1. Introduction
Disciplines represent the categorization of human knowledge into relatively independent systems, arising from the processes of recognition and research [1]. As a crucial force driving scientific advancement and societal development, the construction and continuous improvement of disciplines not only form the foundation for the growth and expansion of higher education institutions but also serve as the driving force for researchers’ innovations and development [2,3,4]. In the context of globalization and rapid advances in information technology, the core value of knowledge structures within disciplines has been highlighted [5]. Knowledge structures provide a roadmap for the development and transformation of entire fields and play an essential role in guiding the advancement of academic disciplines and fostering the growth of researchers [6,7].
The effective advancement of a discipline should adhere to the principle of “maintaining integrity while fostering innovation”. In other words, it is essential to both preserve and inherit foundational knowledge and, at the same time, expand research boundaries by incorporating diverse perspectives. Therefore, disciplinary knowledge structures arise from two key learning behaviors in the process of knowledge creation: autonomous and collaborative learning [8,9]. Autonomous learning enables researchers to study, assimilate, and internalize knowledge, thus expanding and restructuring their knowledge base to foster innovation [10]. A basic knowledge framework within a discipline gradually forms through researchers’ autonomous learning [11]. Collaborative learning refers to the process in which researchers work together, sharing knowledge, resources, and expertise to enhance their understanding and develop new insights [12,13]. It facilitates the horizontal expansion of a discipline by constructing and expanding a knowledge network system [12,14]. Together, these learning behaviors underpin the self-construction of disciplines and extend their boundaries through synergistic knowledge integration. In view of this, this study explores the knowledge system structures of disciplines from the dual perspectives of autonomous and collaborative learning. We construct and analyze the fundamental and network structures of disciplinary knowledge, guided by knowledge structure theory.
Current research on the structure of disciplinary knowledge is primarily focused on uncovering the internal knowledge elements of specific disciplines and the status of interdisciplinary studies at the macro level [1,6,15]. However, systematic research on the deeper structural characteristics of disciplinary knowledge systems, their construction pathways, and potential interconnections remains insufficient. Consequently, this study aims to propose a systematic pathway for constructing the architectural framework of disciplinary knowledge systems. This framework seeks to enable disciplines to “maintain integrity” and “foster innovation”, thereby promoting their sustainable development. Our study is driven by the following research questions: What types of knowledge exist within the structure of a discipline? How do these forms of knowledge interact to create a specific structure? What are their potential interconnections? In-depth research into these issues can assist in creating a knowledge framework that guides the construction of academic disciplines within higher education institutions to be instrumental in fostering an environment conducive to in-depth academic exploration.
2. Literature Review
Knowledge system structure refers to the stable form of a hierarchical knowledge system composed of knowledge elements and the mutual relations contained in an organization [15]. The existing research on disciplinary knowledge system structures can be categorized into two schools. Grounded in practical observation and personal experience, one stream of research qualitatively describes the universal classification of professional knowledge within disciplines. This school focuses on the abstraction and generalization of basic knowledge within disciplines. Based on specialized knowledge classification, Sluyter et al. (2006) examined the formation and characteristics of the knowledge structure in the field of geography from both diachronic and synchronic perspectives [16]. From the perspective of the knowledge process, disciplinary knowledge systems present a sequential structure that includes value orientation, questions, methodology, and factual or conceptual knowledge. From the perspective of the knowledge outcome, disciplinary knowledge systems present hierarchical structures composed of factual or conceptual knowledge, procedural knowledge, and value knowledge [17,18].
Another stream reveals the structure of disciplinary knowledge systems based on bibliometric methods. The development of academic digital platforms and information technology enables the description of the complex relationships and structural connotations of disciplinary knowledge systems by analyzing the objective relationships between the knowledge elements contained in texts [19,20,21]. Some studies highlight the distribution and evolution of disciplinary knowledge structures. The change in knowledge elements contained in research topics, keywords, citations, authors, and other texts reflects the patterns or laws of dynamic iteration in disciplinary knowledge that is often used to reveal the distribution, development, and evolution of knowledge [22,23]. Co-occurring citations and keywords reflect the relevance of the research areas that are often used to reveal the interactive association among different pieces of disciplinary knowledge [15,23,24,25]. These knowledge elements could also be used to help build the complete knowledge structure of individual scholars, core scholars in sub-domains, and the correlation coefficients between each pair of scholars, reflecting the evolution of disciplinary knowledge structures from the perspective of scholars [1]. Similarly, at the individual level, Liu and Guan (2015) used the methods of individual network density, cooperative network capacity, proximity centrality analysis, and aggregation constraint in social network analysis to analyze cooperative networks in China’s nanoenergy research field, thereby revealing the evolution of the overall knowledge network structure behind the individual network structure [26]. Xu et al. (2016) introduced a new measurement index for interdisciplinary topic mining and analyzing the evolution of these topics [27].
Others explore the topic clusters of a research field to better recognize its knowledge structure. Cheng et al. (2020) introduced a Keyword–Citation–Keyword network to discover important knowledge units and the topics with great impact on other topics to analyze the knowledge structure of a discipline [15]. Topic hierarchies were used to provide more detailed information about the subfields of a discipline, including the knowledge base, evolutionary mediators, detailed topical clusters, and marginal topics [7]. Furthermore, topic modeling is a popular way to identify and label the main topics and categories of a knowledge domain to reveal its knowledge structure [28]. These studies mainly identify specific research topics and visualize the knowledge structures of certain disciplines. The research findings indicate that knowledge structures and their characteristics exhibit significant variations and specificity depending on the discipline and over time [29].
While existing research provides a preliminary framework for understanding the structure of disciplinary knowledge systems, certain limitations remain. Firstly, qualitative methods often propose generalized classification standards for the specialized knowledge of specific disciplines, overlooking the multidimensional transmission and reorganization of diverse knowledge in complex research contexts. Secondly, although current quantitative studies offer deep insights into the distribution, flow, and evolution of domain knowledge, they largely reveal phenomena related to the current state of specific disciplinary research. These studies fall short of developing a theoretical framework of knowledge structures that can effectively guide the construction of various disciplines.
