Page 1087
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Exploring the Intersection of AI, Big Data and Business Networks: A
Bibliometric and Scientific Mapping Study
Khyrunnisa. T. P
1*
,
Noora Mohamed Kutty
2
1
Department of Commerce, PSMO College (Autonomous) Tirurangadi, Affiliated to University of
Calicut, Malappuram, Kerala, India
2
Department of Commerce, PSMO College (Autonomous) Tirurangadi, Affiliated to University of
Calicut, Malappuram, Kerala, India
*Corresponding Author
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150400096
Received: 17 April 2026; Accepted: 22 April 2026; Published: 16 May 2026
ABSTRACT
The combination between Artificial Intelligence (AI), Big Data and Business Networks has substantially
transformed the system of performance of organizations and has enhanced inter-firm cooperation leading to
innovation in various industries. This current bibliometric and scientific mapping research paper examines the
intellectual environment of this interdisciplinary discipline in terms of the PRISMA model. The analysis is based
on meta-data obtained on Web of Science (2015 to 2025). A package of visualizations (geographical distribution,
annual publication patterns, author productivity, major journals, country output and keyword co-occurrence) are
done by using RStudio and Vos viewer are used to map the central research topics and developing areas.
Important groups draw the major themes and transformational research patterns. The ten most influential papers
are put in the spotlight, which provides a selective reference to the background and breakthrough articles. The
interpretation of the results on the collected data allows one to have a general picture of the evolution of the field
among scholars. The findings indicate the constant growth of the academic contributions toward the entire world,
with countries such as the United States, China and the United Kingdom taking the top table in terms of matters
regarding research production. Key journals and authors are proposed, confirming the increased significance of
AI and Big Data in optimization of the business networks in terms of its agility and efficiency. This work
provides valuable lessons to artists and scholars due to its ability to capture the most up-to-date trends and
highlight potential future directions of research that could benefit the resilience, scale and innovation in
Networked Business context.
Keywords: Artificial Intelligence, Big data, Business Network, Bibliometric Analysis, Innovation trends
INTRODUCTION
The aggregation of the ranges of Artificial Intelligence (AI), Big Data and Business Networks, the phenomenon
has become one of the most important fields of study, with the ability to revolutionarily transform the
organization behavior, operational processes and strategic motivation in the contemporary business
environments (Sanchez-Lopez, 2023). Intelligent analytics, grounded on large data volumes, allows companies
to attain practical knowledge, maximize the use of resources and maximize the effectiveness of cooperation in a
composite business environment (Ghaouri et al., 2023). Such networks characterized by complex inter-firm
interactions exploit AI and Big Data to innovate, increase their nourishment and address issues like privacy of
data, energy management and adaptive networks. Its particular importance in generating competitive and
sustainable growth expanded new attention in the academic literature as topics in management research have
flourished around the nexus of this intersection (González et al., 2022).
This bibliometric and scientific mapping study aims carefully at examining the intellectual framework, research
trends and thematic development of the interactions between AI, Big Data, and Business Networks.
Page 1088
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
The study is informed by a variety of methods and implementations, including: administered clustering, used in
the analysis of inter-firm performance sceneries in business networks (Sun et al., 2022), meta machine learning,
which enables distributed analytics without violating data privacy (Dsouza et al., 2025) and genetic-algorithm-
based approaches, which assist in energy-efficient management of 5G base stations (Galib et al., 2022).
Moreover, the distributing reinforcement learning technique of multi-agent systems demonstrates the impressive
capability of AI in improving the network interaction and functioning accuracy (Xavier et al., 2022).
Further, AI and Big Data in business networks consume affecting crucial problems. Through bibliometrics, the
paper can outline the scientific map and describe the field with its theoretical development, practical
implementation and unexploited areas of research. We target researchers and practitioners who can explore AI
and Big Data potential to support business networks and boost innovation and strength in a global economy that
will continue growing interconnected (Cantu & Tunisini, 2023).
Systematic Literature Review: AI and Business Networks (2015-2025)
Digital transformation and ai-enabled business models
The Future of Business Model-driven Digital Transformation and AI-Enabled Business Models The power of
AI has become a fundamental driver of digital transformation within businesses networks. It does not just spur
improvement within an organization, but also helps organizations to re-organize their inter-organizational
relationships and ecosystems. (Schroeder et al., 2019) present the case of sterilization through a business network
lens focusing on the industrial setting. They have made some suggestions that AI facilitates adaptive coordination
and changes of service-based business models. This is confirmed by (Camarinha-Matos et al., 2017), who have
come up with a conceptual framework of collaborative networks wherein AI is important in meeting the
interaction of the heterogeneous agents. (Papa et al., 2021) go on to believe that AI helps to redefine dynamic
capabilities of firms, enabling them to participate in the co-creation and innovation on a digital-mediated
platform. Furthermore, (Aguiar Lima Dos Santos et al., 2021) explore the topic of manufacturing networks and
point out that AI allows digitally monitoring the production, predicting its maintenance, and improving the
coordination between different production units.
