Reviewers
Prof. Dr. Borut Jereb (Celje, Slovenia)
Prof. Dr. Drago Pupavac (Rijeka, Croatia)
1
2
Ranka KRIVOKAPIC
Milica DELIBASIC
Mimo DRASKOVIC
ESSAYS ON SMALL SEAPORTS:
contemporary changes, tendencies
and perspectives for the development
SPH - Scientific Publishing Hub
Celje, 2023
3
CIP - Kataložni zapis o publikaciji
Narodna in univerzitetna knjižnica, Ljubljana
338.47(262.3)
656.615(262.3)
KRIVOKAPIĆ, Ranka
Essays on small seaports: contemporary changes, tendencies and
perspectives for the development / Ranka Krivokapic, Milica Delibasic,
Mimo Draskovic. - 1st ed. - Celje: SPH - Scientific Publishing Hub, 2023
ISBN 978-961-6948-27-2
COBISS.SI-ID 156883715
4
Contents
List of Figures
7
List of Tables
11
List of Contributors
13
PREFACE
Milica Delibasic
17
Part 1: SMALL SEAPORTS EVOLUTION
Ranka Krivokapic
31
1. INSTITUTIONAL EFFICIENCY FACTORS OF ADRIATIC
SEAPORTS ……………………………………………………………………...…
33
2. PERSPECTIVES FOR THE DEVELOPMENT OF SMALL
SEAPORTS, WITH REFERENCE TO THE ADRIATIC SEAPORTS ….
47
3. THE ESSENTIAL FACTORS ON THE COMPETITIVENESS OF
SMALL ADRIATIC SEAPORTS ………………………………………………..
67
Part 2: CHANGING THE STRATEGIC ROLE OF SMALL SEAPORTS
Milica Delibasic
85
1. STRATEGIC ADAPTATION OF SEAPORTS ……………………………….. 87
2. INSTITUTIONAL IMBALANCE OF INTERESTS IN MARITIME
TRANSPORT AND SUSTAINABLE DEVELOPMENT …………………… 105
3. INSTITUTIONAL CHANGES AS A FRAMEWORK OF THE
MARITIME DEVELOPMENT ……………………………………………….….. 119
5
Part 3: LOGISTICS DEVELOPMENT OF SMALL SEAPORTS
Mimo Draskovic
133
1. EMPLOYMENT IN THE MARITIME TRANSPORT ……………………... 135
2. SPREADSHEETS IN FUNCTION OF OPTIMISATION
OF LOGISTICS NETWORK ……………………………………………………
153
3. NEGATIVE EXTERNALITIES AND LOGISTICS DEVELOPMENT
OF ADRIATIC SEAPORTS ………………………………………………………
167
4. BUSINESS COOPERATION IN THE ADRIATIC SEAPORTS …………
183
REFERENCES
195
6
Figures
No.
Name
Pages
PREFACE
1
2
3
4
5
Defining the unique qualities of a company in relation to
completion and the supply chain
The process of configuring the port system to improve
competitiveness and integration of the port and
hinterland
Participants of maritime transport and logistics
Future port model: sixth generation ports (6GP) as smart
port
Smart Port models
21
23
24
26
28
Part 1
1
2
3
4
5
6
7
Institutional efficiency factors of adriatic seaports –
the research framework
Percentage of appearing values 1-4 in the set of
dependent variable (Dv)
Mean values of dependent (Dv) and independent
variables (Iv1-5) according to subjective asses-sments of
respondents (all cases)
Dependant variable (Dv) vs. independent variables
(Iv1-5)
Factors Influencing the evolution of small seaports
Evolution Path to the fifth generation port (5GP)
Conditional concept of small seaport development
35
42
43
44
50
55
59
7
8
9
10
11
12
Major container shipping routes in the world
Possibilities of developing adriatic ports
Conceptual framework for port competitiveness
Role of ports through logistics supply chain
Hypothetical research model
61
63
70
73
76
Part 2
1
2
3
4
5
6
7
8
9
10
11
12
Functional environment of the port cluster
The decision flows for port governance
Outside stakeholders in the port
The road to institutional reforms, management reforms,
and governance
Hypothetical model of future development of Luka Bar
The interaction of individuals and institutions
(enviroments and agreements)
Logic of institutional changes
Conceptual model of institutional changes in seaports
Social capital components
The model of economic growth and economic
development under „knowledge economy“
Social innovations and the development formula within
knowledge economy
GEM's conceptual model of economic growth
92
94
96
98
102
109
109
113
122
129
130
131
Part 3
1
2
3
4
5
6
7
8
8
Maritime traffic employees and transport system
employees ratio, 1987-2015
Employment trends in transport system of the Croatia,
1983-2015
Employment trends in maritime transport system of
Croatia (000)
Number of inhabitants per one seafarer in EU countries
Goods carried in sea water and coastal transport (000t)
Logistics network
Logistics network in industrial firm
Global logistics network
139
140
142
143
149
156
157
157
9
10
11
12
13
14
15
Crossdocking
Possibilities of logistical and economic development of
seaports
The values of the dependent variables, estimated by
respondents and those determined by the model, in the
case of Port of Bar
The values of the dependent variables, estimated by
respondents and those determined by the model, in the
case of Port of Rijeka
The values of the dependent variables, estimated by
respondents and those determined by the model, in the
case of Port of Koper
Research framework of the proposed hypothetical model
Dependencies between the dependent variable and the
mean values of the independent variables at the level of
all observed Adriatic seaports
160
170
178
179
180
185
190
9
10
Tables
No.
Name
Pages
PREFACE
1
2
Evolution Path from 5GP to 6GP as Smart Ports
UNCTAD Smart Port model
25
27
Part 1
1
2
3
4
5
6
Linear multiple regression model key parameters and
statistics
Mean values of the dependent variable Dv k in the case
of Montenegro, Serbia, and Bosnia and Herzegovina
(integral)
Mean values of the independent variables and their
impact to the dependant variable
Seaport evolution
Mean values of the dependent and independent
variables in the case of Port of Bar, Port of Rijeka and Port
of Koper
Errors, coefficients of correlation and determination
39
41
45
51
80
81
Part 2
1
2
Evolutions of the functional purpose of seaports
Basic logistics strategies of seaports
89
99
11
Part 3
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
12
Employees in Croatian transport system and maritime
traffic, 2008-2014
Descriptive statistics on employment in maritime
transport in EU-23, 2014.
Number of seafarers, GDP/p.c. and number of ships, EU25+Norway
Correlations between the number of seafarers, GDP/p.c.
and number of ships, EU25+Norway
Movement of employment in sea transport, passenger
ships, cargo ships, passengers carried, passenger miles,
goods carried,tonne-miles and GDP
Interdependence of the number of employed in maritime,
transport and passenger ships, cargo ships, passengers
carried, passenger miles, goods carried, tonne-miles and
gross domestic product
Pessimistic estimates for employment in the maritime
transport of Croatia 2025.
Descriptive statistics for goods carried, 1996-2016.
Optimistic estimates for employment in the maritime
transport of Croatia in 2025.
Type of network
Transportation costs - Plant to DC (€ 000 t)
Transportation costs - DC to Customer (€ 000 t)
Minimum cost network flow problem
Optimal minimum cost network flow problem solution by
use of calculation table
Mean values of the dependent variable Y in the case of
Port of Bar, Port of Rijeka and Port of Koper
Errors, coefficients of correlation and determination
Key parameters and statistical indicators in multiple
linear regression model
Mean values of independent variables (I1-3) and the rank
of their impact on the dependent variable (DV)
138
141
144
145
146
148
148
150
151
154
161
161
164
165
176
177
188
191
CONTRIBUTORS
13
14
Ranka Krivokapic, PhD (Economic), is Assistant Professor at the Maritime Faculty of Kotor, University of Montenegro, Podgorica Montenegro; on
disciplines Port and Shipping Economics, Communication in Shipping and
Shipping Agencies and Chartering, and Economics of Ship Exploitation.
His research interests include Management in Shipping, Shipping Economics, Business Communication and Etiquette in Business.
