INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
A Bibliometric Analysis and Overview: A Study to Understand the  
Trend of Sustainable Logistic Studies in Malaysia  
Mohamad Yusuf Mislam  
Institute of Graduate Studies, Universiti Poly-Tech Malaysia, Kuala Lumpur, Malaysia.  
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000139  
Received: 06 November 2025; Accepted: 14 November 2025; Published: 21 November 2025  
Abstract  
Background: Nowadays, business organization implementing the Sustainable Development Gold (SDG) in their business  
operations and company policy as their support towards sustainability. Sustainable logistic is supporting the United Nations  
Sustainable Development Goals (SDGs) aim to improve economic, social, and environmental outcomes through increased  
efficiency, lower emissions, and improved resource management. It helps to achieve goals such as sustainable energy (SDG 7),  
responsible production and consumption (SDG 12), infrastructure (SDG 9), and fewer traffic accidents (SDG 3). Key factors include  
developing green energy applications, streamlining supply chains, supporting sustainable procurement, and utilizing technologies  
such as artificial intelligence and blockchain to improve supply chain sustainability and performance. This study aims to understand  
the trend of sustainable logistic studies, key theme, publication trend and citation, authors, and co-word related to sustainable  
logistic. This study will provide an insight to for future study on sustainable logistic.  
Method: A bibliometric analysis is use in this study by extracting data from Dimension.Ai database. By using three keyword which  
are ‘Sustainable’ AND ‘Logistic’ AND ‘Malaysia’. Performance analysis, science mapping network analysis use in this study.  
Result: Publication trend show that the post-peak trend suggests that while the initial fervour may have subsided, the topic continues  
to be a subject of ongoing, albeit less intensely focused, academic inquiry. The frequent number of authors are 3 to 5 people. Co-  
words analysis show how the topic being choose and discuss within the industry. This topic become matured and more analysis  
needed for the future study  
Conclusion: Base on the bibliometric analysis, we can conclude that the field of telematic system acceptance is a dynamic and  
deeply interconnected research area. The densest clusters demonstrate that the study has expanded beyond the individual user to  
encompass the broader system performance, transport efficiency, and industry-level integration.  
Keywords: Sustainable, Logistic, Supply Chain, Bibliometric  
I. Introduction  
Sustainable logistics is increasingly recognized as essential for enhancing operational efficiencies and minimizing environmental  
impacts. In Malaysia, several factors contribute to the development of sustainable logistics practices within various sectors,  
particularly in manufacturing and transportation. The logistics industry in Malaysia is also influenced by financial dynamics that  
urge logistics service providers (LSPs) to build value-based strategies. Current research indicates that Malaysian LSPs need to  
improve financial confidence to sustain long-term relationships and achieve business goals effectively. A focus on microeconomic  
factors and collectively beneficial strategies can lead to improvements in logistics performance and positive implications for  
governmental policy-making regarding logistics and supply chain management (Jomthanachai et al., 2023).  
Moreover, the overarching challenge of developing sustainable logistics systems in Malaysia can be well illustrated through  
emerging technologies and best practices. For instance, the integration of Industry 4.0 technologies can enhance logistics processes  
by optimizing supply chain visibility and enabling SMEs to adopt sustainable practices. However, apprehensions regarding the  
investment required for technological integration remain a substantial barrier (Qureshi et al., 2024).  
Sustainable logistics significantly contributes to food supply chains in Malaysia, which face unique sustainability challenges due  
to the perishable nature of products involved. Ensuring logistics systems that are both agile and resilient is crucial in minimizing  
losses and maintaining product quality throughout the supply chain (Kazançoğlu et al., 2021). This highlights the critical interplay  
between logistics efficiency and sustainability within food-based sectors, ultimately affecting consumer choices and market  
dynamics.  
A bibliometric study is essential for exploring the topic of sustainable logistics in Malaysia as it provides a scientifically rigorous  
method to map the intellectual landscape, identify trends, and assess the growth of research in this area. This method serves multiple  
purposes that enhance the understanding and development of sustainable logistics, particularly within a rapidly evolving global  
context.  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025  
II. Research Methodology  
This study used Bibliometric Analysis which using performance analysis to examine the publication-related matrix, citation -related  
matrix and citation-and-publication matrix. Those matrices will give insight to the future research on the literature gap.  