Predictive analytics and strategic decision making
The ability to process big data and detect trends is what makes AI an important asset to business strategy
decision-making. It supports informed decision making on-the-fly, more so in complex business networks. (Hirt
et al., 2025) stress that effective execution of analytics depends on the design relative to the field and business
knowledge integration. They reveal that AI can provide some significant decision-making value in terms of being
united into strategic processes. Simple and practical proof of the benefit of AI systems is given by (Santos &
Campos, 2023) who show that economic forecasting and planning is more accurate when supported by AI
systems, especially in highly uncertain and interdependent conditions. Furthermore, (Rivera, 2024) summaries
the use of AI in the field of public health networks when predictive modeling helps with epidemic response and
organization of healthcare delivery, and algorithmic accountability comes to the face.
Learning and co-creation
Another vital role of AI in business networks is that it helps to share knowledge and innovation. Knowledge
flows are upgraded through AI tools which read, memorize and suggest contextual information to stakeholders.
The article by (Ben Yahia et al., 2021) formulates collaborative intelligence, explaining the broader concept of
the joint problem-solving process in partner firms through an AI-supported approach. The (Papa et al., 2021)
defend the idea that AI-based tools will boost the outcomes of knowledge management strategies, including
supporting innovation with real-time feedbacks, pattern recognition, as well as insights provided by machine
learning. Moreover, the (Camarinha-Matos et al., 2017) claim that, thanks to AI, firms can move beyond
straightforward sharing of information to intelligent, flexible collaboration and accomplish in smarter supply
chains and service innovation.
Page 1089
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Artificial intelligence in business to business (b2b) marketing and supply networks
In a business-to-business environment, AI benefits to reengineer the customer communication process,
relationship management and even supply chain coordination. AI assists companies in understanding customers,
their needs and preferences, increases logistics and optimal distribution. (Pardo et al., 2022) offer the evidence
that AI applications in B2B marketing allow companies to better assess the performance of partners; monitor the
behavior of buyers; and operate with complex, multi-levelled distribution channels. (Penttinen & Frösén, 2022)
are concerned with branding and market positioning and concluded that AI tools also help finite firms align
marketing activities with the strategic issues in the cross-partner networks. In the scope of manufacturing,
(Aguiar Lima Dos Santos et al., 2021) note that AI can play a prominent role in contributing to the agility of the
supplier networks through the forecasting of demand fluctuations and automation of the procurement strategies.
Ethics, governance and human-ai synergy
As the use of AI increases, ethical issues, transparency and the relationship between human and algorithm
employee are dominant. The literature escalates the two-edged sword AI in the networks AI is used to increase
efficiency and an increased risk with respect to accountability and fairness. (Rivera, 2024) provides an in-depth
discussion of ethical AI implementation at the level of the broad networks. His fear is about experiencing opaque
algorithm overly by relying on them in areas that concern human wellbeing. His work reveals why we should
adopt the governance structures that integrate the human control prevail component into AI software. Ethical
implications are indirectly mentioned in most of the reviewed articles, especially in the situations when an
autonomous decision is made or when firms share data
According to the 2015-2025 literature, AI had evolved in a business network as an instrument of automation to
become a strategic enabler. The thematic analysis validates the level of convictions that AI facilitates digital
transformation, teamwork innovation, information-guided marketing and knowledge-based planning. The lack
of representation of the SMEs and emerging economies in the study of AI-business networks. Edging
requirements of AI ethics, governance and regulatory compliance in inter-organizational regions. Further study
on the long-term effects of AI on business ecosystems and generating inclusive, transparent and morally secure
AI are the things that will be obligatory.
Objectives
To examine the trends in publication of Intersection of AI, Big Data and Business Networks during 2015-
2025.
To find out most popular authors, journals, country, sources and themes.
To understand keyword co-occurrence of articles.
Research question
RQ1: How are the publications and citation trend?
RQ2: Which are the clustering dominating research topics, authors, country, sources, journal and themes?
RQ3: What are keywords which most co- occurred?
METHODOLOGY
The figure 1 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a well-
recognized methodological framework that aims to increase the transparency and the extensiveness of systematic
reviews. It also provides a systematic process of finding, filtering and choosing feasible literature with
determined parameters that makes evidence synthesis rigorous and replicable (Ekundayo et al., 2021). The
research is based on a systematic review of literature with the help of the PRISMA framework that helps to find
and review research on the interconnection of artificial intelligence and business networks. The literature review
will be based on readings written before and after 1 January 2015 and not before 15 May 2025, where a search
Page 1090
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
will be done in May 2025. To apply consistency and fit with the research purpose, the eligibility was well
constructed and the document search process was clearly expressed to make it more open and justify the validity
of the research results. As per PRISMA guidelines, the review procedure followed four consecutive stages,
identification, screening, eligibility and inclusion.
In the identification stage, web-based search was conducted in the Web of Science database with a specific
search query: Title/Abstract/Keyword contains (“Artificial Intelligence” OR “AI” OR “Big Data” OR “Machine
Learning”) AND (“Business Network*” OR “Collaborative Network*” OR “Inter-Organizational Network*”
OR “Referral Network*” OR “B2B Network*”). The search approach that restricted the language of the article
originally retrieved 310 records within the scope of search results.
