AI Technology Adoption in Corporate IT Network Operations Based on the TOE Model

Abstract

As the digital environment evolves, the need to integrate artificial intelligence (AI) technology into corporate IT network operations increases. In this study, the aim was to define the factors that influence AI adoption in the network operations and analyze their impact on productivity and service stability. The technology–organization–environment (TOE) framework was employed for this investigation, focusing on technological, organizational, and environmental factors. In addition, in this study, structural equation modeling was employed to analyze the relationships between these influencing factors and the intention to adopt AI. The mediation effect was examined through the network operation productivity and network service stability. A survey was conducted targeting network operations and AI professionals to collect data. The analysis results revealed that technological and environmental factors positively influenced the network operation productivity, while only environmental factors positively influenced the network service stability. Furthermore, the findings of this study highlight that environmental factors are the most significant factors that influence network operation productivity and network service stability. Moreover, the direct positive impact of network operation productivity and IT network service stability on the intention to adopt AI underscores their crucial role. In conclusion, when evaluating AI adoption in terms of network operation productivity and network service stability, prioritizing technological and environmental factors over organizational factors is necessary.

1. Introduction

In recent years, networks have grown exponentially in scale and complexity. The increasingly digital environment forces companies to manage a wider range of domains, leading to increased complexity in architecture, technology, processes, and management tools for network operations [1]. To enhance network management efficiency, companies use operation support systems (OSS), which consist of functions such as configuration management (CM), performance management (PM), fault management (FM), trace management (TM), license management (LM), and security. These functions often operate separately, limiting comprehensive network monitoring and management. The rapid increase in network data and complexity has led to rising operational costs as companies invest more in network equipment and human resources [2]. To address this, companies are adopting AI technology to automate network operations such as network management, network support, network deployment, anomaly detection, intelligent automation, and threat analysis [3].

Artificial intelligence (AI) automates and systematically orchestrates many network operational tasks previously managed manually [4,5,6]. Consequently, the significance of AI for IT operations (AIOps) is increasing. AIOps integrates big data and AI technology to automate IT operations, including tasks such as anomaly detection, correlation analysis, and causality analysis. The primary goal of AIOps is to enhance the availability, scalability, and efficiency of IT operations [7]. Networks often experience failures due to their complex architecture, hardware failures, software errors, operator mistakes, and security threats. These failures can significantly reduce business reliability and impose substantial financial burdens. Consequently, AI adoption for network operations, particularly for troubleshooting tasks such as anomaly detection, root cause analysis (RCA), and recovery, is on the rise [8].

Integrating AI technology into network operations poses several challenges for companies, including measuring value, managing data costs, dealing with implementational and operational complexity, and ensuring seamless integration with existing tasks. AI adoption often conflicts with established systems and operational processes because of varying circumstances, such as the organizational culture, employee capabilities, preferred technologies, internal regulations, and labor unions [9]. Brock and Von Wangenheim [10] highlighted the risks associated with AI adoption without feasibility analysis or strategy. Ransbotham et al. [11] emphasized the difficulties that arise after AI adoption, particularly those related to measuring AI value and the necessary expertise for successful implementation.

However, Gartner [12] highlighted that the AIOps market is growing at an annual rate of approximately 19%, and is projected to reach a market size of approximately USD 2.1 billion by 2025. Despite the challenges associated with adopting AI technology, many companies are enhancing their network operations capabilities to support digital business activities. Recently, there has been an increased demand for next-generation AI-based network operations to provide customized services across various business areas within the digital environment. Achieving this requires a highly flexible and agile infrastructure [13]. Furthermore, the adoption of AI technology is accelerating to ensure more stable network services in terms of performance, quality, security, and availability while maximizing overall business efficiency. Various reference models and frameworks, such as ITU 3GPP’s network data analytics function (NWDAF), ETSI’s zero touch network and service management (ZSM), and network functions virtualization (NFV), as well as TM Forum’s enhanced telecommunications operations map (eTOM) and autonomous networks framework, are being proposed to assist companies in integrating AI technologies into their network operations. Among these, TM Forum’s autonomous networks framework is widely recognized across industries as a reference model for AI integration. Notable examples include AT&T’s Auto-X for automating B2B SD-WAN services, Verizon’s advanced threat detection, Rio Tinto’s closed-loop automation for mineral transportation, and Claro’s self-healing in mobile communication services.

Most studies have highlighted the technical limitations of applying AI or have focused on effectively implementing embedded AI systems. Although the research on AI is actively conducted across many fields, these studies mainly emphasize the theoretical foundations and conceptual applications. Moreover, there is a scarcity of studies that present decision-making frameworks for AI adoption at the organizational level and validate their effectiveness through samples, resulting in a lack of hypothesis testing and empirical verification. Therefore, beyond technology, strategic discussions are necessary for the effective adoption of AI, considering the technological, organizational, and environmental aspects.

In this context, this study was conducted based on the following research questions: “What is the most important factor to consider for the successful adoption of AI in network operations?”; “Among the technological, environmental, and organizational factors, which factors have the greatest influence on decision-making in the adoption of AI in network operations?”; and “How do the network operation productivity and network service stability affect the adoption of AI in network operations?” Through these research questions, in this study, the relationships among the factors influencing AI adoption in network operations are empirically explored. Therefore, in this study, the impact of technological, organizational, and environmental factors on the adoption of AI technology in network operations is analyzed, with a focus on the TOE model, which is mediated by the network operation productivity and network service stability. In this study, specific implications are provided for the strategic organizational approach and effective adoption plans of AI technology for companies seeking to advance their network operations.

Therefore, in this article, in Section 1, the background and necessity for this study are explained, and the purpose of this study is presented. In Section 2, the contents of the hypothesis design are presented based on previous studies. Section 3 designs a structural equation research model as a research methodology and presents the definitions of operational variables for each variable. In addition, it introduces questionnaire items, questionnaires, and analysis methods, and finally presents the information of respondents. Section 4 presents the results of statistical analysis. First, the reliability and validity are verified based on exploratory and confirmatory factor analysis results and correlation analysis. After that, the hypothesis is verified through path analysis and the mediating effect results are presented through direct and indirect effect analysis based on bootstrapping. Section 5 presents the contents of the discussion of the analysis results, and finally, Section 6 explains the implications, limitations of this study, and future plans.

