West Science Social and Humanities Studies Vol.
No.
March 2026, pp.
IoT as a Business Performance Support: A Bibliometric Study Loso Judijanto IPOSS Jakarta.
Indonesia
Article Info
ABSTRACT
Article history:
This study examines the development of research on the Internet of Things as a support for business performance through a bibliometric Using data retrieved from the Scopus, the study analyzes publication trends, influential sources, and the intellectual structure of the field.
The analysis is conducted using VOSviewer and Bibliometrix to map co-authorship, co-citation, and keyword co-occurrence The findings reveal that IoT research has evolved from a primary focus on technological infrastructure, such as security and connectivity, toward more integrated themes including data analytics, artificial intelligence, and industry applications.
Several key clusters emerge, highlighting the role of IoT in operational efficiency, business process transformation, and intelligent decision-making.
The results also indicate a growing convergence between IoT and advanced digital technologies, suggesting a shift toward more strategic and performance-oriented applications.
This study contributes by positioning IoT as a capability that supports business performance and by identifying emerging research directions for future investigation.
Received Mar, 2026 Revised Mar, 2026 Accepted Mar, 2026 Keywords:
Internet of Things IoT Business Performance Bibliometric Analysis VOSviewer This is an open access article under the CC BY-SA license.
Corresponding Author:
Name: Loso Judijanto Institution: IPOSS Jakarta.
Indonesia Email: losojudijantobumn@gmail.
INTRODUCTION
The rapid advancement of digital operations, and compete in increasingly dynamic markets.
Among these technologies, the Internet of Things has emerged as a key driver of organizational transformation by real-time connectivity between physical and digital systems, and enhanced visibility across business processes.
Firms are no longer relying solely on traditional information instead, they are leveraging performance, optimize resource utilization, and respond more quickly to market changes.
This shift reflects a broader transition toward data-driven environments .
Within this context, the relationship between IoT adoption and business performance has attracted growing scholarly Business performance is often understood through multiple dimensions, including operational efficiency, financial customer value creation.
IoT technologies contribute to these dimensions by facilitating automation, improving decision-making accuracy, and enabling predictive capabilities.
For instance, organizations can use IoTgenerated data to anticipate maintenance needs, streamline supply chains, and Journal homepage: https://wsj.
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These capabilities suggest that IoT functions not merely as a technological tool, but as an enabler of improved organizational outcomes .
Despite the increasing volume of research on IoT, the existing literature remains fragmented across disciplines such as engineering, information systems, and operations management.
Many studies emphasize technical development or specific industry applications, such as smart manufacturing or logistics, while fewer studies explicitly connect IoT implementation to broader measures of business performance.
As a result, there is limited understanding of how the intellectual structure of this research field has evolved, what key themes dominate the discourse, and how different research This fragmentation creates a need for a systematic approach to synthesize and map the existing body of knowledge .
, .
To address this gap, bibliometric analysis offers a robust method for examining the development and structure of scientific By analyzing publication patterns, citation relationships, and keyword networks, bibliometric techniques can reveal the underlying themes, influential contributors, and emerging trends within a research field.
Tools such as VOSviewer and Bibliometrix enable researchers to visualize complex relationships and identify clusters of knowledge that may not be immediately apparent through traditional literature This approach allows for a more comprehensive understanding of how IoT research relates to business performance.
Based on these considerations, this study aims to map the intellectual structure and research trends of IoT in the context of business performance using a bibliometric Specifically, the study seeks to identify key themes, influential publications, and collaborative networks that shape this field of research.
By doing so, the study contributes to the literature by positioning IoT as a strategic enabler of business performance and by offering insights into future research The findings are expected to provide both academic and practical implications, particularly for organizations seeking to leverage IoT technologies to enhance their performance in an increasingly competitive environment.
METHODS
This study adopts a bibliometric approach to systematically analyze the scientific literature on the role of the Internet Things The data were collected from the Scopus database due to its broad coverage of high-quality peer-reviewed publications.
structured search query combining keywords such as AuInternet of Things,Ay AuIoT,Ay and Aubusiness performanceAy was applied to retrieve relevant documents within a defined time span.