In the screening process, results were refined according to a group of exclusion criteria. In particular, the
publications and documents not in English; proceedings papers, review articles, book reviews and editorial
materials were excluded. As a consequence of that process, 59 records were disregarded, which brought 251
available articles to be considered as appropriate for further assessment.
During the step of eligibility, the entire texts of these 251 articles were carefully evaluated. The articles which
were deemed not relevant with the main scope- those not talking about integration of AI technologies into the
sphere of business or collaborative networks- were also excluded.
The final number of articles is 229 as a total of 22 articles were excluded fulfilling all inclusion criteria. These
229 full-text articles were used as the last step in the inclusion of the qualitative synthesis. Such documents are
a rich source of a literature that can be related to the research purpose
Figure 1: PRISMA four-phase flow diagram
RESULTS AND DISCUSSION
Country scientific production of articles
Figure 2 displays the visualization of the geographical behavior of the research articles in the field Artificial
Intelligence (AI) and business networks. Such bibliometric mapping shows the research output of the different
Page 1091
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
countries around the planet, reflecting high levels of regional variation in the number of publications. The
stronger shade of blue is associated with a larger number of articles, the more the research is active. The statistics
show that the large share of research output in this area was represented by 356 articles by China. This is followed
by United States that has 102 articles and Australia with 64 articles. The other significant contributions are those
of the United Kingdom (14 articles), Brazil (12 articles), Russia (10 articles) and Argentina (1 article).
With this graphic depiction, one will be able to better understand that the geographical focus on output can
embody the regional research priorities and invest in innovation in AI-driven business networks. In its turn, the
figure also indicates low contributions of Africa, the Middle East and to a certain extent, Europe and Latin
America, which suggests the possible areas of gaps and opportunities in the future cross-border research
activities
Figure 2: country scientific production of articles
Main information about data
The table 1 provided bibliometric data represents 2015-2025 with the total amount of 229 documents on the
basis of 172 sources, i.e., journals and books. This literature body has shown a strong rate of annual growth rate
of 25.25% implying that the field of study is growing at a very fast rate. An average age of the documents is 2.57
years, which implies that the field is young and currently developing. The average number of citations per
document is 10.16 with a relatively high academic impact. In a total, these documents refer to 13881 sources,
which indicates an impressive number of references to existing literature. When it comes to content there are
747 Keywords plus (ID) and 922 key words chosen by the author suggesting rich diversity in its themes. The
authorship is very collaborative as 3,945 authors were involved in the dataset. Nevertheless, only 6 articles are
one-authored and it means that almost every publication is made in collaboration. The average number of authors
in a document is 19.1 with a total of 35.81% international collaborations, which indicate internationalization of
the research study. As far as the type of documents is concerned, 219 of them will be classified as articles with
10 more being an early access article. The dominance of peer-reviewed journal publications in the dataset and
topicality of timely publishing of research findings is represented in this distribution. On the whole, the
information shows an active and community-based and globally-oriented research environment.
Table 1: Summative Data of Web of Science (2015–2025) in 2022
Description
Results
MAIN INFORMATION ABOUT DATA
Timespan
2015:2025
Sources (Journals, Books etc.)
172
Documents
229
Page 1092
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Annual Growth Rate %
25.25
Document Average Age
2.57
Average citations per doc
10.16
References
13881
DOCUMENT CONTENTS
Keywords Plus (ID)
747
Author's Keywords (DE)
922
AUTHORS
Authors
3945
Authors of single-authored docs
6
AUTHORS COLLABORATION
Single-authored docs
6
Co-Authors per Doc
19.1
International co-authorships %
35.81
DOCUMENT TYPES
article
219
article; early access
10
Annual number of research articles
The figure 3 presents a combined analysis of annual research output (bars) and mean citations per article (orange
line) in the domain of AI, big data, and business networks from 2015 to 2025, revealing a dynamic interplay
between knowledge production and scholarly impact. In the initial phase (2015–2018), the number of
publications remains relatively low and inconsistent, yet the citation line shows comparatively higher values
with a sharp peak around 2018–2019. This indicates that although few studies were produced, they carried
disproportionately high academic influence, suggesting the presence of foundational or pioneering works that
significantly shaped the direction of the field.
From 2019 onwards, the bars show a clear and sustained increase in publications, marking a rapid expansion
phase especially after 2021, reaching its highest level around 2024. However, this growth in publication volume
is accompanied by a steady decline in the citation line, indicating that the average impact per article is decreasing.
This inverse relationship highlights a critical structural shift: as the field matures and attracts more researchers,
the focus moves from high-impact, theory-building studies to a larger volume of incremental and application-
oriented research. Additionally, recent publications (2022–2025) naturally exhibit lower citation averages due
to the citation time-lag effect, as they have had less time to accumulate scholarly attention.
