5 percent of its users are under 30 years old. The primary product functionalities for current users are “recipe browsing” and “function startup”, which indicates a market opportunity that has led to the development of the micro-steaming and baking all-in-one machine [14]. Dynamic capabilities, as a superior organizational competency, are essential in the management and innovation processes within the contexts of the “digital economy” and “new technology” [45]. Therefore, this study will further investigate the role of dynamic capacities in promoting innovation through digital technology, drawing on prior studies from the literature.

2.4. Disruptive Innovation

In 1995, Christensen first proposed the concept of disruptive technology [46]. Then, in 1997, he formally proposed the theory of disruptive innovation, which states that enterprises develop new products or technologies ignored by the mainstream market to satisfy niche markets’ requirements and capture the mainstream market share by constantly improving technologies and products [3].

In the following two decades, many scholars have conducted many studies on disruptive innovation. The research mainly focuses on three aspects: First, the existing literature has answered the question of “what is disruptive innovation” by explaining its concept, connotation, and characteristics. Early research emphasized the development of technologies ignored by the mainstream market to enter a niche or new markets and finally enter the mainstream market after continuous improvement [4]. Subsequently, scholars expanded the connotation of disruptive innovation to include products, services, markets, and business models [5,47,48]. Second, previous research has investigated the factors that influence disruptive innovation from the organization’s internal and external perspectives. It was found that internal factors such as managers’ competence [49,50,51], resources [51,52,53], and dynamic capabilities [21], as well as external factors such as industrial structure [54], industrial innovation cycle [55], and stakeholders [56,57,58], can affect the disruptive innovation of enterprises. Third, various academics have studied the process and path of disruptive innovation at different levels of enterprises, industries, and countries. After reviewing the relevant literature, it was found that most of the studies on disruptive innovation are grounded in traditional innovation theories. The richness of the research findings provides substantial theoretical support to this study. First of all, based on the definition of disruptive innovation in previous research, this study emphasizes that enterprises need to develop new products and technologies based on customers’ pain points and potential needs. Then, enterprises expand the user base by continuously optimizing products and technologies, ultimately occupying the mainstream market. Secondly, disruptive innovation is not just a result but a complete and progressive process [59,60]. Each part of the process is critical to whether the innovation is disruptive or not [2]. Therefore, the “fuzzy front-end–development–commercialization” innovation phases are applied in this study to analyze disruptive innovation. Finally, this study on the impact of disruptive innovation has theoretical backing from various scholars’ discussions of the drivers and pathways of disruptive innovation.

The traditional innovation theory has recently changed due to the ongoing development of emerging technologies. Some researchers have extended the notion of disruptive innovation by investigating the process driving disruptive innovation in the setting of digitalization. Liu et al., for instance, investigated the disruptive innovation process mechanism driven by digital technology [14]. However, few quantitative studies are exploring the mechanism of digital technology’s impact on disruptive innovation in the setting of the shifting paradigm of technological innovation. Thus, more thorough research is required.

Therefore, to address the issues mentioned above, this study will investigate how digital technology’s affordability affects disruptive innovation from the standpoint of the innovation process.

3. Research Hypotheses

3.1. The Effect of Digital Technology Affordance on the Disruptive Innovation of Enterprises

Digital technology’s affordance exploits data’s homogeneity and programmability characteristics to significantly reduce the costs and risks associated with disruptive innovation while increasing its effectiveness. Specifically, cumulative and variant affordances facilitate disruptive innovation.

The following aspects primarily indicate how cumulative affordance has affected the three phases of disruptive innovation. First, the disruptive innovation process begins with the fuzzy front-end. Enterprises need to accurately judge the pain points of user demands and identify innovation opportunities to reduce uncertainty and ambiguity. In the process of enterprises interacting with users, the cumulative affordance of digital technology can generate various homogenized data [37]. The enterprise’s collection, processing, and evaluation of these data will help clarify and achieve the goal of disruptive innovation. Specifically, enterprises can use digital technology to observe, record, and analyze users’ behaviors and habits during the fuzzy front-end stage. Digital technology, which relies on cumulative affordance, can accurately analyze extensive homogenized data produced in the interaction process [18], assist enterprises in analyzing the unsatisfied demands of the mainstream market, and then accurately identify market opportunities [61,62]. This provides conditions to realize disruptive innovation. Second, in the development stage, cumulative affordance facilitates collaboration between different innovation entities and promotes the effectiveness of disruptive innovation. From the internal perspective of the enterprise, cumulative affordance can promote the enterprise to open up different data access channels and standardize heterogeneous data from multiple sources, including R&D, production, and sales. Enterprises can break down the boundaries of different departments and prompt employees to communicate with high efficiency. The process of disruptive innovation is no longer composed of discrete, linear, and continuous links but is a process in which different participating subjects constantly interact dynamically, and various links cross over [12]. The resultant distributed collaboration shortens the innovation cycle and improves innovation efficiency. From an external perspective, cumulative affordance can accelerate the coordination and cooperation of various firms. The resources and capabilities required for disruptive innovation are often distributed across different enterprises [63]. To achieve disruptive innovation, enterprises need to cross organizational boundaries to acquire the required knowledge and external resources [64,65]. However, it is challenging to effectively exchange and move resources across organizations because of knowledge stickiness [66,67]. The cumulative affordance of digital technology can homogenize data to effectively solve the problem, promoting cooperation among innovation participants [68]. It also helps enterprises find and integrate the knowledge and technology needed for disruptive innovation quickly. Then, enterprises establish synergistic advantages with other enterprises and accelerate the realization of disruptive innovation. Third, in the final commercialization stage, the enterprise can use digital technology to monitor product sales, user feedback, and other pertinent data when the disruptive product enters the market. Then, it can carry out homogenized data analysis utilizing cumulative affordance to discover product issues promptly and ultimately achieve continuous product iteration until it disrupts the market.