Understanding and constructing these publication-related matrices is the first and most critical step in any sophisticated bibliometric  
analysis. They transform raw publication data into a structured format ready for quantitative and visual exploration. VOS Viewer  
is use to visualise the relationship, connection and mapping. From the visualization, we analyse and interpret the mapping, cluster  
and exploration through zooming, panning, and clicking to understand the structure of the research landscape.  
A comprehensive search was conducted across over 170 million research papers in Consensus, encompassing databases such as  
Semantic Scholar, PubMed, and others. The search strategy included targeted queries on acceptance, telematic systems, and  
logistics, as well as adjacent topics like technology adoption models, organizational factors, privacy, and measurement methods. In  
total, 991 papers were identified, 289 removed due to missing abstract, 248 removed due to low semantic relevance to each other.  
454 eligible papers. 150 best papers after removing paper not related to logistic scope. The extraction, screening process flow and  
protocol show in figure 1 as below:  
Figure 1: Data Extraction and Paper Selection Flow  
Identification  
N= 991  
Screening  
N= 702  
Eligibility  
N= 454  
Included  
N= 150  
289 removed  
(Missing abstract)  
248 removed  
(Semantic relevant)  
323 removed  
(Not related to  
logistic scope)  
III. Results and Findings  
Publication-Related Matrix  
150 out of 991 papers included in this study. A screening has been done to ensure only best paper use in this study. Paper with zero  
citation removed during the last screening process. The volume, growth and distribution of the papers as below:  
Figure 2: Distribution of Publish Paper from 1995-2025 (TP)  
30  
25  
20  
15  
10  
5
0
1995 1997 1999 2000 2002 2004 2006 2007 2008 2009 2012 2013 2014 2014 2015 2016 2017 2018 2019 2020 2021 2023 2024 2025  
From 1995 to 2006, the landscape of published research on this subject was remarkably sparse. During this twelve-year span, the  
annual number of papers consistently hovered between zero and two. This sustained low output suggests that the topic was either  
in its nascent stages of development, lacked significant academic recognition, or was simply not considered a primary area of focus  
within logistics research at the time. The minimal scholarly discourse during this period indicates a foundational phase, where the  
concepts of telematic systems in logistics were perhaps just beginning to emerge or were confined to very specialized circles.  
A subtle shift becomes discernible in the decade spanning 2007 to 2016. While still far from prolific, this period witnessed a modest  
increase and some minor fluctuations in publication numbers. Peaks of around three papers in years like 2007 and 2009 hint at a  
nascent, albeit limited, growth in interest. This phase can be characterized as a period of gradual awakening, where the potential  
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implications of telematic systems for logistics acceptance started to garner slightly more attention, yet remained a relatively niche  
area within the broader academic landscape.  
The most striking transformation in research output occurred between 2017 and 2021, marking a period of exponential growth and  
peak interest. Beginning with seven papers in 2017, the numbers climbed steadily to nine in 2018 and ten in 2019. The true  
explosion, however, is evident in 2020 and 2021, where the volume of published papers soared to an unprecedented 28 in each  
year. This dramatic leap signifies a critical inflection point, suggesting that the acceptance of telematic systems in logistics had, by  
this time, become a highly relevant and pressing subject for researchers, possibly driven by technological advancements, industry  
adoption, or emerging challenges in the sector. The sustained peak over two consecutive years underscores a robust and widespread  
academic engagement with the topic.  
Following this intense period of research, the years 2022 to 2025 demonstrate a degree of volatility, although publication numbers  
remain significantly elevated compared to the pre-2017 era. A dip to nine papers in 2022 was followed by a rebound to thirteen in  
2023. The sharp decline to one paper in 2024 and 2025, however, warrants cautious interpretation. The data for these most recent  
years may be incomplete, especially for 2025, and might not fully represent the eventual annual totals. Nevertheless, the post-peak  
trend suggests that while the initial fervour may have subsided, the topic continues to be a subject of ongoing, albeit less intensely  
focused, academic inquiry.  
The most striking feature of the graph is the clear peak at three contributing authors, accounting for 44 papers. This suggests that a  
three-person team is the most common and perhaps most effective configuration for research in this domain. Following closely,  
both papers with two authors and those with four authors are equally prevalent, each represented by 30 instances. This indicates a  
strong preference for smaller to moderately sized research groups, with two to four authors collectively making up the vast majority  
of publications.  
