Digital Transformation Management in Logistics Security and Supply Chain Sustainability
Background. Digital technologies have significantly improved logistics efficiency and transparency. An analysis of TBC Logistics data (Georgian logistics company) indicates faster delivery, improved security, and higher customer satisfaction. Workforce competence, technological innovation, and organ...
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| author | Goguadze, Mariam |
| author_facet | Goguadze, Mariam |
| author_institution_txt_mv | [
{
"author": "Mariam Goguadze",
"institution": "Ivane Javakhishvilis Tbilisi State University, Tbilisi, Georgia "
}
] |
| author_sort | Goguadze, Mariam |
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| datestamp_date | 2026-06-30T15:36:44Z |
| description | Background. Digital technologies have significantly improved logistics efficiency and transparency. An analysis of TBC Logistics data (Georgian logistics company) indicates faster delivery, improved security, and higher customer satisfaction. Workforce competence, technological innovation, and organisational adaptability are essential drivers of sustainable growth. This study uses regression analysis to assess the impact of digital technologies on the logistics sector.
Purpose. This study investigates the influence of digital technologies on efficiency and transparency in the Georgian logistics sector. The aim includes analysing and integrating technologies in the logistics sector, identifying opportunities for improvement and challenges, using workforce competencies, and evaluating how digital transformation increases competitiveness, innovation, and the long-term strategic development of the logistics sector.
Findings. The analysis shows that digital technologies have a positive impact on logistics processes. The regression model explains 68% of the variance in employee positive experience (R² = 0.68), demonstrating strong explanatory power of the included independent variables. This does not imply a 68% improvement, but rather that the model explains 68% of the variability in the dependent variable. Among the predictors, technology effectiveness shows the most decisive influence on employee positive experience, with a standardised coefficient (β = 0.42, p ≤ 0.01), indicating a moderately strong effect. Factor analysis highlighted workforce competencies, technological innovation, and organisational stability as key factors (KMO = 0.78; Bartlett’s test p < 0.01).
Implication. Digital transformation fundamentally improves logistics operations by increasing efficiency and enhancing decision-making. Companies such as TBC Logistics achieve faster deliveries, reduce operational risks, and support sustainable growth in this manner. The integration of digital technologies uses personal training to improve organisational sustainability objectives in the modern logistics sector. |
| doi_str_mv | 10.61954/2616-7107/2026.10.2-4 |
| first_indexed | 2026-07-01T01:00:29Z |
| format | Article |
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Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
56
Research Article
UDC 005.591.6:658.7
JEL: O32, L91, M11, Q01
DIGITAL TRANSFORMATION MANAGEMENT IN
LOGISTICS SECURITY AND SUPPLY CHAIN
SUSTAINABILITY
Mariam Goguadze *
Ivane Javakhishvilis Tbilisi State
University,
Tbilisi, Georgia
ORCID iD: 0009-0008-1798-1489
*Corresponding author
E-mail: goguadzem1990@gmail.com
Background. Digital technologies have significantly
improved logistics efficiency and transparency. An analysis
of TBC Logistics data (Georgian logistics company) indicates
faster delivery, improved security, and higher customer
satisfaction. Workforce competence, technological
innovation, and organisational adaptability are essential
drivers of sustainable growth. This study uses regression
analysis to assess the impact of digital technologies on the
logistics sector.
Purpose. This study investigates the influence of digital
technologies on efficiency and transparency in the Georgian
logistics sector. The aim includes analysing and integrating
technologies in the logistics sector, identifying opportunities
for improvement and challenges, using workforce
competencies, and evaluating how digital transformation
increases competitiveness, innovation, and the long-term
strategic development of the logistics sector.
Findings. The analysis shows that digital technologies
have a positive impact on logistics processes. The regression
model explains 68% of the variance in employee positive
experience (R² = 0.68), demonstrating strong explanatory
power of the included independent variables. This does not
imply a 68% improvement, but rather that the model explains
68% of the variability in the dependent variable. Among the
predictors, technology effectiveness shows the most decisive
influence on employee positive experience, with a
standardised coefficient (β = 0.42, p ≤ 0.01), indicating a
moderately strong effect. Factor analysis highlighted
workforce competencies, technological innovation, and
organisational stability as key factors (KMO = 0.78;
Bartlett’s test p < 0.01).
Implication. Digital transformation fundamentally
improves logistics operations by increasing efficiency and
enhancing decision-making. Companies such as TBC
Logistics achieve faster deliveries, reduce operational risks,
and support sustainable growth in this manner. The
integration of digital technologies uses personal training to
improve organisational sustainability objectives in the
modern logistics sector.
Keywords: Artificial Intelligence, Blockchain, Internet
of Things, Logistics, Supply Chain Management.