Therefore, we propose the following hypothesis and sub-hypotheses (as shown in Figure 1):

H1. 

Cumulative affordance is positively related to firms’ disruptive innovation.

H1a. 

Cumulative affordance plays a positive role in the fuzzy front-end stage of firms’ disruptive innovation.

H1b. 

Cumulative affordance plays a positive role in the development stage of firms’ disruptive innovation.

H1c. 

Cumulative affordance plays a positive role in the commercialization stage of firms’ disruptive innovation.

Digital technology variant affordance also impacts disruptive innovation’s “fuzzy front-end–development–commercialization” process. In the fuzzy front-end stage, enterprises use digital technology to profile users and collect their demand pain points. Based on variant affordance, the enterprise determines the best combination of concepts by analyzing the product matrix formed by the functions that address these demand pain points [14]. This provides the goal and direction for research and development as well as establishes the foundation for the enterprise to realize disruptive innovation. In the development stage, based on variant affordance, the procedures and results of existing data processing by digital technology are likewise stored and processed as data. This method effectively promotes the flexibility and editability of procedures connected to innovation. Enterprises can utilize this feature to conduct simulation experiments in the process of implementing disruptive innovation. Specifically, enterprises utilize digital technology in virtual environments to conduct simulation experiments, generating data for the following experimentation cycle. Ultimately, enterprises build solutions for decision making in an ongoing learning and iteration process. Variant affordance lowers innovation risk and expense while increasing disruptive innovation’s effectiveness. In addition, variant affordance can couple technologies into different levels [10], facilitating the subjects to innovate at different levels. The decomposition of technology promotes collaborative innovation while strongly protecting the commercial and technical secrets of enterprises. It also reduces the risk associated with innovation and encourages businesses to implement disruptive innovation. In the commercialization stage, digital technology variant affordance can effectively complete the iterative upgrading of disruptive products and provide conditions for the ultimate realization of disruptive markets. The forms and functions that digital technology brings might be delayed due to its variable affordance features [69]. This means that functions arising from different needs can be added after the product is designed and produced [18]. Once the product is on the market, the enterprise needs to further enhance it based on customer feedback and evaluations. Currently, variant affordance is used to upgrade the technology and functions efficiently. Then, enterprises improve customer loyalty and satisfaction by the iterative upgrading of products. Ultimately, variant affordance establishes a foundation for enterprises to realize disruptive innovation. In addition, the scalable and editable nature of variant affordance enables enterprises to iterate their digital products in real time, thus improving the efficiency of disruptive innovation [12]. Therefore, Figure 1 shows the following hypothesis and sub-hypotheses:

H2. 

Variant affordance is positively related to firms’ disruptive innovation.

H2a. 

Variant affordance plays a positive role in the fuzzy front-end stage of firms’ disruptive innovation.

H2b. 

Variant affordance plays a positive role in the development stage of firms’ disruptive innovation.

H2c. 

Variant affordance plays a positive role in the commercialization stage of firms’ disruptive innovation.

3.2. Mediating Role of Dynamic Capabilities

The market environment is constantly changing, and consumer demand is diversified and personalized. Strong dynamic capabilities in this context enable companies to adjust to environmental changes quickly [70]. Digital technologies, including big data, serve as valuable tools for enterprises to enhance their dynamic capabilities [71]. Companies that use digital technology internally also contribute to the positive role that dynamic capabilities play [45]. That is, digital technology’s affordance plays a significant role in promoting enterprises’ dynamic capabilities.

While implementing disruptive innovation, companies “profile” users in mainstream markets using digital technology. They collect much information about these users [14], including their statuses, locations, consumption records, and consumption habits. This information is then used to analyze the potential needs of consumers in current markets. Digital technology’s cumulative affordance can analyze, organize, and convert these raw data in a short time, thus reducing the information asymmetry between the enterprise and the users. With the support of a large amount of empirical data, enterprises sensitively perceive market opportunities. Therefore, enterprises can develop the capability to respond to complicated and changing settings [70]. Furthermore, in the process of innovation, cumulative affordance blurs the boundaries between employees within the enterprise and between the enterprise and its partners, facilitating a more fluid exchange of data across different subjects. Within the enterprise, cumulative affordance provides suitable conditions for interaction and communication among employees in different departments. This function accelerates the flow and exchange of knowledge within the enterprise and improves employees’ capacity for innovation. All of these factors support the enterprise’s ability to implement and sustain innovation while enhancing its capability for dynamic capabilities. Outside of enterprises, enterprises can use cumulative affordance to strengthen their connections with other organizations. The enterprise can establish a value network by forming an “ecosystem” with suppliers, investors, partners, and other stakeholders. The cumulative affordance of digital technology enables the quick organization and analysis of new knowledge obtained from the value network, thereby increasing the frequency and speed of information and resource exchange among various entities. Enterprises can foster new, creative ideas and improve their capability to allocate and integrate resources [72]. Moreover, enterprises will improve dynamic capabilities to recognize opportunities and adjust to change in a complex and dynamic environment [73].