By 2024–2025, the figure suggests a high-output but lower-average-impact equilibrium, where the domain has
achieved widespread academic engagement but with more diffused and specialized contributions. Overall, the
combined visualization reflects the evolution of the research field from concentrated, high-impact beginnings to
a phase of rapid expansion and diversification, emphasizing the trade-off between quantity and average citation
impact.
Page 1093
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Figure 3: Annual Number of Research Articles on Intersection of AI, Big Data, and Business Networks from
2015-2025
Most productive authors
The figure 4 Top 10 Most Productive Authors is demonstrated by number of documents that is represented in
the form of the horizontal bar chart. The most productive author is JAKSE N with about 9 documents and it is
far ahead of the rest by numbers. LI WJ has authored approximately 6 documents following JAKSE N, followed
by WONG J and LI J, with about 4 documents each, FERREIRA M with about 4 documents, DUCHALAIS E,
DELIBEGOVIC S and BAUTISTA OA with about 4 documents each. Finally, AKTAS MK and AGUILERA
ML have published approximately 3 publications each. We can see the existence of an apparent inequality in
productivity, as JAKSE N is a major player in the region.
Figure 4: Most productive authors on Intersection of AI, Big Data, and Business Networks
Page 1094
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Number of articles published by sources and influential publication
The figure 5 represents Top 10 Sources by Number of Articles in the form of a horizontal bar chart. It is
arguments the academic journals and publishing platforms that have made the largest contribution of articles in
the analyzed data set. The most productive source was SUSTAINABILITY of which close to 10 articles were
published. The top priorities after it are IEEE ACCESS that has added up by about 8 articles These two journals
are the most effective platforms to present the academic work. Approximately 5 articles have already been
published on PHYSICAL REVIEW B and INDUSTRIAL MARKETING MANAGEMENT, making the journal
a reputable contributor to finding the research in the area. The rest of the sources JOURNAL OF
MULTIDISCIPLINARY HEALTHCARE, ELECTRONICS, CHAOS SOLITONS & FRACTALS, BMJ
OPEN, APPLIED SCIENCES-BASEL and ACTA POLYTECHNICA HUNGARICA have published 2.5 to 4
articles each. These journals divide many disciplines suggesting that the research is interdisciplinary.
Figure 5: Top Ten Articles with the Highest Number of Publications on Intersection of AI, Big Data and Business
Networks
Top ten influential papers
The table 2 features the 10 most internationally quoted research paper in terms of the total number of citations,
annual citation and normalized citation counts. The analysis reveals that citation impact is not solely determined
by total citations, but by how rapidly and contextually a paper is recognized. While some studies show high
cumulative citations, their annual growth rate is relatively moderate, indicating stabilized influence (Schroeder,
2019 – 98 citations; Ali, 2019 – 89 citations). In contrast, recent publications demonstrate higher citation
intensity, suggesting strong emerging relevance in the field (Papa, 2021 – 93 citations; Chang, 2020 – 72
citations). Papers with high normalized citation scores reflect greater influence within their specific research
domain, regardless of total counts (Papa, 2021 – 93 citations; Hu, 2021 – 59 citations). This indicates a shift
toward more impactful and timely contributions, particularly in technology-driven and interdisciplinary areas.
Additionally, variations between normalized and total citations highlight the importance of field-adjusted metrics
in evaluating true scholarly significance (Hadidi, 2020 – 52 citations; Krishnan, 2019 – 59 citations). Overall,
the findings suggest a transition from foundational studies to more dynamic, high-impact recent research shaping
the current academic discourse.
Page 1095
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Table 2: Top Ten Influential Papers for Intersection of AI, Big Data and Business Networks.
Paper
DOI
Total
Citations
TC per
Year
Normalized
TC
SCHROEDER A, 2019,
PROD PLAN CONTROL
10.1080/09537287.2019.16121
11
98
14.00
2.71
PAPA A, 2021, J
KNOWL MANAG
10.1108/JKM-04-2020-0300
93
18.60
4.38
ALI F, 2019, INT J
CONTEMP HOSP M
10.1108/IJCHM-10-2018-0832
89
12.71
2.46
AKHTAR P, 2018, BRIT
J MANAGE
10.1111/1467-8551.12233
78
9.75
2.64
CHANG KC, 2020, IEEE
ACCESS
10.1109/ACCESS.2020.297364
8
72
12.00
3.75
KRISHNAN P, 2019,
COMPUT COMMUN
10.1016/j.comcom.2019.09.014
59
8.43
1.63
HU N, 2021, INT J
MACH LEARN CYB
10.1007/s13042-020-01253-w
59
11.80
2.78
LI WJ, 2017, J NETW
COMPUT APPL
10.1016/j.jnca.2016.09.014
59
6.56
2.62
DOS SANTOS LMAL,
2021, J MANUF
TECHNOL MANA
10.1108/JMTM-04-2020-0156
52
10.40
2.45
HADIDI R, 2020, IEEE
INTERNET THINGS
10.1109/JIOT.2020.2972000
52
8.67
2.71
Global research distribution and collaboration
The figure 6 demonstrates the mode of usefulness of research and horizontal cooperation of different universities
formed through the number of items produced. The number of documents represents the horizontal axis and the
universities are presented in the vertical axis. The bars are broken down into two colors which depict the varying
forms of collaboration, Red Multiple Country Publication (MCP), collaborations involve international
collaborations with foreign countries.