The dataset was then refined through inclusion and exclusion criteria, focusing on articles and reviews published in English.
The analysis was conducted using VOSviewer and Bibliometrix to perform performance analysis and science mapping, including co-authorship, co-citation, and keyword co-occurrence.
These techniques enable the identification of research trends, thematic clusters, and intellectual structures, providing a comprehensive overview of how IoT research has evolved in relation to business performance.
RESULTS AND DISCUSSION
1 Keyword Co-Occurrence Network Vol.
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West Science Social and Humanities Studies Figure 1.
Network Visualization Source: Data Analysis Result, 2026 Figure 1 highlights the central role of the Internet of Things as the core node connecting multiple research domains.
Its position at the center of the map, with dense and multidirectional links, indicates that IoT serves as an integrative platform that bridges various technological and business-related The strong connectivity suggests that IoT is not studied in isolation but is deeply transformation discussions, particularly those involving data, infrastructure, and system One prominent cluster .
reflects the intersection of IoT with industry applications and operational systems, including terms such as industry 4.
manufacturing, supply chain management, sustainability, and artificial intelligence.
This cluster shows that a significant portion of IoT research is grounded in improving operational performance and industrial The presence of predictive maintenance and data mining further indicates a focus on using IoT-generated data to enhance decision-making and reduce operational risks, aligning closely with business performance outcomes.
Another cluster .
emphasizes business processes and organizational aspects, including business models, supply chains, efficiency, and data analytics.
This cluster suggests that IoT is increasingly being examined as a driver of process optimization and value creation.
The linkage between business models and ecosystems indicates that IoT is reshaping how organizations design and deliver value, moving toward more interconnected and platform-based This reinforces the idea that IoT contributes to performance not only through technology but also through organizational The blue and yellow clusters highlight the technological infrastructure and security dimensions of IoT.
The blue cluster includes concepts such as network security, digital storage, edge computing, and foundational systems that support IoT Meanwhile, the yellow cluster focuses on blockchain, security, data privacy, and access control, indicating growing concerns around trust, data protection, and system reliability.
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The purple cluster centers on advanced analytics and intelligent systems, algorithms, and edge computing.
This cluster shows that IoT is increasingly combined with artificial intelligence to generate predictive and adaptive capabilities.
The integration of these technologies points toward a shift from simple data collection to intelligent, automated decision-making systems.
Figure 2.
Overlay Visualization Source: Data Analysis Result, 2026 Figure 2 reveals the temporal evolution of research on the Internet of Things, where colors indicate the average publication year of each keyword.
Earlier studies .
lue to dark tones, around 2020Ae2.
are concentrated on foundational aspects such as network security, cryptography, digital storage, and basic IoT infrastructure.
This suggests that initial research efforts were primarily concerned with building reliable addressing security challenges as the technological backbone of IoT began to As the field progresses into the middle phase .
reen tones, around 2022Ae 2.
, the focus shifts toward integration and Keywords such as cloud computing, big data, data analytics, and 0 become more prominent, indicating a growing emphasis on how IoT systems can be utilized within organizational and industrial contexts.
This phase reflects the transition from purely technical development to value creation, where IoT is increasingly linked to operational efficiency, supply chain transformation initiatives.
More recent research trends .
ellow tones, around 2023Ae2.
highlight the emergence of advanced and intelligent capabilities, including machine learning, learning systems, artificial intelligence, and These developments indicate a autonomous systems that can generate predictive insights and support strategic decision-making.
The presence of topics such as data privacy, low latency, and access control also shows that as IoT applications Vol.
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Figure 3.
Density Visualization Source: Data Analysis Result, 2026 Figure 3 highlights the most intensively studied areas within the Internet of Things research landscape.
The brightest .
region is concentrated around Auinternet of things,Ay indicating its central and dominant role in the network.
Surrounding this core, high-density areas include keywords such as network security, digital storage, blockchain, and cloud computing, suggesting that a large portion of the literature focuses on the technological infrastructure that enables IoT systems.