Milica Delibasic, PhD, is Assistant Professor at the Faculty of Business
Studies, Podgorica, Mediterranean University, Podgorica, Montenegro;
Faculty of Mediterranean Business Studies Tivat, Niksic study centre; and
Maritime Faculty Kotor, Universita of Montenegro, Montenegro on disciplines: Basics of Economics, Microeconomics, and Corporative Management. Editor in Chief of the international scientific journal Montenegrin
Journal of Ecology. Her research interests include institutional economics,
corporate governance, corporate social responsibility and management
problems.
Mimo Draskovic, PhD, is full Professor at the University of Montenegro. He teaches Maritime Management, Marketing Logistics and Organization of Maritime Company at the Faculty of Maritime Studies, Kotor.
His fields of research are strategic management, economy and international relations. He is a member of Association of Young Scientists at
Montenegrin Academy of Sciences and Arts. Moreover, he is the Editor in
Chief of the International Scientific Journal Media Dialogues.
15
16
PREFACE
17
18
S
eaports play a crucial role in logistics and supply chain management as well as economy on a regional, national, and international level. Those enable accessibility to essential resources
through transportation and services, in terms of energy, health, labor aspects, passenger mobility, and accessibility, especially when it comes to
the land-locked or island regions, as well as providing safety and security.
Irrespective of their size and location, those always serve as gateways and
act as epicenters of economic and social interactions. They pave the way
for regional economic development and social inclusion. Yet, ports are
subject to growing interdependencies and imperatives to leverage interests of nature preservation, economic efficiency, and legal compliance.
Most of the published literature about port development, is made in a
context of the large global ports such as Rotterdam, Antwerp, Hong Kong
and Shanghai, as exemplified by the work of Notteboom and Rodrigue
(2005) and Wang et al. (2006).
Proceeding from the facts that: “the port industry accounts for a substantial part of regional and national economies in most nations; She facilitates trade and has therefore an indirect impact on the economy; The
performance of ports is thus important for the prosperity of regions and
national economies”, De Langen (1998) is analyzed it a long time ago
made of the position of small and medium sized ports (SMSP's) in the
maritime transport market. He states: “Small ports, finally, merely serve
a local hinterland. They do not have good hinterland accessibility. The differences between medium-sized and small ports are not sharp. They have
the same characteristics, but a different scale. Consequently, small and
medium sized ports are taken as one group” (Ibid.).
The smaller ports do not have the same resources and knowledge to
implement the techniques that have been developed for the larger ports,
as these methods often are very complex in nature. Smaller ports do not
have the economics of scale to support more complex developments
(Olesen et al., 2012). For smaller ports, however, one of the main challenges is to engage in better supply chain development and integration.
According to Brooks et al. (2010) small and medium-sized ports need to
focus on developing their coordination competencies in their own operations and the collaboration competencies in relation to other ports and
companies. “This can be seen as the key driver in converting a port from
a gateway to an integrated logistics hub. One of the main motivations for
small and medium-sized ports is to improve the growth of the port’s hin-
19
terland, both in terms of job creation and moving goods from land to sea”
(Olesen et al., Ibid.).
SMSPs show lower integration into both horizontal value chains and
vertical supply chains, thus, suffering from less freight volumes, missing
smart specialization, low cognitive, organizational, or institutional proximity to and between macro-regional, national, EU, and international actors,
outdated infrastructure, lack of investments and new business models
accompanied by missing hands-on strategic foresight.
To define small and medium-sized ports, the EU Commission’s (2003)
definition, for small and medium-sized companies is used. The definition
is that small companies have up to 50 employees, medium-sized companies have up to 250 employees. Competitive advantage is introduced as
a concept to add further depth to the development models and relate
them to what is practical applicable in a smaller port context. The reason
for this is that the small ports have to choose which functions to invest in,
and this has to be based on their competencies and what creates value
for the hinterland.
Although differing definitions and notions of SMSPs prevail in the topical literature, three main functional characteristics can be applied to all
them:
− enhancers of the Blue Economy competitiveness;
− actors in regionalization processes; and
− key capacity to set up multiport gateway regions.
In addition, SMSPs also are defined by their limited competitive position in their port cluster regions and their role in a port hierarchy as the
lowest, based on cost and efficiency. However, empirical statistics revealed positive relationships between SMSPs and local economies (Karimah and Yudhistira, 2020). Finally, SMSPs have not only low trade volume
and disproportioned freight turnover, but also geographic, economic, and
environmental disadvantages (Lu et al., 2018; UNCTAD, 2014).
According to the International Transport Forum Forecast (2015), waterborne transport will grow with 327% by 2050, thus, producing 238%
more CO2 emissions. In Europe, freight volumes will increase by 216% by
2050, with 174% more CO2 emissions. There will be an enormous shift in
commodity transportation (Gerlitz et al., 2018). This also presupposes
growing challenges of SMSPs to comply with growing needs, in particular,
within the port environmental performance domain.
20
Figure 1. Defining the unique qualities of a company in relation to
completion and the supply chain
Source: Hafeez et al., 2002.
21
The number of SMSPs within and along the Adriatic, Ionian, and Liguria (Mediterranean area) are 93. The Adriatic Sea Region has 67 ports in
five countries, of which nine are categorized as Core Ports, while the rest
(58) are SMSPs. The Adriatic and Ionian Region, especially, are deeply affected by a long-term decline in the competitiveness of the regions’ main
industries. Therefore, for these ports, most efforts are geared toward the
preservation of existing conditions and economies rather than toward environmental sustainability (Kovacevic ans Pagella, 2015).
Hamel (1990) who stated that focusing on the organisation’s core
competencies relates to the success of the business. Hafeez et al. (2002)
show that the uniqueness of a company’s competencies are key in how
sustainable the competitive advantage is (Figure 1). A port can use the
model in Figure 1 to define what competencies it needs to develop, in
order to improve its competitiveness in regards to the hinterland and the
different supply chains.
The competitive advantage is a key driver in converting a port from a
gateway to an integrated logistics hub, which is in line with Prahalad and
and Hamel (1990) who stated that focusing on the organisation’s core
competencies relates to the success of the business. Hafeez et al. (2002)
show that the uniqueness of a company’s competencies are key in how
sustainable the competitive advantage is. In Figure 1 physical assets are
considered resources that the capabilities are related to, but in a port system the physical asset can be part of the core competencies, as it can
provide a sustainable competitive advantage in certain specialised supply
chains. Therefore, ports need to ensure that the physical assets are seen
as a driver for growth but at the same time ensure that the physical assets
are strategically tied to a specific product or service.
The framework in Figure 2 provides the steps that will allow smaller
ports to follow and modify the development of the larger ports.
Small and medium-sized ports (SMSPs) notify barriers in their development, due to the fact that suffer from lower trade volumes to bigger
ports, but also face economic, geographic and environmental disadvantages (Unctad, 2014; Lu et al., 2018). Also, SMSPs have to deal with missing policy compliance (Puig et al., 2020) and disadvantages in fund allocation (Baltic Ports Organisation, 2021). SMSPs are bound to limited resources and low cognitive, organizational, or institutional proximity, compared to their bigger counterparts. In the literature, there is no unified definition for a SMSP.
22
Figure 2. The process of configuring the port system to improve
competitiveness and integration of the port and hinterland
Source: Olesen et. al., Ibid., p. 11.
The Seaports’ classification of small and medium-sized can be implemented by their limited position in existing port clusters (Feng and Notteboom, 2013) or regarding their lowermost positioning in port hierarchy in
terms of costs and efficiency (Robinson, 1998). In addition, SMSPs can
be defined by their main functionalities as enhancer for blue economy,
actor in regionalisation processes and institution in multiport gateways
(Notteboom, 2005, 2010; Feng and Notteboom, 2013).
23
Figure 3. Participants of maritime transport and logistics
Source: Ibid.
Maritime transport is an enormous global network of fleets, companies, transport, industrial and infrastructure systems, which deliver everything we buy, from the manufacturer to the warehouse, from the store to
the home. Although maritime transport technologies have evolved significantly, the roles and functions of seaports have remained relatively similar: they are transit areas, i.e. a passage through which goods and people
move from and to the sea; point of contact between land and sea space,
hub of interaction between oceanic and inland transport systems, point of
convergence of different types of transport; unloading place where cargo
is consolidated or unconsolidated.