To address these shortcomings, this study introduces a dual-system approach to theoretically analyze the structural characteristics of disciplinary knowledge systems. The foundational knowledge system clearly delineates the core knowledge structure of a discipline and its dynamic changes. This helps universities identify priority areas for development and enhancement within their disciplines, thereby improving overall disciplinary standards. Knowledge network systems offer universities conceptual frameworks for the improved planning of interdisciplinary integration and synergy, fostering innovation and breakthroughs. By combining these two knowledge systems, universities can better evaluate faculty research directions and collaborative relationships, strategically organizing principal investigators and various research teams to maximize the synergistic effects of the research community.
3. Theoretical Framework
Autonomous learning and collaborative learning represent two crucial mechanisms for knowledge creation among researchers [8]. Autonomous learning constitutes the foundational approach to fostering knowledge creation and is deemed indispensable. It is through the acquisition and mastery of core knowledge that researchers build the knowledge repositories essential for their scientific inquiries, thereby facilitating innovation in knowledge. Due to the burden of accumulating knowledge, autonomous learning is time-consuming and requires extra effort, making it challenging for complex knowledge, and posing a high risk [30]. Knowledge collaboration, on the other hand, brings together complementary resources, diverse perspectives, and a reasonable division of labor, thus making collaborative learning an important supplement to autonomous learning [30,31]. The combination of autonomous and collaborative learning effectively facilitates knowledge creation for researchers.
With autonomous learning, researchers independently acquire and apply knowledge based on their own deliberation and research needs, internalizing diverse knowledge into their personal knowledge base [10]. This process gradually forms the foundational knowledge system (vertical structure) of the discipline. This aligns with Kuhn’s theory of knowledge, which posits that the construction of a basic disciplinary knowledge system and the promotion of paradigm shifts occur through conventional research, puzzle-solving, anomaly detection, and crisis resolution [11]. Basic disciplinary knowledge systems, therefore, consist of systematic norms, principles, and theories, forming the core foundation for scientific inquiry and knowledge creation [32]. Autonomous learning enables researchers to integrate knowledge from various sources, facilitating the continuous adaptation and refinement of this basic knowledge system [10,33].
In contrast, collaborative learning involves interaction, sharing, and cooperation among researchers from diverse disciplines, promoting the integration of specialized knowledge [34]. Through ongoing collaboration, a unique knowledge network (circle structure) emerges, reflecting Kant’s view that individuals expand their knowledge by engaging in active knowledge exchange [35]. The disciplinary knowledge network comprises various theories, concepts, and methods, synthesizing the knowledge created by researchers to address complex problems that transcend individual disciplines [36,37]. Collaborative learning, therefore, facilitates the construction and evolution of a comprehensive knowledge network system [19,34].
Thus, disciplinary knowledge is composed of two interconnected systems: the basic knowledge system (vertical structure) and the knowledge network system (horizontal structure).
3.1. Basic Knowledge System
Discipline is a systematically constructed knowledge classification system [32]. The basic knowledge system serves as the foundational framework that guides researchers in addressing current issues and directs future knowledge creation within the field [11]. According to constructivism, knowledge is an interpretation of the objective world, not an absolute law of reality [38]. As our understanding of complex problems deepens, disciplines must continuously incorporate new knowledge to address evolving challenges [39].
Building on the discipline’s knowledge traditions, researchers use autonomous learning strategies to integrate diverse knowledge tailored to specific contexts, leading to the absorption and internalization of interdisciplinary insights [40]. This process facilitates the adjustment and updating of basic knowledge systems [10]. Researchers, through personal reflection and critique, recombine traditional knowledge with new insights, fostering innovation and evolving research norms [40]. This iterative process encourages the evolution of research norms and discourse systems within disciplines, ultimately shaping a distinctive basic knowledge system. Thus, given the complexity of modern scientific problems, basic knowledge systems increasingly draw from multiple fields. The integration of diverse knowledge during knowledge creation results in the emergence of a hierarchical structure within the basic disciplinary knowledge system.
Researchers employ effective autonomous learning strategies to study, absorb, and internalize knowledge persistently, gradually refining the scope and topics within specific fields. Throughout this dynamic process, foundational concepts, key propositions, and core theories gradually emerge, shaping the trajectory of disciplinary development [11]. These elements collectively form the bedrock knowledge within a discipline, representing its distinctive tradition and forming a foundation for consensus in scientific inquiry [41]. Bedrock knowledge guides and regulates research, promoting disciplined knowledge creation and ongoing review, thereby contributing to the discipline’s sustained advancement.
As society advances and our understanding of the real world deepens, it becomes apparent that bedrock knowledge from a single discipline cannot address the complexities of frontier issues [11]. Thus, researchers assimilate domain-specific knowledge from other fields, integrating diverse sources into existing knowledge systems. As such knowledge is increasingly applied, its significant impact becomes evident, prompting more researchers to internalize it, thereby expanding the knowledge base of the discipline [6]. Thus, this knowledge forms the expansive knowledge in the discipline. Learning such knowledge can offer researchers multiple perspectives, and it may even give rise to different research schools, leading to the emergence of new research paradigms. For example, in public policy-making, computer science and machine learning have been utilized to extract information from extensive policy documents, illustrating its expansive application potential in public administration [42].
When researchers begin to learn and apply certain knowledge but have not yet gained widespread acceptance and recognition, such knowledge can be referred to as reference knowledge. Due to novelty bias and cognitive constraints, it requires time to evaluate its potential impact [43]. Nonetheless, reference knowledge can foster breakthroughs in target disciplines [43]. It demands the precise comprehension of research issues and the validation of novel integrations of traditional and reference knowledge through theoretical and practical exploration. For instance, marketing involves understanding consumer decisions and behaviors. The digital age has transformed access to information and consumption, leading to marketing’s involvement in information behavior research [19,44]. When researchers decide to autonomously learn this type of knowledge, they need to carefully weigh the investment against the return, as this knowledge has yet to demonstrate high applicability within a discipline.