2. Literature Review and Hypothesis Development

2.1. AI Technology Adoption and the TOE Model

Network technology has continually evolved to meet business demands across various domains, including transport and switching, control and management, wireline and wireless communication, and multimedia. In recent times, networks have become closely intertwined with emerging technologies such as IoT, cloud computing, 5G mobile networks, and artificial intelligence (AI). Companies are actively leveraging AI specialized in network operations for data analysis, simulation, and insights [14]. Kim et al. [15] highlighted the following four key areas of AI technology development related to networks: traffic analysis and security, proactive incident management, signal transport optimization, and operational management automation. They particularly emphasized a complementary relationship between two technological aspects, “Network by AI” which refers to the AI technology directly applied to the network, such as resource replacement, network slicing, and network service quality, and “Network for AI” which refers to the network technology facilitating connections among distributed AIs, including orchestration and network connectivity.

Previous studies have explored the adoption of specific technologies or operational information systems by companies to develop new operational models or solutions. Collins et al. [16] emphasized the importance of existing technology, which limits the speed and scope of change during the adoption process of new technology. Rogers [17] asserted that adopting innovative technologies should align with an organization’s values, demands, and experiences. Baker [18] highlighted that even technologies not currently in use outside the organization can influence adoption decisions. Organizations should consider the advantages of new technology over existing ones, such as its usefulness, ease of use, compatibility with existing technologies, and functional benefits. Phuoc [19] identified several factors that significantly affect the adoption of AI, including technical compatibility, relative advantage, technical complexity, technical capability, managerial capability, organizational readiness, government involvement, market uncertainty, and vendor partnership.

Companies, as described by Depietro [20], are influenced by three key aspects, technology, organization, and the environment when adopting new technologies. These influencing factors are based on the TOE model presented by Tornatzky and Fleischer [21] in 1990. The model defines three critical factors that companies should consider when introducing new technologies to enhance processes through technological innovation. First, technological factors relate to the characteristics of both internal and external technologies, including relative advantages, compatibility, and complexity. Relative advantage refers to the perceived degree of benefits after adoption compared with those before adoption [22].

AI technology is mainly used for customer service chatbots, customer-facing voice assistants, and network operation automation. It significantly contributes to activating and widely deploying new business models for companies by leveraging relative advantages such as cost reduction, improved service quality, enhanced customer experience, and operational efficiency [23]. According to Rogers [17] and Plessis and Smuts [24], the tendency of organizations to adopt new technologies is influenced by perceived advantages, which play a crucial role in the decision-making process. In addition, compatibility, which refers to the degree to which the value and experience provided by the innovative technologies align with the organization’s needs, is a crucial factor in technology adoption [25]. AI systems that seamlessly integrate with existing network operational technologies and legacy systems not only reduce adoption time and cost but also enhance network service reliability through self-detection, self-adjustment, self-remediation, and self-healing, thus driving digital transformation [26].

Second, organizational factors play a crucial role in technology adoption. These factors include characteristics and resources such as managerial support, organizational readiness, and organizational size. They encompass the structure of connections between employees and teams, internal communications within the organization, and the availability of resources, all of which influence decision-making regarding the adoption of innovative technologies [18,27]. Tushman and Nadler [28] highlighted that top management plays a crucial role in fostering technological innovation by creating an organizational environment that embraces change and encourages innovation. Elbanna [29] emphasized the significance of allocating key talented members within the organization for AI technology adoption along with management support in allocating sufficient finances and other resources. Furthermore, Ghobakhloo and Ching [30] argued that in highly uncertain business environments, successful AI adoption requires clear operational standards, governance, and systematic support. As indicated by Kim and Kim [31], establishing formal or informal reward programs within the organization for technology innovation and change management is essential for the flexible and smooth progression of technology adoption. Overall, the adoption of AI technology is influenced by organizational readiness, including policies, culture, and resources, as well as the perceptions of organizational members.

Third, environmental factors play a crucial role in influencing the adoption of new technology within organizations. These factors refer to external social and technological support, including competition, relationships with technology suppliers, and the regulatory environment [18,21,32,33,34]. Among these factors, competitive pressure is a significant driver of technological innovation. When companies face pressure from competitors who have adopted a new technology, they are compelled to swiftly embrace the same or more advanced solutions to maintain their competitive edge. Competitive pressure, as highlighted by Oliveira and Martins [35], plays a crucial role in driving companies to adopt AI technology. When a competitor embraces a new technology, other companies in the same industry often follow suit, either adopting the same solution or advancing beyond to maintain their competitive edge. Furthermore, the involvement of technology suppliers, as explained by Assael [36] and Johnsen [37], significantly influences the speed of technology adoption and its diffusion. Successful AI implementation requires close collaboration with partners for operational tasks, such as AI model training and maintenance, over a certain period after the implementation. Therefore, the relationships with consulting firms, technology suppliers, and maintenance service providers significantly influence the adoption of AI [38].

2.2. Corporate Network Operation Productivity and Network Service Stability

Artificial intelligence (AI) analyzes data from diverse sources to detect inefficiencies or bottlenecks in operational processes, streamlining workflows, and optimizing resource allocation [39]. Furthermore, AI insights, derived from high-quality data and aimed at optimizing limited resources within organizations, lead to enhanced operational efficiency and cost savings [40]. Moreover, AI’s ability to analyze extensive data, identify patterns, and make accurate predictions enables companies to mitigate potential risks in business by optimizing operational management. Therefore, successful AI adoption not only enhances productivity in network operations but also improves overall network service stability.

In a review of the previous studies on network operation productivity, Banica et al. [41] emphasized the use of AI for monitoring computing resources and promptly notifying operators when critical events exceed thresholds. To reduce the mean time to recovery (MTTR), they categorized alarms into the three distinct types of web services, social media, and network. Their framework automates event alarm processes, demonstrating the effectiveness of AI adoption in enhancing operational productivity. Wang et al. [42] empirically demonstrated the positive impact of AI on operational tasks, including cost reduction and shortened worktime, ultimately contributing to productivity improvement. They also underscored the importance of a human-centered approach alongside AI initiatives and advocated for a systematic portfolio of AI implementations. In addition, Dastane [43] explored the effects of adopting innovative technologies such as AI on organizational productivity. They underscored the critical need for organizations to maintain up-to-date IT infrastructure and remain agile and responsive to evolving technological advancements.

As organizations increasingly adopt AI technology to automate and optimize operational tasks within complex multi-vendor, multi-domain networks, they contribute to improving network operation productivity. Chen et al. [44] and Masood and Hashmi [45] highlighted the importance of addressing challenges related to technological factors, including data standardization, dynamic network resource management, and network traffic classification. These factors play a crucial role in improving productivity for rapid incident response and overall operational improvement in complex environments. In another study, Rijal et al. [46] emphasized the importance of organizational support and perceived advantages among team members for successful AI adoption. By driving operational productivity through process automation and continuous improvement, AI technology positively affects operational productivity. Furthermore, Mughal [47] asserted that adopting AI technology capable of real-time prediction and rapid adaptation to changing business environments enhances market competitiveness and overall network operation productivity.

