{"id":393,"date":"2026-05-11T10:52:43","date_gmt":"2026-05-11T02:52:43","guid":{"rendered":"https:\/\/zhaoyanqi.cn\/?p=393"},"modified":"2026-05-11T10:52:44","modified_gmt":"2026-05-11T02:52:44","slug":"enhancing-server-management-innovation-with-ai-integrating-chatgpt-and-the-seci-model","status":"publish","type":"post","link":"https:\/\/zhaoyanqi.cn\/?p=393","title":{"rendered":"Enhancing Server Management Innovation with AI: Integrating ChatGPT and the SECI Model"},"content":{"rendered":"\n

Author\uff1aZhao Yanqi<\/strong><\/p>\n\n\n\n

Date\uff1a2024-06-06<\/strong><\/p>\n\n\n\n

Introduction<\/h1>\n\n\n\n

In recent years, AI natural language processing (NLP) technology has rapidly developed, providing solutions to various technical problems. In this paper, I will explore the innovations and challenges brought by NLP technologies, such as ChatGPT, in the server management processes of large internet enterprises by combining a practical view of knowledge management using the interactive approach and the SECI model. By analyzing the application of ChatGPT in problem identification, idea generation, solution selection, and optimization implementation, I will demonstrate how to use it to promote the creation and sharing of knowledge, thereby enhancing the innovation capabilities and overall efficiency of server management teams.<\/p>\n\n\n\n

Background<\/h2>\n\n\n\n

Large internet companies rely on extensive backend server infrastructure to provide their services. Due to the rapid technological advancements of these internet giants, the technology updates for backend server management have also accelerated accordingly. Therefore, maintaining efficient, reliable, and scalable server operations is crucial for business success. With the increasing number of users and the growing amount of data, the complexity of server management is continuously increasing (Oppenheimer et al., 2003). Unlike traditional large companies, such as banks, which directly purchase equipment and services from established solution providers, internet companies usually need their own backend solutions due to business needs and cost reasons. For example, they often build clusters using hosts with x86 architecture to provide backend services for their systems (Oppenheimer et al., 2003). This raises higher requirements for backend server management.<\/p>\n\n\n\n

The main focus of this paper is on the backend server management teams within these large internet companies. These teams are responsible for ensuring the smooth operation of server infrastructure. However, due to rapid technological updates, they must constantly learn and master new management skills and knowledge, which requires strong innovation capabilities and a sound knowledge management system. These teams face challenges in server upgrades, problem-solving, and collaboration and knowledge sharing among team members. Addressing these issues is not only time-consuming and labor-intensive but also prone to errors, affecting service quality and user experience.<\/p>\n\n\n\n

AI natural language processing (NLP) technologies, such as ChatGPT, may provide innovative solutions to the challenges mentioned above. I believe that ChatGPT, using advanced NLP technology, can achieve functions that traditional server management tools cannot, such as integrating existing solutions, continuously providing the latest solutions, and offering real-time feedback on problem-solving. According to Rotolo et al. (2015), this type of innovation is revolutionary rather than incremental. Moreover, ChatGPT has the potential to significantly impact server management, improving efficiency and productivity, creating new solutions, and enhancing team collaboration (Rajbhoj et al., 2024). By promoting the creation and sharing of knowledge, ChatGPT can help a large server management team quickly learn and master new management skills and knowledge, improving the knowledge management system (Hu et al., 2023), and better coping with the challenges of rapid technological updates.<\/p>\n\n\n\n

Innovation Process Method<\/h2>\n\n\n\n

Next, I will use the interactive approach to study how ChatGPT addresses the issue of rapid technological updates in the backend server management of large internet companies. Unlike linear innovation methods, the interactive approach emphasizes continuous feedback loops and dynamic adjustments, which can better adapt to rapidly changing technological environments. In server management, frequent technological updates require the management team not only to master new technologies quickly but also to adjust management strategies to cope with constantly evolving challenges flexibly. The interactive approach, by emphasizing real-time interaction and continuous learning among team members, can effectively promote the creation and sharing of knowledge, thereby improving the team’s adaptability and innovation capabilities.<\/p>\n\n\n\n