Multi-functions of Ports (Lee, 2020):
− Node of multimodal transport systems:
➢ Sea-River integrated transportation system.
➢ Sea-Rail multimodal transport system.
➢ Seaport and Dry port (Inland Port) by land and river
transport.
− Sustainable connectivity of ports for international trade.
− Enabler of economic growth of national and regional economy.
− Node of value chains in global supply chain system in association with global production lines.
24
− A port as an organic system, i.e. sustainable and resilient system
As generic as the term "port" may seem, it expresses the considerable
variety of sizes, functions and locations used for port activities. They are
complex and multifaceted. This is why it is common to approach them primarily from the perspective of the supply chain and their network connectivity. This led to the following (general) definition: A seaport is a logistic
and industrial hub in global supply chains with a strong maritime character and functional and spatial clusters of activities, which are directly or
indirectly related to transport, transformation and information flows and
processes within these global delivery chains. The development of the
seaport definition can be seen from the table below.
Table 1. Evolution Path from 5GP to 6GP as Smart Ports
Source: Lee, 2020.
25
Figure 4. Future Port Model: Sixth Generation Ports (6GP) as Smart Port
Source: Lee, 2020.
A modern seaport is a transit transport and logistics point that creates
added value. This is exactly where maritime logistics is most important.
Modern performances of seaports and their very complex generic structures are the results of the application of the most modern technologies,
organizational solutions, property reforms, private-public partnerships, active government, and entrepreneurial policies, as well as specific innovation strategies in this area. This allows them to perform complex and numerous services, such as:
26
− Transportation of goods,
− Warehousing: equipment handling, transit storage, receiving and delivery, road transport, assembly and processing, storage and manipulation, transfers, packaging,
− Ship services: assistance in navigation, pilotage, towing, mooring, bunkering, disposal, garbage collection, anchorage, buoys, ship repair,
− Infrastructure: hydrographic research, dredging, repair and maintenance, engineering design, port construction,
− Procurement of equipment: marketing research,
− Management and information,
− Accounting: data processing and analysis,
− HR policy,
− Safety: firefighters and rescue work, application of standards and
safety regulations.
Table 2: UNCTAD Smart Port model
1st
Generation
‐‐‐/1940
Mechanic
Port
Mechanical
operation
Handicraft
works
2st
Generation
1960
Container
Port
Free Zone
Industrial
area
Free tax port
3st
Generation
1980
4st
Generation
2000
5st
Generation
2020
EDI Port
Internet Port
Smart Port
International
network
Integrated
centre
Commercial
area
Global
Network
Port
community
Logistic
community
Logistic area
Smart City
Intermodal
services
Internet
services
Smart
Hinterland
Multimodal
services
Sustainable
port
EDI services
ITS port
Source: Seaport development, 2020.
Smart City is a new, emerging and evolving concept which rose the last
years. Smart Port is newest, with no international accepted definition and with
27
several parallel initiatives from both main international Ports and Sectoral Associations (Table 2). Like cities from the megalopolis to the rural village have
embarked on projects called "Smart City" regardless of content or budgets
Ports also follow a similar path, from large international ports to the smallest
local port, labeling as "Smart Port" any initiative, project or service that has
any content or technological support, if belonging to their normal field of operations. Many of the Smart Ports (projects) are in port cities, which in turn
have a Smart City project that does not have included the port, focusing mostly
in urban transport, but both projects should converge and cooperate where
appropriate. We can roughly identify two large approach to the Smart Port issue of emerging definition: a) Regulatory and b) Technological (Figure 5). The
first is based on policies supported by institutions such as IMO and EU, one
issuing technical recommendations and the other with mandatory Directives.
The second is used by Ports itself and by the UNCTAD, both of them based on
economy aims through the technology implementations.
Figure 5. Smart Port models
Source: Seaport development, 2020.
28
In the academic literature, small and medium-sized ports as a central
research subject has been sparsely in focus. According to Rozmarynowska and Oldakowski (2013), 66% of all BSR ports are small and mediumsized ports, which handle less than two million tonnes of cargo per year.
Since these ports form the majority and are an essential source of entrepreneurial spirit and innovation, they represent an important object of investigation. Thus, there is a need to support small and medium-sized
ports in their crucial role of generating economic growth, triggering innovations, attracting new investments and businesses, enabling clusters to
evolve, ensuring employability and fostering social integration.
It is considered (Rodrigue and Schulman, 2013; Olesen et al., 2014;
Simkins and Stewart, 2015) that the basic development constraints of
small ports are finance, resources, knowledge, experience, small economies of scale, disconnection from the hinterland, lower cargo-generating
and cargo-binding potential and weak supply chain integration. Olesen et
al. (2014) stressed that small and medium-sized ports are lacking in elementary strategic concepts.
According to Castillo-Manzano et al. (2013), efficient supply chains
need to be established by small and medium-sized ports to ensure that
freight is shipped smoothly and more cost effectively, which, in turn, will
allow the ports to be more competitive. Moreover, small and mediumsized ports in the BSR suffer from lower cargo volumes, missing smart
specialisation, out-dated infrastructure and the absence of new business
models. Therefore, in order to keep pace with the rapidly changing market
environment and customer needs, small and medium-sized ports, as a
dormant gateway of economic and social interactions for regional development and growth, must develop digitization strategies as well as new
initiatives and sustainable measures (Philipp, 2021). The growing interest
in digitalisation and related novel technologies has evolved over the last
decade especially. Digitalisation is often regarded as the saviour in terms
of managing the challenges of the increasing globalisation, competition,
environmental issues and customer-oriented supply chain focus.
Especially in the context of the novel visionary idea of smart ports, the
investigation of digitalisation possibilities and the application and integration of novel technologies becomes more and more important (Ibid.). The
brainchild of a smart port development is associated with an innovative
endeavour in which the focus is on improving the competitiveness of the
port and facilitating entrepreneurial collaboration between different port
stakeholders in order to achieve the horizontal and vertical integration of
29
supply chains (Douaioui et al., 2018). According to Yang et al. (2018), a
smart port can be defined as a fully automated port in which all devices
are connected via the IoT.
We hope that this collective scientific monograph will contribute to the
spread and adoption of knowledge in the subject area, especially among
students, for whom it is primarily intended.
We sincerely thank the respected reviewers, university professors: Borut Jereb (Celje, Slovenia), and Drago Pupavac (Rijeka, Croatia).
June 30, 2023
30
Milica Delibasic
Part 1
SMALL SEAPORTS EVOLUTION
Ranka Krivokapic
31
32
1. INSTITUTIONAL EFFICIENCY FACTORS
OF ADRIATIC SEAPORTS
T
he subject of the paper is to investigate the hypothetical perceptions of the impact of the selected institutional factors on the efficiency of the selected Adriatic seaports.
The aim of the paper is to show that Adriatic Seaports must accept and
implement much faster and bigger institutional changes, which can be
the basis for their efficiency increase, expansion, and development.
Therefore, this paper starts with the basic hypothesis that there is a
direct and proportionate dependence between the efficiency of the
Adriatic Seaports of Koper, Rijeka and Bar, and the level
of their institutional strength.
To research the perception of the institutional impact factors,
the multiple linear regression method is used.
It is concluded that the level of the selected institutional factors differs
not only in the selected ports individually but also between them.
The research results have verified the initial hypothesis.
33
A
driatic seaports that are the subject of this research (Bar - Montenegro, Rijeka – Croatia, and Koper - Slovenia) belong to the
countries in transition, which are at different levels of socio-economic development, but they have their specificities and specific development problems and priorities. There are some important similarities that
are reflected in the long-term and inertial reproduction of crisis development and the presence of conflict between formal and informal, on one
hand, and alternative institutions, on the other, which are the basic generators of economic and social crisis. It is natural that these seaports and
their efficiency in principle shared the fate and implementation of appropriate institutional reforms. In this sense, our modeling in this paper has
been created so that the efficiency of Adriatic seaports appears as dependent variable. We have also defined four institutional factors, which
appear as independent variables, namely (Krivokapic. 2020):
−
−
−
−
corporate governance (concretely: the “corporatisation” of government port agencies) – Iv1,
possibilities for realization of real, transparent, and fair privatization – Iv2,
transparent institutional (primarily ownership: public, private or
joint public/private, regulatory and management) structure – Iv3
and
opportunities for forming a private-public partnership – Iv4.