As the number of contributing authors increases beyond four, there is a consistent and sharp decline in frequency. Papers with five  
authors drop to 15, while those with six authors are further reduced to 8. This trend continues, with seven and eight authors each  
appearing in only 3 papers. Notably, there were no papers found with nine contributing authors. The distribution extends to a single  
instance of a paper with ten authors, representing a rare occurrence of a very large collaborative effort.  
This pattern suggests that while collaboration is clearly favoured over single authorship (only 16 papers had a single author), there  
appears to be an optimal team size that balances diverse perspectives with efficient coordination. Single authorship (SA) is about  
10.6% from total published paper. Larger teams, while sometimes necessary for complex projects, become increasingly less  
common, possibly due to challenges in management, communication, or the nature of the research questions being addressed. The  
graph thus paints a picture of a research environment where focused, small-to-medium sized collaborations are the norm.  
50  
44  
45  
40  
35  
30  
30  
30  
25  
20  
15  
10  
5
16  
15  
8
6
3
7
3
8
1
0
9
0
1
2
3
4
5
10  
Figure 2: Number of Contributing Author (NCA)  
Citation-related Matrix  
Acceptance, telematic system and logistic attract increasing number for scholar to study in details. For acceptance, most of the past  
study are using Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) as  
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underpinning theory to their studies. To identify the leading paper in measuring acceptance, Table 1 below is top 50 publication by  
total citations (TC) and average citation per year (AC).  
Table 1: The Top 50 Most Influential Publication in Technology Acceptance  
R
Title  
Authors  
Year  
SJR  
TC  
AC/Y  
1
A Theoretical Extension of the Technology  
Acceptance Model: Four Longitudinal Field  
Studies  
Fred D. Davis  
2000  
1
20633  
825.32  
2
3
4
5
Examining the Technology Acceptance  
Model Using Physician Acceptance of  
Telemedicine Technology  
H. Heijden  
1999  
1999  
2002  
2021  
1
1
1
1
2126  
1719  
924  
81.77  
66.12  
40.17  
214.00  
Extending the technology acceptance model  
with task-technology fit constructs  
P. H. Hu, Patrick Y.  
K. Chau, O. R. Sheng,  
K. Tam  
User Acceptance Enablers in Individual  
Decision Making About Technology: Toward  
an Integrated Model  
Bernadette Szajna  
Blockchain technology and the sustainable  
supply chain: Theoretically exploring  
adoption barriers  
M. Chuttur  
856  
6
7
8
A simple procedure for the assessment of  
acceptance of advanced transport telematics  
J. V. D. Laan, A.  
Heino, D. de Waard  
1997  
2019  
2020  
1
1
1
822  
722  
648  
29.36  
120.33  
129.60  
Technology acceptance model in educational  
context: A systematic literature review  
Investigating acceptance of telemedicine  
services through an extended technology  
acceptance model (TAM)  
B. Rahimi, H. Nadri,  
H. Afshar, T. Timpka  
Using a modified technology acceptance  
model in hospitals  
9
Priyanka Surendran  
2009  
2015  
1
1
586  
548  
36.63  
54.80  
10 An empirical study of wearable technology  
acceptance in healthcare  
Michael R. Shirts, E.  
Bair, Giles Hooker,  
V. Pande  
11 An extension of trust and TAM model with  
Lt T. Wong, K. Mui,  
2008  
2016  
1
1
496  
477  
29.18  
53.00  
IDT in the adoption of the electronic logistics P. Hui  
information system in HIS in the medical  
industry  
12 Mobile technology acceptance model: An  
investigation using mobile users to explore  
smartphone credit card  
M.  
Gagnon,  
E.  
Orruño, J. Asua, A.  
Abdeljelil,  
Emparanza  
J.  
13 Analyzing older users' home telehealth  
services acceptance behavior - applying an  
Extended UTAUT model  
R.  
Kantam,  
K.  