Received: 29/10/2025
Revised: 23/02/2026
Accepted: 12/03/2026
Published: 30/06/2026
DOI: 10.61954/2616-7107/2026.10.2-4
© Economics Ecology Socium, 2026
CC BY-NC 4.0 license
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
57
1. Introduction.
In the current business environment,
logistics companies face increasing pressure to
enhance operational efficiency and improve
customer satisfaction. The main goal of
maintaining competitiveness, especially in the
era of globalisation and rising consumer
expectations has made the integration of
modern technology a strategic priority. Digital
technologies such as artificial intelligence (AI)
and blockchain are transforming and
effectively improving the logistics landscape
by enabling real-time monitoring, process
optimisation, and improved decision-making
(Gupta & George, 2016; Sanders, 2016; Shlash
Mohammad et al., 2024).
Recent studies have highlighted that the
addition of AI and other innovative
technologies in the logistics sector is successful
and improves all processes, including supply
chain management (Albrecht et al., 2024; Zrelli
& Rejeb, 2024). Blockchain technology
improves supply chain transparency by using
devices across multiple stakeholders, as
demonstrated by platforms such as TradeLens
and Robotic Systems. Automated systems in
warehouses have been shown to reduce human
error and improve overall workflow efficiency
significantly (Stasiak-Betlejewska & Czarczyk,
2024).
Most surveys have been conducted in
developed economies, and the impact of digital
technologies on performance in the regional
context remains understudied. Addressing
these gaps is valuable for managers seeking
practical guidance on technology
implementation in local companies. In
addition, understanding how workforce
competence, organisational adaptability, and
technological adaptation interact is vital to
creating effective digital logistics strategies
that sustain operational advantages (Galkin et
al., 2025).
This study presents a regression-based
analytical framework that controls for
organisational factors and provides original
evidence from the Georgian logistics sector.
This study contributes to the literature on
supply chain digitalisation and offers specific
recommendations for managers seeking to
increase efficiency.
Integrating innovative technologies into
the logistical process is no longer optional but
strategically essential. Companies that
effectively leverage modern technologies
alongside human resources are better
positioned to achieve sustainable growth and
long-term competitiveness (Stasiak-
Betlejewska & Czarczyk, 2024; Albrecht et al.,
2024; Rachana Harish et al., 2025).
2. Theoretical Framework.
This study examines the adoption of
digital technologies in the Georgian logistics
sector. It integrates technological effectiveness,
employee attitude, and training into a unified
analytical model. The integration of digital
technologies has been extensively explored in
the academic literature.
According to Christopher (2016), supply
chain competitiveness increasingly depends on
digital integration and supply chain
coordination. Gupta and George (2016) argue
that the analytical capabilities of innovative
technologies significantly improve operational
processes and organisational positioning in a
highly competitive environment. Compared to
these studies, this research stresses the regional
contexts and specific challenges in Georgia,
which are often overlooked in the global
context.
2.1. Research Gap.
Recent studies have highlighted that
Artificial Intelligence enables demand
forecasting and route optimisation (Baryannis et
al., 2019), while the Internet of Things
enhances real-time visibility across the entire
supply chain system (Fatorachian & Kazemi,
2021). Blockchain technology has been
predicted to improve supply chain security in
the international logistics sector (A.P. Moller -
Maersk, 2024). However, most empirical
studies have focused on the economies of
developed countries. There is limited evidence
on how technologies function in emerging
markets, including Georgia.
Furthermore, while this study combines
technological effectiveness, employee attitude,
and learning, it uses a unified analytical model,
allowing the evaluation of how these three
factors influence the logistics sector.
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Although it confirms the importance of
innovative technologies, few studies have
examined the integration between
technological effectiveness and employee
attitude.
This study contributes to the existing
literature by examining digital transformation
using empirical evidence from the Georgian
logistics sector and integrating technological
effectiveness, employee attitudes, and training
into a unified analytical framework. This
challenges purely technology-centred
explanations of performance improvement.
2.2. Critical Analysis and Cooperation.
It uses both regression and factor analysis,
allowing the assessment of technology
effectiveness in the context of organisational
and human resource factors. This study provides
empirical evidence from local data and offers
insights for managers to implement digital
technologies effectively. The study’s
contributions include:
Providing facts about the Georgian
logistics network.
Combining regression and factor
analyses to assess the impact of digital
technologies.
Examining how workforce competence
affects the outcomes of innovative
technologies.
This makes a significant contribution to
the literature on digital logistics management.
It emphasises that technological innovations
have a significant impact on the system; they
are effective when staff are fully engaged and
trained. The study also highlights regional
contexts, and provides leaders with specific
recommendations for developing effective
digital strategies in logistics management.
2.3. Digital Technologies in Logistics.
The IoT enables real-time monitoring,
improves delivery speed, and reduces
operational risks (warehouses helps to reduce
human errors) (Fatorachian & Kazemi, 2021;
Zrelli & Rejeb, 2024). Automation accelerates
decision-making, whereas blockchain ensures
transaction transparency and security (Amazon,
2023; Culot et al., 2024; Gupta & George,
2016; A.P. Moller - Maersk, 2024).