Figure 2 shows the following hypothesis and sub-hypotheses:

H3. 

Cumulative affordance has a significant positive effect on dynamic capabilities.

H3a. 

Cumulative affordance is conducive to improving firms’ sensing capabilities.

H3b. 

Cumulative affordance is conducive to improving firms’ seizing capabilities.

H3c. 

Cumulative affordance is conducive to improving firms’ reconfiguring capabilities.

Digital technology variant affordance helps enterprises implement hierarchical modular architecture [10]. It creates new organizational methods for enterprises and improves their reconfiguration capabilities. In particular, variant affordance decomposes traditional business processes into different levels, forming a layered architecture consisting of equipment, network, service, and content [74,75]. The structure allows for establishing connections with external subjects at various levels and increases the flexibility and malleability of each layered module [76]. Enterprises will improve their ability to manage business processes and strengthen dynamic capabilities. In addition, variant affordance can realize the coupling and reorganization of digital technology. Thus, it promotes the effective integration of customer demand, enterprise processes, and supply networks. Moreover, variant affordance can realize the value co-creation of an innovation network, thereby improving the ability of enterprises to obtain resources. Combining digital technologies will encourage different digital resources to create new solutions in the process of continuous analysis, learning, and decision making, which creates new dynamics and opportunities for enterprises [77], improving their ability to adapt to the external environment and perceive opportunities [78]. Figure 2 shows the following hypothesis and sub-hypotheses:

H4. 

Variant affordance has a significant positive effect on dynamic capabilities.

H4a. 

Variant affordance is conducive to improving firms’ sensing capabilities.

H4b. 

Variant affordance is conducive to improving firms’ seizing capabilities.

H4c. 

Variant affordance is conducive to improving firms’ reconfiguring capabilities.

According to research conducted by experts, dynamic capabilities help enterprises improve innovation performance and long-term competitive advantage [20,40], which provides theoretical support for enterprises to implement disruptive innovation and improve core competitiveness. In order to ultimately dominate the mainstream market share and achieve market disruption, disruptive innovation emphasizes breaking through the current technological trajectory [4,46,79], providing goods or services that satisfy the requirements of customers, and continuously improving the performance and attributes of products through technological updates [60,80,81,82,83]. It can be seen that the foundation element for businesses to achieve disruptive innovation is identifying and meeting consumer demand. With the support of dynamic capabilities, enterprises can continuously monitor the external environment and search for and excavate information on users’ needs in the current mainstream market [84] to capture new market demands and market opportunities and provide a foundation for realizing disruptive innovation. According to the resource-based view, an organization’s primary competitive advantage lies in its rare, valuable, unique, and irreplaceable resources [85]. Therefore, enterprises need to obtain the necessary technologies and resources to realize disruptive innovation while interacting with other stakeholders. The dynamic capabilities of enterprises facilitate the rapid sharing and coordination of resources [86,87] and improve disruptive innovation’s efficiency and performance. Firms’ dynamic capabilities enable rapid sharing and resource coordination, improving disruptive innovation’s effectiveness and performance. In addition, it is difficult for an enterprise to develop without corresponding capabilities in a dynamic environment. Therefore, in addition to obtaining the necessary resources from internal and external sources, an enterprise needs to maximize the utilization and integration of resources through dynamic capabilities [88]. This promotes enterprises to realize technological and product innovations [40,89], which ultimately promotes the formation of disruptive innovations.

Therefore, we propose the following hypotheses:

H5. 

Dynamic capabilities have a significant positive effect on firms’ disruptive innovation.

H6. 

Dynamic capabilities mediate the relationship between digital technology cumulative affordance and firms’ disruptive innovation.

H7. 

Dynamic capabilities mediate the relationship between digital technology variant affordance and firms’ disruptive innovation.

Figure 3 shows a conceptual model based on the hypotheses mentioned above.

4. Research Design

4.1. Sample and Data

This study is based on a sample of Chinese enterprises. The sample businesses are dispersed among major cities in China’s eastern, central, and western regions to guarantee that the sample is sufficiently representative. The enterprises include state-owned enterprises, private enterprises, wholly foreign-owned enterprises, and Chinese–foreign joint ventures. The industries in which the enterprises are situated include electronic information, medical manufacturing, and machinery manufacturing. Moreover, to ensure the questionnaire topic’s relevance and the data’s accuracy, we randomly selected some enterprises for a pre-survey. Then, we developed a more reasonable questionnaire topic based on the survey results. To further reduce the possibility of bias brought on by social expectations, this study’s questionnaire clearly states that there are no right or wrong responses. The participants will remain completely anonymous.