2016  
2018  
2016  
1
2
1
473  
446  
381  
52.56  
63.71  
42.33  
Rosaiah, G. S. Rao  
A Systematic Review of the Technology  
Acceptance Model in Health Informatics  
Yi-Hsuan Lee, Yi-  
Chuan Hsieh, Yen-  
Hsun Chen  
14  
15 Do context and personality matter? Trust and D. Persico, S. Manca,  
privacy concerns in disclosing private  
information online  
F. Pozzi  
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16 Mobile computing acceptance factors in the  
healthcare industry: A structural equation  
model  
Patrick Y. K. Chau  
2007  
2020  
2020  
2016  
2007  
2012  
2020  
1
1
1
2
1
1
1
345  
282  
269  
267  
258  
256  
255  
19.17  
56.40  
53.80  
29.67  
14.33  
19.69  
51.00  
17 Impact of Trust and Privacy Concerns on  
Technology Acceptance in Healthcare: An  
Indian Perspective  
Anastasia Revythi,  
Nikolaos Tselios  
18 Acceptance of autonomous delivery vehicles  
for last-mile delivery in Germany –  
Bassam Hasan  
T. Chesney  
Extending UTAUT2 with risk perceptions  
19 Acceptance of Automated Road Transport  
Systems (ARTS): An adaptation of the  
UTAUT model  
20 A Conceptual Framework and Propositions  
for the Acceptance of Mobile Services  
Namkee Park, Mohja  
Rhoads, J. Hou, K. M.  
Lee  
21 Using a modified technology acceptance  
model to evaluate healthcare professionals'  
adoption of a new telemonitoring system.  
T. Chesney  
22 Chatbots in retailers’ customer  
communication: How to measure their  
acceptance?  
C.  
Villaret,  
J.  
R.  
Hervouet,  
Kopmann, U. Merkel,  
A. Davies  
23 What drives FinTech adoption? A multi-  
method evaluation using an adapted  
technology acceptance model  
Trevor T. Moores  
2020  
2019  
2018  
1
1
1
238  
233  
225  
47.60  
38.83  
32.14  
24 Theories Predicting End-User Acceptance of  
Telemedicine Use: Systematic Review  
Jessica  
Pesantez-  
Narvaez, Montserrat  
Guillén, M. Alcañiz  
25 An exploratory study of Internet of Things  
(IoT) adoption intention in logistics and  
supply chain management - a mixed research  
approach  
O. Kwon, Keunho  
Choi, M. Kim  
26 Antecedents of Adopting Corporate  
Environmental Responsibility and Green  
Practices  
H.  
Seidling,  
S.  
2018  
2020  
2021  
2008  
1
1
2
1
182  
166  
166  
162  
26.00  
33.20  
41.50  
9.53  
Phansalkar, D. Seger,  
M.  
Shaykevich,  
Haefeli, D. Bates  
Paterno,  
S.  
W.  
27 Blockchain technology in supply chain  
management: an empirical study of the  
factors affecting user adoption/acceptance  
H.  
Seidling,  
S.  
Phansalkar, D. Seger,  
M.  
Shaykevich,  
Haefeli, D. Bates  
Paterno,  
S.  
W.  
28 Technology Acceptance in Healthcare: A  
Systematic Review  
P. W. Handayani, A.  
Hidayanto, A. Pinem,  
Ika Chandra Hapsari,  
P. Sandhyaduhita, I.  
Budi  
29 An expanded model of logistics service  
quality: Incorporating logistics information  
technology  
Patrícia Silva  
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30 A systematic review and meta-analysis of  
user acceptance of consumer-oriented health  
information technologies  
E.  
Orruño,  
M.  
2020  
1
161  
32.20  
Gagnon, J. Asua, A.  
Abdeljelil  
31 Big Data Analytics and IoT in logistics: a  
case study  
M. V. Offenbeek, A.  
Boonstra, D. Seo  
2018  
2016  
158  
157  
22.57  
17.44  
32 Conceptual Model to Explain, Predict, and  
Improve User Acceptance of Driverless  
Podlike Vehicles  
M. Aslam, C. Jun  
2
Insurance Telematics: Opportunities and  
Challenges with the Smartphone Solution  
Liping  
Qingxiong Ma  
Liu,  
33  
2014  
2016  
1
1
147  
141  
13.36  
15.67  
34 Exploring students' awareness and  
perceptions: Influencing factors and  
individual differences driving m-learning  
adoption  
Yifan  
Shengwang Meng  
Huang,  
35 Factors influencing behavior intentions to  
telehealth by Chinese elderly: An extended  
TAM model  
Yujong  
Mohanned  
Arabiat,  
Hwang,  
Al-  
Donghee  
2019  
2018  
1
1
139  
137  
23.17  
19.57  
Don Shin  
36 Development of an adoption model to assess  
user acceptance of e-service technology: E-  
Service Technology Acceptance Model  
J. Asua, E. Orruño, E.  
Reviriego,  
Gagnon  
M.  