Based on these previous studies, the technological, organizational, and environmental factors that influence the adoption of AI technology in network operations ultimately have a direct impact on network operation productivity. Taking this into consideration, in this study, the following hypotheses were established:

H1. 

Technological factors that influence the adoption of AI in corporate IT network operations will have a positive impact on network operation productivity.

H2. 

Organizational factors that influence the adoption of AI in corporate IT network operations will have a positive impact on network operation productivity.

H3. 

Environmental factors that influence the adoption of AI in corporate IT network operations will have a positive impact on the network operation productivity.

The network service stability refers to the capability of networks to operate consistently despite failures, external threats, or attacks [48,49]. When networks integrate AI, they become intelligent networks. These network intelligence technologies automatically handle tasks such as network configuration, control, management, and orchestration by analyzing data collected from diverse network devices. Through autonomous decision-making using AI, these technologies enhance the stability and reliability of network services by predicting and preventing failures [50].

Yeruva [51] presented an IT operation framework that leverages AI to monitor infrastructure components, alert operators to detected abnormal patterns, and predict potential failures for self-healing based on an understanding of dependencies between IT assets. The primary goal was to enhance service quality and stability by minimizing network failures. Clemm et al. [52] emphasized the importance of accurate end-to-end service assurance monitoring, which is necessary for predicting and preventing failures, ensuring consistent network performance, and maintaining service quality beyond mere problem resolution. Luo et al. [53] investigated the potential of AI in monitoring mobile networks, making intelligent decisions, and taking proactive actions. They advocated for an AI-based network architecture that is directly integrated with network quality of service (QoS) to ensure stable network services. Coronado et al. [54] emphasized the significance of adopting zero-touch networks (ZTNs). In addition, they emphasized the need for standardization to enhance network stability. This is particularly relevant given the operational challenges posed by complex and heterogeneous network architectures involving products from multiple vendors.

In the context of increasingly complex networks, traditional manual network operations encounter significant challenges in maintaining service quality and stability. The need for rapid responses to evolving business demands often strains operational teams. Consequently, there is a growing demand for an AI-driven network operational system that can predict and maintain stability and reliability. Khan et al. [55] introduced an intent-based networking (IBN) framework that leverages software-based intelligence analytics and service orchestration for autonomous network operations and control. They emphasized the importance of stability in network operations by actively leveraging open-source technologies and solutions that offer rapid technological advancements and cost-effectiveness. In addition, Velasco et al. [56] highlighted the critical role of networks in providing agile and stable services for business operations. Their network control and the management framework, along with relevant use cases, automatically reconfigure and optimize networks in response to dynamic traffic changes.

In contrast, Yates and Ge [57] argued that leveraging AI to streamline complex network processes not only reduces the involvement of the operational organization but also enhances network performance and service stability. They emphasized redefining the operational organization’s role to achieve more efficient AI-driven network operations. Furthermore, Zeb et al. [58] highlighted the evolving competitive technological landscape, advancing toward intelligent networks. This transformation results from the convergence of advanced technologies such as AI, hybrid cloud, edge-native computing, and network softwarization. Consequently, the ability to predict failures and ensure network service stability through AI adoption becomes a crucial competitive advantage for companies in an environment where intelligent networks are increasingly secure.

Based on these previous studies, the technological, organizational, and environmental factors that influence the adoption of AI technology in network operations ultimately have a direct impact on network service stability. Taking this into consideration, in this study, the following hypotheses were established:

H4. 

Technological factors that influence the adoption of AI corporate IT network operations will have a positive impact on network service stability.

H5. 

Organizational factors that influence the adoption of AI corporate IT network operations will have a positive impact on network service stability.

H6. 

Environmental factors that influence the adoption of AI corporate IT network operations will have a positive impact on network service stability.

2.3. Productivity, Stability, and AI Technology Adoption

AI has a positive impact on businesses by automating routine operational tasks, improving customer experiences, and enabling customer-centric services [59,60,61]. However, relying solely on human expertise for network operations results in limited automation. As network scale and complexity grow, this manual approach leads to increased costs and inefficiencies. To address these challenges, Altamimi et al. [62] advocated exploring reinforcement learning (RL) in AI to create more productive and stable network operational systems. By adopting AI technology, network operators receive prompt recommendations for remedial actions, enabling autonomous problem resolution and significantly improving network operation productivity and service stability.

Duan et al. [63] argued that AI adoption within organizations enhances productivity by automating tasks and optimizing processes. In addition, it facilitates rapid decision-making through data-driven insights. Balakrishnan and Dwivedi [64] noted that in network operations, failure management and traffic engineering rely on implicit operational guidelines and principles. However, automating these complex processes remains challenging. Organizations often resort to purchasing expensive commercial products or relying on manual efforts from network experts. Consequently, such inefficient network operations lead to reduced productivity, emphasizing the importance of AI adoption for achieving more productive network operations.

Yeruva [65] investigated the potential of AI as an effective solution for reducing the mean time to detect (MTTD) and mean time to recover (MTTR) in network operations by mitigating the inefficiencies caused by redundant and repetitive tasks performed by network operators. Yeruva emphasized that an AI-based network operations framework, which comprises technical architecture, learning algorithms, and operational applications, allows customization to a company’s specific conditions and available data. This customization is crucial for achieving the optimal productivity desired by companies.

From the perspective of network service stability, Mohammed et al. [66] emphasized the logical relationship between network problems and the corresponding actions required for resolution. They highlighted the significance of autonomous networks through AI adoption, particularly in automating operational processes such as failure management and traffic engineering. These processes constitute a substantial portion of network operations. By advancing AI capabilities, this approach has been proven to improve problem-solving efficiency and reduce costs. Moreover, AI-based automation contributes to network service stability by reducing the network failure rate, even as data volume and service complexity increase.

Most companies operate a network operations center (NOC) to monitor network status and respond to failures, ensuring stable business services. The NOC systematically handles a high volume of messages, errors, warnings, and network failures daily. While numerous studies have explored process automation to control or reduce network operational costs, several factors hinder improvements in network capabilities. These factors include network scale, complexity, legal regulations, and the dynamic technological landscape. However, as indicated by Khan et al. [55], adopting AI is strongly recommended to overcome these limitations.

As such, the adoption of AI plays a critical role in discussions about improving the productivity and stability of network operations. Network operation productivity and network service stability are significant factors that directly influence the decision and intention to adopt AI. Considering this, in our study, the following hypotheses were formulated:

H7. 