Then comes to domestic collaborations; these collaborations occur within a single country are labeled as Single
Country Publication (SCP). Based on the figure, the majority of universities depict greater percentage of MCPs,
which indicates a high tendency to cooperate in international research. Guangzhou University and the
Communaute Universite Grenoble Alpes are the most fruitful of all the institutions, four documents respectively.
There are three publications of Wuhan university, Central South University, and the Ministry of Education -
China who follows in that order with three publications each suggesting the level of research interest. Both SCP
and MCP contributions are presented in a few universities, including Hubei University of Chinese Medicine,
Jiangsu University and Beijing, University of Posts and Telecommunications. This means that such institutions
are involved in national and international research projects. All in all, the diagram indicates that different
universities are involved actively in the joint scholarship and emphasizes the reach of the production of cross-
border in research in the academic publication.
Page 1096
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Figure 6: Corresponding Author Country Productivity
*MCP: multiple country publication *SCP: Single country publication
Thematic map on intersection of ai, big data and business networks
The figure 7 thematic map presented in the given visual examines two critical dimensions, the level of degree of
density of development and relevance (centrality) in the academic literature.
Theme Category
Keywords
Meaning
Stage
Motor themes
Big data, management, performance,
system architecture, dynamic.
Developed and Core
Mature
Niche Themes
Disease, disparities, experiences,
classification, privacy, and state-of-the-
art techniques.
less crucial
Isolated
Theoretical
Themes
Artificial intelligence, models,
intelligence, analytics, diagnosis, and
impact.
Underdeveloped
Diagnostics
Emerging or
Declining
Themes
Molecular dynamics, meta-analysis,
prevalence, and data bank
Not very central or dense
Limited relevance
or development
The bibliometric analysis for this study was primarily conducted using the RStudio environment in conjunction
with the Biblioshiny interface, which enabled comprehensive data analysis and visualization. These tools
facilitated the systematic examination of publication trends, citation structures, and thematic evolution within
the dataset. On the whole, this map is a valuable resource for identifying both well-established research areas
and promising avenues for future study.
Page 1097
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Figure 7: Thematic Map
Co-occurrence of keywords
In contrast, Figure 8 presents the keyword co-occurrence network map was generated using VOS viewer, which
is widely recognized for its effectiveness in constructing and visualizing bibliometric networks. For this analysis,
a minimum occurrence threshold of 5 was applied to ensure the inclusion of only significant and frequently
occurring keywords. Additionally, irrelevant or non-informative keywords were manually excluded to enhance
the clarity and interpretability of the network. A thesaurus file was also employed to standardize terminology by
merging synonymous or closely related keywords into unified terms, thereby improving the accuracy and
coherence of the co-occurrence structure. This combined methodological approach ensured both analytical rigor
and visual clarity in identifying key research themes and relationships within the study domain.
From an analytical standpoint, the central positioning of the keyword “business network” indicates its high
degree of connectivity and co-occurrence with multiple thematic domains, signifying its role as the conceptual
anchor of the field. This centrality reflects not only frequency but also structural importance, suggesting that
most research streams are directly or indirectly linked to this core construct. The cluster structure reveals distinct
yet interrelated research trajectories. The red cluster, comprising keywords such as “social capital,”
“entrepreneurship,” and “collaboration,” reflects a strong theoretical orientation toward relational and
resource-based perspectives. This indicates that scholars predominantly interpret business networks through the
lens of social capital theory and entrepreneurial dynamics, emphasizing trust, relationships, and cooperative
value creation.
In contrast, the green cluster, including “social network analysis,” “networking,” and “internationalization,”
represents a more methodological and expansion-oriented stream. This suggests a shift toward analytical rigor
and the application of quantitative network techniques, alongside a growing interest in how networks facilitate
cross-border business activities. The blue cluster, which links “innovation,” “startups,” and “social networks,”
highlights the increasing integration of business network research with innovation ecosystem literature. This
indicates an emerging emphasis on networks as enablers of knowledge diffusion, creativity and startup growth,
reflecting the field’s alignment with contemporary innovation-driven economic contexts.
Page 1098
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Importantly, the presence of peripheral and weakly connected keywords such as “blockchain” and “network
capability” signals emerging but still underdeveloped research areas. Their relatively low density and weaker
link strength suggest that these topics have not yet been fully integrated into the mainstream discourse,
representing potential avenues for future research. Overall, the co-occurrence network moves beyond a
descriptive mapping by revealing a multi-layered intellectual structure: a well-established theoretical core (social
capital and collaboration), a methodological and a set of emerging, technology-driven themes. This pattern
indicates a field that is both conceptually mature and dynamically evolving, with increasing interdisciplinarity
and opportunities for theoretical integration and empirical advancement.