37 Educational Technology Adoption: A  
systematic review  
P. W. Handayani, A.  
Hidayanto, I. Budi  
2022  
2020  
1
1
137  
129  
45.67  
25.80  
38 Analysis of barriers to implement drone  
logistics  
R. Fensli, P. E.  
Pedersen,  
Gundersen,  
Hejlesen  
Torstein  
O.  
39 User acceptance of context-aware services:  
self-efficacy, user innovativeness and  
Michael P. Johnson,  
K. Zheng, R. Padman  
2007  
2022  
1
1
127  
123  
7.06  
perceived sensitivity on contextual pressure  
40 Precision Positioning for Smart Logistics  
Using Ultra-Wideband Technology-Based  
Indoor Navigation: A Review  
G. Lowry  
41.00  
41 A technology acceptance model of  
W. Money, Arch  
Turner  
2004  
2020  
1
1
122  
118  
5.81  
innovation adoption: the case of teleworking  
42 A study on users' willingness to accept  
mobility as a service based on UTAUT  
model  
Soussan Djamasbi, A.  
Fruhling, Eleanor T.  
Loiacono  
23.60  
43 The most used questionnaires for evaluating  
telemedicine services  
Huei-Huang Chen,  
Shih‐Chih Chen  
2021  
2017  
2020  
1
1
1
117  
114  
113  
29.25  
14.25  
22.60  
44 Acceptance model of a Hospital Information  
System  
Jing Zhao, J. Malenje,  
Yu Tang, Yin Han  
Factors Affecting Organizations’ Resistance  
to the Adoption of Blockchain Technology in  
Supply Networks  
45  
Nai-Hua Chen  
46 User acceptance of automated public  
transport  
M. Cermack  
2020  
2
112  
22.40  
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47 TAM-UTAUT and the acceptance of remote  
healthcare technologies by healthcare  
professionals: A systematic review  
L. Seiford, Joe Zhu  
2022  
2013  
2008  
2013  
2
1
1
1
101  
99  
33.67  
8.25  
5.47  
7.50  
48 Towards integrating acceptance and  
resistance research: evidence from a telecare  
case study  
Jacky Chin, Shu-  
Chiang Lin  
49 Logistics information systems: The Hong  
Kong experience  
E. Park, B. Hwang,  
Kyungwan Ko, Dae-  
cheol Kim  
93  
50 Tracking and Tracing: Geographies of  
Harsh Tripathi, S.  
90  
Logistical Governance and Labouring Bodies Dey, Mahendra Saha  
Three papers are published before year 2000. Eight papers publish within year 2000-2010. Twenty papers published within 2011-  
2019. For the last 5 year, 19 paper influential paper published. The increase in the production of academic papers from 2020 to  
2025 correlates strongly with the broader acceptance of technology, particularly in the context of the COVID-19 pandemic and the  
accelerated adoption of various digital tools across multiple sectors. Researchers have observed a significant rise in publication  
volumes related to telemedicine and digital health as a direct response to the pandemic's challenges (Xie et al., 2022). Moreover,  
public opinion plays a vital role in technology acceptance, particularly regarding smart cities and artificial intelligence (Yiğitcanlar  
et al., 2022). Research indicates that stakeholders often face barriers stemming from public scepticism about new technologies. As  
authorities and tech developers attempt to build trust and educate the public on the benefits of these innovations, there is an  
accompanying increase in discourse and scholarly inquiry, culminating in higher publication rates (Robles & Mallinson, 2023).  
The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) derivatives have  
been employed to analyse a wide range of technologies, such as AI, telehealth, and service robots, to gauge user acceptance trends  
(Dash & Mohanty, 2023; Cabrilo et al., 2024). The implications of these analyses contribute not only to theoretical advancements  
but also to practical applications that reinforce the importance of aligning technological developments with user needs and societal  
expectations, thus encouraging further scholarly exploration (Ball et al., 2025; Zhao et al., 2023). The significant increase in  
academic publications from 2020 to 2025 is emblematic of a broader societal shift toward the acceptance and integration of  
technology. As researchers continue to explore the frameworks governing technology adoption and its societal implications, this  
body of work will likely inform ongoing discourse about effectively harnessing technology in future policy and practice.  