In this process, I will focus on creating and sharing knowledge. Knowledge creation involves capturing and generating new solutions and optimization strategies, which are crucial for addressing new technological challenges. Knowledge sharing ensures that team members can timely access and apply the latest management skills and knowledge, thereby enhancing overall efficiency and collaborative capability. Combining these two aspects of the knowledge process can continuously improve the team’s overall capability and management level while keeping up with technological updates. Additionally, by examining some characteristics of the practical view, such as “Situated,” I learned that teams could effectively apply new knowledge in specific operational environments, ensuring that management strategies are closely aligned with actual needs, and “Provisional” emphasizes the continuous development and updating of knowledge, highlighting that teams can dynamically adjust and optimize their knowledge and strategies through the interactive approach and the real-time feedback provided by ChatGPT.<\/p>\n\n\n\n

Theory: Overview of the SECI Model Combined with ChatGPT<\/h2>\n\n\n\n

To address the issue of rapid technological updates more effectively in the backend server management of large internet companies, combining the SECI model with ChatGPT is an ideal strategy. The SECI model systematically promotes the creation and sharing of knowledge through four stages: Socialization, Externalization, Combination, and Internalization (Nonaka, 1994). ChatGPT, as a powerful NLP tool, can provide critical support at each stage.<\/p>\n\n\n\n

Socialization<\/h3>\n\n\n\n

Socialization is the process of converting tacit knowledge into new tacit knowledge through shared experiences and interactions (Nonaka, 1994). Still, during socialization, ChatGPT can capture interactions with team members and extract valuable experiences, helping convert tacit knowledge into explicit knowledge. For example, team members can acquire skills through observation and imitation during work. In this case, ChatGPT can record and analyze these interactions, transforming them into textual records that other team members can study and reference. This method effectively captures tacit knowledge and transmits it to more team members.<\/p>\n\n\n\n

Externalization<\/h3>\n\n\n\n

In the externalization stage, ChatGPT can generate reports and suggestions, externalizing the personal knowledge of team members into a shared knowledge base. Externalization is the key process of converting tacit knowledge into explicit knowledge by articulating and reflecting personal knowledge into sharable concepts (Nonaka, 1994). ChatGPT can capture team discussions and generate detailed reports and analyses, transforming these discussions into actionable strategies and solutions.<\/p>\n\n\n\n

Combination<\/h3>\n\n\n\n

In the combination stage, ChatGPT can integrate explicit knowledge from different sources to generate new insights and solutions, thereby enhancing the optimization of management strategies. Combination involves synthesizing, adding, reclassifying, and recontextualizing existing explicit knowledge to generate new knowledge (Nonaka, 1994). For instance, ChatGPT can consolidate reports and data from various departments to create comprehensive analytical reports that support decision-making. By doing so, ChatGPT helps the team integrate scattered explicit knowledge into a systematic body of knowledge, improving the efficiency and effectiveness of knowledge management.<\/p>\n\n\n\n

Internalization<\/h3>\n\n\n\n

In the internalization stage, team members can use the knowledge generated by ChatGPT, internalizing it into personal skills and understanding, thereby enhancing the overall technical level. Internalization is the process of converting explicit knowledge into tacit knowledge through practice and repetition, making explicit knowledge part of an individual’s skill set. Through this method, knowledge circulates between individuals and teams, forming a dynamic process of knowledge creation (Nonaka, 1994). For example, ChatGPT can provide personalized learning suggestions and feedback, helping team members quickly master new technologies and knowledge and improving the technical level and adaptability of the entire team.<\/p>\n\n\n\n

By combining the SECI model and ChatGPT, the process of knowledge creation and sharing is significantly enhanced. ChatGPT helps teams quickly adapt to technological updates, improve management efficiency, and promote the cycle and recreation of knowledge, forming a comprehensive knowledge management system. Combining the SECI model and ChatGPT, this interactive approach ensures that teams can timely access and apply the latest knowledge, effectively addressing the challenges of rapid technological updates in backend server management for large internet companies.<\/p>\n\n\n\n

In-depth Discussion on Promoting Knowledge Sharing with SECI Model and ChatGPT<\/h2>\n\n\n\n