The survey of the perceptions of the respondents on the above-mentioned variables encompassed three seaports, located in the countries of
the former Yugoslavia. They are geographically connected by the Adriatic
Sea: the port of Bar is in Montenegro, the port of Rijeka is in Croatia, and
the port of Koper is in Slovenia (Ibid.).
For the purposes of this research, a questionnaire was created in accordance with the previously presented theoretical framework. A total of
240 highly educated respondents were surveyed: 80 in Montenegro, 80
in Croatia, and 80 in Slovenia. Respondets were asked to answer five
questions related to the subjective perception of the degree in which the
independent variable (indicated in the model as Iv1, Iv2 Iv3 and Iv4) impacted the dependent variable (denoted as Dv). A schematic representation of our research framework is given in Figure 1 (Ibid.).
Constructs from the research framework were measured by 1-5 point
multi-item Likert scale. In fact, respondents answered the questions using
34
linguistic qualifications: very strong (5), strong (4), medium (3), weak (2),
and very weak (1).
Figure 1. Institutional efficiency factors of adriatic seaports –
the research framework
Source: own
35
The constructs from the research framework were measured with 15 point Likert-type multi-item scale. In fact, the respondents used linguistic qualifications: very strong (5), strong (4), medium (3), weak (2) and
very weak (1) to answer the questions.
The seaport efficiency, as well as the entire transport economy sector
(Pupavac et al., 2019) is viewed as the relationship between the results
achieved and the spent resources and time. It implies their maximum utilization with the maximum satisfaction of the port users’ needs. This corresponds to the method of the port services production which minimizes
the alternative costs of the available and used resources of a particular
seaport. In doing so, an optimal relationship is established between the
cost of port resources, the applied modern technologies (Bauk et al.,
2017), and the port services provided, which presuppose achieving the
maximum possible profit. In modern conditions, for the seaport efficiency,
cargo freight forwarding companies no longer choose a specific seaport
according to its essence or general competences (efficiency, location, low
port taxes, speed and quality of services, etc.), but according to quality of
their logistics port services package (Draskovic, 2019).
Practice shows that logistics networks represent the most reliable and
effective methods for maximizing the cargo value, both for the supplier
and for the user. The selected transport route involves a combination of
optimal logistics operations of a seaport (warehouses, tarminals, management, dispatch services, etc.).
Effective use of port resources involves a large number of synchronized impact factors, among which are (Ibid.):
− modern port capacities (suprastructural facilities),
− modern technological procedures in realization processes of port
services,
− quality work organization,
− high livel of application of marketing logistics and port logistics,
− application of state-of-the-art information technologies,
− quality personnel structure and continuous improvement of personnel,
− institutionalized, stable and stimulating policy of port tariffs,
− stable demand for port services,
− wide gravity zones,
− wide range of quality port services,
36
− application of modern achievements in the field of outsourcing,
controlling, etc. (Bilan et al., 2017),
− several significant institutional factors (Delibasic, 201; Draskovic
et al, 2017),
− confidential relations with the transfer companies,
− flexible pricing policy,
− sufficient connections, etc.
Economic seaport efficiency is merely a theoretical (abstract) concept,
but it is measurable (Clark et al., 2004, Park and De, 2004, Blonigen and
Wilson, 2006, Cullinane et al., 2006, De Monie, 2009; Panayides et al.,
2009; Polyzos and Niavis, 2013). This means that it has its practical concretization, which is reflected in various economic indicators of port operations. There are many factors affecting the efficiency of seaport operations. The most significant among them are: natural, technological, human, social, organizational, and market factors (the pricing of port services, competition, the demand for port services, the possibility of creating
free zones, the possibility of investments, business relations with customers, etc.).
For the purposes of this paper, the focus of research are institutional
factors, which have been mention in the Introduction as independent variables. Traditionally, in most of transitional countries, seaports are owned
by public institutions that manage them (Brooks and Cullinane, 2007),
primarily because of their role in national economies. However, the participation of private capital (and its management) in seaports is not a novelty
in their institutional structure. For the past three decades, institutional investors have become increasingly important entities in the shareholder
participation in the port industry (Krivokapic. 2020).
The idea is to determine functional relationship between the dependent variable (Dv): efficiency of Adriatic Seaports and independent ones
(Iv1, Iv2, Iv3, and Iv4). Our goal was to estimate the realistically expected
mean value of the dependent variable ( Dv ), based on individual estimation of the respondents. Since the respondents have estimated the dependent variable Dv and independent variables (Iv1, Iv2, Iv3, and Iv4) on
their own discretion, our task was, in line with the requirements of multiple
linear regression, to determine the coefficients: I, S1, S2, S3 and S4, and to
calculate Dv , using Eg. (1):
37
Dv = I + S 1 * Dv1 + S 2 * Dv 2 + S 3 * Dv 3 + S 4 * Dv 4 (1)
Where,
Dv - is the mean expected value of the dependent variable;
I - is the intercept, determined on the basis of an appropriate sample;
S1, S2, S3, and S4 – are coefficients of independent variables Iv1, Iv2,
Iv3, and Iv4, or slopes of the correspondent lines. This practically means
that for any new value of each independent variable from a predefined
interval, we can estimate the value of the dependent variable. It should
be said that Dv is an average estimated value, since it is a mean value of
Iv1, Iv2, Iv3, and Iv4. To determine Dv it is used the last square method. In
fact, our goal was to determine the coefficients: I, S1, S2, S3, and S4, so as
to minimize the sum of squared errors (SSE), which is represented by Eq.
(2):
 (Dv
n
SSE =
k
− Dv k
)
2
=
k =1
n
=
 (Dv
k
− (I + S 1 * Iv1 + S 2 * Iv 2 + S 3 * Iv3 + S 4 * Iv 4 ))2
(2)
k =1
Where,
Dv k - is actual value of the dependent variable, given by the k respondents ( k = 1, n );
Dv k - is the estimated value of the dependent variable on the basis of the
model, in the case of k respondents ( k = 1, n );
n – is the total number of respondents (240 of them from port of bar, port
of Rijeka, and port of Koper), k = 1, n (Krivokapic. 2020).
Using the least-squares method, here is actually determined a straight
line, which minimizes the sum of vertical differences for each pair of
points (Balakrishnan et al., 2007). In other words, identified is a straight
line that best fits the given set of points, by determining the optimal value
of intercept (I), as well as coefficients (S1, S2, S3, and S4), in order to obtain
38
a more accurate value of Dv for the given (estimated) values of Iv1, Iv2,
Iv3, Iv4, and Dv ( k , k = 1, n ).
The multiple linear regression has been deployed (SPSS), and the obtained numerical results are presented in Table 1. Besides linear regression key parameters: I, S1, S2, S3, and S4, following statistical parameters
are calculated: MAD (mean absolute deviation), MSE (mean square error),
MAPE (mean absolute percent error), and SE (standard error of the regression estimate).
Table 1. Linear multiple regression model key parameters and statistics
Param.
I
S1
S2
S3
S4
MAD
MSE
MAPE
SE
Case 1: Port
of Bar (MON)
1,667
-0,289
0,065
0,082
0,108
0,675
0,664
29%
0,83
Case 2: Port of
Rijeka (CRO)
3,221
-0,047
0,183
-0,114
0,117
0,556
0,491
21%
0,72
Case 3: Port of
Koper (SLO)
2,590
0,0470
0,127
-0,109
0,12097
0,660
0,673
22%
0,85
Source: own calculations.