Network operation productivity will have a positive impact on the intention to adopt AI in network operations.

H8. 

Network service stability will have a positive impact on the intention to adopt AI in network operations.

3. Methods

3.1. Research Model

In this study, the impact of technological, organizational, and environmental factors on the adoption of artificial intelligence (AI) in network operations was empirically analyzed. We classified three main factors as the independent variables, technological, organizational, and environmental factors. The network operation productivity and network service stability were set as the mediating variables, whereas the intention to adopt AI in network operations was set as the dependent variable. Causal relationships between these variables were established, and a research model was designed, as shown in Figure 1, using path analysis based on structural equation modeling (SEM).

3.2. Measurement Variable and Data Collection

To collect data for analyzing the model, we conducted a survey. The survey items were developed based on previous studies, as shown in

Table 1. Variable definitions and measurement items.

Factors		Measurement Items	References
Technology	Relative Advantage (REA)	▪ More efficient execution of network operational tasks (monitoring, configuration management, etc.). ▪ Reduction in recovery time in the case of network problems. ▪ Operational cost-saving effects. ▪ Agility in responding to business requirements.	[54,67,68,69,70,71]
	Compatibility (COM)	▪ Ease of integration with existing systems and technologies. ▪ Compatibility with existing workflows and processes. ▪ A good reflection of the requirements for business services. ▪ Low risk of security threats.	[56,69,70,71,72,73]
Organization	Top Management Support (TMS)	▪ Top management’s clear goals and problem awareness. ▪ Top management’s good understanding of the benefits of AI adoption. ▪ Top management’s proactive willingness to adopt AI. ▪ Top management’s active support for providing necessary resources (personnel, facilities, polities, etc.)	[25,69,74,75,76]
	Organizational Readiness (ORR)	▪ Sufficient investment budget secured for AI adoption. ▪ Professional technical skills and manpower in place. ▪ A positive and collaborative organizational culture fostered for successful AI adoption. ▪ Compliance with essential rules and procedures.	[27,71,72,73,75,77]
Environment	Competitive Pressure (COP)	▪ Urgency for enhancing network operational tasks. ▪ Secure competitive advantage. ▪ Alignment with the desires of industry stakeholders (customers, partners, etc.) ▪ Market demands from customers and consumers within the industry environment.	[45,69,70,71,78]
	Collaborative Environment (COE)	▪ Direct and indirect support from partners for AI technology implementation. ▪ Maintenance of close relationships with partners. ▪ Sufficient information provided by partners. ▪ Work with partners with strong technical expertise.	[65,67,69,75,79]
Network Operation Productivity (NOP)		▪ Improvement in the speed of operational task processing. ▪ Simplification of complex network operations. ▪ Contribution to increasing productivity in network operations. ▪ Strengthening network operation efficiency.	[39,41,43,44,45]
Network Service Stability (NSS)		▪ Reduction in error and outage rates during network operations. ▪ Improvement in the quality of network services. ▪ Enhancement of the reliability of network services. ▪ Protection of information.	[53,54,55,56,80]
AI Adoption in Network Operations (ANO)		▪ Strong willingness to adopt and use AI in network operations. ▪ Intention to leverage AI in various operational fields. ▪ Recognition of AI technology as one of the organization’s key competitive strategies. ▪ Commitment to continuous development and improvement in the future.	[64,65,67,68,81]

. Operational variables for the survey constructs were defined accordingly. In this study, the factors that influence the adoption of AI technology in network operations refer to considerations when applying AI technology to the network operations of a specific organization or company. These influencing factors represent the considerations that must be addressed during the adoption and implementation of the technology. By effectively managing and using these factors, companies can facilitate successful network operation through AI.

In this study, the factors influencing the adoption of AI technology in network operations were classified based on the TOE model, which is a widely recognized framework used to explain and predict the likelihood of innovation and technology adoption and diffusion within organizations or industries. The independent variables were categorized into the three groups of technological factors, organizational factors, and environmental factors.

Technological factors refer to the technological aspects that influence the adoption and integration of AI technology into network operations. From a technological perspective, companies should consider the two key subcategories of the relative advantages and compatibility. The relative advantages encompass factors such as cost-effectiveness, resource efficiency, flexibility for internal or external changes, resilience to incidents, and manageability. Compatibility focuses on ease of use, usefulness, integration with existing systems, and security. Technological factors significantly influence the network operations because these operations must manage the network infrastructure using various technologies and tools to ensure secure and efficient networking services and resource access for users. Therefore, in this study, the technological factors that have a positive impact on the adoption of AI for network operations are defined as the variables.

Organizational factors refer to the support that organizations must provide to enhance network operation capabilities through the adoption of AI technology. From an organizational perspective, the two key subcategories need to be considered of top management support and organizational readiness. Top management support encompasses factors such as goal alignment, resource allocation commitment, and leadership competency with insights on benefits. Organizational readiness involves financial, technological, managerial, and cultural preparedness. These organizational factors should be considered alongside technological factors, and effective and successful AI adoption requires strategic and comprehensive efforts across the entire organization. Therefore, in this study, we defined organizational factors as variables that influence AI adoption and establishment for network operations.

Environmental factors refer to external influences on AI adoption in network operations. From an environmental perspective, companies must consider the two key subcategories of competitive pressure and a collaborative environment. Competitive pressure encompasses factors such as changes in the industrial structure, market uncertainty, and intensified competition. The collaborative environment involves vendor technological expertise, availability of vendor services, and relationships with partners. These environmental factors provide essential insights for developing technology adoption strategies and significantly impact the organization’s effective management of interactions with the external environment. Therefore, in this study, we defined environmental factors as variables that influence the adoption of AI technology in network operations.

Network operation productivity, which is defined as a mediating variable, refers to the capability of generating the performance required for organizations to achieve business objectives and goals by effectively operating and managing their networks. From a productivity perspective, companies must consider factors such as agility in task processing, operational simplification through automation, and cost reduction. These productivity factors play a crucial role in minimizing costs and maximizing business performance by effectively using information technology (IT) resources, in conjunction with technological, organizational, and environmental factors. Considering these various aspects, the network operations is defined as a variable that directly influences the intention to apply AI technology to network operations.

Network service stability, which is defined as another mediating variable in this study, refers to the capability of ensuring seamless communication and data access for users without any failures and to consistently maintain high-quality service across organizational networks. In practical terms, network service stability requires the capability of proactively minimizing failures and issues that may occur while users are using network services and prompt recovery when required. From a stability perspective, companies must consider factors such as high availability for zero downtime, resilient architecture, observability for detection and analytics, low latency performance, and security. These stability factors significantly impact user experience and reliability based on network service quality, in conjunction with technological, organizational, and environmental factors. Ensuring stability empowers users to consistently use network services, which increases service continuity and user satisfaction. In summary, network service stability directly influences the intention to adopt AI technology for network operations.