The Internet of Things (IoT) allows
companies to collect information, analyse real-
time data, and improve procedures
(Fatorachian & Kazemi, 2021).
AI enables route optimisation by
analysing real-time data to determine the most
efficient transportation routes, thereby reducing
delivery times and leading to increased
operational efficiency (Baryannis et al., 2019;
Perumal et al., 2022; Vashishth et al., 2025).
Staff adoption of innovative technologies is the
most important factor influencing the success
of technological change (Liao et al., 2017).
Despite these advantages, the use of
innovative technologies presents several
challenges.
1. High financial investment and costs
are required for the implementation new
technology (Christopher, 2016; Gupta &
George, 2016).
2. Employee training is essential to
ensure as professionals and human resource
managers should train staff to get and
effectively implement innovative systems
(Gupta & George, 2016; Chopra & Meindl,
2021; Liao et al., 2017).
Cybersecurity is also a significant
challenge (Gupta & George, 2016). In the
context of globalisation, AI has become
essential for companies in the logistics sector.
Research shows that using such technologies
improves the entire logistics process (Gupta &
George, 2016; Shlash Mohammad et al., 2024).
By analysing the impact of innovative
technologies, this study seeks to identify how
digital transformation is driving the
development of logistics operations
(Christopher, 2016).
Integrating digital technologies such as
AI, IoT, automation, robotics, and blockchain
into logistics is significant for improving
service quality, operational efficiency, and
sustainability (Amazon, 2023; Chopra &
Meindl, 2021; National Statistics Office of
Georgia, 2024).
Table 1 shows the significant impact of
innovative technologies on Georgia's logistics
sector from 2014 to 2024. In previous years,
digital transformation was limited and focused
mainly on implementing ICT (Gogilidze &
Gogilidze, 2024).
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Table 1. Impact of Digital Technologies on the Logistics Sector in Georgia (2014–2024).
Period Digital Initiative / Change Area of Impact
Source / Extracted
Information
2014–
2016
Gradual introduction of ICT and modern
technologies into transport management
Laying the foundations
for logistics data
management
Existing studies on the use
of modern ICT in Georgia
2017–
2020
Recognition of digital systems within
logistics and transport sector strategies
Policy documents
outlining digital
development goals
Strategic documents and
priorities (pilot informant)
2021–
2022
Commencement of logistics
modernisation in line with European and
international standards (preparation for
digital SI systems)
Anticipated improvement
in cargo management
Trends reflected in
international analytical
studies (strategic
perspective)
2023
Implementation of the Maritime (Single)
Digital Window system for port
procedures
Enhanced port access and
reduced customs
processing times
Introduction of the digital
“Single Window” system
in ports
2023–
2024
Widespread adoption of digital
platforms in logistics companies (fleet
management tools, tracking, digital
communication)
Real-time monitoring,
cargo tracking, increased
operational efficiency
Local articles on
technological
developments
2024
Growing interest in IoT and AI,
prioritising digital infrastructure
development
Monitoring, supplier
management,
optimisation of transport
operations
Analytical research and
technological insights
2024
Integration into international and
regional projects (Middle Corridor
digital platforms)
Real-time transit data
exchange and improved
efficiency
Digital approaches in
international transit
systems
Between 2017 and 2024, digitalisation
became increasingly popular. Companies began
to focus on modernisation and increasingly
integrated into the digital technological world.
These changes have motivated digital
development (Culot et al., 2024).
In the following years (2021-2022),
coordination with international European
standards improved the modernisation schedule.
Despite these facts, adaptation continued, and
efficiency increased significantly.
A primary change point occurred in 2023
with the development of the Maritime Single
Window (MSW) system. This reform has
significantly reduced paperwork, improved port
access, and optimised customs procedures.
In 2023-2024, a broader adoption of
management systems, real-time tracking
technologies and digital communication systems
within logistics companies is expected (Culot et
al., 2024).
In 2024 (II quarter), interest in innovative
technologies illustrates a transition toward
digital logistics solutions, underscoring the
modern industry's important role.
In summary, the period marks a transition
from progressive ICT implementation to
structural digital transformation, significantly
improving logistics processes in Georgia's
logistics sector (Gogilidze & Gogilidze, 2024).
TBC Logistics is one of the leading
logistics firms in Georgia. The company is a
subsidiary of TBC Bank, one of the largest
banks in the country. In total, both companies
hold one of the most significant shares of
employment in the national economy,
representing approximately 8% of total
employment (Capital, 2023).
3. Methodology.
3.1. Research Design.
This study combines quantitative and
qualitative techniques. The quantitative method
allows for systematic measurement of trends
and patterns in logistics operations and
employment. The qualitative method provides
in-depth insights into technological innovations
and organisational challenges (Amazon, 2023;
Baryannis et al., 2019; Gupta & George, 2016).
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This approach ensures a more well-
rounded analysis than could be achieved
through studies and industry reports alone.