Brief description of the statistics given in Table 1 is given below:
−
−
Mean absolute deviation (MAD), indicates the numbers on how much
the value of the dependent variable, obtained through multiple regression analysis, corresponds to the estimated value by the respondents,
or in other words, to what extent the model reflects the perception of
the respondents. It takes the values: 0,675, 0,556, and 0.660 for
three analysed cases (MON, CRO, and SLO). These values represent
the satisfied level of correspondence between the model and the values assessed by the respondents;
Mean square error (MSE) is the mean value of squares of the individual errors of assessment. It points expressed deviations. It takes the
values: 0,664, 0,491, and 0,673 for three analysed cases (MON, CRO,
39
−
−
and SLO). These values represent also the satisfied level of correspondence between the model and the values assessed by the respondents;
Mean absolute percent error (MAPE), indicates the error between the
estimated value and value of dependent variable as a percentage, obtained by using the model. It is the simplest statistical value for interpretation. It takes the values: 29%, 21% and 22% for three analysed
cases (MNO, CRO, and SLO). These values represent again the satisfied level of correspondence between the model and the values assessed by the respondents;
Standard error of the regression estimate (SE), is also called the
standard deviation of regression. This statistical value is suitable for
the formation of the so-called confidence intervals around the regression line. It indicates how much the value of the dependent variable,
obtained by the model, can vary. It corresponds to the values:  0.83
in the case of MNO,  0.72 in the case of CRO, and  0.85 in the case
of SLO. These variations are reasonable if we have in mind the fact
that Likert’s scale (1-5) has been used in assessing variables’ values
in the model (Ibid.).
By using the specified model, and calculated data from Table 1, the
lines that represent linearly the impact of independent variables (Iv1-5) to
the dependant variable (Dv) are given below:
− Case 1: Port of Bar, Eq. (3):
DvB = 1,667 − 0.289 * Iv 1 + 0.065 * Iv 2 − 0.082 * Iv 3 + 0.108 * Iv 4
(3)
− Case 2: Port of Rijeka, Eq. (4):
DvR = 3,221 − 0.047 * Iv 1 + 0.183 * Iv 2 − 0.114 * Iv 3 + 0,117 * Iv 4
(4)
− Case 3: Port of Coper, Eq. (5):
DvC = 2,590 + 0.047 * Iv 1 − 0.127 * Iv 2 − 0,109 * Iv 3 − 0.120 * Iv 4
(5)
Based on Eq. 1, 2, and 3 Mean values of the dependent variable Dv k
in the case of MNE, CRO, and SLO were calculated (Table 2).
40
Table 2. Mean values of the dependent variable Dv k in the case of Montenegro, Serbia, and Bosnia and Herzegovina (integral)
Montenegro
Croatia
Slovenia
(MNE)
(CRO)
(SLO)
Iv1 Iv2 Iv3 Iv4 Iv1 Iv2 Iv3
Iv4 Iv1 Iv2 Iv3 Iv4
1,68 2,06 2,67 1,94 4,32 3,00 3,91 2,92 2,9 3,94 4,01 3,57
Dv
1,667
3,221
2,590
S1
-0,289
-0,047
0,0470
S2
0,065
0,183
0,127
S3
0,082
-0,114
-0,109
S4
0,108
0,117
0,12097
1,71
2,91
3,225
Dv k
Source: own calculations.
The average values of the dependant variable estimated by the respondents are shown in Figure 2, and expressed in %, for each of the analysed categories, i.e., MNE, CRO, and SLO. It is obvious that the highest
percentage of respondents (over 40%) from CRO and SLO rated the level
of performances of sea medium (3), and for MNE is low (1,5). The smaller
percentages were associated to strong (5) influences of efficiency Adriatic
Seaports to which the citizens are exposed in accordance to the subjective
judgments of the respondents from the analysed countries.
Due to the analysis of the linear dependence between dependent and
mean values of independent variables (Figure 2-4), it becomes clear that
variable Iv3, and Iv2 have the most pronounced influence on the dependent variable. This analysis is done throughout the whole sample.
.
41
Figure 2. Percentage of appearing values 1-4 in the set of dependent
variable (Dv)
Source: own calculations
42
Percent
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
,50
1,50
2,00
Dv
3,00
3,50
Error bars: 95% CI
2,50
4,00
5,00
Case
Slovenia
Croatia
Montenego
Iv4
Iv3
Iv2
Iv1
Dv
0.00
1.00
2.00
3.00
4.00
Mean
Figure 3. Mean values of dependent (Dv) and independent variables (Iv15) according to subjective assessments of respondents (all ca
ses)
Source: own calculations
43
1.00
2.00
3.00
4.00
5.00
6.00
2,00
3,00
Source: own calculations
44
Error bars: +/- 2 SD
Dv
4,00
5,00
Iv4
Iv3
Iv2
Iv1
7.00
Mean
Figure 4. Dependant variable (Dv) vs. independent variables (Iv1-5)
Based on the analysis for each of three different countries, the data
given in Table 3, were obtained
Table 3. Mean values of the independent variables and their impact
to the dependant variable
Rank
1
2
3
4
Case 1: Port of Case 1: Port of Case 3: Port of
Bar (MNE)
Rijeka (CRO)
Coper (SLO)
Iv3 [2,67]
Iv1 [4,325]
Iv3 [4,012]
Iv2 [2,06]
Iv3 [3,912]
Iv2 [3,948]
Iv3 [1,94]
Iv2 [3]
Iv4 [3,575]
Iv1 [1,68]
Iv4 [2,925]
Iv1 [2,900]
Source: own calculations.
In accordance to the mean values of the independent variables (Table 3), it can be concluded that the highest impact to the Efficiency of
Adriatic Seports in the cases of MNE and SLO have transparent institutional structure. In the case of CRO, corporate governance has the greatest influence on the phenomena of efficiency Adriatic Seaports (Stjepcevic, 2018). On the other side, the lowest impact to the efficiency of Adriatic
Seaports has corporate governance in the cases of MNE, and SLO, while
opportunities for forming a private-public partnership has the lowest impact to efficiency of Adriatic seaports in the case of CRO.
  
T
he paper presents a theoretical and methodological framework
for the quantitative modelling of the efficiency of Adriatic Seaports, caused by corporate governance (concretely: the ‘corporatisation’ of government port agencies), possibilities for realization of
real, transparent and fair privatization, transparent institutional (primarily
ownership: public, private or joint public/private, regulatory and
45
management) and opportunities for forming a private-public partnership.
The research is based on the analysis conducted among the highly educated part of the population in MNO, CRO, and SLO. For the modelling
purposes multiple linear regression method was used.
The functional dependencies between the efficiency of Adriatic
Seaports as the dependent variable and the afore mentioned the independent variables, were established. Based on the conducted analysis, it
was found that the prevailing influence on efficiency Adriatic Seaports in
CRO and SLO was estimated as medium (3), for CRO, and SLO with smaller
or larger variations. For MNO is low (1,5). On the basis of statistical modelling it has been shown that mean expected values of the dependent variable are: 1,71; 2,91; and 3,22 in the cases of Port of Bar (MNE), Port of
Rijeka (CRO) and Port of Koper (SLO), respectively. Also, the rank of independent variables influence to the dependant variable has been established on the basis of each of the analysed sample from the three observed ports. In addition, the level of independent variables impact to the
efficiency Adriatic Seaports at the level of the entire sample was determined, while the transparent institutional structure prevails. According to
the above stated, the starting hypothesis in the paper has been fully verified.
Further research should be carried out in the direction of the possible
inclusion of additional independent variables, or the establishment of different, more complex aspects of functional dependence among the variables. There are significant internal reserves for the improvement of all independent factors in order to achieve the highest level of performance in
observed ports (Krivokapic, 2020).
46
2. PERSPECTIVES FOR THE
DEVELOPMENT OF SMALL SEAPORTS,
WITH REFERENCE TO THE ADRIATIC
SEAPORTS
T
he subject of the paper is to consider some basic perspectives
for the development of seaport, i.e., their strategic adaptation
to modern technological, economic, commercial, institutional,
environmental, and other trends. The aims of the paper are:
a) to explore the possibilities of developing small (peripheral) seaports in
the process of adapting to the contemporary world trends and finding
ways to overcome their backward status and include them in global
trade routes, with reference to the three selected Adriatic seaports
b) to contribute to the understanding of complex relationships, which affect the evolution of small seaports, and
c) to explain ways and means better quality (faster, cheaper and
greater) customer satisfaction, i.e. strengthening seaports competence
and competitiveness, in the context of globalisation.
It starts with the basic hypothesis that the developmental adaptation of
small seaports requires a broader proactive and reactive strategic
approach, which implies of institutional, functional, operational,
economical, ecological, technological, geographical, legal, political, and
other frameworks, relations, and determinants.