AI adoption in network operations, which is defined as the dependent variable in this study, refers to the goals and intentions of organizations to use AI technology in network operations. From the perspective of adoption intention, companies must consider various factors, such as business goals, competitive strategies, technological advancements, operational productivity, service stability, and user experience. Operational productivity and service stability represent core values in network operations. The adoption of AI technology enhances these values, thereby strengthening competitiveness. This improvement is crucial for companies and significantly influences adoption intention. In this study, adoption intention refers to the level of willingness and activity to adopt AI technology for network operational tasks and to further leverage it across various organizational fields.

The variables consisted of 36 survey items, as shown in Table 1. However, we excluded item 2 from the network operation productivity factor and item 1 from the network service stability factor based on the exploratory factor analysis results. Therefore, a total of three items were retained for the network operation productivity and network service stability factors. For data analysis, SPSS 28.0 was used to conduct demographic, reliability, descriptive statistics, and exploratory factor analyses. In addition, AMOS 28.0 was employed for confirmatory factor analysis, model validation, and path analysis based on structural equation modeling (SEM) for the defined hypothesis.

3.3. Demographic Information About the Data

In this study, we conducted an online survey using a random sampling method targeting practical professionals. The survey, which consisted of 36 questions derived from previous studies on artificial intelligence (AI) and networks, was distributed in Korean to numerous respondents using Google Forms for convenience and accessibility. A 5-point Likert scale was utilized to collect data on the adoption of AI technology in network operations (see Figure A1). Before conducting the actual survey, questionnaire validation and pilot sampling were conducted with five experts, including AI and network specialists, to enhance the reliability and validity of the survey. Furthermore, the survey included detailed explanations of the research objectives and instructions on how to respond. To address any potential confusion regarding the meaning of specific questions, clarifications were provided to respondents through video conferencing or in-person meetings. The survey respondents included professionals from various industries, such as high technology, media and communications, manufacturing and distribution, finance and healthcare, as well as the government and public sector, all of whom were directly or indirectly involved with AI and network operations. We collected 212 responses, excluding 52 unreliable responses, resulting in 160 valid samples for analysis. The survey was conducted over two weeks, from 8 January 2024 to 19 January 2024. To assess the reliability and validity of the respondents’ answers, we applied the following criteria: we thoroughly examined whether the respondents demonstrated a comprehensive understanding of the subject, whether their answers were consistent with the overall context, whether their opinions were objective and data-driven, and how closely their responses aligned with the key points of the questions. The demographic characteristics of the respondents are presented in

Table 2. Demographic information of survey participants.

Classification		Frequency (n)	Percentage (%)
Sex	Male	142	88.8
	Female	18	11.3
Age	Less than 30	3	1.9
	30–less than 40	34	21.3
	40–less than 50	70	43.8
	50 or older	53	33.1
Academic background	College Degree	4	2.5
	Bachelor’s Degree	92	57.5
	Master’s degree (and above)	64	40.0
Industrial Area	High Technology, Media, and Communications	106	66.3
	Manufacturing and Distribution	18	11.3
	Finance and Healthcare	23	14.4
	Government and Public Sector	8	5.0
	Other	5	3.1
Job Area	Management and Strategy	24	15.0
	Finance, Procurement, and Human Resources	3	1.9
	Information Technology (IT)	108	67.5
	Sales and Marketing	23	14.4
	Other	2	1.3
Work Experience	Less than 5 years	8	5.0
	5–less than 10 years	20	12.5
	10–less than 15 years	23	14.4
	15–less than 20 years	35	21.9
	More than 20 years	74	46.3
Professional Area	Demander	86	53.8
	Provider	74	46.3

.

4. Results

4.1. Reliability and Validity Analysis Results

In

Table 3. Results of reliability and convergent validity test.

Variables	Measurement Item	Standard Loading	SE	t-Value	p-Value	CR	AVE	Cronbach α
Technology	REA	0.783				0.844	0.734	0.626
	COM	0.587	0.100	6.445	***
Organization	TMS	0.635				0.896	0.814	0.693
	ORR	0.837	0.209	6.874	***
Environment	COP	0.786				0.800	0.671	0.637
	COE	0.598	0.088	7.759	***
Productivity	NOP1	0.852				0.912	0.777	0.859
	NOP3	0.838	0.086	12.050	***
	NOP4	0.768	0.081	10.804	***
Stability	NSS2	0.863				0.858	0.671	0.843
	NSS3	0.832	0.098	9.837	***
	NSS4	0.654	0.117	7.576	***
AI Adoption	ANO1	0.877				0.920	0.743	0.919
	ANO2	0.948	0.065	17.083	***
	ANO3	0.775	0.082	12.255	***
	ANO4	0.788	0.072	12.611	***

Structural model fit: χ²(df) 141.011(87), χ²/degree of freedom 1.621, RMR 0.028, GFI 0.897, AGFI 0.840, NFI 0.912, TLI 0.950, CFI 0.964, RMSEA 0.062./Note: *** p < 0.001.

, the results of the reliability and convergent validity analysis for the measurement model appear to be adequate. The factor loadings ranged from 0.587 to 0.948, and all values exceeded the recommended threshold of 0.5. In addition, the composite reliability (CR) ranged from 0.800 to 0.920, further supporting the internal consistency. The t-values were all at least 6.445, further confirming statistical significance. Furthermore, the average variance extracted (AVE) values ranged from 0.671 to 0.814, and the Cronbach’s alpha values ranged from 0.626 to 0.919, both confirming the convergent validity.

To analyze the overall fit of the measurement model, we examined several fit indices. The chi-square (χ²) statistic was 141.011 (df = 87), and the χ²/degree of freedom ratio was 1.621. In addition, the goodness-of-fit index (GFI) was 0.897, the adjusted goodness-of-fit index (AGFI) was 0.840, the normal fit index (NFI) was 0.912, and the root mean square error of approximation (RMSEA) was 0.062. These values indicate that the fit indices for the measurement model were statistically significant, confirming the overall validity of the model.