In summary, from an autonomous learning perspective, basic knowledge systems can be categorized into three levels: bedrock knowledge, expansive knowledge, and reference knowledge. Bedrock knowledge refers to the foundational knowledge within a discipline, including basic concepts, key propositions, and core theories. These elements collectively define the trajectory of disciplinary development and represent the distinctive tradition of the field. Expansive knowledge involves domain-specific knowledge from other fields that are gradually assimilated and integrated into the existing knowledge systems of a discipline. As this knowledge is increasingly applied, its significant impact becomes evident, prompting more researchers to internalize it. Reference knowledge refers to the knowledge that researchers start to learn and apply but has not yet gained widespread acceptance and recognition. As illustrated in Figure 1, bedrock knowledge forms the foundational level, encompassing the essential content that researchers must master due to its broad applicability and practicality. The intermediate level consists of expanded knowledge which researchers might learn selectively for scientific research purposes. At the top is reference knowledge which is used less frequently. As knowledge advances and research demands shift, certain basic knowledge types may transition from one category to another based on researchers’ study and application.
3.2. Knowledge Network System
After developing a fundamental knowledge base to support its construction needs, a discipline can enhance its knowledge creation capacity by forming collaborative relationships across different knowledge domains and professional fields [6,45]. By employing collaborative learning, researchers in the target discipline can establish multilateral relationships with experts from diverse fields, fostering the growth of a disciplinary knowledge network [31,46,47]. This network reflects the convergence of innovative ideas from researchers across various fields, ultimately aiding in the discovery of optimal solutions for complex problems [48].
This approach is closely aligned with the ecosystem-as-affiliation concept in ecosystem research, where an ecosystem is seen as a network of interconnected and mutually supportive participants, with central participants acting as the core [49,50]. At the heart of this concept lies the “center-periphery” structure, which emphasizes the critical importance of moving beyond traditional organizational boundaries to cultivate collaboration and mutual benefit [51,52]. Within this framework, the disciplinary knowledge network can be conceptualized as a star-shaped, layered structure. This structure features the knowledge repository of a target discipline at its core, surrounded by the knowledge of other disciplines that engage through dynamic interactions, thereby highlighting the central role of the “center” and the supportive role of the “periphery” in fostering an integrated and collaborative knowledge ecosystem.
Some knowledge domains share a common foundation or framework, enabling easier and more efficient collaboration due to similar methodologies, principles, or terminologies. This kind of knowledge offers researchers from the target discipline quick access to the necessary tools, ideas, or methods, promoting synergy and reducing transaction costs in collaborative efforts, which form the proximal knowledge of the target discipline. Proximal knowledge comprises interconnected disciplines that share a similar knowledge framework with the target discipline, as well as the foundational knowledge essential for the target discipline’s knowledge creation. These elements form a closely knit network surrounding the target discipline. For example, business administration and public administration both fundamentally rely on management principles, making them proximal disciplines to each other. Similarly, the reliance of STEM fields on mathematics positions mathematics as a proximal discipline to these fields. The choice of collaborative learning depends on research needs and the costs associated with knowledge exchange [45,53]. Researchers often engage with holders of proximal knowledge due to lower cooperation costs and individual needs, such as access to ideas, methods, or tools [54,55]. Due to the inherent academic logic of disciplines, it is more efficient for researchers to collaborate with experts who possess such proximal knowledge [45]. Given the comprehensive demand and correlation characteristics of research problems, there exists a certain intercommunication and interconnectedness among proximal knowledge.
On the other hand, engaging with other knowledge requires more deliberate efforts but can introduce novel perspectives and approaches that lead to significant breakthroughs. Collaborating with them can help to broaden the scope of inquiry and innovation by incorporating diverse and sometimes unconventional insights of the target discipline. These elements collectively form the remote knowledge of the target discipline. Remote knowledge refers to the knowledge that forms a limited yet stable connection through the collaborative learning efforts of researchers. Collaborations with holders of remote knowledge can introduce diverse perspectives, potentially leading to significant breakthroughs in research [54,56]. For example, plant studies have been working to explain the trigger mechanism of the RNA interference of gene silencing that has been found in animal studies and has received much attention and positive citation [43]. The integration of target disciplines with remote knowledge facilitates interdisciplinary research and the discovery of new knowledge domains, potentially charting new directions for exploration within the target discipline [57]. Challenges faced in the target discipline might be mitigated or completely addressed through insights from remote knowledge [43]. Therefore, incorporating remote knowledge through collaborative strategies is crucial for fostering innovative and transformative discoveries.
Knowledge creation occurs when researchers from the target discipline integrate diverse perspectives by collaborating with related fields, which results in the formation of new concepts, theories, or applications. This process is facilitated by cross-disciplinary collaboration, where the exchange in knowledge leads to innovative breakthroughs that would not have been possible within the confines of a single discipline. The newly created knowledge then serves as knowledge feedback for the relevant disciplines, primarily in the form of publicly published research. This feedback provides researchers in both the target and related disciplines with novel ideas and insights, which, through learning and internalization, contribute to the updating and expansion of their own knowledge bases, thereby driving further knowledge creation. This iterative process not only strengthens the knowledge structure of the target discipline but also fosters the ongoing development of related fields, creating a continuous loop of knowledge generation and feedback.
In summary, from a collaborative learning perspective, knowledge from other domains can be deconstructed into two levels: proximal knowledge and remote knowledge, based on their relationship to the target discipline. Figure 2 illustrates the structure of disciplinary knowledge network systems. At their core lies the knowledge stock of the target discipline, or basic knowledge system. Surrounding this core, the first circle represents proximal knowledge, and the second represents remote knowledge. Solid lines indicate the interconnected relationships established through the collaborative efforts of researchers, while dotted lines signify these concentric structures. Dashed arrows demonstrate the creation and feedback of new knowledge, highlighting its dynamic evolution as it flows from the periphery towards the center.
4. Method
In academia, high-quality papers are seen as the primary achievements of disciplinary knowledge creation and serve as indicators of research strength and innovative impact [43]. This has led to the emergence of bibliometrics. As bibliometric methods have matured, researchers are increasingly able to detect the interrelationships and knowledge structures within a discipline by examining the objective relationships between academic entities. As a result, bibliometrics has been widely used to investigate academic knowledge structures [1]. On one hand, references, as widely accepted norms in scientific communication, have long been seen as a means of acknowledging the works of scholarly predecessors [15]. Therefore, references reflect the knowledge that authors have acquired and integrated during the process of knowledge creation, illustrating the autonomous learning of researchers [58,59]. The higher the citation frequency, the greater the impact of that knowledge on the discipline, indicating that researchers are more likely to engage with and internalize it within their own knowledge framework [6,60]. Thus, it can be inferred that knowledge that is frequently cited and subject to autonomous learning is closely related to the researcher’s work and constitutes an integral part of the foundational knowledge system of a discipline [60].