47
The auxiliary hypothesis is that small seaports must adapt their development to the dominant world trade, maritime, and port trends through
the application of various strategies in a complex environment.
In terms of methodology, the paper uses common methods of social
And economic sciences, among them description, abstraction,
concretisation, induction, and deduction, as well as analysis
and synthesis.
In conclusion, it is stated that the basic and auxiliary hypotheses were
verified using the mentioned methods. In addition, it is emphasised that
competent and sustainable seaport management needs to overcome
various constraints and conflicting relationships in the inner and outer
environment by applying new knowledge, skills, technologies,
investments, strategies, and information.
M
ost seaports are very important for all maritime states and
are often the main industrial, commercial and macroeconomic subject of economic and social development. The port
sector is under the constant influence of changes (social, economic, institutional, technological, environmental, and others). This is due to the development of needs in domicile countries, as well as due to their commitment to the principles of free trade (in modern contexts of globalisation)
and new restrictions and opportunities imposed by maritime transport. In
this regard, the development of maritime transport has led to significant
changes in the port environment. These changes have led to its complication and the resulting fundamental improvement of the seaport concept
(Hlali and Hammami, 2017).
The seaport remained a multidimensional system, combined with an
economic function, infrastructure system, geographical area, trade,
transport technologies, and specific port management. Of course, he was
constantly adapting to the complex legal and organisational concept,
which is based on and improved along with various forms of convergence
of public and private partnerships (Roa et al., 2013; Alghaffari, 2018). All
of these factors have been strongly influenced by institutional reforms and
government policy decisions, which are often related to special economic
48
zones. (Wang and Slack, 2000). Tourret (Hlali and Hammami, 2017) defined the port, in its traditional conception, as a set of moles, basins, and
docks, which prove to treat all kinds of ships and goods.
Beyond the terminological meaning, the seaport can be defined according to different approaches such as economic, geographical, legal,
and institutional. In economic terms, the port is defined as „the place
where the ships are sheltered, also the place where the goods pass, or
even where they are transformed” (Ibid.). This definition designates the
port by its three essential functions. In addition, with these port functions
commercial and industrial functions can also be added. In a geographical
sense, the port is the point of contact between land and sea freight and
passenger traffic. Also, the port is an integrated logistics centre and multifunctional socioeconomic space (Krivokapic, 2022).
Finally, in modern conditions, seaports have become a key part of the
supply chain network. As such, seaports are under the constant pressure
of the need for their business sustainability, especially in less developed
countries (Streimikiene, 2022; Lahane et al., 2021; Warris et al., 2021;
Sahora et al., 2020; Chowdhury, Paul Sanjoy, 2020; Borimdesouza et al.,
2020). The development of small seaports involves attaining higher port
performance levels. In order to achieve this, it is necessary to repeatedly
and continuously adapt seaports to exponential changes in economic
(market, competition), institutional, legal, operational (optimisation of
transport, handling, and other processes), organisational (network integration, the growing concentration and consolidation in the liner shipping
market, public-private partnerships and port concessions), technological,
security, social and environmental environment.
Globalisation has significantly increased the importance of seaports
in integrated supply chains. The port business has expanded from cargo
handling to the provision of logistics services. Growth in logistics performance has increased the competitiveness of the seaport. She directly depends, among other things, on the amount of logistics costs and the reliability of supply chains. Two and a half decades ago, the world port economy underwent substantial changes, which significantly influenced the
evolution of port systems (Figure 5). They are shown in Figure 1 basic
changes, customisation options, opportunities for improvement, and seaport functions (Krivokapic, 2022).
49
Changes:
social, economic, institutional,
technological, environmental, and others)

Customisation options:
Globalisation of economic flows, technological development, internationalisation of production and exchange,
Concentration of capital in some significant
entities of port activity
(especially in logistics
operators)
Opportunities
for improvement:
→
SEAPORT

Convergence of public
and private partnership,
New knowledge and
skills, Investments, Strategies, New organisational solutions, Structural
changes in international
trade

Functions:
market, infrastructure, organisational, security, operational, geographical, trade,
transport, network, information, port management)
Figure 5. Factors influencing the evolution of small seaports
Source: Delibasic, 2021.
The functional evolution of seaports is essentially always geared towards sustainable business and the creation of new competitive advantages. In a changing international environment, seaports have undergone radical changes over time, especially in terms of their organisation
and structure. In spatial and temporal evolution, the expansion of seaports was realised either by the evolution of maritime production technologies or by improving the handling of freight. Many authors present different periodisations of seaport development. For the ports evolution today
the current new five-stage models. Some authors (Bichou, Gray 2005) are
more focused on different aspects of the port’s evolution, such as services, concerted development of ports and cities, financial activities. Notteboom, Rodrigue (2005) study on the port regionalisation phase and related functional focus. Taafe et al. (1963) put forward six phased
50
transportation development. UNCTAD (1991) division (a classification
which includes three port generations) has been accepted by many authors, among them Rimmer (1967), Hayuth (1981), Flynn et al. (2011),
Lee, Lam (2013), Lee et al., 2014, Sun et al. (2022). Their views and classifications are presented in Table 4.
Table 4. Seaport evolution
Generation
1st
2st
3st
Time
Before
1960
Location
Remark
Connect to the
platform. Ports
functioned as
commercial
centres
Semi-bulk cargo; conservative, means of transport change point; loading, unloading, storage, navigation services; independent operations within the port, informal
connections between the port and
its users; cargo flow, simple, single
services, no/low added value; deciding factors: work/capital
Centre for tranDry semi-bulk cargo and liquid bulk
sportation and
cargo; expansive, transport, indusindustrial and
trial, and commercial centre; cargo
commercial serprocessing, industrial and
vices Points of
commercial services - territorial
ship handling
expansion; closer ties between the
1960– within the bimoport and its users. No connections
1980
dal transport
between different types of operatisystem: e.g.,
ons within the port, provisional ties
maritime tranbetween the port and the city;
sport - rail trancargo flow, cargo processing. Varisport; maritime
ous services, higher added value;
transport - road
deciding factors: capital
transport.
Bulk, general, and containerised
Comprehensive
cargo; commercial, logistics and
logistics centre.
1981distribution centre for international
Ports functio2000
trade; cargo and information distrined as indusbution, logistic operations; unified
trial centres.
port community. Port integration
51
4st
20012007
5st
Since
2007
52
with the transport commercial
chain. Close connections between
the port and the city. Extensive
port organisation; cargo and information flow. Cargo and information
distribution. Wide package of various services. High added value;
deciding factors: technology, knowhow
Compliance with regulatory requirements and general standards; limited to customs clearance and
tracking the cargoes in the port; liGlobal resource
mited to compliance with planning
allocation hub.
procedures and environmental
Port functioned
planning procedures; operated
as a logistic
under procedures of port area decentre as a
velopment; examined independennode for hantly of port functions; development
dling multimoof the logistic function, as an
dal transports.
expansion of port functions, as
well as creating duty-free zones
and logistic parks near the ports;
deciding factors: know-how
Co-operated
Exceeding the standard of services
hub-and-spoke expected by port stakeholders; fonetwork. ports
cusing on the level of quality of
of this genera- services, security, and increasingly
tion should
better efficiency. Application of
combine the
computer technology for the provifollowing functi- sion of port services and to predict
ons: a) whole- events and measure results; active
sale centres
approach towards stakeholders in
which will cut
order to coordinate planning and
the time of
the process of mutual decision-macargo delivery;
king; port services fully integrated
b) points of joi- with the port’s mission and vision.
ning of water
The port authority plays the role of
and land
a „cluster leader”, contributing to
passenger
the increase of added value in the
streams; c)
port; functionally still outside the
industrial cenport cluster, however, by establistres with com- hing creative financial incentives, it
prehensive
draws new ship owners and loaintermodal tran- ders, creating new jobs and added
sport handling;
value; logistics is a part of a marid) logistic cen- time supply chain, air transport for
tres as
valuable cargo and cargo requiring
connecting pofast delivery. Advanced dutyfree
ints for multizones, as well as logistic parks
modal cargo
near ports; ports develop stratetransport.
gies of connections with the hinterland by their pricing policy and constructing a system of economic
incentives aimed at securing loaders against such a development
of the connection network which
would harm the customers’ interests; deciding factors: know-how
Source: according to UNCTAD, 2017; Flynn et al., 2011; Kaliszewski,
2017; Sun et al., 2022.