The formal research began after clarifying the research area, the research enterprise, and the questionnaire. The data were collected by distributing the questionnaires online, using Questionnaire Star and promoting it on social platforms. Finally, 295 questionnaires were obtained from the Questionnaire Star website. A total of 241 questionnaires that might be utilized for data analysis were gathered after 54 invalid questions were eliminated.

4.2. Measures of Variables

To guarantee the validity and precision of the survey, this research draws on maturity scales in existing studies and makes adjustments according to the actual situation.

Table A1. The scale of the main variables.

Variables		Items
Cumulative affordance (CA)		In your enterprise, digital technology is used to store, process and share data and information of enterprise production, manufacturing, sales and operation, as follows: Q1: Your company can analyze various business data such as R&D, design, manufacturing, product and service Q2: Your enterprise can acquire and reuse historical business data Q3: Your enterprise can store, archive, retrieve, and share business data Q4: Your enterprise has a high level of information management in the relationship with customers
Variant affordance (VA)		In your organization, digital technology is being used to enable collaboration at the collective level in the following ways: Q1: Your company is comprehensively promoting digital design, production and management Q2: Your company can achieve production process coordination and implement collaboration plans Q3: Your company effectively enables members of the organization to work together
Dynamic capabilities (DC)	Sensing (DC1)	Q1: Your company has the ability to respond quickly to changes in the market environment Q2: Your business has the ability to react quickly to the actions of your competitors Q3: Your company is good at using business capabilities to respond to external opportunities
	Seizing (DC2)	Q1: Your company has abundant access to opportunities and is good at taking advantage of them Q2: When the external environment changes, your company can seize unexpected opportunities Q3: Your company pays close attention to the situation of the market, consumers and competitors, and implements countermeasures in a timely manner
	Reconfiguring (DC3)	Q1: Your enterprise can use the new organizational norms to update the working mode or management mode Q2: Your company often changes its organizational structure according to new organizational norms Q3: Your company frequently updates its business processes in line with new organizational values
Disruptive innovation (DI)	Fuzzy front-end (DI1)	Q1: Your company develops products based on customers’ pain points and potential needs Q2: Your company develops products by proactively anticipating future market demand
	Development (DI2)	Q1: Your company uses leading and cutting-edge technologies in the development and production of products Q2: Your company implements innovations that significantly change existing products, technologies, businesses, or services
	Commercialization (DI3)	Q1: Your company improves and optimizes products or services through user feedback and continuously expands its user base Q2: The products developed by your company have a high market share and can squeeze out the original market and form a new mainstream market

in Appendix A shows the specific items. This research uses a seven-point Likert scale in the questionnaire, where 1 represents “strongly disagree”, 4 represents “neutral”, and 7 represents “strongly agree”.

(1)

Digital technology affordance. This study draws on the research by Cheng and other scholars to examine the use of digital technology and its affordances [37]. Digital technology cumulative affordance is measured by four items, such as “Your company can analyze various business data such as R&D, design, manufacturing, product and service”. Digital technology variant affordance is measured by three items, such as “Your enterprise can realize production process collaboration and implement collaboration plans”.

(2)

Dynamic capabilities. Current research on dynamic capabilities is very rich, and the scale is relatively mature. According to the research by scholars Wilden et al. [90], Chiua et al. [91], and Lee et al. [92], the dynamic capabilities are divided into sensing, seizing, and reconfiguring. Then, we utilize nine questions in Xi’s study to measure dynamic capabilities [41].

(3)

Disruptive innovation. This study utilizes the research by Liu and other scholars to analyze disruptive innovation according to the process of “Fuzzy front-end–Development–Commercialization” [14]. Based on the different characteristics of each stage, disruptive innovation is measured by six items, including “your company develops products based on customers’ pain points and potential needs” [93].

Furthermore, to precisely assess the impact of digital technology affordance on firms’ disruptive innovation, this study uses firm age, firm nature, industry background, and firm size as control variables. Precisely, firm age is measured by the time since the firm was established, where “1” indicates up to two years, “2” indicates two to five years, “3” indicates six to ten years, and “4” indicates eleven years and above. The nature of the enterprise is divided into wholly foreign-owned enterprises, Sino-foreign joint ventures, state-owned enterprises, private enterprises, and others, which are indicated by “1” to “5”, respectively. The industry background is divided into 12 categories, including the medical and pharmaceutical industry, machinery manufacturing industry, food manufacturing industry, etc., which are indicated by “1” to “12”, respectively. The size of an enterprise is indicated in this study by the number of staff members, where “1” indicates 100 people or less, “2” indicates 101 to 1000 people, “3” indicates 1001 to 2000, and so on, with “6” denoting 4001 to 5000 and “7” denoting more than 5000.