On the other hand, co-authorship data are considered a visible, quantifiable, and reliable indicator of collaborative activities, enabling the capture of key elements of collaboration [31,61]. Therefore, the co-authors of a paper highlight the relationships between researchers that reflect the outcomes of collaborative learning. At the disciplinary level, co-authorship relationships emphasize the horizontal connections between knowledge elements, forming a collaborative network that represents the knowledge network system of the discipline.
4.1. Data
To elucidate the structure of disciplinary knowledge systems, case analysis was undertaken following theoretical framework. This process can provide a methodological reference for disciplines to construct their knowledge structure from a practical perspective. We selected chemical engineering as our case study, exploring its knowledge system using top journal articles. Utilizing data from the Literature and Information Center of the Chinese Academy of Sciences (CAS), periodicals in the “Engineering: Chemical Engineering” sub-category were ranked. Within the Q1 category, three journals had a 3-year average impact factor (IF) exceeding 10. Table 1 lists the journals and their IF values.
Considering the differences between core themes and IF values, Energy and Environmental Science and Progress in Energy and Combustion Science were identified as leading journals in energy chemical engineering for this study. This selection methodology is endorsed by experts in the field. Consequently, the knowledge structure of energy chemical engineering was analyzed based on papers from these two journals spanning the past five years (2016–2020). A total of 1582 articles were obtained, with 334 published in 2016, 262 in 2017, 337 in 2018, 298 in 2019, and 351 in 2020. The references of these published papers were retrieved from the Web of Science (WoS) database, totaling 62,798 data entries. The information on authors and their institutions was obtained from the WoS database, resulting in 15,296 records.
4.2. Data Processing
4.2.1. Data Processing of References
The fields of references were classified based on the research field categories of the journals listed in the WoS [10]. This classification method relies on the journal rather than the content. Research has shown that this method is closely aligned with content-based classification [62]. We then summarized and counted the research fields of the cited papers, identifying a total of 103 distinct fields. By analyzing all research fields appearing in the WoS categories, we calculated the frequency of each type of knowledge present in the references to evaluate the application level of various knowledge domains within energy chemical engineering.
4.2.2. Data Processing of Co-Authorship
First, we extracted keywords from the affiliation details of all the authors. Keywords were derived from institutional details such as department names, laboratories, and companies, helping to determine the researchers’ knowledge backgrounds [63].
Next, we annotated the keywords with corresponding knowledge domains. Following the classification of first-level disciplines and sub-disciplines as revised by the Degree Committee of the State Council and the Ministry of Education of China, keywords directly reflecting a discipline’s background were annotated accordingly. When a keyword or organizational name did not align clearly with these disciplines, a website search was conducted, and it was annotated based on the official description. Multidisciplinary or comprehensive keywords were annotated with multiple disciplines, representing all fields they encompass. For instance, “Biochem” was tagged as both biology and chemistry to ensure an accurate representation of knowledge domains.
Subsequently, we matched these tagged keywords with authors by examining the smallest organizational unit in the authors’ addresses. If an author’s address indicated a specific department, the discipline was confirmed using keywords from the department’s name. If a specific laboratory was mentioned, keywords associated with the laboratory were used. For example, if an author was affiliated with the “Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA”, we determined his/her disciplinary background as materials science and engineering based on the smallest unit of the institution which is the department. This approach allowed us to statistically summarize the authors’ knowledge backgrounds, identifying a total of 51 disciplinary fields within our sampled dataset.
Finally, we constructed a disciplinary knowledge collaboration matrix using co-authorship data. As the co-occurrence of different disciplines increased, the knowledge connection among these fields grew closer [64]. The matrix was based on authors’ disciplinary backgrounds and their collaborative relationships in each paper. For example, if the authors of a paper were affiliated with disciplines A, B, and C, each pair of disciplines (A and B, A and C, B and C) would see their connection count increase by 1. This method allowed us to calculate the number of connections between each pair of the 51 identified fields, forming a knowledge collaboration matrix reflecting the trends in top journals in energy chemical engineering.
4.3. Social Network Analysis
The core concept of Social Network Analysis (SNA) is to represent social relationships through a graph structure, where nodes represent individuals or entities, and edges represent the relationships or interactions between them. By analyzing these structures, along with factors such as relationship density and strength, SNA helps identify key nodes within the network as well as the overall characteristics of the network. Nodes are the fundamental units within the network, typically representing individuals, organizations, groups, or any entities that need to be analyzed. Edges, on the other hand, are the lines or relationships that connect two nodes, signifying the connections or interactions between them. In social networks, edges can represent various types of relationships, such as intimate connections, cooperative ties, or information exchanges.
Degree centrality (CD) is a metric that describes the number of direct connections a node has with other nodes in the network [19], which is a key index to evaluate the embeddedness and connectivity of individual nodes within a network [65]. A higher degree of centrality indicates a more prominent role for the node within the network. The degree centrality was calculated using the following formula:
(1)
where represents an element of the collaboration matrix, indicating the frequency of interactions between disciplines i and j.5. Results
5.1. Basic Knowledge System of Energy Chemical Engineering
To evaluate the level of application of various knowledge domains within high-level paper references, we calculated the frequency of their occurrence. The results, sorted in descending order, are detailed in Appendix A. Notably, the cumulative proportion of knowledge from different fields in the references exceeds 1. This is due to the integration and recombination of knowledge, where certain references encompass multiple fields. To maintain the authenticity and comprehensiveness of knowledge application, we included all relevant fields in these calculations, leading to a total proportion greater than 1. Figure 3 illustrates the application of knowledge from multiple domains in energy chemical engineering research, highlighting only those with an application frequency exceeding 0.1% due to space constraints.