The new concept of the seaport is about customer services (providing
services at a higher level). It argues that the fifth-generation port has a
stronger focus on customer requirement port throughput supply chain logistics and networks (Notteboom, 2011). In doing so, it should be borne
in mind that literature on the subject differentiates seaports by their capacity and throughput results as well as other parameters such as the
management system, the port’s effectiveness as the supply centre for creating added value, and innovativeness.
In accordance with the basic factors affecting port performances (infrastructural, network and connectivity, operational, sustainability, and
environmental, can be defined basic principles of small seaport evolution.
They must start from:
− exemplary world models of seaport development,
− modern development trends, especially logistics and container (Nze
et al., 2021; Ikeogu et al., 2021),
− own needs, limitations, and possibilities,
53
− opportunities for inclusion in global supply chains (in terms of integration, foreign investment, business cooperation, etc.) and
− application of state-of-the-art information and others technologies,
quality work organisation, and application of modern achievements in the
field of outsourcing, controlling, and others.
It is considered that smaller, and lesser-known ports have numerous
advantages. Often deliver substantial benefits to shippers, letting them
bypass congestion while leveraging advanced technology. Less congestion applies not only to berths but to rail and truck access as well. The
customs process in smaller ports often moves more smoothly because
fewer people are involved. We can give more attention to each shipper
and shipment and work closely with the terminal operator.
Smaller ports tend to be more entrepreneurial and flexible when solving problems. In all this, seaports must adhere to some basic principles,
among which they stand out: − the principle of balance of technological,
economic, and environmental requirements, − the business networking
and cluster organisation of port resources principle, − the principle of logistics efficiency (smart shipping, big data and analytics, advanced materials, robotics, communications and sensors), − the principle of institutional adjustment, − the principle of competitiveness, − the principle of
adjusting to the dominant trends of container shipping concentration, −
the principle of an attractive environment for investors, and − the principle
of sustainability. Small seaports need to adapt and approach access to
new technologies (advances in shipbuilding, propulsion, smart shipping,
advanced materials, big data and analytics, robotics, sensors, autonomous drones, self-driving trucks and communications) - Krivokapic, 2022.
Modern technologies increase the security and efficiency of operations in seaports. A seaport is increasingly becoming a cluster in which all
port resources are combined: the environment of the port industrial-logistics zone, port terminals, transport-logistics and distribution centres, and
cargo handling complex, with numerous service complexes, specialised
storage facilities, and intermediary agencies. The port clusters are formed
in order to increase the competitiveness of all transport nodes. It is based
on volume effects, stimulating innovation in the environment of transshipment and terminal port complexes and creating synergies between clusters. The various clusters are interconnected by information, material and
financial flows, transport and storage infrastructure, and various steward
terminal functions (loading and unloading operations). The cluster is networking customs, freight forwarding, service, brokerage, overhaul, produc54
tion, inspection, and other port functions. No matter how many new principles and criteria emerge, economies of scale remain dominant. As a result, seaports no longer compete with each other individually, but supply
chains do.
Figure 6. Evolution path to the fifth generation port (5GP)
Source: Lee, 2020.
55
Notteboom and Rodruque (2004) conclude the following: „Observed
logistics integration and network orientation in the port and maritime industry have redefined the functional role of ports in value chains and have
generated new patterns of freight distribution and new approaches to
port hierarchy”. In maritime logistics participate three types of actors in
cargo handling: port authorities, shipping lines with terminal operations,
and independent container terminal management companies. Their activities depend on global supply chains (Heaver, 2006). One of the major
tasks of supply chain management is the coordination of relations within
it. This implies assessing the position of each supplier in the chain and
assessing its importance, ie. contributions to the overall success of the
cooperation.
Each organisational link in that chain strives to maximise its own performance and profit. Seaports are actively involved in supply chains
through various adaptation strategies, depending on their own capabilities, conditions, and constraints. Transport and logistics centres are being
formed on the seaport territory, while industrial and logistics zones are
being formed in the hinterland. These are realistic conditions for the realisation of new logistics port functions, which enable seaports to be included in logistics supply chains. Practice shows that logistics networks
represent the most reliable and effective methods for maximising cargo
value, both for the supplier and for the user. Ways of integration can be
various. They must be based on the specifics and actual goals of the seaport. It depends on the geographical, location, infrastructural, supra-structural, and logistical characteristics of the port, the economic development
of the port hinterland and the home country, its political relations, and
integrations in the region (Krivokapic, 2022).
All those factors are necessarily networked, as well as local port operation management with a global manufacturing supply chain in various
fields such as service, organisation, value-adding, and flow (information,
material, and financial). Above all, it is necessary to valorise and harmonise different institutional levels and relations (Draskovic et al., 2020b;
Nguen and Nguen, 2021; Tran, 2022). In the institutional adaptation and
evolution of seaports, the main change was the involvement of private
operators as intermediaries between seaports, freight forwarders, and
shipping companies. In addition to state bodies and port authorities, the
institutional structure was supplemented by private operators, who are
responsible for the development of superstructure, management strategies, and procurement of modern technological equipment. A strong insti56
tutional framework is a prerequisite for any major investment. The transition from the transport functions to the logistics functions essentially
means a change in the character of the seaport's functional purpose.
That is why the seaport goals are increasingly identified with the basic
goals of logistics. In principle, it means optimal utilisation of bandwidth,
high flexibility in the production industry, rapid response to customer requirements, willingness to provide complex services in the package, security in the execution of services, short deadlines for service delivery, cost
reduction, continuous and comprehensive customer support, etc. The realisation of these goals leads to an increase in the competitiveness of
seaports. With the aggravation of global environmental problems (climate
change, energy consumption, etc. - Streimikiene, 2021) and the growing
institutional pressure of regulatory authorities, the involvement of seaports in solving environmental problems has grown. The main environmental issues of seaports relate to the handling of ships and cargo, port
extensions, and accessibility to the hinterland. The concept of seaport
sustainability includes three main aspects:
− economic, which refers to the return on investment, the efficiency
of the use of the port area, and the provision of facilities for companies to maximise their performance,
− social, which refers to the direct contribution to employment in
port companies and activities related to the port (the relationship
between the port and the city, the contribution to the development
of knowledge and education, and the vitality of the area around
the port), and
− environmental, which implies solving the problems of pollution,
noise, air quality, various port operations, and waste disposal.
There are few types of research in the literature whose subject is the
Adriatic’s seaports. Nevertheless, some authors have explored certain aspects of this research question. In this sense, Draskovic (2019) showed
that Adriatic seaports must accept and apply the integration strategy as a
key business and logistic competence, which can be the basis for their
expansion and development. He verified in the quoted article hypothesis
that a partner business performance and cooperation between the Adriatic seaports of Koper, Rijeka, and Bar is a crucial condition for easier
finding of large foreign investors and global logistics providers. Draskovic
et al. (2020a) have identified three important factors, which dominantly
57
negatively affect the establishment of business cooperation between the
Adriatic seaports of Bar, Koper, and Rijeka. They are:
− negative impact of institutional, infrastructural, suprastructural,
and corporate factors,
− applied level of logistics services, and
− political and economic barriers (Krivokapic, 2022).
They concluded that the greatest limitations in terms of the level of
possible business-partnership cooperation characterise the seaport of
Bar and that in all mentioned seaports it is necessary to invest large investment, organisational, institutional, and other efforts to improve certain development factors. The common characteristics of the development barrier of Adriatic ports imply the need for their wider business cooperation. These are:
− relatively low level of quality of port and logistics services, which
reduce port competitiveness,
− poor development of logistics infrastructure and port superstructure, which is associated with the long-standing economic crisis,
failed privatisation, and economic erosion,
− chronic deficit of investment funds, which negatively affects the
introduction of new technologies, business models and meeting
customer expectations, and
− insufficient use of port capacities, especially logistics outsourcing.