When using the interactive approach to study how ChatGPT handles the issue of rapid technological updates in the backend server management of large internet companies, knowledge sharing is a crucial aspect that can be applied at various stages of the SECI model. Knowledge sharing is a means to enhance team collaboration efficiency and a core driver for innovation and organizational performance improvement. This method emphasizes the effective dissemination and utilization of knowledge through real-time interaction and continuous feedback in a dynamic environment. Knowledge sharing becomes a key driver for organizational adaptability and innovation capability in the context of rapidly developing server management technologies. According to Olan et al.’s (2022) research, artificial intelligence significantly promotes the flow of knowledge within organizations and enhances overall organizational performance. This perspective is especially important in the current scenario of rapid technological updates in backend server management, where timely knowledge sharing is crucial to maintaining technological advancement and competitive advantage.<\/p>\n\n\n\n

Accelerating Knowledge Dissemination<\/h3>\n\n\n\n

Firstly, in the combination stage, ChatGPT can accelerate the dissemination and application of technical knowledge through the automated knowledge management process, reducing the steepness of the learning curve. Adopting new technologies and procedures is essential in rapidly changing technological environments such as server management. Olan et al.’s research emphasizes that through effective AI-driven knowledge management strategies, organizations can quickly obtain critical technical knowledge from internal experts and disseminate this knowledge swiftly throughout the team. This speeds up problem-solving and technological implementation and significantly improves operational efficiency by reducing repetitive work and errors.<\/p>\n\n\n\n

Promoting Cross-functional Collaboration<\/h3>\n\n\n\n

Secondly, in the socialization and externalization stages, ChatGPT is important in promoting cross-functional team collaboration, thereby stimulating innovation potential. Olan et al. point out that AI-driven knowledge-sharing platforms create a multidisciplinary exchange platform where employees can cross-validate ideas and collaborate to solve complex problems. In the context of server management, for example, system administrators, network engineers, and security analysts can utilize ChatGPT to work together, share their expertise, and develop more effective technological updates and troubleshooting strategies. This cross-disciplinary collaboration not only improves the quality of solutions but also accelerates the innovation process.<\/p>\n\n\n\n

Fostering a Knowledge Culture<\/h3>\n\n\n\n

Lastly, in the internalization stage, knowledge sharing through ChatGPT is crucial for fostering a knowledge culture within the organization that supports continuous learning and self-improvement. According to Olan et al.’s analysis, a culture that emphasizes knowledge sharing can motivate employees to actively seek new knowledge and skills, which is particularly important in the technology field where iteration and updates are rapid. AI technology not only enhances individual employee capabilities but also strengthens the overall adaptability and competitiveness of the organization.<\/p>\n\n\n\n

The Challenge of Knowledge Hiding<\/h3>\n\n\n\n

However, critics argue that artificial intelligence in rapidly changing technological environments may also negatively impact knowledge sharing. According to Arias-P\u00e9rez and V\u00e9lez-Jaramillo (2022) research, as AI technology becomes widespread, employees may feel insecure about their jobs, leading to phenomena such as knowledge hiding. This phenomenon includes three forms: playing dumb, evasiveness, and rationalized hiding, all of which affect the flow of knowledge and overall efficiency within the organization. For example, the rapid development and application of ChatGPT in enterprises may cause employees to feel threatened about their careers, leading them to hide knowledge to protect their positions deliberately. Arias-P\u00e9rez and V\u00e9lez-Jaramillo’s research indicates that in response to threats posed by intelligent robots, employees might engage in opportunistic behaviors, hiding knowledge to reduce the risk of being replaced. Such behaviors weaken the positive effects of knowledge sharing and potentially create tension and distrust within the organization.<\/p>\n\n\n\n

Furthermore, AI-driven knowledge-hiding behaviors can undermine the effectiveness of cross-functional team collaboration. While knowledge sharing can foster innovation, the potential for such collaboration will not be fully realized if employees hide knowledge due to fear of AI technology replacement. Arias-P\u00e9rez and V\u00e9lez-Jaramillo also note that in such situations, knowledge-hiding behaviors can hinder the flow of information, making it difficult for teams to solve complex problems, thus impacting the innovation process effectively. Additionally, research by Husted and Michailova (2002) points out that knowledge hiding affects knowledge flow and leads to hostile emotions within the organization. These hostile emotions stem from employees’ resistance to knowledge sharing, manifesting as a rejection of “external knowledge” and a negative attitude toward mistakes and failures. In highly hostile environments, knowledge-hiding behaviors are more prevalent, impacting the overall knowledge management effectiveness of the organization.<\/p>\n\n\n\n