4.3. Models and Data Analysis Procedure

We construct a linear regression model based on the questionnaire data to test the proposed hypotheses. To be specific, this study verifies the positive effects of two types of affordances on disruptive innovation (H1 and H2) by constructing Equations (1) and (2). Disruptive innovation (DI), cumulative affordance (CA), and variant affordance (VA) are the terms used in the equations. The coefficients a₁ and b₁ represent the degrees of effect of cumulative and variant affordances on disruptive innovation. If a₁ and b₁ are significantly positively correlated, H1 and H2 are supported. In addition, Equations (3) and (4) are constructed to verify the effect of digital technology affordance on the phases of “Fuzzy front-end–development–commercialization” of disruptive innovation (H1a–H1c and H2a–H2c), where DI_(1–3) stands for the three stages of the disruptive innovation process.

DI = a₀ + a₁ × CA + a₂ × controls + ε

(1)

DI = b₀ + b₁ × VA + b₂ × controls + ε

(2)

DI_(1–3) = a₀’_(1–3) + a₁’_(1–3) × CA + a₂’_(1–3) × controls + ε

(3)

DI_(1–3) = b₀’_(1–3) + b₁’_(1–3) × VA + b₂’_(1–3) × controls + ε

(4)

To investigate the role that dynamic capabilities play as a mediator between disruptive innovation and cumulative affordance as well as variant affordance, this study constructs Equations (5)–(7). DC stands for dynamic capability. The relationship between disruptive innovation and cumulative affordance may be mediated by dynamic capabilities if c₁ is significantly positive and d₁ is significantly less than a₁, supporting H6; dynamic capabilities may mediate the relationship between disruptive innovation and variant affordance if c₁ is significantly positive and e₁ is significantly less than b₁, supporting H7.

DI = c₀ + c₁ × DC + c₂ × controls + ε

(5)

DI = d₀ + d₁ × CA + d₂ × DC + d₃ × controls + ε

(6)

DI = e₀ + e₁ × VA + e₂ × DC + c₃ × controls + ε

(7)

This study constructs Equations (8) and (9) to further explore the effects of cumulative and variant affordances on the dimensions of dynamic capabilities. In the Equations, DC_(1–3) stand for perceived capability, acquisition capability, and reconfiguration capability.

DC_(1–3) = m_0(1–3) + m_1(1–3) × CA + m_2(1–3) × controls + ε

(8)

DC_(1–3) = n_0(1–3) + n_1(1–3) × VA + n_2(1–3) × controls + ε

(9)

5. Results

5.1. Descriptive Statistics

This study adopts the SPSS 22.0 software to conduct a descriptive statistical analysis of the survey data, examining characteristics across multiple dimensions, including the mean and standard deviation, to gain a basic understanding (

Table 1. Min and max values, means, and standard deviations.

Variables	n	Min	Max	Mean	S.D.
Firm age	241	1.00	4.00	3.622	0.721
Firm nature	241	1.00	5.00	3.548	0.841
Industry background	241	1.00	12.00	8.838	3.762
Firm size	241	1.00	7.00	3.573	2.394
Cumulative affordance	241	1.00	7.00	5.010	1.527
Variant affordance	241	1.00	7.00	5.008	1.565
Dynamic capabilities	241	1.00	7.00	4.916	1.416
Disruptive innovation	241	1.00	7.00	4.730	1.550

). All variables’ mean values are within a reasonable range. For example, the mean age of firms is 3.622, suggesting that most of the surveyed enterprises have been in operation for over ten years. These firms exhibit greater maturity and may possess a more comprehensive organizational structure. The mean value of the nature of enterprises is 3.548, which state-owned and private enterprises dominate. The standard deviations of these variables exceed 0.8, signifying that the data acquired in this study possess research value and are not stagnant, thus enabling the analysis of variations among various model variables.

5.2. Correlation Analysis

This paper analyzes the correlation between different variables according to the Pearson correlation coefficient.

Table 2. The correlations between the variables.

Variables	1	2	3	4	5	6	7	8
1 Firm age	1
2 Firm nature	−0.194 **	1
3 Industry background	0.019	0.076	1
4 Firm size	0.341 **	−0.289 **	0.011	1
5 Cumulative affordance	0.241 **	−0.278 **	−0.077	0.435 **	1
6 Variant affordance	0.309 **	−0.216 **	−0.112	0.394 **	0.878 **	1
7 Dynamic capabilities	0.189 **	−0.191 **	−0.098	0.329 **	0.804 **	0.778 **	1
8 Disruptive innovation	0.209 **	−0.184 **	−0.109	0.310 **	0.748 **	0.714 **	0.843 **	1

Note: ** p < 0.01; two-tailed test; N = 241.

shows the specific calculation results. At the level of 1%, the Pearson correlation coefficients of cumulative affordance and variant affordance with disruptive innovation are 0.748 and 0.714, respectively. This indicates that cumulative and variant affordances have a positive relationship with disruptive innovation. Similarly, the Pearson correlation coefficients of cumulative affordance and variant affordance with dynamic capabilities are 0.804 and 0.778, respectively, indicating a positive correlation. The Pearson correlation coefficient between dynamic capabilities and disruptive innovation is 0.843, reflecting that there is a positive correlation between dynamic capabilities and disruptive innovation. The core variables of cumulative affordance, variant affordance, and dynamic capabilities have significant correlations (p < 0.01). The results serve as preliminary validation for the subsequent testing of the hypotheses.