A limited applicability of knowledge in high-level papers indicates its lesser significance in the field of knowledge creation. This is often reflected by inadequate acceptance and recognition among researchers, potentially due to barriers that hinder effective learning and absorption [66]. Therefore, knowledge with an application frequency over 1% was included in the basic knowledge system of energy chemical engineering. After discussions with experts in the chemical engineering field, thresholds of 10% and 50% were then used for classification: knowledge with an application frequency of 50% or more is designated as bedrock knowledge; those with frequencies between 10% and 50% are classified as expansive knowledge; and frequencies below 10% are considered reference knowledge. Consequently, the basic knowledge system of energy chemical engineering was organized into a hierarchical structure, as depicted in Figure 4.
Chemistry plays a critical role in high-level papers within the field of energy chemical engineering, exhibiting an application level of 61.04%. Consequently, it serves as foundational knowledge within the basic knowledge system of energy chemical engineering. It is therefore imperative for departments or universities to focus on enhancing and accumulating chemistry knowledge to establish a solid foundation for the development of energy chemical engineering.
Materials science, science and technology, energy and fuels, physics, and engineering collectively account for 15–30% of the knowledge utilized by researchers in energy chemical engineering. These domains are considered as expansive knowledge within the discipline’s basic knowledge system. Their study and integration can inspire researchers to develop innovative approaches to contemporary scientific challenges. Researchers should selectively draw upon this knowledge to augment their theoretical understanding and facilitate the discovery of innovative solutions to current challenges.
The application of knowledge from environmental sciences and ecology, electrochemistry, thermodynamics, polymer science, biotechnology and applied microbiology is notably limited, comprising less than 10% of the knowledge base. This knowledge is classified as reference knowledge within the basic knowledge system of energy chemical engineering. The limited immediate applicability of reference knowledge may be attributed to two factors. First, the integration of reference knowledge with core, discipline-specific knowledge is relatively novel, potentially posing learning challenges due to differing theories and paradigms. Second, research incorporating this knowledge may be at the forefront of the discipline, yet currently receives limited attention and recognition. Nevertheless, it could possess significant reference value and offer broader opportunities for further exploration, potentially leading to a future surge in research interest.
5.2. Knowledge Network System of Energy Chemical Engineering
We developed a knowledge network system for energy chemical engineering using the knowledge collaboration matrix. Sporadic author collaborations are insufficient to effectively drive continuous innovation. To address this, we filtered out knowledge connections with fewer than 20 collaboration instances. The resulting knowledge collaboration network diagram for energy chemical engineering, as shown in Figure 5, provides a refined view of these relationships.
In this network, the nodes represent disciplinary knowledge, with their sizes proportional to the number of authors in each discipline. Connections between nodes indicate collaborations between researchers from different disciplines, with the thickness (or edge weight) representing the intensity of these collaborations. Stronger knowledge collaborations are depicted by thicker connections, signifying tighter relationships between disciplines. The centrality or peripherality of a node reflects its disciplinary circle within the knowledge network.
Figure 5 provides an intuitive depiction of the knowledge circle structure that promotes high-level knowledge creation in energy chemical engineering. To further analyze the criteria underlying this circular structure, we used social network analysis to examine the characteristics of the nodes in the knowledge collaboration network. The degree centrality values for each discipline were calculated by Formula (1), as detailed in Table 2. Degree signifies the values of degree centrality, while NrmDegree refers to the standardized measure, commonly termed relative centrality. We identified disciplines with relative centrality values exceeding 0.5. Detailed results of the centrality calculations for all disciplines can be found in Appendix B.
The average value of the NrmDegree in the network is 1.169, with a standard deviation of 2.341. From the knowledge collaboration network diagram and node degree centrality results, proximal knowledge in energy chemical engineering includes disciplines where the NrmDegree exceeds the mean by two standard deviations (NrmDegree > 5.8). Proximal knowledge of energy chemical engineering comprises material science and engineering, chemistry, physics, and chemical engineering and technology. These disciplines are crucial in the knowledge network system of energy chemical engineering, demonstrating strong collaboration (edge weights exceeding 300).
Meanwhile, remote knowledge includes disciplines with NrmDegree values greater than the approximate mean (NrmDegree > 1). Environmental science and engineering, electronic science and technology, mechanical engineering, biology, biological engineering, optical engineering, power engineering, and engineering thermophysics maintain stable and consistent connections with energy chemical engineering and its proximal knowledge (edge weights exceeding 50), establishing them as the remote knowledge of energy chemical engineering.
Figure 6 illustrates the structural diagram of the knowledge network system in energy chemical engineering. Advancing energy chemical engineering relies not only on close collaboration with the four proximal knowledge areas but also on robust support from remote knowledge fields. Researchers in energy chemical engineering should seek collaboration opportunities with proximal knowledge and expand engagement with remote knowledge areas. By collaborating with professionals across a range of disciplines, researchers can explore problems from broader, more diverse, and in-depth perspectives.
Moreover, higher education institutions should focus on establishing platforms and spaces that foster the growth of energy chemical engineering disciplines and their proximal knowledge while emphasizing the collaborative development of remote knowledge areas. This approach promotes the formation of a cross-discipline knowledge integration model, providing diversified knowledge nutrients for the growth of energy chemical engineering.
5.3. Intrinsic Connection of Disciplinary Knowledge Systems
There exists a significant connection between basic knowledge systems and knowledge network systems. Specifically, we found that the proximal knowledge of the target discipline also exists in the basic knowledge system presenting as the bedrock knowledge (chemistry) and expansive knowledge (materials science and physics). This phenomenon demonstrates that knowledge from adjacent disciplines is more readily integrated into a field than knowledge from more distant fields [10]. Due to the similarity of the knowledge and the relevance of research problems, years of accumulated knowledge have enabled most researchers to establish a solid foundation in closely related knowledge, allowing for seamless autonomous learning and application of that knowledge. Beyond the immediate or surface-level requirements driven by research questions, researchers may also seek more specialized and profound insights and recommendations for reasons of research interest, innovation goals, or developmental needs [45,67]. In such cases, they often turn to experts for assistance. Through collaboration, researchers gain closer access to the research paradigms of the field, correct potential errors promptly, and deepen their understanding of related knowledge. This enhances the capacity of domain researchers to absorb closely related knowledge, accelerating the internalization process, facilitating the inward convergence and transformation of disciplinary knowledge, and promoting the iteration and updating of fundamental disciplinary knowledge structures.