Dive down to logistic industry scope, three key papers identified for the last five year. All three papers using TAM and UTAUT as  
underpinning theory in their studies. Table 2 below show the paper mentions. Zalewski et al (2022) use survey to 500 companies  
to measure acceptance of telematic system. Nguyen et al (2025) also use TAM to measure acceptance of logistic robot. Trust is  
measure as new variable to extend the existing TAM. While Kapser & Abdelrahman (2020) use UTAUT toward 500 consumers to  
measure acceptance of autonomous delivery vehicles.  
Table 2: Key Papers  
Author  
Methodology  
Sample Size  
Key Results  
Zalewski et al.,2022 Survey, TAM, SEM  
500 companies  
80% reported telematics improved efficiency and  
larger firms more motivated to adopt  
Nguyen et al.,2025  
Kapser  
Survey, TAM, SEM  
401 logistic  
employee  
Trust (dispositional/situational) and TAM factors  
drive acceptance of logistics robots  
&
Survey, UTAUT2,SEM  
500 consumers  
Price sensitivity, performance expectancy,  
risk,and social influence predict acceptance of  
autonomous delivery vehicles  
Abdelrahman, 2020  
The research on telematic system acceptance in logistics is robust, with multiple high-quality empirical studies and systematic  
reviews supporting the centrality of perceived usefulness, ease of use, and trust as key determinants (Chen, 2019; Zalewski et al.,  
2022; Nguyen et al., 2025; Kapser & Abdelrahman, 2020). The consistent application of TAM and UTAUT frameworks across  
diverse contexts (road transport, last-mile delivery, logistics robots) strengthens the validity of these findings. However, the field  
faces challenges in measurement rigor, data transparency, and the inclusion of user perspectives, especially regarding privacy and  
risk concerns (Milanović et al.,2020). The COVID-19 pandemic has acted as a catalyst for telematics adoption, but long-term effects  
and sustainability remain to be fully understood (Zalewski et al., 2021; Zalewski et al., 2023). The importance of organizational  
readiness and top management support suggests that successful telematics implementation is as much about change management  
as it is about technology (Zalewski et al., 2022). Future research should prioritize longitudinal studies, user-cantered design, and  
the development of standardized and transparent measurement tools.  
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Co-word Analysis  
Figure 3 below show a bibliometric co-word network visualization for studies on the acceptance of telematics systems. The network  
illustrates the relationships between keywords appearing together in academic publications. Words that are frequently used together  
are closer and connected by lines, indicating a strong thematic link. The colour of each cluster signifies a distinct research theme,  
while the size of a node (keyword) indicates its frequency of occurrence.  
Figure 3: Bibliometric Co-Word Network Visualization for Studies on The Acceptance of Telematics Systems  
Green cluster referring to the user acceptance and behavioural intention. This cluster focuses on the human-centric aspects of  
technology adoption. Key terms like 'intention,' 'acceptance,' 'attitude,' 'consumer,' and 'trust' are central here. The inclusion of  
'technology acceptance model' (TAM) is a clear indicator that a significant portion of the literature applies established theories of  
technology adoption to the context of telematics. The presence of ‘usefulness' and 'behavioural intention' further reinforces this  
theme. This cluster explores what drives a user to adopt or reject a telematic system, often examining psychological factors and  
perceived benefits. The strong connection between 'acceptance' and 'service' suggests that the perceived quality and utility of the  
telematic service are crucial for user adoption (Bird et al. ,2021). The link to 'performance' indicates that the system's actual  
performance is a key driver of user acceptance (Seguí et al., 2020)  
Yellow cluster are about vehicle, user and service perception. This cluster is characterized by keywords directly related to the  
physical implementation and user interaction with telematics. The prominent terms are 'car,' 'vehicle,' 'person,' 'user,' and  
'perception.' This suggests a focus on how telematics is integrated into vehicles and how individuals (the 'person' or 'user') perceive  
these systems. The link to 'service' and 'quality' shows an overlap with the green and red clusters, highlighting the importance of  
the service's perceived quality in a vehicle context. This cluster likely examines the practical aspects of telematics, such as the user  
interface, the physical experience of interacting with the system, and how these factors shape perception and, ultimately, acceptance  
(Seguí et al., 2020).  
Red cluster referring to system performance and quality of transport. This is the most central and dense cluster, indicating its pivotal  
role in the research landscape. Key terms include 'performance,' 'quality,' 'transport,' 'data,' and 'service.' This cluster primarily deals  
with the functional and operational aspects of telematics. The term 'transport' is a central node, linking to concepts like 'innovation,'  
'safety,' and 'data.' This suggests that research in this area explores how telematics systems can be used to improve the overall  
quality of transport, enhance safety, and drive innovation within the industry. The presence of 'data' is significant, as telematics  
systems are fundamentally data-driven. This cluster likely examines how the data collected by these systems is used to measure and  
improve performance, service quality, and safety. The connection to 'literature' and 'review' suggests a body of research that  
synthesizes and evaluates the findings on these topics.  