Addressing Knowledge Hiding<\/h3>\n\n\n\n

To address these challenges, server management departments in internet companies need to take specific measures to reduce the negative impact of knowledge hiding. According to Husted and Michailova’s research, several methods can effectively address the phenomenon of knowledge hiding, such as building and maintaining trust relationships among employees through various means, including transparent communication and fair reward mechanisms or providing appropriate incentives. Organizations can also offer training to help employees improve their ability and willingness to share knowledge. Additionally, organizations can adopt methods like BP Amoco’s “After Action Review” to promote knowledge reflection and summarization. In summary, by implementing these measures, server management departments in internet companies can leverage ChatGPT to enhance knowledge sharing while reducing the negative impact of knowledge hiding, thus maintaining a competitive advantage in the face of rapidly updating technologies.<\/p>\n\n\n\n

Through this in-depth discussion, we can see that while AI-driven knowledge sharing in a rapidly changing technological environment can enhance organizational adaptability, efficiency, and innovation capability, it also faces the challenge of knowledge hiding. ChatGPT is both a tool for promoting knowledge sharing and innovation and a potential factor for knowledge hiding. Organizations may need to balance the relationship between knowledge sharing and knowledge hiding, taking measures to alleviate employees’ insecurities, and ensuring consistency between technological implementation and organizational goals to maintain a competitive edge in a fierce market.<\/p>\n\n\n\n

Exploring Knowledge Creation in the SECI Model Framework through ChatGPT Improvement Limitations<\/h2>\n\n\n\n

In the knowledge creation process, individuals may encounter various limitations, particularly evident in large internet companies’ server management processes. However, after using the interactive approach to study how ChatGPT can improve these limitations, I believe that ChatGPT might significantly enhance the efficiency and quality of knowledge creation in practical applications.<\/p>\n\n\n\n

Limitations in Knowledge Creation<\/h3>\n\n\n\n

In the server management teams of large Internet companies, knowledge creation may be limited by certain factors. According to Garbuio and Lin’s (2021) research, individuals are prone to cognitive load limitations during innovation. Cognitive load theory suggests that the capacity of human working memory is limited, making it challenging to process a large amount of complex information simultaneously. In server management, administrators need to handle a vast amount of complex data and information, posing a significant challenge to working memory. This limitation might hinder the efficiency and effectiveness of generating and evaluating innovative hypotheses, negatively affecting knowledge creation.<\/p>\n\n\n\n

Garbuio and Lin also point out that individuals tend to be influenced by confirmation bias during the innovation process. Confirmation bias leads server management teams to seek and value information supporting their beliefs while ignoring or rejecting contradictory information. This cognitive bias may limit the team’s perspective, making it difficult for them to discover and utilize potential innovation opportunities for knowledge creation. Especially in complex and uncertain technological environments, confirmation bias can significantly impede effective hypothesis generation and the proposal of innovative solutions (Garbuio & Lin, 2021).<\/p>\n\n\n\n

How ChatGPT Improves These Limitations<\/h3>\n\n\n\n

ChatGPT shows great potential in improving these aspects and can significantly enhance the knowledge-creation process. Particularly in the combination and internalization stages of the SECI model, ChatGPT can greatly improve innovation efficiency and quality in practical applications, thereby promoting the knowledge creation capability of server management teams in large internet companies.<\/p>\n\n\n\n

Mitigating Cognitive Load<\/h4>\n\n\n\n

For cognitive load, ChatGPT can significantly mitigate this limitation in the innovation process. ChatGPT can automate the analysis and synthesis of vast amounts of data, reducing human cognitive load and helping innovators generate and evaluate hypotheses more efficiently. For example, ChatGPT can quickly generate valuable insights by processing and summarizing large amounts of unstructured data, such as videos, audio, and social media posts, accelerating innovation (Garbuio & Lin, 2021). Moreover, in the internalization stage of the SECI model, according to Hutchinson’s (2021) research, AI can help organizational members internalize explicit knowledge into personal skills and understanding through automated knowledge management. AI can assist system administrators in understanding complex technical concepts and optimization strategies through simulation and modeling techniques, enabling them to master new technologies more quickly. According to Hutchinson, ChatGPT can provide personalized learning modules that adjust the content and difficulty based on the administrators’ learning progress and feedback, enhancing the learning effect. This personalized learning support not only improves employees’ skill levels but also enhances the overall knowledge-creation capability of the organization.<\/p>\n\n\n\n