This concentration reflects a strong research emphasis on ensuring system reliability, data management, and secure connectivity as IoT Beyond the central core, moderately dense .
regions extend toward themes such as industry 4.
0, data analytics, artificial intelligence, and machine learning.
These areas indicate growing attention to the application of IoT in enhancing business processes and generating value from data.
Meanwhile, less dense .
regions, such as sustainability, manufacturing, and predictive maintenance, suggest emerging or more specialized topics that are still developing within the field.
3 Citation Analysis Table 1.
Top Cited Research Citations Authors and year Title .
Internet of things for enterprise systems of modern manufacturing .
The role of digital technologies for the service transformation of industrial companies The role and impact of industry 4.
0 and the internet of things on the business strategy of the value chain-the case of hungary Vol.
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Impact of industry 4.
0 on environmental sustainability .
Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review Big data analytics: Applications, prospects and challenges .
Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia Blockchain-based business process management (BPM) framework for service composition in industry 4.
Architecture design approach for IoT-based farm management information systems Predictive maintenance architecture development for nuclear infrastructure using machine learning Source: Scopus, 2026 Discussion The findings of this bibliometric study demonstrate that the Internet of Things has evolved from a purely technological concept into a central element within broader discussions of business transformation and performance enhancement.
The dominance of IoT as a core node across all visualizations indicates its integrative role in connecting multiple domains, including data analytics, security, artificial intelligence, and industrial This pattern suggests that IoT is increasingly positioned not as an isolated infrastructure that supports interconnected data-driven The clustering structure further reveals that IoT research is strongly anchored in operational and industrial contexts, particularly through its association with 0, supply chain management, and manufacturing systems.
These linkages indicate that IoT contributes to business optimization, and real-time monitoring.
the same time, the presence of business models and ecosystems within the same cluster highlights a shift toward more strategic considerations, where IoT enables new forms of value creation and organizational design.
This reflects a transition from operational benefits toward broader competitive positioning.
Another important insight emerges from the prominence of technological infrastructure and security-related themes, including blockchain, network security, and data privacy.
The close proximity of these concepts to IoT suggests that trust, reliability, and data governance are central concerns in the adoption and scaling of IoT systems.
Rather than being peripheral issues, these elements appear to shape how organizations integrate IoT into their operations and how they manage associated risks.
This reinforces the idea that the performance implications of IoT are closely tied to the robustness and security of the underlying technological The temporal and density patterns also indicate a clear progression in the research focus, moving from foundational infrastructure toward advanced analytics and The prominence of machine learning, artificial intelligence, and predictive capabilities suggests that IoT is becoming part of a more sophisticated digital ecosystem where data is not only collected but actively used to inform decisions and automate processes.
This shift points to a deeper level of integration, where IoT supports adaptive and responsive organizational capabilities that can enhance performance in dynamic environments.
These findings support the view that IoT functions as a strategic enabler of business performance by facilitating data integration, enhancing decision-making processes, and Vol.
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From a theoretical perspective, this positions IoT within the logic of resource-based and capability-oriented frameworks, where its value lies not merely in the technology itself, but in how it is embedded within organizational systems and practices.
For future research, there remains a need to further explore the direct linkage between IoT adoption and measurable performance outcomes, particularly in underrepresented contexts such as small and medium enterprises and developing economies.
CONCLUSION
This study provides a comprehensive overview of how the Internet of Things has A 372 evolved within the academic landscape as a key enabler of business performance.
Through bibliometric analysis, the findings reveal that IoT research has progressed from a focus on technological infrastructure and security toward more integrated applications involving data analytics, artificial intelligence, and strategic business processes.
The intellectual structure of the field highlights IoTAos role in enhancing operational efficiency, supporting decision-making, and enabling innovation across various organizational This study reinforces the position of IoT as a strategic resource that contributes to performance outcomes, while also identifying opportunities for future research to further examine its impact in diverse industries and emerging markets.
REFERENCES