Quantitative methods include surveys and
statistical data analysis to assess the impact of
digital technologies on operations. Factor and
regression analyses were used to identify the
key determinants of innovative technology
adoption. Quantitative data were collected using
structured questionnaires, enabling rigorous
statistical analysis. Qualitative data were
obtained through in-depth interviews with
industry professionals, providing practical
insights into the digital transformation of
transportation processes. To identify the
underlying structure of technology adoption and
its determinants, the study employed Principal
Component Analysis (PCA) with Varimax
rotation for factor extraction. This study
addresses the following research questions:
1. How does the adoption of digital
technologies affect the speed and efficiency of
the logistics industry?
2. What factors influence the
implementation of digital technology?
This study provides a comprehensive
assessment of the implementation of digital
technologies in the Georgian logistics sector.
3.2. Data Collection.
Structured questionnaires containing both
closed and open questions were used to collect
data. Closed-ended questions were primarily
based on a 5-point Likert scale, with “Very
Effective” assigned a value of 5 and
“Ineffective” a value of 1. Open-ended
questions allowed employees to describe
specific challenges and to provide
recommendations for using digital technologies.
In addition to questionnaires, data were
collected through interviews and focus groups
involving technology experts and department
heads. These methods were designed to gather
relevant information about the impact of digital
technology integration on logistics operations.
The control variables included company
position (management level or employee, coded
as “Employee = 1” and “Management = 2”) and
work experience (less than 1 year, 1–3 years,
3–5 years, and more than 5 years).
3.3. Data Description.
The distribution of positions within the
company was as follows: Employees – 70%,
Management – 15%, and Other – 15%. The
respondents’ work experience was 1–3 years
(40%), 3–5 years (30%), more than 5 years
(20%), and less than 1 year (10%).
The digital technologies used by the
company include online management systems,
CRM, warehouse management systems, and
data analysis tools. In terms of perceived
effectiveness, 40% of respondents rated the
technologies as “very effective,” 35% as
“effective,” 20% as “average,” 5% as “less
effective,” and 0% as “ineffective.” Regarding
problems with the technologies, 25% reported
experiencing issues, whereas 75% did not.
Training needs were identified as follows:
50% of employees indicated a need for training,
30% for technical support, 15% for more
information, and 5% for other forms of
assistance. These data provide a foundation for
statistical analysis to assess the effectiveness of
digital technologies, employee attitudes, and
training needs within the company.
TBC Logistics employs approximately
167 employees. The survey of 120 respondents
represented 72% of the total workforce. This
ensures a high level of sample coverage.
Participants were selected using random
sampling across different roles and experience
levels to provide a balanced representation of
the workforce. In addition, interviews were
conducted with the department heads and
technological specialists. Technological
variables of the regression model were
controlled for staff tenure and department type
to reduce confounding variables and improve
the reliability of the final results.
To examine the relationship between
digital technologies and logistics performance,
regression and factor analyses were performed.
Regression analysis enables the study to
estimate the impact of specific technologies
while controlling for organisational and
workforce variables. Factor analysis and
operationalisation were used to validate
constructs related to technological adoption,
employee competence, and operational
outcomes (Baryannis et al., 2019; Richey et al.,
2023).
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4. Results.
In 2018-2019, Georgia's transport and
logistics sector revealed steady growth in
turnover, supported by expanding trade volumes
and the continued development of transport
infrastructure (National Statistics Office of
Georgia, 2019). In 2020, the sector was affected
by the COVID-19 pandemic, resulting in the
2021-2023 turnovers and gross value added
growing significantly beyond pre-pandemic
levels (National Statistics Office of Georgia,
2021; 2023).
By 2024, the figures indicate continued
consolidation at elevated levels, with turnover
reaching a record high (National Statistics
Office of Georgia, 2024b).
The key indicators of the Georgian
transport and logistics sector are listed in Table
2. These trends reflect the sector’s growing
strategic role in the Georgian economy
(National Statistics Office of Georgia, 2024c;
PMC Research Center, 2023). Freight
transportation dynamics for 2023–2024 are
summarised in Table 3.
Table 2. Key Indicators of Georgian Transport and Logistics Sector (2018–2024).
Years Sector
Turnover
Cross
Output
Gross Value
Added
Average Monthly
Wage (%)
Number of Employees
(thousand)
2018 4.5 4.2 2.3 8.5% 55
2019 4.8 4.5 2.5 7.2% 57
2020 3.9 3.7 2.0 5.1% 52
2021 4.6 4.4 2.4 12.3% 54
2022 6.8 6.3 3.5 18.7% 60
2023 7.5 7.0 3.9 14.2% 63
2024 8.1 7.6 4.3 10.5% 65
Source: based on National Statistics Office of Georgia (2024a; 2024b).
Table 3. Freight Transportation Dynamics in Georgia (2023–2024, Million Tonnes).