Figure 8: Co-Occurrence Analysis of Concepts and Keywords
KEY FINDINGS
Analysis Type
Key Findings
Interpretation
Theoretical
Link
Practical Implication
Annual Trends
Publications increased
sharply after 2020;
citations peaked earlier
Field is growing
but maturing
Knowledge
diffusion
theory
Increasing research
interest; emerging
saturation
Country/Institution
China dominates MCP;
limited SCP collaboration
Low
international
collaboration
Social
capital
theory
Need for stronger
global academic ties
Thematic Map
Motor themes: AI, big
data; Basic themes:
models, analytics
Tech-driven shift
in research
Innovation
diffusion
theory
Firms must adopt AI-
driven networking
tools
Keyword Network
“Business network”
central; links to social
capital, innovation, SMEs
Strong
conceptual core
forming
Network
theory
Emphasizes relational
value creation
CONCLUSION
This bibliometric and scientific mapping research, implemented by the PRISMA framework is based on an
extensive examination of the artificially intelligent (AI) data flood and business networks research context over
the 2015-2025 axis. Analysis shows that research is spread worldwide, and a lot of work is done by a collection
of countries and thus, the interdisciplinary field has international nature. The fact that the number of publications
increased gradually throughout the decade highlights the growing academic, as well as practical concerns on
using AI and big data to optimize business network dynamics. They reveal that highly productive authors and
prominent journals have had a pivotal role in the development of this field, giving out thematic analyses based
Page 1099
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
on co-occurrence of concepts and keywords, showing key themes that include analytics powered by AI, network
optimization and decision-making supported by data as the most popular ones which form the modern research.
Having listed the most influential papers, it is possible to note that they have contributed to theoretical and
practical conclusions in the area extensively.
The paper will give to the body of knowledge by providing systematic representation of the intellectual structure
and research extractions of AI, big data and business networks, which will be useful to academics, policymakers
and practitioners. In the future, it would be appropriate to investigate new sub-themes, including ethical issues
that appear in AI and big data in business networks, a role in privacy-preserving technology and cross-sectoral
refers to the sustainability or healthcare sector. The study of business network analytics with the latest AI
potential, similar to generative models or reinforcement learning can also be productive.
The contribution to academic literature is to trace the evolution of research in the field during the period of 2015
to 2025. It accumulates knowledge regarding subjects in the other disciplines and includes a straightforward
account of the connection and integration of the subjects into a complex framework of field study of systems.
The scholars can understand the role of data-based technologies in impacting current organizational networks.
It also serves as an excellent source of reference in prospective research when one observes the most impulsive
writers, journals and pioneer research.
To people in the industry, the report sheds light on how organizations around the world are utilizing digital
capacities and data intelligence to drive innovations, smarter decisions and partnerships. Other areas considered
in the findings include predictive analytics, digital collaboration and intelligent supply chains that are massively
promising to businesses interested in staying afloat. These insights can help business leaders and policymakers
to prioritize investments that may not only produce global best practices, but also harness cross-industry
cooperation. All in all, this research paper demonstrates the revolutionary possible technology of incorporating
AI and big data in business networks, marking the beginning of new ways of finding solution to the complex
organizational problems.
ACKNOWLEDGEMENTS
This research was not funded by any grant
Limitations and Future Scope
Although the current research contains a thorough bibliometric and scientific mapping of research carried out on
the topics of Artificial Intelligence (AI), Big Data and Business Networks, this analysis is done using
publications included in the Web of Science database only, which can rule out the relevant articles of other
sources of similar quality. Secondly, bibliometric methods are mainly dependent on citation information and co-
occurrence trends, which is not necessarily revealing of the level or quality of intellectual wisdom. The articles
included in this is between 2015-2025 (after 2025) are not represented. Lastly, no qualitative evaluation of the
content or methodologies in the articles analyzed is present, which would have been a more inspiring contextual
interpretation.
Future studies may build on this study by combining the various databases to build a more representative and
inclusive dataset. To cover more of the theoretical/applicational face of the field, scholars can complement
bibliometric techniques with systematic literature review or meta-analysis. An interindustry or inter-regional
comparison can also be useful to generate localized differences in attempts to shape AI and Big Data into
business networks. A mixture of bibliometric knowledge and the use of interviews or case studies could also
represent a more refined concept of how the digital transformation affects organizational behavior, performance
and resilience.
REFERENCES
1. Abayomi Abraham Adesina, Toluwalase Vanessa Iyelolu, & Patience Okpeke Paul. (2024). Leveraging
predictive analytics for strategic decision-making: Enhancing business performance through data-driven
Page 1100
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
insights. World Journal of Advanced Research and Reviews, 22(3), 1927–1934.
https://doi.org/10.30574/wjarr.2024.22.3.1961
2. Aguiar Lima Dos Santos, L. M., da Costa, M. B., Kothe, J. V., Benitez, G. B., Schaefer, J. L., Baierle, I.
C., & Benitez Nara, E. O. (2021). Industry 4.0 collaborative networks for industrial performance.
JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 32(2), 245–265.
https://doi.org/10.1108/JMTM-04-2020-0156
3. Albarrati, A. M., Nazer, R., Abdelwahab, S. I., & Albratty, M. (2025). Artificial intelligence applications
and aging (1995-2024): Trends , challenges, and future directions in frailty research. ARCHIVES OF
GERONTOLOGY AND GERIATRICS, 134. https://doi.org/10.1016/j.archger.2025.105837
4. Andres, B., Urze, P., Araujo, E., & Camarinha-Matos, L. M. (2025). Artificial intelligence use in
collaborative network processes. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 47.
https://doi.org/10.1016/j.jii.2025.100883
5. Arasteh, S. T., Kuhl, C., Saehn, M.-J., Isfort, P., Truhn, D., & Nebelung, S. (2023). Enhancing domain
generalization in the AI-based analysis of chest radiographs with federated learning. SCIENTIFIC
REPORTS, 13(1). https://doi.org/10.1038/s41598-023-49956-8
6. Ben Yahia, N., Eljaoued, W., Ben Saoud, N. B., & Colomo-Palacios, R. (2021). Towards sustainable
collaborative networks for smart cities co-governance. INTERNATIONAL JOURNAL OF
INFORMATION MANAGEMENT, 56. https://doi.org/10.1016/j.ijinfomgt.2019.11.005
7. Benaben, F., Fertier, A., Montarnal, A., Mu, W., Jiang, Z., Truptil, S., Barthe-Delanoe, A.-M., Lauras,
M., Mace-Ramete, G., Wang, T., Bidoux, L., & Lamothe, J. (2020). An AI framework and a metamodel
for collaborative situations: Application to crisis management contexts. JOURNAL OF
CONTINGENCIES AND CRISIS MANAGEMENT, 28(3, SI), 291–306. https://doi.org/10.1111/1468-
5973.12310
8. Camarinha-Matos, L. M., Oliveira, A. I., Ferrada, F., & Thamburaj, V. (2017). Collaborative services
provision for solar power plants. INDUSTRIAL MANAGEMENT \& DATA SYSTEMS, 117(5), 946–
966. https://doi.org/10.1108/IMDS-06-2016-0246
9. Cantu, C. L., & Tunisini, A. (2023). A circular innovation strategy in a supply network context: evidence
from the packaging industry. Journal of Business and Industrial Marketing, 38(13), 220 – 238.
https://doi.org/10.1108/JBIM-07-2021-0325
10. David Iyanuoluwa Ajiga, Ndubuisi Leonard Ndubuisi, Onyeka Franca Asuzu, Oluwaseyi Rita Owolabi,
Tula Sunday Tubokirifuruar, & Rhoda Adura Adeleye. (2024). Ai-Driven Predictive Analytics in Retail:
a Review of Emerging Trends and Customer Engagement Strategies. International Journal of Management
& Entrepreneurship Research, 6(2), 307–321. https://doi.org/10.51594/ijmer.v6i2.772
11. Domenteanu, A., Cotfas, L.-A., Diaconu, P., Tudor, G.-A., & Delcea, C. (2025). AI on Wheels:
Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric
Vehicles. ELECTRONICS, 14(2). https://doi.org/10.3390/electronics14020378
12. Dsouza, V. S., Leyens, L., Kurian, J. R., Brand, A., & Brand, H. (2025). Artificial intelligence (AI) in
pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized
patients. RESEARCH IN SOCIAL \& ADMINISTRATIVE PHARMACY, 21(6), 453–462.
https://doi.org/10.1016/j.sapharm.2025.02.008
13. Ekundayo, T. C., Olasehinde, T. A., Okaiyeto, K., & Okoh, A. I. (2021). Microbial Pathogenesis and
Pathophysiology of Alzheimer’s Disease: A Systematic Assessment of Microorganisms’ Implications in
the Neurodegenerative Disease. FRONTIERS IN NEUROSCIENCE, 15.
https://doi.org/10.3389/fnins.2021.648484
14. Galib, M., Haerani, S., Mamimg, J., & Munir, A. R. (2022). The Role of SMT and Business Network
Accentuation on Value Distribution and Performance Consequences. Journal of Distribution Science,
20(5), 97 – 104. https://doi.org/10.15722/jds.20.05.202205.97
15. Ghaouri, M. H., Kassim, S., & Rashid, H. (2023). Waqf-Linked Islamic Fintech Microfinance as a
Business Enabler in Post-pandemic Economy: The Experience of Hal Microfinance, Kenya. Studies in
Systems, Decision and Control, 470, 357 – 370. https://doi.org/10.1007/978-3-031-28314-7_30
16. González, E. P., Martín Martín, J. M., Martínez, J. M. G., & Khodja, H. H. A. (2022). The Disruptive
Effect of Technological Innovation in the Tourist Accommodation Industry. International Journal of
Innovation and Technology Management, 19(1). https://doi.org/10.1142/S0219877021400083
17. Hirt, R., Kühl, N., Martin, D., & Satzger, G. (2025). Enabling inter-organizational analytics in business
Page 1101
www.rsisinternational.org
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
networks through meta machine learning. Information Technology and Management, 26(1), 57 – 81.