5.3. Reliability and Validity

In this paper, internal consistency reliability is used to measure the reliability level of the main variables. We used Cronbach’s α coefficient to measure the internal consistency reliability. It is generally believed that when the Cronbach’s α coefficient of a variable is more significant than 0.7, it indicates high internal consistency. According to the analysis results in

Table 3. The results of the reliability and validity analysis.

Variables		Item	Factor Loading	Cronbach’s α	AVE	CR
Cumulative affordance		Q1	0.841	0.924	0.756	0.925
		Q2	0.902
		Q3	0.879
		Q4	0.859
Variant affordance		Q1	0.925	0.937	0.840	0.940
		Q2	0.950
		Q3	0.867
Dynamic capabilities	Sensing	Q1	0.928	0.950	0.866	0.951
		Q2	0.937
		Q3	0.926
	Seizing	Q4	0.918	0.944	0.851	0.945
		Q5	0.908
		Q6	0.941
	Reconfiguring	Q7	0.890	0.953	0.884	0.958
		Q8	0.960
		Q9	0.965
Disruptive innovation	Fuzzy front-end	Q1	0.926	0.936	0.879	0.936
		Q2	0.949
	Development	Q3	0.966	0.940	0.888	0.940
		Q4	0.917
	Commercialization	Q5	0.926	0.864	0.763	0.866
		Q6	0.821

, the Cronbach’s α values of the main variables are all higher than the critical value of 0.7, indicating that the scale has good reliability. Furthermore, we tested the scale’s validity using convergent and discriminant validity. On the one hand, the factor loadings of the question items of all variables were more significant than 0.7, and the AVE values were higher than 0.5. Therefore, the scale had good convergent validity. On the other hand, the square root of the AVE was greater than the correlation coefficient between the variables, indicating that the measurement of the variables had good discriminant validity. In general, the reliability and validity of the scale in this paper are satisfactory, fulfilling the criteria for the subsequent empirical analysis.

5.4. Hypothesis Testing and Results

An ordinary least squares (OLS) regression analysis was carried out to test the hypotheses. We determined whether the independent and control variables are multicollinear before executing the regression analysis. The results indicate that the variance inflation factors (VIFs) are less than 5, suggesting no issue with multicollinearity among the variables.

The test results of the main effect are shown in

Table 4. Regression results of main effects.

	Disruptive Innovation		Phase of Disruptive Innovation
			Fuzzy Front-End		Development		Commercialization
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
	β	β	β	β	β	β	β	β
Firm age	0.042	−0.024	0.076	0.011	0.028	−0.037	0.003	−0.057
Firm nature	0.030	−0.026	0.065	0.013	−0.007	−0.063	0.030	−0.025
Industry background	−0.054	−0.029	−0.026	0.000	−0.080	−0.055	−0.049	−0.027
Firm size	−0.022	0.035	−0.016	0.033	−0.044	0.014	0.007	0.067
Cumulative affordance	0.752 ***		0.707 ***		0.749 ***		0.717 ***
Variant affordance		0.699 ***		0.671 ***		0.694 ***		0.649 ***
R2	0.565	0.513	0.501	0.470	0.564	0.509	0.516	0.451
Adjusted R2	0.555	0.502	0.491	0.459	0.554	0.499	0.506	0.440
F-value	60.937 ***	49.475 ***	47.263 ***	41.724 ***	60.679 ***	48.728 ***	50.199 ***	38.651 ***

Note: *** p < 0.001; two-tailed test; N = 241.

. Models 1 and 2 were used to test two types of affordance’s impact on disruptive innovation. The coefficient of cumulative affordance in Model 1 is 0.752, which passes the significance level test at 1%. This indicates that cumulative affordance has a significantly positive impact on disruptive innovation, supporting H1. The coefficient of variation affordance in Model 2 is 0.699, indicating that variant affordance positively impacts disruptive innovation, supporting H2. After a further analysis of the coefficients, it was found that cumulative affordance may have a more significant impact on disruptive innovation than variant affordance.

The three phases of disruptive innovation are all significantly positively impacted by the cumulative affordance of digital technology, as demonstrated by Models 3, 5, and 7 (β = 0.707, p < 0.01; β = 0.749, p < 0.01; β = 0.717, p < 0.01), supporting H1a, H1b, and H1c. Examining the coefficients revealed that cumulative affordance has the most substantial influence on the development stage, followed by the commercialization stage, and lastly, the fuzzy front-end stage. According to Model 4, Model 6, and Model 8, variant affordance also improves each of the three phases of disruptive innovation (β = 0.671, p < 0.01; β = 0.694, p < 0.01; β = 0.649, p < 0.01), supporting H2a, H2b, and H2c. In addition, the coefficients of the independent variables in these three models reflect that variant affordance may also have the most significant impact on the development stage, followed by the fuzzy front-end stage, and finally, the commercialization stage.

Table 5. Regression results of mediating effect.