In contrast, knowledge from more distant disciplines, although represented within the basic knowledge system as reference knowledge (such as environmental science or biology), overlaps less significantly. Typically, researchers selectively engage with this knowledge or collaborate with distant disciplines in the short term. Limited interactions might result from the complexity of the knowledge, novelty biases, and differences in research paradigms, necessitating greater time and effort for researchers to proficiently master and apply this knowledge, along with higher communication and coordination costs for collaborative efforts [43,68]. Once new research opportunities gain acceptance, such concerns might be offset by the legitimacy they bring, leading to a surge in learning and collaboration [11].
6. Discussion
6.1. Theoretical Contributions
This study presents a multi-level process of disciplinary knowledge structure formation from a systems perspective. Through the study of disciplinary knowledge structures, this paper proposes a more comprehensive theoretical framework that integrates both vertical and horizontal dimensions of knowledge by integrating Kuhn’s concept of scientific paradigms with an ecosystem approach, highlighting that the creation of disciplinary knowledge relies on both basic knowledge systems and knowledge network systems. This multi-level knowledge structure has not been fully addressed in previous research.
Traditionally, the formation of disciplinary knowledge has been viewed as a relatively closed, linear process, focusing on the accumulation and transmission of foundational theories [11]. In contrast, the dual-system framework suggests that the formation of disciplinary knowledge is a multidimensional, multilevel interactive process, which includes both the internal knowledge accumulation within a discipline (vertical dimension) and the knowledge flow and exchange between disciplines (horizontal dimension). This framework encourages long-term academic growth and fosters interdisciplinary innovation by promoting the creation and evolution of knowledge systems.
Another key theoretical contribution of this study is exploring disciplinary knowledge system structure from researchers’ learning behaviors. Knowledge accumulation models typically emphasize individual comprehension and the gradual acquisition of knowledge [33], while social constructivism stresses the social and interactive nature of learning [38]. By integrating these two perspectives, this research recognizes and incorporates the two types of learning behaviors exhibited by researchers—autonomous and collaborative—thereby offering a novel theoretical viewpoint on the structure of disciplinary knowledge. Traditionally, discussions on knowledge structure have predominantly focused on the organizations and knowledge itself. However, this perspective may overlook the true source of knowledge vitality: the learning behaviors of researchers. These behaviors encompass autonomous knowledge acquisition and collaborative learning with experts [69,70].
Researchers’ learning behaviors are characterized by autonomy and dynamism, as they actively seek and acquire new knowledge beyond existing frameworks [71]. Through an examination of what knowledge researchers learn and adapt, we can observe the gradual construction and refinement of disciplinary knowledge systems. In addition, collaborative learning with experts is a crucial mechanism for their development; through interdisciplinary collaboration and exploration, researchers can incorporate perspectives and methodologies from diverse fields, leading to innovative insights and theories [45]. Researchers test new ideas by collaborating with others, thus pushing the boundaries of their disciplines as they seek innovative solutions. By incorporating researchers’ learning behaviors into this analysis, we gain a deeper understanding of the self-constructing and expanding nature of disciplinary knowledge system at the micro level. This approach clarifies how knowledge evolves within academic domains and provides valuable insights into the formation of knowledge structures. Also, this dynamic process of knowledge acquisition emphasizes that disciplinary knowledge is not static but rather the result of the reflective and innovative interactions of researchers.
Furthermore, this study, through a comparative analysis of the basic knowledge system and the knowledge network system, provides deeper insights into the dynamic and complementary coexistence of autonomous and collaborative learning behaviors in the process of knowledge creation. It offers a fresh perspective on research into disciplinary knowledge structures. Autonomous learning facilitates efficient communication and collaboration, while the interaction and cooperation intrinsic to knowledge processes are essential for learning and understanding. Therefore, it is crucial not to overlook the structural distinctions within disciplinary knowledge systems that arise from the varied strategic behaviors of researchers. This helps explain the significant inconsistencies observed when reflecting the diversity of disciplinary research, particularly when using references and author affiliations [36].
6.2. Policy Suggestion
The dual-system framework—combining basic knowledge systems and knowledge network systems—systematically advances expertise within disciplines, supporting a progression from foundational knowledge to cutting-edge research. The vertical structure is hierarchical, with bedrock knowledge forming the core foundation of a discipline, expansive knowledge extending insights from other fields, and reference knowledge driving breakthroughs that require careful evaluation. This system enables academic institutions to provide students with a solid foundation while exposing them to cutting-edge developments. It also encourages tiered research opportunities, from novices focusing on bedrock knowledge to seasoned scholars pursuing theoretical refinement and applied exploration of reference knowledge, which enhances learning experiences.
The horizontal structure emphasizes the interconnectedness of knowledge across disciplines, facilitating a more holistic understanding of how various fields relate. For higher education institutions, leveraging this structure is critical for building interdisciplinary curricula and fostering collaboration. The transparency and visualization of the knowledge network system can help universities and scholars quickly identify potential collaborators, provide a basis for resource integration, and facilitate efficient cooperation by determining priority areas for interdisciplinary collaboration. Moreover, it can fully present the intersecting areas between different disciplines, as well as the “boundary zones” that have not yet been fully explored but often harbor significant potential for innovation, thereby driving the realization of innovation.
The combination of basic knowledge systems and knowledge network systems allows disciplinary knowledge systems to become more flexible and open, enabling them to adapt to the ever-changing academic environment and promote the continuous updating and evolution of knowledge systems. By applying this framework, institutions can better evaluate their strengths and identify areas for further exploration and development. From a policy perspective, governments can play a pivotal role by supporting the creation of robust knowledge systems rather than merely providing material incentives. This shift aligns better with the goals of sustained academic development and innovation, fostering an environment that enables both individual expertise and collaborative breakthroughs.
6.3. Limitations and Prospects
There are several limitations in this study that warrant further exploration and analysis. First, this paper describes the static structure of disciplinary knowledge systems but has yet to explore their dynamic characteristics. The current study defines the foundational knowledge system of the discipline as a hierarchical pyramid structure, based on its level of knowledge application. However, from a dynamic perspective of disciplinary development, the applicability of existing concepts, models, and theories is limited, necessitating continuous updates and refinements [11]. Consequently, the extension of disciplinary knowledge through the incorporation of additional knowledge is a divergent and continuously iterative process. However, the current hierarchical structure of the basic knowledge system does not accurately capture this dynamic and divergent process.