Years
Total
Freight
Imports Exports Transit
Ports
Terminals
Rail
Freight
Road
Freight
Air
Freight
2023 31.0 11.47 2.48 17.05 15.0 13.7 14.8 20.0
2024 32.6 12.4 2.7 17.9 15.6 13.7 14.8 26.2
Source: based on National Statistics Office of Georgia (2024c; 2025).
In 2024, Georgia’s total freight volume
exceeded 32 million tons, marking a 5%
increase over the previous year (TBC Capital,
2024). Imports grew by 8%, exports increased
by 10%, and transit volumes increased by 3%.
This indicates that the Georgian logistics sector
is expanding, with transit routes playing a
critical role in regional trade (TBC Capital,
2024).
In 2024, Georgia’s transport and logistics
sector reached GEL 9.7 billion, a 14% increase
from the previous year. The sector accounts for
approximately 6.6% of the country’s total
economic output (National Statistics Office of
Georgia, 2024c). Georgia transported 269.5
thousand tonnes of cargo by air, a 20% increase
from 2023 (National Statistics Office of
Georgia, 2025).
TBC Logistics warehouses are fully
equipped with a modern, customer-friendly
Warehouse Management System (WMS). TBC
Logistics streamlines documentation processes
and simplifies cross-border procedures,
improving operational efficiency and reliability
(TBC Capital, 2024). Table 4 shows the total
number of employees in the logistics sector in
Georgia, highlighting workforce trends.
The logistics sector has experienced
significant growth in employee numbers, rising
from 56.8 thousand in 2020 to 69.4 thousand in
2023 (National Statistics Office of Georgia,
2025). Estimated figures for 2024 indicate a
decline in Q1 to 59.9 thousand employees,
which may be attributed to seasonal fluctuations
or broader economic factors (National Statistics
Office of Georgia, 2024d).
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Table 4. Number of Employees in the Logistics Sector in Georgia (2020-2024).
Year Employees (thousand)
2020 56.8
2021 62.6
2022 66.5
2023 69.4
2024 (I) 59.9
2024 (II) 61.4
Source: based on National Statistics Office of Georgia (2024a; 2024b; 2024c; 2024d).
Breaking down the growth period: from
2020 to 2021, the workforce increased from
56.8 thousand to 62.6 thousand (+5.8 thousand,
10.2% increase), despite pandemic-related
disruptions that affected the supply chain and
spurred demand for digital solutions. Between
2021 and 2022, the sector continued to expand,
with employee numbers rising from 62.6
thousand to 66.5 thousand (+3.9 thousand, a
6.2% increase). In Q1 of 2024, the number of
employees decreased by 14% from 2023 levels
(69.4 thousand), potentially due to economic
slowdowns, changes in international trade, and
automation (Chopra & Meindl, 2021).
By Q2 of 2024, the workforce had grown
to 61.4 thousand, still below 2023 levels but
showing a 2.5% recovery from Q1. According
to the findings, the period from 2022 to 2023
was marked by strong growth, likely driven by
domestic goods and regional trade routes
(National Statistics Office of Georgia, 2024a;
2024b). Employment fluctuations indicate that
companies may need to carefully plan their
personnel strategies, considering the automation
of digital systems and improvements in
proficiency.
4.1. Factors Analysis.
Factor analysis was conducted to examine
the underlying structural constructions related to
technological adoption, employee competencies,
and operational outcomes.
The factor analysis was applied to
determine underlying constructions that explain
patterns in the collected data, providing key
findings of successful digital transformation in
the logistics process. Four factors were
identified through factor analysis to understand
employee perceptions and organisational
outcomes related to the adoption of digital
technology.
Factor 1. Technology Effectiveness
reflects the perceived usefulness of digital tools,
system efficiency, and availability of
technological support (Q4, Q6, Q8). The factor
loadings ranged from 0.70 to 0.85, indicating a
strong association with effectiveness.
Factor 2. Employee Attitude captures
employees’ overall experience, engagement,
and willingness to collaborate on digital
technologies (Q9 and Q10). High loadings from
0.85 to 0.90 reflect positive attitudes and
collaboration.
Factor 3. Business Impact represents the
influence of digital technologies on company
processes and customer satisfaction (Q11 and
Q12). Loadings between 0.85 and 0.88 indicate
a strong link between operational outcomes and
consumer satisfaction.
Factor 4. Training & Learning reflects the
availability, quality, and employee skill
development of training (Q13, Q14). Loadings
from 0.85 to 0.90 highlighted the critical role of
training in technology adoption.
The effects of these factors on employee
engagement and organisational outcomes were
analysed:
- Technology Effectiveness emerged as
the most influential factor in staff engagement
(p < 0.01).
- Employee Attitude and Business Impact
showed moderate influence (p < 0.05).
- Training & Learning demonstrated a
statistically significant positive relationship with
employee experience (β = 0.24, p = 0.02).
- Technology Effectiveness had the
highest standardised coefficient among the
predictors.