	Dynamic Capabilities		Disruptive Innovation
	Model 9	Model 10	Model 17	Model 18
	β	β	β	β
Firm age	0.006	−0.069	0.038	0.026
Firm nature	0.035	−0.025	0.007	−0.008
Industry background	−0.038	−0.008	−0.028	−0.022
Firm size	−0.018	0.038	−0.010	0.007
Cumulative affordance	0.817 ***		0.197 ***
Variant affordance		0.778 ***		0.131 ***
Dynamic capabilities			0.679 ***	0.730 ***
R2	0.649	0.611	0.726	0.720
Adjusted R2	0.642	0.603	0.719	0.713
F-value	87.083 ***	73.802 ***	103.351 ***	100.264 ***

Note: *** p < 0.001; two-tailed test; N = 241.

verifies whether the mediating effect is valid. Based on the results of testing the main effect between digital technology affordance and disruptive innovation, we first verify the relationship between dynamic capabilities and cumulative affordance as well as variant affordance. Model 9 and Model 10 illustrate the impacts of cumulative affordance and variant affordance on dynamic capability. The results show that the cumulative and variant affordance coefficients are 0.817 and 0.778, respectively, passing the significance level test. Cumulative affordance and variant affordance have a positive effect on dynamic capabilities, supporting H5. Then, based on the results of the existing regression equation, Model 17 is obtained by adding the mediating variable of dynamic capability on the basis of Model 1. The coefficient of cumulative affordance on disruptive innovation decreased from 0.752 (p < 0.01) to 0.197 (p < 0.01), which indicates that dynamic capability produces an indirect effect, supporting H6. Similarly, to verify the mediating role of dynamic capabilities between variant affordance and disruptive innovation, the mediating variable is added to Model 2, and the coefficient of variant affordance on disruptive innovation is reduced from 0.699 (p < 0.01) to 0.131 (p < 0.01), supporting H7.

We used Bootstrap in SPSS22.0 to test the mediating effect further [94]. The results are shown in

Table 6. The results of the Bootstrap test.

Item	Mediating Effect	BootSE	BootLLCI	BootULCI
Cumulative affordance = > Dynamic capabilities = > Disruptive innovation	0.563	0.085	0.418	0.766
Variant affordance = > Dynamic capabilities = > Disruptive innovation	0.563	0.072	0.438	0.723

. The mediating effect value of the path of cumulative affordance through dynamic capabilities to disruptive innovation is 0.563 (path: cumulative affordance–dynamic capabilities–disruptive innovation), with a 95% confidence interval of [0.418, 0.766], which does not contain 0. This suggests that the mediating effect is significant. Similarly, the mediating effect value of the path of variant affordance through dynamic capabilities to disruptive innovation is 0.563 (path: variant affordance–dynamic capabilities–disruptive innovation), with a 95% confidence interval of [0.438, 0.723], which does not contain 0. This indicates that the mediating role of dynamic capabilities between variant affordance and disruptive innovation is significant. The Bootstrap test again verifies that hypotheses H6 and H7 are valid.

This study divides dynamic capabilities into sensing, seizing, and reconfiguring (

Table 7. Regression results for different dimensions of dynamic capabilities.

	Dimensions of Dynamic Capabilities
	Sensing		Seizing		Reconfiguring
	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16
	β	β	β	β	β	β
Firm age	0.009	−0.062	0.013	−0.057	−0.009	−0.085
Firm nature	0.013	−0.045	0.017	−0.039	0.082	0.024
Industry background	−0.021	0.007	−0.047	−0.019	−0.044	−0.013
Firm size	−0.022	0.034	−0.028	0.027	0.003	0.054
Cumulative affordance	0.787 ***		0.773 ***		0.801 ***
Variant affordance		0.745 ***		0.730 ***		0.777 ***
R2	0.605	0.564	0.585	0.543	0.618	0.597
Adjusted R2	0.597	0.555	0.576	0.533	0.609	0.589
F-value	72.023 ***	60.832 ***	66.131 ***	55.850 ***	75.904 ***	69.758 ***

Note: *** p < 0.001; two-tailed test; N = 241.

). It also verifies the effects of digital technology affordance on the three dimensions of dynamic capabilities.

Models 11, 13, and 15 list the effects of cumulative affordance on sensing, seizing, and reconfiguring capabilities. In these three models, the coefficients of the cumulative affordance are 0.787, 0.773, and 0.801, respectively. They all pass the significance level test of 1%. This shows that digital technology cumulative affordance can promote firms to improve sensing capability, seizing capability, and reconfiguring capability, supporting H3a, H3b, and H3c. Models 12, 14, and 16 list the effects of variant affordance on sensing, seizing, and reconfiguring capabilities. The coefficients of variant affordance of digital technology are 0.745, 0.730, and 0.777, respectively, and are all significantly positive. This also shows that digital technology variant affordance also has a significant positive effect on firms’ sensing, seizing, and reconfiguring capabilities (β = 0.745, p < 0.01; β = 0.730, p < 0.01; β = 0.777, p < 0.01), supporting H4a, H4b, and H4c. The regression results also reflect that cumulative affordance and variant affordance have the most significant impacts on the reconfiguring capability, followed by sensing capability and seizing capability.