In this study, a systematic approach was employed to analyze the quantitative data collected from survey responses. Specifically, structural equation modeling (SEM) was utilized to derive model coefficients based on the respondents’ answers, as shown in Figure 2. Each response was quantified using a 5-point Likert scale, accurately reflecting the perceptions of the respondents. The reliability and validity of the derived coefficients, including their adequacy, were verified through exploratory and confirmatory factor analyses, as well as correlation analysis, prior to testing the hypothesis through path analysis and examining mediating effects. These analyses confirm that the model coefficients are both reliable and valid, indicating that the collected data are appropriately represented within the model. Furthermore, it was determined that there are no issues with the measurement model fit, and the fit indices of the structural equation model support this conclusion. This indicates that the reliability and validity of the collected data and the research findings are robust.

As indicated in

Table 4. Correlation matrix and AVE.

Factors	AVE	Technology	Organization	Environment	Productivity	Stability	AI Adoption
Technology	0.734	0.857
Organization	0.814	0.742	0.902
Environment	0.671	0.654	0.784	0.819
Productivity	0.777	0.766	0.639	0.738	0.881
Stability	0.671	0.541	0.477	0.783	0.577	0.819
AI Adoption	0.743	0.542	0.486	0.791	0.442	0.429	0.862

Note: The square root of AVE is indicated in bold.

, the analysis of the AVE values and correlation coefficients between the latent variables in this study revealed that the square root of the AVE value for each latent variable exceeded the correlation coefficients between the latent variables. This confirms the discriminant validity of the measurement model.

4.2. Analysis Results of the Structural Model

In

Table 5. Results of hypothesis test.

Hypothesis (Path)		Standardized Regression Weights	t-Value	Hypothesis Adoption
H1	Technology → Productivity	0.578	2.761 **	Adopted
H2	Organization → Productivity	−0.120	−0.604	Rejected
H3	Environment → Productivity	0.445	2.744 **	Adopted
H4	Technology → Stability	0.175	0.862	Rejected
H5	Organization → Stability	−0.403	−1.515	Rejected
H6	Environment → Stability	0.983	3.397 ***	Adopted
H7	Productivity → AI Adoption	0.313	2.992 **	Adopted
H8	Stability → AI Adoption	0.297	2.718 **	Adopted

Structural model fit: χ²(df) 184.219(91), χ²/degree of freedom 2.024, RMR 0.046, GFI 0.874, AGFI 0.812, NFI 0.885, TLI 0.917, CFI 0.937, RMSEA 0.080. Note: ** p < 0.01, *** p < 0.001.

and Figure 3, the results of the structural equation model fit analysis indicate that the chi-square (χ²) statistic is 184.219 (df = 91), and the χ²/degree of freedom ratio is 2.024. In addition, the root mean square residual (RMR) value is 0.046, the goodness-of-fit index (GFI) value is 0.874, the adjusted goodness-of-fit index (AGFI) value is 0.812, the normal fit index (NFI) value is 0.885, and the root mean square error of approximation (RMSEA) value is 0.080. These fit indices confirm that the model fit is statistically significant. Furthermore, the comparative fit index (CFI), which reflects the explanatory power of the model and is not influenced by sample size, is 0.937, while the TLI (Turker–Lewis index), which assesses the structural model’s explanatory power, is 0.917. These values indicate that the proposed model demonstrates an excellent fit.

The path analysis of the structural equation model resulted in the rejection of three of the eight hypotheses. Specifically, organizational factors related to the adoption of AI for network operations were observed to have no significant impact on either network operation productivity or network service stability, leading to the rejection of the corresponding hypothesis. In addition, technological factors were observed to have no significant impact on network service stability, resulting in the rejection of another hypothesis. On the contrary, technological factors (with a coefficient of 2.761, p < 0.01) and environmental factors (with a coefficient of 2.744, p < 0.01) exhibited a positive impact on network operation productivity. Furthermore, environmental factors (with a coefficient of 3.397, p < 0.001) were observed to have a positive impact on network service stability. Finally, network operation productivity (with a coefficient of 2.992, p < 0.01) and network service stability (with a coefficient of 2.718, p < 0.01) positively influenced the intention to adopt AI.

4.3. Mediated Effect

In this study, the significance of indirect effects was validated using the bootstrap method to derive the direct, indirect, and total effects, as shown in

Table 6. Results of the mediated effect.

Dependent Variable	Explanatory Variable	Direct Effect	Indirect Effect	Total Effect
AI Adoption	Productivity	0.313 **		0.313
	Stability	0.297 **		0.297
	Technology		0.233	0.233
	Organization		−0.157	−0.157
	Environment		0.432 *	0.432

Note: * p < 0.05, ** p < 0.01.

. The path analysis results revealed that independent variables—technological and organizational factors—did not influence the intention to adopt AI through the mediation of network operation productivity and network service stability. However, environmental factors (with a coefficient of 0.432, p < 0.05) significantly impacted the intention to adopt AI.

5. Discussion

Based on previous studies, in this study, the factors were defined that influence the adoption of artificial intelligence (AI) technology in network operations according to the TOE model, representing the technological, organizational, and environmental factors. Additionally, we examined the relationships between these influencing factors and AI adoption using network operation productivity and network service stability as mediating variables. Our analysis yielded the following conclusions:

First, we observed that the environmental factors exerted the greatest influence on AI technology adoption in network operations. These environmental factors directly and positively influenced network operation productivity and network service stability, thereby influencing AI technology adoption through these mediators. These results underscore the significant impact of competitive market environments and collaborative relationships on AI technology adoption in network operations. This aligns with previous studies [18,34,35], which highlighted that fierce competition within industries and markets fosters the rapid adoption of innovative technologies. Technical support and maintenance by professional consulting firms and other suppliers can influence companies innovation efforts and promote innovation among partners within the company’s value chain [82]. Therefore, as highlighted by Lippert and Govindarajulu [83], maintaining a competitive edge in a dynamic industry landscape requires the swift and flexible adoption and use of AI technology to outpace competitors. This strategic approach requires a comprehensive understanding of environmental influences. In this context, proactive responses to competitive pressures and collaborative engagement with external partners and suppliers play a critical role in enhancing network operation productivity and network service stability through AI technology adoption.

Companies typically prefer outsourcing services to specialized firms rather than internally developing AI solutions. In this context, partners often require access to substantial data volumes, which may include sensitive customer information, for effective AI model training. Therefore, to facilitate seamless collaboration with partners, companies must standardize data collection and collaboration processes while ensuring a secure and open work environment. In addition, after AI adoption, maintaining close working relationships with partners becomes crucial for proper model training and maintenance. Consequently, the quality of relationships with partners in consulting, technology supply, and operations outsourcing significantly influences companies’ success in AI adoption. These findings underscore the pivotal role of environmental factors, particularly collaboration with external partners, in shaping AI decisions related to network operations, emphasizing productivity and stability.