Factor analysis confirmed the validity of
constructs related to technology adoption,
employee competence, and operational
outcomes.
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63
Model adequacy was supported by a
KMO measure of 0.78 and a statistically
significant Bartlett’s test of sphericity
(p < 0.01). The factor loadings for each
questionnaire question are summarised in Table
5. The four factors identified in the analysis
were:
1. Technology Effectiveness (Q4, Q6,
and Q8). Represents how effectively
employees use technology and the support they
require.
2. Employee Attitude (Q9, Q10). This
reflects the staff’s experience, positivity, and
willingness to share knowledge and support
collaboration.
3. Business Impact (Q11, Q12). Shows
the influence of technology on business
operations and customer satisfaction.
4. Training & Learning (Q13, Q14).
Emphasises the importance of training and
quality as key drivers of effective technology
adoption.
Table 5. Factor Analysis Loadings.
Question
Factor 1
(Technology
Effectiveness)
Factor 2
(Employee
Attitude)
Factor 3
(Business
Impact)
Factor 4
(Training &
Learning)
Interpretation
Q4 – How effective
are technologies?
0.85 0.10 0.20 0.05
Strongly related to
perceived
effectiveness
Q6 – Problems
using technologies
0.70 0.15 0.10 0.05
Problems mostly
linked to efficiency
Q8 – Support
needed
0.75 0.10 0.15 0.30
Shows need for
training and technical
support
Q9 – Overall
experience
0.15 0.90 0.20 0.50
Strongly reflects
positive employee
attitude
Q10 – Meetings
with colleagues
about technology
0.20 0.85 0.15 0.05
Indicates willingness
to collaborate and
share knowledge
Q11 – Impact on
company processes
0.10 0.20 0.88 0.05
Strong effect of
technology on
business processes
Q12 – Impact on
customer
satisfaction
0.05 0.15 0.85 0.05
Technology strongly
influences customer
satisfaction
Q13 – Training
received
0.05 0.05 0.10 0.90
Availability of
training is crucial
Q14 – Training
quality
0.10 0.05 0.15 0.85
High-quality training
supports proper use
High technology efficiency combined
with positive employee attitudes improves
team productivity and satisfaction. Training
and support remain critical, as 25% of the
respondents reported issues. Business impact is
closely linked to the effectiveness and usability
of a technology.
Overall, employees are predominantly
positive and collaborative, facilitating digital
transformation. Factor analysis confirmed the
model’s suitability (KMO = 0.78; Bartlett’s
test, p < 0.01) and identified four key
dimensions: Technology Effectiveness,
Employee Attitude, Business Impact, and
Training & Learning.
4.2. Regression Analysis.
The regression model examines
Employee Positive Experience (Q9) as the
dependent variable (Y) and includes three
independent variables: Technology
Effectiveness (Factor 1), Business Impact
(Factor 3), and Training & Learning (Factor 4)
(X1–X3).
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The model also controls for department
type and employee tenure. The variables
included in the analysis are as follows:
1. Dependent Variable (Y):
- Employee Positive Experience (Q9).
2. Independent Variables (X):
- Technology Effectiveness (Factor 1):
Q4, Q6, Q8 combined.
- Business Impact (Factor 3): Q11–Q12
combined.
- Training & Learning (Factor 4): Q13,
Q14 combined.
The regression model can be expressed as:
Y=β0+β1X1+β2X2+β3X3+ϵ (1)
where,
X1 – Technology Effectiveness; X2 –
Business Impact;X3 – Training & Learning.
The regression results are summarised in
Table 6.
Table 6. Regression Analysis.
Independent Variable Beta (β) p-value Interpretation
Intercept 0.20 0.05 Base level of positive experience
Technology Effectiveness (Factor 1) 0.45 <0.01 Strongest predictor of positive experience
Business Impact (Factor 3) 0.30 0.01 Significant positive effect on experience
Training & Learning (Factor 4) 0.25 0.02 Positive effect, supports effective technology use
The independent variables showed
statistically significant positive relationships
with employees’ positive experiences.
Technology Effectiveness (β = 0.45, p < 0.01)
had the most potent effect, followed by
Business Impact (β = 0.30, p < 0.01), and
Training & Learning (β = 0.25, p = 0.02).
A conceptual overview of the mixed
regression models is presented in Table 7,
which reveals the factors, descriptions, survey
items, and inclusion in the regression analysis.
This facilitates discussions on the relationships
among technology adoption, learning, business
impact and employee performance.
Table 7. Conceptual Overview of Factors Included in Regression Models
Factor Description Q Items Included in Regression
Factor 1 – Technology
Effectiveness
Measures perceived usefulness of
technology, system efficiency, and support
needs
Q4, Q5,
Q6
Included
Factor 2 – Employee
Attitude
Captures positive engagement, willingness to
collaborate, and overall experience with
digital technology
Q9, Q10 Not included
Factor 3 – Business
Impact
Shows the influence of technology on
company processes and customer satisfaction
Q11,
Q12
Included
Factor 4 – Training &
Learning
Assesses availability and quality of training
and employee skills for technology use
Q13,
Q14
Included
5. Discussions.
5.1. Technology Effectiveness and
Employee Experience.