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Blue cluster highlight on industry, supply chain and Technology. This cluster focuses on the broader industry and technological  
context of telematics. Prominent keywords are 'supply chain,' 'industry,' 'blockchain technology,' 'thing,' and ‘supply chain  
management’. This cluster explores the business and technological infrastructure surrounding telematic systems. The strong link  
between 'supply chain' and 'blockchain technology' suggests research into how new technologies are being leveraged to improve  
supply chain management through telematics. The presence of 'practitioner' and 'organization' indicates a focus on the practical  
implementation of these systems within companies and the challenges they face. The term 'barrier' suggests an exploration of  
obstacles to adoption and integration at an organizational level. The connection to 'internet' and 'thing' (referring to the Internet of  
Things or IoT) highlights the integration of telematics with wider technological ecosystems. This cluster, therefore, looks beyond  
individual user acceptance to the broader organizational and technological challenges and opportunities.  
The figure as a whole demonstrates a shift in the research focus from the individual user (green and yellow clusters) to the broader  
organizational and technological context (red and blue clusters). The early research likely focused on why individuals accept or  
reject a system, applying models like TAM. Later research, as indicated by the central and interconnected nature of the red and blue  
clusters, has expanded to explore the system's impact on supply chains, transport efficiency, safety, and the role of new technologies  
like blockchain. The inclusion of 'literature' and 'review' nodes, particularly in the red and blue clusters, suggests that a significant  
amount of the research is now focused on synthesizing and consolidating findings from different areas. The network, therefore,  
provides a comprehensive overview of the research evolution, highlighting the key themes and their interdependencies. It's a holistic  
representation of the field, showing how the study of user psychology, vehicle technology, system performance, and industry-level  
implementation are all intertwined.  
IV. Conclusion  
Base on the bibliometric analysis, we can conclude that the field of telematic system acceptance is a dynamic and deeply  
interconnected research area. Rather than focusing on a single aspect, the literature draws from several distinct yet related themes.  
The early focus on user acceptance and behavioural intention provides a foundational understanding of the psychological factors  
driving adoption. This is closely linked to the practical, physical interaction with the system, as seen in the vehicle-centric yellow  
cluster, where perception and service quality are key.  
However, the analysis also reveals a crucial evolution in the field. The central and most dense clusters, red and blue, demonstrate  
that the conversation has expanded beyond the individual user to encompass the broader system performance, transport efficiency,  
and industry-level integration. The prominence of nodes like 'performance,' 'data,' and 'supply chain' shows that modern research is  
heavily invested in understanding the functional and organizational impacts of telematics. The strong connections between these  
clusters, particularly the central role of 'performance' and 'service,' highlight a core finding: a system's technical and operational  
effectiveness is the ultimate determinant of its acceptance at both the individual and organizational level. We could expand on the  
"barriers" to adoption in the blue cluster or dive deeper into the specific ways "performance" links the different research areas.  
V. Recommendation  
A key recommendation is to conduct studies that directly link the technical performance of telematic systems (e.g., data accuracy,  
reliability, and real-time responsiveness) to user acceptance factors like trust and satisfaction. A strong connection between these  
nodes, but more granular research is needed to quantify how specific performance metrics influence user behaviour and  
organizational adoption. Future studies should move beyond general acknowledgments of challenges to systematically identify,  
categorize, and quantify specific organizational, regulatory, and financial hurdles that impede the integration of telematic systems  
in supply chains and other industries. To provide a richer context, future research also should consider comparative studies across  
different industries (e.g., public transport vs. logistics) or geographical regions. Additionally, a longitudinal approach is needed to  
understand how user attitudes and acceptance evolve over time with prolonged exposure to telematic systems, moving beyond a  
single point-in-time assessment.  
Conflict of Interest  
The author declares no conflict of interest  
Funding  
No funding was involved in this study. All cost covered by the author.  
Data Availability Statement  
Data supporting the findings are available from the author upon reasonable request  
Acknowledgements  
Thank you to Dimensions for the bibliometric data availability and access.  
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