Second, the network operation productivity and network service stability were found to positively impact the intention to adopt AI for network operations. These core values in network operations play a crucial role in influencing AI adoption. Previous studies [63,64] have highlighted that advances in AI, particularly in areas such as task automation, performance monitoring, event correlation analysis for network operation productivity, decision-making automation, resource management, and control automation for network service stability, can lead to cost reduction, competitive advantages, and more secure network services.

Furthermore, as Oi et al. [3] mentioned, AI adoption in network operations is synonymous with automation and enables network operation automation for reactive incident response rather than network service automation for proactive fault prevention. Shenoy [84] asserted that companies are grappling with increased demands for network operations because of diverse business expansions. In addition, the complexity of the architecture, technology, processes, and management tools increases with network scale. To address these challenges, the importance of network automation in enhancing network service stability is emphasized, which aligns with the maturity of network automation. Therefore, a strategic approach is essential for achieving seamless integration with existing systems and processes to realize AI technology adoption, considering a balanced focus on network operation productivity and network service stability.

Third, when considering the adoption of AI for network operations, the significance of network operation productivity and network service stability becomes evident. Technological and environmental factors played crucial roles, whereas organizational factors were less relevant. These findings align with companies network management practices. Small–medium companies often lack a dedicated network operation team; instead, they rely on cloud services. Even large companies often operate their networks with minimal personnel and delegate operational tasks to specialized partners in various fields. Davenport and Ronanki [85] highlighted that the adoption of new technologies heavily relies on top management’s attention and support because it entails important decision-making, such as financial investments and changes in organizational structure and business processes. Andenmatten [60] emphasized that while technological and environmental competitiveness play a role, an organization’s strategy and readiness are crucial for AI adoption in network operations.

However, in contrast to previous studies, networks are specialized within organizations. Most networks, excluding those of telecommunications service providers, exhibit characteristics such as restricted external technology adoption, limited information sharing, and low interdependence within the organization. Therefore, improving network operation productivity via AI remains a challenge primarily confined to the network operation team. It relies less on top management support and organizational readiness. Furthermore, when AI adoption is considered in the context of network operation productivity and network service stability as mediators, challenges related to technological improvement in terms of productivity and stability should be addressed through strategic collaboration with partners rather than relying solely on internal organizational capabilities.

Given the significant cost and time associated with enhancing network operation productivity and network service stability through AI adoption, strategic collaboration with partners in various areas, such as best practice benchmarking, consulting, architecture, technology, processes, and management tools is crucial. Notably, the key influencing factors in AI adoption decision-making may differ when considering network operation productivity and network service stability rather than broader business and organizational changes.

6. Conclusions

6.1. Research Implications

AI adoption in recent network operations has manifested in various forms, including network ChatGPT, change impact analysis, toxic factor detection, configuration drift detection, End-to-End (E2E) service activation, proactive alarm detection and mitigation, closed-loop automation, and large language model (LLM)-based configuration audits. The research model proposed in this study, along with the reliability, convergent validity tests, hypothesis testing, and mediation effect analysis results, can serve as a valuable decision-making tool for companies seeking to integrate and optimize AI technologies in network operations. In particular, it helps address the challenges faced by companies in prioritizing and focusing efforts during AI adoption process. Unlike most previous studies that have focused solely on the technical aspects, this research model takes a comprehensive approach by considering the technological, organizational, and environmental factors, and by directly linking the intention of AI adoption to the core values of network operations such as operational productivity and service stability. In summary, the proposed model can be effectively applied by companies to maximize the potential of AI and optimize their network operations.

Further, the era of hyper-connectivity, hyper-intelligence, and hyper-convergence has led to a significant increase in network scale and complexity. This evolution renders manual network operations not only costlier but also less efficient, thereby substantially increasing the risk of network failures. In addition, network architecture is rapidly transitioning from hardware-centric to software-centric, fostering a favorable environment for artificial intelligence (AI) adoption. Given that these network transformations profoundly affect digital transformation and innovative service delivery, there is an increased need for AI-based network operation strategies and systems to enhance network operation productivity and service stability. In this study, the importance is emphasized of analyzing and understanding the factors and their correlations associated with process automation for network operations, the transition to intelligent networks, and AI adoption.

The success of adopting AI technology within the organization for network operation is influenced by various external and internal factors. Therefore, the model presented was constructed based on the technology–organization–environment (TOE) framework to cover these factors. This model covers market trends, competitive pressures, partner cooperation, regulatory requirements as external factors, and the organizational culture, existing infrastructure and technology, and resource availability as internal factors. This provides a framework for comprehensively evaluating the various factors influencing AI adoption. As a result, this model helps stakeholders prioritize areas of investment and improvement that align with their strategic goals. It also leads to a harmonization of technical capabilities and organizational readiness. It will also serve as a diagnostic tool to identify potential challenges and barriers, facilitating informed and effective decision-making.

The findings of this study highlight significant implications. First, companies are transforming their business models to adapt to widespread digitalization. However, network complexity is the greatest obstacle to digital transformation. This complexity hinders digital transformation and business growth by increasing the time required for network operations, complicating operational tasks, and incurring high costs. As businesses increasingly rely on networking capabilities, network failures can result in severe financial losses and reputational damage. Although these failures are often caused by external security threats, they often stem from human error by network operators. These mistakes can be attributed to a lack of standardization and compliance, but fundamentally, they arise from network complexity.

To address the complexity of networks and network operations within organizations, companies have been emphasizing operational process automation, which is typically categorized into engineering, operations, and analytics. Automation, which automatically executes, monitors, and controls complex processes, is particularly effective for repetitive or rule-based tasks. This represents the optimal scenario for adopting AI. However, the adoption of AI-based network operation automation involves various pitfalls and considerations. The most critical factor is familiarity with complexity. Overcoming this familiarity with existing environments presents an opportunity to enhance network operation productivity in a highly competitive environment. Therefore, companies should prioritize the value of network operation productivity and pursue long-term strategies and goals for efficiently managing and advancing their networks. These strategies should be implemented by considering the complexity and urgency of operational tasks and making incremental changes.

Second, companies continually strive to improve their network architecture to ensure that network services are as resilient as possible, even in the event of network failures. The transition from hardware-centric to software-centric architecture in network operations not only provides business growth opportunities but also establishes an optimal environment for AI adoption. Network service stability, which ensures normal network operations even during failures or external attacks, is becoming increasingly important due to network softwareization and AI development.