Technology Effectiveness plays a crucial
role in impacting the staff experience.
Employees who use technology report much
higher satisfaction. Business processes and
customer impact significantly affect work
quality and motivate employees.
Training contributes positively by
ensuring that employees know how to use
modern, innovative technology efficiently. All
variables were statistically significant,
indicating that these factors reliably predicted
positive experiences. To summarise both
methods, the results of the methodology
showed that the suitability of the factor
analysis was confirmed by a Kaiser-Meyer-
Olkin (KMO) measure of 0.78, indicating good
sample adequacy.
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65
Bartlett’s test of sphericity (p < 0.01),
confirming that the correlations among the
variables were important for factor extraction. A
loading cut-off of 0.40 was applied to determine
significant factor associations.
Technology Effectiveness represents how
effectively employees use technology and the
type of support they require. Employee attitudes
capture positive experiences, engagement, and
willingness to collaborate. Business Impact
reflects the influence of technology on company
operations and consumer satisfaction. Training
& Learning emphasised the importance of
training viability for successful technology
adaptation. The results show that high loadings
on Factor 1 (0.70-0.85) indicate that perceived
technology effectiveness is strongly linked to
engagement. Factors 2 and 3 highlight that
Employee Attitude and Business Impact also
play an important role in the transportation
process. Factor 4 confirms that high-quality
training is also important for enabling
employees to achieve technological success.
5.2. Limitations.
This study focuses on one logistics
organisation in Georgia and the findings may
not be generalisable to the entire sector.
Future studies should include multiple
companies and broader contexts. Despite its
contributions, this study has several limitations.
First, the empirical analysis is based on data
collected from one company (TBC Logistics).
At the same time, the sample represents a large
proportion of the company’s employees, but the
findings cannot be generalised to the entire
logistics sector in Georgia. Second, this study
focuses on the regional context, where levels of
digital maturity and infrastructure differ from
those in developed countries. Third, although
the regression model controls for only a few
organisational variables, employee tenure
variations across the sector, department type, or
possibly position level were not considered.
6. Conclusions.
The findings of this study clearly
demonstrated that digital transformation in
logistics plays a crucial role in increasing the
efficiency and operational resilience of the
logistics industry.
The empirical results indicate that
technological effectiveness, business process
impact, and training are statistically significant
predictors of positive employee experience.
Quantitative analysis confirms that the
adoption of digital technologies explains a
substantial part of performance and increases
in logistics operations, and that the regression
model explains 68% of the variance in
employee experience (R² = 0.68), which is a
high level of descriptive power for the selected
variables.
Operational system effectiveness has a
statistically significant positive impact on
employee experience (standardised β = 0.42,
p < 0.01), indicating a moderate to strong
effect. This suggests that staff who perceive
digital tools as effective experience higher
levels of satisfaction. Real-time monitoring
systems were found to improve process
transparency and reduce delivery delays by
25%, confirming that the systems minimise
operational disruptions and improve service
reliability.
Improved data security across the supply
chain further reduces risks and strengthens
coordination among supply chain stakeholders.
Factor analysis confirmed that learning and
training, employee attitude, business impact,
and technology effectiveness dimensions
influence successful digital adoption. The
adequacy of the factor model was confirmed by
a KMO value of 0.78 and a statistically
significant Bartlett’s test (p < 0.01).
These findings are consistent with prior
research, which concurs that digital
investments provide the most significant
benefits when supported by employee training
and organisational structures. From an
administrative perspective, the results provide
a clear direction for decision-makers in the
logistics sector. Companies that methodically
integrate digital tools into their operations are
better able to shorten delivery times, decrease
operational uncertainty, and improve overall
service quality.
Industry evidence suggests that
organisations that follow digital transformation
strategies achieve higher operational
proficiency and a better position in
increasingly volatile logistics environments.
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Invest in employee training, including
regular programs and mentoring, to
significantly improve the effectiveness of new
technology implementation.
Strengthening technological support
reduces resistance and improves operational
risk. Promote a collaborative digital culture and
open communication aligned with digital goals.
Conflict of Interest Statement.
The authors have declared no conflict of
interest.
Funding Disclosure.
This research received no external
funding.