Overcoming Confirmation Bias<\/h4>\n\n\n\n

For confirmation bias, in the combination stage of the SECI model, according to Garbuio and Lin’s research, AI can help identify and integrate anomalies and contradictory information, generating more relevant and feasible hypotheses and thereby overcoming the limitations of confirmation bias. ChatGPT can generate new insights and solutions through real-time analysis and integration of various data sources, reducing the impact of confirmation bias and broadening the perspective of the server management team, making them more likely to engage in knowledge creation. Furthermore, ChatGPT can optimize server management further and even directly create knowledge. Hutchinson’s research indicates this point, showing that AI-driven self-innovation (SAI) technologies can continuously improve and develop products using internal, external, and user-generated data. ChatGPT can integrate performance data and log information from different systems to identify new methods for optimizing server configurations and generate detailed optimization reports in server management. This real-time data integration capability enables organizations to respond quickly to technological changes, reducing downtime risks and enhancing server reliability and efficiency (Hutchinson, 2021).<\/p>\n\n\n\n

In summary, combining the SECI model with ChatGPT may significantly enhance the knowledge-creation process in the combination and internalization stages. ChatGPT may not only overcome human limitations in the innovation process, such as cognitive load and confirmation bias but also improve the organization’s innovation capability through automated knowledge management and data integration. In the practical application of server management, the introduction of ChatGPT may enable organizations to cope with technological updates and market changes more efficiently, maintaining a competitive advantage.<\/p>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

In this paper, we have explored the innovative applications and challenges of ChatGPT within the server management processes of large Internet enterprises. By integrating the SECI model with ChatGPT, we demonstrated how this combination enhances knowledge creation and sharing, thereby improving server management teams’ innovation capabilities and overall efficiency.<\/p>\n\n\n\n

The rapid technological advancements in the server management domain necessitate continuous learning and adaptation. ChatGPT, with its advanced NLP capabilities, facilitates this by capturing, synthesizing, and disseminating knowledge across various stages of the SECI model. The Socialization stage transforms tacit knowledge into explicit knowledge through shared experiences and interactions. During Externalization, it articulates personal knowledge into sharable concepts, while in the Combination stage, it integrates disparate pieces of explicit knowledge to generate new insights. Finally, the Internalization stage helps team members internalize new knowledge, enhancing their skills and understanding.<\/p>\n\n\n\n

The interactive approach further supports this by emphasizing real-time interaction and continuous feedback, which are crucial in a rapidly changing technological environment. However, while ChatGPT and similar AI technologies hold great potential for improving efficiency and innovation, they also present challenges, such as the risk of knowledge hiding among employees due to job insecurity. Addressing these challenges requires building trust, providing incentives for knowledge sharing, and fostering a culture of continuous learning.<\/p>\n\n\n\n

In conclusion, the integration of ChatGPT with the SECI model offers a robust framework for enhancing knowledge management in server management processes. By promoting efficient knowledge creation and sharing, ChatGPT helps large internet enterprises maintain a competitive edge in the face of rapid technological updates. Future research should address the challenges of knowledge hiding and further refine the interactive approach to maximize the benefits of AI-driven knowledge management systems.<\/p>\n\n\n\n

Reference<\/h2>\n\n\n\n
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  • Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology? Research Policy<\/em>, 44(10), 1827\u20131843.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    Author\uff1aZhao Yanqi Date\uff1a2024-06-06 Introduction In recen […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-393","post","type-post","status-publish","format-standard","hentry","category-20"],"_links":{"self":[{"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/posts\/393","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=393"}],"version-history":[{"count":1,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/posts\/393\/revisions"}],"predecessor-version":[{"id":394,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=\/wp\/v2\/posts\/393\/revisions\/394"}],"wp:attachment":[{"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=393"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=393"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zhaoyanqi.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=393"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}