Second, many literature database platforms lack field-specific categorization for the literature [72]. Due to limited data access, the research fields of the references (articles) were approximated based on the categorization of the WoS database corresponding to the journal. Journals in the WoS are classified into multiple research area categories, resulting in a paper usually assigned to multiple disciplines according to the attributes of the journal. However, not all the papers necessarily include every field of the journal. This may lead to an article being categorized into an unrelated field. Although this method is currently satisfactory, achieving precise knowledge classification remains challenging.
Third, the information in the papers cannot explicitly reflect the authors’ knowledge backgrounds. This paper inferred the authors’ knowledge backgrounds from their organizational information [36]. Yet, some researchers’ work may extend beyond the knowledge sphere of their affiliated organizations, and researchers in interdisciplinary organizations may specialize in a specific domain. As a result, there may be bias in assessing the knowledge backgrounds of certain authors.
Future research can extend in these ways. First, future studies can develop the basic knowledge system’s structure from a dynamic perspective. This approach should adopt a diachronic perspective that encompasses longitudinal studies or the integration of real-time data in emerging fields to capture the dynamic evolution of knowledge systems. Second, researchers should pay attention to the method of reference–discipline and author–discipline matching. On one hand, future studies may address the issue with misclassifying references by thematic analysis or expert validation instead of relying on journal categories. On the other hand, when data permit, future efforts can track and precisely identify each collaborator’s institutional affiliations and research outputs based on the author information provided by the database. By employing thematic analysis, the research themes and keywords can be examined. Additionally, relevant experts can be invited to delineate the knowledge background based on each scholar’s research, thereby constructing a more accurate and practically relevant disciplinary knowledge network structure.
7. Conclusions
This study, from the perspective of coexistence between autonomous learning and collaborative learning behaviors among researchers, builds basic knowledge system and knowledge network system structures of disciplinary knowledge based on knowledge structure theory and ecosystem perspective. The basic knowledge system is characterized by a pyramid structure including bedrock knowledge, expansive knowledge and reference knowledge. The knowledge network system assumes a center–periphery circle structure including proximal and remote knowledge. Using energy chemistry engineering as a case study, this study constructs its knowledge structure and compares its basic knowledge system with the knowledge network system. The dual-system framework of disciplinary knowledge structures reflects the continuous updating and evolution of the discipline’s knowledge system. This framework enables the discipline to establish a more flexible and open knowledge structure, which not only adapts to the ever-changing academic environment but also enhances the discipline’s capacity for knowledge creation, thereby promoting its development and advancement. The aim is to theoretically expand the theoretical extension of disciplinary knowledge structure theory, and practically summarize a mechanism for acquiring disciplinary knowledge structures, offering both interpretive insights and methodological references from a knowledge perspective for academic development.
Conceptualization, H.Z. and L.C.; methodology, L.C. and Z.Y.; formal analysis, L.C.; data curation, L.C. and Z.Y.; writing—original draft preparation, L.C.; writing—review and editing, H.Z., L.C. and J.L.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.
The data supporting reported results can be found in the Web of Science (WoS) database.
The authors would like to thank the editor and anonymous referees for their helpful suggestions.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Vertical pyramid structure diagram of basic disciplinary knowledge system.
Enlarge this image.Figure 2. Horizontal circle structure diagram of disciplinary knowledge network systems.
Enlarge this image.Figure 5. Knowledge collaboration network of high-level paper authors in energy chemical engineering (after the screening process).
Information on Q1 Journals in the “Engineering: Chemical Engineering” Category from the Literature and Information Center of CAS.
| Name | Category | 3-Year Average IF |
|---|---|---|
| Energy and Environmental Science | Q1 | 31.202 |
| Progress in Energy and Combustion Science | Q1 | 26.882 |
| Applied Catalysis B-Environmental | Q1 | 14.204 |
Node degree centrality statistics of knowledge network system of energy chemical engineering (Part).
| Disciplines | Degree | NrmDegree | Disciplines | Degree | NrmDegree |
|---|---|---|---|---|---|
| material science and engineering | 1520 | 9.325 | biological engineering | 393 | 2.411 |
| chemistry | 1501 | 9.209 | optical engineering | 219 | 1.344 |
| physics | 1415 | 8.681 | power engineering and engineering thermophysics | 186 | 1.141 |
| chemical engineering and technology | 1208 | 7.411 | information and communication engineering | 130 | 0.798 |
| environmental science and engineering | 575 | 3.528 | electrical engineering | 111 | 0.681 |
| electronic science and technology | 471 | 2.89 | aerospace science and technology | 110 | 0.675 |
| mechanical engineering | 448 | 2.748 | civil engineering | 96 | 0.589 |
| biology | 411 | 2.521 |
Appendix A
Statistics of knowledge application of citations in high-level papers in energy chemical engineering (from 2016 to 2020).