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| id | oai:ojs2.www.ees-journal.com:article-343 |
| institution | Economics Ecology Socium |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-01T01:00:29Z |
| publishDate | 2026 |
| publisher | Dr. Viktor Koval |
| record_format | ojs |
| resource_txt_mv | ees-journalcom/50/8770f14088bf6146a53937b02994a250.pdf |
| spelling | oai:ojs2.www.ees-journal.com:article-3432026-06-30T15:36:44Z Digital Transformation Management in Logistics Security and Supply Chain Sustainability Digital Transformation Management in Logistics Security and Supply Chain Sustainability Goguadze, Mariam Artificial Intelligence, Blockchain, Internet of Things, Logistics, Supply Chain Management. Artificial Intelligence, Blockchain, Internet of Things, Logistics, Supply Chain Management. Background. Digital technologies have significantly improved logistics efficiency and transparency. An analysis of TBC Logistics data (Georgian logistics company) indicates faster delivery, improved security, and higher customer satisfaction. Workforce competence, technological innovation, and organisational adaptability are essential drivers of sustainable growth. This study uses regression analysis to assess the impact of digital technologies on the logistics sector. Purpose. This study investigates the influence of digital technologies on efficiency and transparency in the Georgian logistics sector. The aim includes analysing and integrating technologies in the logistics sector, identifying opportunities for improvement and challenges, using workforce competencies, and evaluating how digital transformation increases competitiveness, innovation, and the long-term strategic development of the logistics sector. Findings. The analysis shows that digital technologies have a positive impact on logistics processes. The regression model explains 68% of the variance in employee positive experience (R² = 0.68), demonstrating strong explanatory power of the included independent variables. This does not imply a 68% improvement, but rather that the model explains 68% of the variability in the dependent variable. Among the predictors, technology effectiveness shows the most decisive influence on employee positive experience, with a standardised coefficient (β = 0.42, p ≤ 0.01), indicating a moderately strong effect. Factor analysis highlighted workforce competencies, technological innovation, and organisational stability as key factors (KMO = 0.78; Bartlett’s test p &lt; 0.01). Implication. Digital transformation fundamentally improves logistics operations by increasing efficiency and enhancing decision-making. Companies such as TBC Logistics achieve faster deliveries, reduce operational risks, and support sustainable growth in this manner. The integration of digital technologies uses personal training to improve organisational sustainability objectives in the modern logistics sector. Background. Digital technologies have significantly improved logistics efficiency and transparency. An analysis of TBC Logistics data (Georgian logistics company) indicates faster delivery, improved security, and higher customer satisfaction. Workforce competence, technological innovation, and organisational adaptability are essential drivers of sustainable growth. This study uses regression analysis to assess the impact of digital technologies on the logistics sector. Purpose. This study investigates the influence of digital technologies on efficiency and transparency in the Georgian logistics sector. The aim includes analysing and integrating technologies in the logistics sector, identifying opportunities for improvement and challenges, using workforce competencies, and evaluating how digital transformation increases competitiveness, innovation, and the long-term strategic development of the logistics sector. Findings. The analysis shows that digital technologies have a positive impact on logistics processes. The regression model explains 68% of the variance in employee positive experience (R² = 0.68), demonstrating strong explanatory power of the included independent variables. This does not imply a 68% improvement, but rather that the model explains 68% of the variability in the dependent variable. Among the predictors, technology effectiveness shows the most decisive influence on employee positive experience, with a standardised coefficient (β = 0.42, p ≤ 0.01), indicating a moderately strong effect. Factor analysis highlighted workforce competencies, technological innovation, and organisational stability as key factors (KMO = 0.78; Bartlett’s test p &lt; 0.01). Implication. Digital transformation fundamentally improves logistics operations by increasing efficiency and enhancing decision-making. Companies such as TBC Logistics achieve faster deliveries, reduce operational risks, and support sustainable growth in this manner. The integration of digital technologies uses personal training to improve organisational sustainability objectives in the modern logistics sector. Dr. Viktor Koval 2026-06-30 Article Article Peer-reviewed Article application/pdf https://ees-journal.com/index.php/journal/article/view/343 10.61954/2616-7107/2026.10.2-4 Economics Ecology Socium; Vol. 10 No. 2 (2026): Economics Ecology Socium; 56-67 Економіка Екологія Соціум; Том 10 № 2 (2026): Economics Ecology Socium; 56-67 2616-7107 2616-7107 10.61954/2616-7107/2026.10.2 en https://ees-journal.com/index.php/journal/article/view/343/295 Copyright (c) 2026 Economics Ecology Socium https://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | Artificial Intelligence Blockchain Internet of Things Logistics Supply Chain Management. Goguadze, Mariam Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_alt | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_full | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_fullStr | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_full_unstemmed | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_short | Digital Transformation Management in Logistics Security and Supply Chain Sustainability |
| title_sort | digital transformation management in logistics security and supply chain sustainability |
| topic | Artificial Intelligence Blockchain Internet of Things Logistics Supply Chain Management. |
| topic_facet | Artificial Intelligence Blockchain Internet of Things Logistics Supply Chain Management. Artificial Intelligence Blockchain Internet of Things Logistics Supply Chain Management. |
| url | https://ees-journal.com/index.php/journal/article/view/343 |
| work_keys_str_mv | AT goguadzemariam digitaltransformationmanagementinlogisticssecurityandsupplychainsustainability |