https://doi.org/10.1007/s10799-023-00399-7
18. Holland, C. P., Thornton, S. C., & Naude, P. (2020). B2B analytics in the airline market: Harnessing the
power of consumer big data. INDUSTRIAL MARKETING MANAGEMENT, 86.
https://doi.org/10.1016/j.indmarman.2019.11.002
19. Keegan, B. J., Iredale, S., & Naude, P. (2023). Examining the dark force consequences of AI as a new
actor in B2B relationships. INDUSTRIAL MARKETING MANAGEMENT, 115, 228–239.
https://doi.org/10.1016/j.indmarman.2023.10.001
20. Liao, S.-H., Hu, D.-C., & Chen, S.-T. (2022). Supply chain integration, capability and performance – a
business-to-business network cooperation. Journal of Business and Industrial Marketing, 37(5), 1127 –
1137. https://doi.org/10.1108/JBIM-10-2020-0467
21. Papa, A., Chierici, R., Ballestra, L. V., Meissner, D., & Orhan, M. A. (2021). Harvesting reflective
knowledge exchange for inbound open innovation in complex collaborative networks: an empirical
verification in Europe. JOURNAL OF KNOWLEDGE MANAGEMENT, 25(4, SI), 669–692.
https://doi.org/10.1108/JKM-04-2020-0300
22. Pardo, C., Wei, R., & Ivens, B. S. (2022). Integrating the business networks and internet of things
perspectives: A system of systems (SoS) approach for industrial markets. Industrial Marketing
Management, 104, 258 – 275. https://doi.org/10.1016/j.indmarman.2022.04.012
23. Patrick Azuka Okeleke, Daniel Ajiga, Samuel Olaoluwa Folorunsho, & Chinedu Ezeigweneme. (2024).
Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of
Engineering Research Updates, 7(1), 036–049. https://doi.org/10.53430/ijeru.2024.7.1.0032
24. Penttinen, V., & Frösén, J. (2022). Shared analytical capabilities in business networks. Industrial
Marketing Management, 106, 308 – 322. https://doi.org/10.1016/j.indmarman.2022.08.018
25. Pinter, D. G., & Ihasz, P. L. (2019). Bridging Natural Language Processing AI techniques and Corporate
Communications: towards an integrative model. INFORMACIOS TARSADALOM, 19(4), 77–99.
26. Rivera, N. V. (2024). Big data in sarcoidosis. CURRENT OPINION IN PULMONARY MEDICINE,
30(5), 561–569. https://doi.org/10.1097/MCP.0000000000001102
27. Sanchez-Lopez, M. D. (2023). Geopolitics of the Li-ion battery value chain and the Lithium Triangle in
South America. Latin American Policy, 14(1), 22 – 45. https://doi.org/10.1111/lamp.12285
28. Santos, B., & Campos, P. (2023). SUWAN: A supervised clustering algorithm with attributed networks.
INTELLIGENT DATA ANALYSIS, 27(2), 423–441. https://doi.org/10.3233/IDA-216436
29. Schroeder, A., Bigdeli, A. Z., Zarcos, C. G., & Baines, T. (2019). Capturing the benefits of industry 4.0:
a business network perspective. PRODUCTION PLANNING \& CONTROL, 30(16), 1305–1321.
https://doi.org/10.1080/09537287.2019.1612111
30. Sun, Y., Qiao, F., Wang, W., Xu, B., Zhu, J., Mansour, R. F., & Qi, J. (2022). Dynamic Intelligent Supply-
Demand Adaptation Model Towards Intelligent Cloud Manufacturing. CMC-COMPUTERS
MATERIALS \& CONTINUA, 72(2), 2825–2843. https://doi.org/10.32604/cmc.2022.026574
31. Uchenna Joseph Umoga, Enoch Oluwademilade Sodiya, Ejike David Ugwuanyi, Boma Sonimitiem Jacks,
Oluwaseun Augustine Lottu, Obinna Donald Daraojimba, & Alexander Obaigbena. (2024). Exploring the
potential of AI-driven optimization in enhancing network performance and efficiency. Magna Scientia
Advanced Research and Reviews, 10(1), 368–378. https://doi.org/10.30574/msarr.2024.10.1.0028
32. Wang, H., Smith, D., & Demir, U. (2025). Scaling AI filmmaking with collaborative networking. IET
COMMUNICATIONS, 19(1). https://doi.org/10.1049/cmu2.12877
33. Xavier, J. A., Siong, G. K., Yew, L. K., Jiang, N., & Alagappar, P. N. (2022). Fostering Business Growth
through Generational Change: Is Guanxi Still Important? Translocal Chinese: East Asian Perspectives,
16(2), 228 – 248. https://doi.org/10.1163/24522015-16020004
34. Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex and
Intelligent Systems, 7(1), 439–457. https://doi.org/10.1007/s40747-020-00212-w
35. Zhu, Y. P., & Park, H. W. (2024). Publication, Collaboration, Citation Performance, and Triple Helix
Innovation Gene of Artificial Intelligence Research in the Communication Field: Comparing Asia to the
Rest of the World. JOURNAL OF THE KNOWLEDGE ECONOMY. https://doi.org/10.1007/s13132-
024-02280-6