| Disciplines | Frequency | Proportion (%) | Disciplines | Frequency | Proportion (%) |
| chemistry | 38,333 | 61.0418 | genetics and heredity | 8 | 0.0127 |
| materials science | 26,955 | 42.9233 | dermatology | 7 | 0.0111 |
| science and technology—other topics | 20,627 | 32.8466 | physiology | 7 | 0.0111 |
| energy and fuels | 16,802 | 26.7556 | research and experimental medicine | 7 | 0.0111 |
| physics | 16,108 | 25.6505 | biodiversity and conservation | 6 | 0.0096 |
| engineering | 9708 | 15.4591 | oceanography | 6 | 0.0096 |
| environmental sciences and ecology | 5348 | 8.5162 | oncology | 6 | 0.0096 |
| electrochemistry | 5135 | 8.1770 | neurosciences and neurology | 5 | 0.0080 |
| thermodynamics | 1288 | 2.0510 | psychology | 5 | 0.0080 |
| polymer science | 970 | 1.5446 | substance abuse | 5 | 0.0080 |
| biotechnology and applied microbiology | 769 | 1.2246 | astronomy and astrophysics | 4 | 0.0064 |
| optics | 550 | 0.8758 | government and law | 4 | 0.0064 |
| biochemistry and molecular biology | 389 | 0.6194 | imaging science and photographic technology | 4 | 0.0064 |
| metallurgy and metallurgical engineering | 333 | 0.5303 | pediatrics | 4 | 0.0064 |
| agriculture | 311 | 0.4952 | robotics | 4 | 0.0064 |
| mathematics | 276 | 0.4395 | veterinary sciences | 4 | 0.0064 |
| mechanics | 222 | 0.3535 | endocrinology and metabolism | 3 | 0.0048 |
| water resources | 222 | 0.3535 | fisheries | 3 | 0.0048 |
| meteorology and atmospheric sciences | 198 | 0.3153 | general and internal medicine | 3 | 0.0048 |
| instruments and instrumentation | 196 | 0.3121 | immunology | 3 | 0.0048 |
| crystallography | 152 | 0.2420 | infectious diseases | 3 | 0.0048 |
| microbiology | 146 | 0.2325 | international relations | 3 | 0.0048 |
| biophysics | 130 | 0.2070 | ophthalmology | 3 | 0.0048 |
| computer science | 117 | 0.1863 | radiology, nuclear medicine and medical imaging | 3 | 0.0048 |
| business and economics | 114 | 0.1815 | social sciences—other topics | 3 | 0.0048 |
| geology | 75 | 0.1194 | behavioral sciences | 2 | 0.0032 |
| nuclear science and technology | 68 | 0.1083 | mathematical methods in social sciences | 2 | 0.0032 |
| plant sciences | 62 | 0.0987 | physical geography | 2 | 0.0032 |
| transportation | 55 | 0.0876 | surgery | 2 | 0.0032 |
| geochemistry and geophysics | 52 | 0.0828 | zoology | 2 | 0.0032 |
| spectroscopy | 47 | 0.0748 | anatomy and morphology | 1 | 0.0016 |
| construction and building technology | 40 | 0.0637 | anthropology | 1 | 0.0016 |
| cell biology | 34 | 0.0541 | archeology | 1 | 0.0016 |
| life sciences and biomedicine—other topics | 33 | 0.0525 | art | 1 | 0.0016 |
| toxicology | 32 | 0.0510 | arts and humanities—other topics | 1 | 0.0016 |
| pharmacology and pharmacy | 31 | 0.0494 | cardiovascular system and cardiology | 1 | 0.0016 |
| food science and technology | 30 | 0.0478 | cultural studies | 1 | 0.0016 |
| telecommunications | 30 | 0.0478 | development studies | 1 | 0.0016 |
| automation and control systems | 28 | 0.0446 | entomology | 1 | 0.0016 |
| microscopy | 27 | 0.0430 | evolutionary biology | 1 | 0.0016 |
| mineralogy | 27 | 0.0430 | hematology | 1 | 0.0016 |
| public, environmental and occupational health | 21 | 0.0334 | history and philosophy of science | 1 | 0.0016 |
| marine and freshwater biology | 18 | 0.0287 | information science and library science | 1 | 0.0016 |
| mining and mineral processing | 18 | 0.0287 | integrative and complementary medicine | 1 | 0.0016 |
| acoustics | 17 | 0.0271 | medical informatics | 1 | 0.0016 |
| public administration | 14 | 0.0223 | mycology | 1 | 0.0016 |
| geography | 13 | 0.0207 | orthopedics | 1 | 0.0016 |
| operations research and management science | 10 | 0.0159 | philosophy | 1 | 0.0016 |
| education and educational research | 9 | 0.0143 | remote sensing | 1 | 0.0016 |
| forestry | 9 | 0.0143 | sport sciences | 1 | 0.0016 |
| mathematical and computational biology | 9 | 0.0143 | virology | 1 | 0.0016 |
| nutrition and dietetics | 9 | 0.0143 |
Appendix B
Network node degree centrality statistics of knowledge network system of energy chemical engineering.
| Disciplines | Degree | NrmDegree | Disciplines | Degree | NrmDegree |
| material science and engineering | 1520 | 9.325 | mining engineering | 32 | 0.196 |
| chemistry | 1501 | 9.209 | control science and engineering | 29 | 0.178 |
| physics | 1415 | 8.681 | public administration | 29 | 0.178 |
| chemical engineering and technology | 1208 | 7.411 | architecture | 28 | 0.172 |
| environmental science and engineering | 575 | 3.528 | atmospheric science | 28 | 0.172 |
| electronic science and technology | 471 | 2.89 | agricultural resources and environment | 17 | 0.104 |
| mechanical engineering | 448 | 2.748 | pharmacy | 14 | 0.086 |
| biology | 411 | 2.521 | agricultural engineering | 12 | 0.074 |
| biological engineering | 393 | 2.411 | instrument science and technology | 10 | 0.061 |
| optical engineering | 219 | 1.344 | management science and engineering | 10 | 0.061 |
| power engineering and engineering thermophysics | 186 | 1.141 | transportation engineering | 10 | 0.061 |
| information and communication engineering | 130 | 0.798 | basic medical sciences | 8 | 0.049 |
| electrical engineering | 111 | 0.681 | ecology | 8 | 0.049 |
| aerospace science and technology | 110 | 0.675 | art studies | 9 | 0.055 |
| civil engineering | 96 | 0.589 | safety science and engineering | 7 | 0.043 |
| education | 76 | 0.466 | clinical medicine | 6 | 0.037 |
| mathematics | 70 | 0.429 | design studies | 6 | 0.037 |
| computer science and technology | 69 | 0.423 | food science and engineering | 6 | 0.037 |
| geology | 67 | 0.411 | food science and technology | 4 | 0.025 |
| biomedical engineering | 64 | 0.393 | marine science | 4 | 0.025 |
| plant pathology | 61 | 0.374 | sociology | 4 | 0.025 |
| metallurgical engineering | 55 | 0.337 | astronomy | 2 | 0.012 |
| petroleum and natural gas engineering | 46 | 0.282 | crop science | 2 | 0.012 |
| geography | 45 | 0.276 | safety engineering | 2 | 0.012 |
| nuclear science and technology | 44 | 0.270 | applied economics | 1 | 0.006 |
| mechanics | 43 | 0.264 |
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; Chang, Le; Yang, Zuguo; Lu, Juan