6. Conclusions

This research developed a theoretical model to explore the relationship between affordances of digital technology and disruptive innovation, as well as its process. The following conclusions are drawn based on the empirical analysis of 241 data points: (1) Cumulative affordance and variant affordance significantly foster disruptive innovation. The influence of cumulative affordance on disruptive innovation is higher than that of variant affordance. (2) The “fuzzy front-end–development–commercialization” phases of disruptive innovation are positively influenced by cumulative affordance and variable affordance. Specifically, among the three phases, cumulative and variant affordances play a more significant role in influencing the development stage. However, the impact on the other two phases is different. The impact of cumulative affordance on the commercialization phase is more significant than that on the fuzzy front-end phase. However, the effect of variant affordance on the fuzzy front-end phase is more significant than that on the commercialization phase. (3) Dynamic capabilities partially mediate between cumulative affordance and disruptive innovation and partially mediate between variant affordance and disruptive innovation. (4) The sensing, seizing, and reconfiguring capabilities are greatly influenced by cumulative affordance and variant affordance. Among them, both cumulative affordance and variant affordance significantly impact reconfiguring capability, followed by sensing and seizing capability. Overall, this study’s conclusions enrich and expand the findings of existing studies. They also have managerial implications for firms to capitalize on digital technology affordance to achieve disruptive innovation.

6.1. Theoretical Contribution

First, research on the effects of digital technology affordance is expanded upon in this study. In the era of the digital economy, the characteristics of digital technology have changed many aspects of innovation [12]. However, contemporary scholars prioritize analyzing digital technology in its entirety over examining how specific aspects of digital technology influence innovation. This study starts with the affordance of digital technology, dividing it into two categories—cumulative affordance and variant affordance. We explore the mechanisms underlying their effects on disruptive innovation, enhancing and broadening the literature on digital technology and its affordance.

Second, this study proposes a mechanism for enterprises to realize disruptive innovation from the micro level. The process and trajectory of enterprise disruptive innovation have changed due to digital technology. However, few studies explore the mechanism for generating disruptive innovation in the digital context [95]. Little has been written about it, and it has been examined primarily through the exploratory case study method, which is not supported by extensive sample data. Based on data from various industries and firm types, this study proposes a micro-mechanism for enterprises to realize disruptive innovation. The results highlight the critical role that dynamic capabilities play in enabling companies to drive and deploy innovation in digital contexts. Strengthening dynamic capabilities is critical for firms to achieve disruptive innovation through digital technology affordance [96]. In addition, this study finds that cumulative affordance and variant affordance play a positive role in the “fuzzy front-end–development–commercialization” phases of disruptive innovation.

Third, dynamic capabilities, which received a lot of attention from academics, assist enterprises in identifying opportunities in challenging external situations, acquiring resources, and integrating and allocating them to create long-term competitive advantages. Through empirical research, this study enriches the research on dynamic capabilities in digital settings by demonstrating that the affordances of digital technology significantly improve an organization’s capability for sensing, seizing, and reconfiguring.

6.2. Managerial Implications

This article has the following three practical insights.

First, disruptive innovation is an effective way for firms looking to establish a competitive edge in the face of intensifying global competition. Along with developing digital technology, enterprises need to utilize digital technology affordance to reduce the risk and cost of disruptive innovation and drive its realization. Specifically, enterprises can utilize digital technology affordance to improve their dynamic capabilities, hence facilitating the rapid integration and allocation of resources to satisfy consumer demands.

Second, in the process of “fuzzy front-end–development–commercialization”, enterprises can leverage the affordance of digital technology to promote disruptive innovation. For instance, during the fuzzy front-end phase, enterprises can leverage digital technology to perform “user profiling”, thus producing and analyzing substantial volumes of homogeneous data using cumulative affordance. Subsequently, enterprises utilize variant affordance to develop the optimal product concept combination. During the development phase, enterprises can facilitate the sharing and integration of internal and external resources through cumulative affordance and conduct simulation tests and technological iterations through variant affordance. During the commercialization phase, enterprises gather extensive feedback via cumulative affordance and utilize variant affordance to modify and enhance product functionalities, thereby achieving ongoing product iteration until they disrupt the market.

Third, enterprises can fully utilize digital technology affordance to enhance sensing, seizing, and reconfiguring capabilities to cope with complex and dynamic environments and form sustainable competitive advantages.

6.3. Limitations

Although this study enriches and supplements the literature on digital technology and disruptive innovation, the following shortcomings still require further study: (1) The research sample and methodology can be more diversified. The sample companies in this study are limited to China. Future research can expand the sample to include companies in other countries to draw more generalized conclusions. This study was mainly conducted through a questionnaire survey. Future research can use a combination of methods to obtain a more extended period and objective data. (2) The research topic could be more detailed and comprehensive. The attributes of digital technology include generativity, affordance, and openness [15], but this study only studied the affordance of digital technology. In the future, a thorough investigation is required to determine how other aspects of digital technology affect innovation. (3) This paper only examines how the affordance of digital technology fosters the perception of firms’ disruptive innovation through dynamic capabilities. Different perspectives can be studied to examine how the affordance of digital technology influences firms’ disruptive innovation to enhance the pertinent literature and management implications.