Network operation productivity and network service stability are both driven by automation. Network operation productivity relies on automation to promptly resolve issues that arise after they occur, while network service stability relies on automation for rapid service recovery, minimizing downtime. The key concept in adopting AI for network service stability is closed-loop automation. This involves the real-time monitoring of the network and resource status based on network operation policies, thereby ensuring optimal network service quality and resource use. Closed-loop automation requires the fulfillment of several criteria, including the workflow for process automation, assurance for monitoring network behavior and status, provisions for automatically performing network operational tasks based on policies, and multi-vendor support for accommodating network devices from various manufacturers. These elements constitute the conceptual and foundational framework of the ‘intelligent network’.

The transition to intelligent networks introduces several considerations, with the most significant being resistance to AI. Overcoming this resistance and entrusting network services to autonomous AI decision-making provides an opportunity to improve network service stability in a highly competitive environment. Therefore, companies should prioritize network service stability, establish a vision, and set goals for improving the flexibility and availability of network architectures and operational systems. This requires a clear understanding of AI and related technologies as well as gradual pursuit through collaboration with external specialized partners, professionals, and academia.

Third, when integrating AI into network operations, the most affected are employees. Essentially, AI adoption automates and optimizes repetitive yet complex network operational tasks, thereby reducing employees’ workloads. This allows employees to allocate more time to strategic and creative work, thereby enhancing overall operational efficiency. However, there are also drawbacks. Some employees may struggle to adapt to new AI-related technologies, and certain tasks may be replaced by AI, leading to job insecurity. AI adoption enables productivity and reliability improvements through network operation automation and service optimization, as well as data-driven rapid decision-making. However, attitudes and willingness to accept AI significantly influence its adoption. Therefore, companies must develop strategies and roadmaps that account for employees directly and indirectly affected by AI adoption. This approach is crucial for enhancing network operation productivity and network service stability, fostering a mutually complementary relationship between AI and employees.

Moreover, companies should support employees’ capability development and enhance their understanding of change through appropriate training and assistance. Actively involving employees in the AI adoption process, gathering their feedback, and transparently communicating changes in their roles—positioning AI not as a job replacement but as a complementary tool to help them focus on higher-value tasks—are all critical elements. Sharing successful AI adoption cases from other companies is also essential. When these actions are combined with structured training for skill development, best practices can be implemented to ensure that employees feel valued and included in the transition process.

Ultimately, companies must address employee resistance to change and adapt their approach accordingly. Furthermore, establishing and enforcing key performance indicators and compensation systems for network operations are essential for sustaining momentum. The indicators for evaluating the success of AI adoption can be broadly categorized into operational efficiency indicators and employee morale indicators. Companies should establish a set of key performance indicators (KPIs) to monitor the impact of AI on operational performance.

The operational efficiency indicators should assess the following six key aspects: maximizing network availability; accelerating detection and resolution; enhancing customer experience; reducing operational costs; empowering and scaling teams; and promoting business agility and informed decision-making. As for the indicators of employee morale, it is essential to evaluate whether the adoption of AI positively impacts employee morale through regular employee satisfaction and job satisfaction surveys, as well as turnover rates. In the long term, continuous performance reviews, benchmarking, surveys, feedback, and monitoring of key performance indicators are essential for evaluating the effectiveness of AI adoption in network operations.

Hence, the model in this study provides a practical application framework for comprehensively assessing the various factors that influence AI adoption. It can be used as a company-wide decision-making system, AI technology adoption guidelines, and an evaluation tool for companies considering AI technology adoption by covering market trends, competitive pressures, partner collaboration, and regulatory requirements as external factors, as well as organizational culture, existing infrastructure and technology, and resource availability as internal factors. It is important to evaluate the complexity and potential quality in implementing various technologies in the corporate production process. In this regard, most of the existing research has focused on the technical aspects or problems of the process itself.

However, the model presented in this study suggests that the problem of AI technology adoption is not just a technical problem, but also requires consideration of the organizational and environmental factors in an integrated manner. Partner cooperation that adopts AI technology in network operation, the competitive advantage gained through network operation productivity, service stability, AI-based network operation automation, and organizational problem-solving that hinders AI adoption can be used as a model for AI technology adoption as they are all important factors in AI technology adoption.

Choosing AI technology that fits the digital technology status will minimize the organization’s operating costs while providing the basis for making the right level of investment in line with overall organizational operations and corporate efficiency policies. In this regard, the research model will set the criteria for companies to consider when adopting AI technology in their network operations and provide guidelines for decision-making.

It can also be used as a reference model to propose measurement methods in measuring the effectiveness of network operations by adopting artificial intelligence technology. For example, British Telecom (BT) proposes building a portfolio of various AI initiatives as one of the success factors of AI adoption. In addition to measuring the technical effects of AI adoption on its internal impact, it is also possible to measure the role of artificial intelligence in strengthening the company’s business and products and promoting development strategies by measuring the effectiveness of the environment and organization and evaluating its overall impact. Therefore, the research model of this article can be used within the enterprise as a model for subsequent operational evaluations along with decision-making guidelines.

6.2. Research Limitations and Future Plans

In this study, the relationships were highlighted between technological, organizational, and environmental factors and the adoption of artificial intelligence (AI) in network operations, focusing on the network operation productivity and network service stability, which are the core values of network operations. However, the following limitations were identified in this study. First, the findings of this study may not be generalizable due to its surveys focusing on network operations and AI professionals in South Korea. Future research should expand the sample size to include practitioners from diverse countries with advanced network and AI technologies.

Second, although the TOE model employed in this study considers technological, organizational, and environmental factors as determinants of AI adoption in network operations, a more comprehensive set of variables influencing AI adoption in this context needs to be explored. Future research should categorize real-world cases of AI adoption in network operations and analyze influencing factors by adoption type to provide decision-making guides from a practical perspective.

Third, in this study, the impact was explored of network operation productivity and network service stability on AI adoption. However, given the increasing complexity of network operations, it is essential to consider other relevant factors beyond productivity and stability. The future research should propose studies that evaluate and measure the impact of various factors influencing network operations, identified through prototyping in real environments. This evaluation should consider a network operations framework based on the concept of closed-loop automation and AI hallucination that extends beyond mere productivity and stability considerations.

Lastly, the results of this study provide a decision model and variables for network AI adoption. These results can promote the use of the system within the organization by establishing a decision support system in the future. In general, a decision support system is developed by decision tree methodology, and when data values according to measurement criteria and variables are entered, an automatic support system that presents a direction for decision making can be developed through technologies such as artificial intelligence or machine learning. However, since in this study research was not conducted that considers the implementation of such a system, future studies will be able to conduct research that considers the development of a decision support system for the introduction of artificial intelligence technology by companies.