Only the largest seaports in the world located at the junction of key
navigation routes and belonging to developed economic systems are able
to function as part of the most advanced global logistic platforms which
concentrate a large part of the unit cargo streams. The development of
small seaports is it depends on the possibility of overcoming numerous
limitations and affirmation of certain possibilities (Figure 7). Of course, the
development of seaports is a result of their multifunctionality and multimodality focused on the widening of the range of services. Ports servicing
the trade of developing countries, especially, relatively smaller and secondary ports, will need to adjust to remain competitive and continue to
attract business, whether through direct connections or feedering services (UNCTAD, 2017, p. 78).
58
Figure 7. Conditional concept of small seaport development
Source: adapted to Hlali, Hammami, 2017; Delibasic 2021.
59
Without wishing to precisely determine the current level of development of selected Adriatic seaports (Koper, Rijeka, and Bar), we believe
that they can be characterised as generationally obsolete (backward) in
terms of their level of modernity and openness to innovation. They belong
to the countries in transition, which are at different levels of socioeconomic development, though have their specificities and specific development problems and priorities. Observed objectively and in general, the
Port of Bar is probably at the level of the second generation of seaports
(Bar), while Koper and Rijeka are at the crossroads between the second
and third generation. Pelevic (2021) researched the development of logistics routes of intermodal transport in the Eastern Adriatic. He came to
a conclusion that the seaport of Bar is noticeably behind the seaports of
Rijeka and Koper, because of the low level of infrastructural, superstructural, and logistical development, high costs and bad logistics services,
deteriorating political relations between Montenegro and Serbia, the poor
infrastructural transport connections of the development investment deficit, high percentage of idling of engaged containers in the return direction, etc.
Based on the conducted analysis, he found that the prevailing influence on achieved levels of development of logistics routes of intermodal
transport in Adriatic ports is the next factors: low Liner Shipping Connectivity Index LSCI (Liner Shipping Connectivity Index), weak seaport development, and week seaport connectivity. He pointed out that the ports with
the higher level of listed factors have achieved greater levels of development of logistics routes of intermodal transport. The port of Bar belongs
to the second-generation ports because, according to UNCTAD, it integrates with its surroundings via its transport, industrial, and commercial
function. This is evidenced by the realisation Container throughput 2020
(53591/TEU) and LSCI 2020 (5.25). Within the port areas, industrial
parks are created which receive imported raw materials delivered by sea.
The development of the industrial function is connected with access to
land, efficient land transport, as well as worker, and utility (power and water) availability. From the operational perspective, the efficient functioning
of the port centre led to a larger degree of coordination of activities with
the port city and region.
The importance of cooperation between the various service providers
within the seaport in order to handle cargo efficiently also increases. Although the third-generation seaports first appeared in the 1980s in the
period of accelerated development of containerised cargo volumes, the
60
creation of an intermodal connections network, and increasing requirements resulting from the development of international transport, the Slovenian Port of Koper and the Croatian Port of Rijeka (according to our estimates) do not yet fall into this category. This is evidenced by the realisation Container throughput 2020 (Koper 945007/TEU and Rijeka
303626/TEU) and LSCI 2020 (Koper 35.32 and Rijeka 33.35). They are
characterised by higher and more modern activity than the port previous
generation (Bar), especially in view creation of integrated logistics centres
and even logistics platforms supporting international trade.
Figure 8. Major container shipping routes in the world
Source: Lee, 2020.
They do a much larger scope of services (stevedoring, storage, and
navigation services with the use of modern technologies, organisation,
and management). They have better skills (know-how), electronic data
processing and exchange, as well as higher quality environmental functions. In addition, they are more efficient in terms of administrative-commercial handling of cargo information, the necessary bank, insurance,
and legal services for the port. These ports have better quality road and
rail connections with facilities, modern warehouses, and distribution
parks, as well as a bigger symbiosis between the port and the city. Finally,
61
they are more advanced in terms of new logistic-distribution function
which results from including seaports in the integrated concept of the
land-sea transport chain. The idea elaborated by Draskovic (2019) is significant for our topic. Namely, he believes, being with а good political will,
economic logic, and institutional elaboration (harmonisation) in the future, can increase the level of development of intermodal transport logistics routes in Adriatic seaports. It is about the implementation of partial
business integration, with a certain redistribution of transport, port, and
logistics services in the region, which would strengthen the key competencies of the considered seaports. The implementation of this idea also considers a significant degree of partnership and the associated long-term
forms of partial business integration (Pelevic, 2021). In addition, we believe that this topic can also be successfully considered over the theoretical model (Figure 9), proposed by Montwiłł (2014, p.260) in accordance
with UNCTAD recommendations (2004). We note that Figure 9 contains
an adaptation by Pelevic (Ibid.), as well as our supplement, which refers
to the improvement of the institutional and ecologic environment and possible business-partnership integration.
Major points to ponder for container port development (Lee, 2020):
−
−
−
−
−
−
Efficient and competitive port;
Safety Port (natural disasters, terrorist attacks, pandemic);
Sustainability/Resilience;
Green Port;
Smart Port; and
Port/City interface.
Between the 1960s and early 2000s, seaports went through four generations in their development (according to the UNCTAD model). Due to
the evolutionary (and not abrupt) process of development, the WORKPORT model assumes the co-existence of ports and terminals of varying
generations. Politicians, investors and the general public often challenge
and hinder investment in improving the quality of port infrastructure. This
contradicts the proven fact that maritime and urban development enable
the economic progress of many countries. In doing so, one must start with
the fact that small seaports access global trade via large hub ports. Also,
small feeder vessels connect small and medium ports. The modern trend
is yes shipping lines and major port terminal operators consolidate and
integrate their portfolios, to enable they have enabled the provision of
62
seamless intermodal transport services from port to port, strengthen port
competition, and occupy as much hinterland as possible.
Figure 9. Possibilities of developing adriatic ports
Source: supplemented by UNCTAD, 2004; Montwiłł, 2014; Pelevic, 2021.
63
In addition, seaports are expanding their institutional capacities in
various ways (privatisation, strengthening the competencies of the port
authority, and restructuring business models). These experiences should
respect the Adriatic ports. In the future, they should invest great efforts
and resources to improve the attractiveness of their size, location, infrastructure, logistics, or management. Their transformation implies integration into the network delivery transport system and the creation of modern
logistics centres (platforms). Such a development strategy must be based
on the greater application of containerisation, the use of advanced automation and information technology, and full integration in the transport
forwarder & logistics sector, intermodalism, and standardisation of information. In particular, the considered Adriatic seaports must:
− significantly change, improve, and push the boundaries of its complex relationships with the city and hinterland,
− create an ever more complicated system of connections between
the participants in the port services market both from the supply
and the demand side,
− develop seaports' capacity to handle various ship types and the
cargo transported thereby (including unitised),
− develop the computer link networks and the automation of the
processes executed therein,
− increase the depth of their water areas, − cooperate with all entities and factors within the intermodal chain, which refers to the
ports of the Eastern Adriatic (Beskovnik, 2010), − offer better conditions to foreign investment in order to significantly increase it
(Pelevic, 2021),
− harmonise institutional conditions with exemplary world models, −
strengthen its infrastructure container capacity (Pupavac et al.,
2019), − accompany proportional increase flow of goods by the
development of dry ports,
− expand and modernise railway and road infrastructure − raise to
a higher level its multimodal connectivity and involvement in
global supply chains (Baran and Górecka, 2019),
− constantly strengthen your organisational and management skills,
and
− accept favorable private-public partnership arrangements.
Empirical research to date significantly confirms these statements. The research of Pelevic (2021) showed that the seaport of Bar
64
is noticeably behind the seaports of Rijeka and Koper in terms of the
development of logistics routes for intermodal transport. The basic
reasons are numerous, and they are dominated by the low level of
port infrastructural, superstructural and logistical development, high
costs of its port and logistics services, deteriorating political relations
in the region, the poor infrastructural transport connections, development investment deficit, orientation of Serbia to other seaports, a percentage of idling of engaged containers in the return direction, etc.
Based on the conducted analysis, it was found that the prevailing influence on achieved levels of development of logistics routes of intermodal transport in Adriatic ports has a low level of the following factors: Liner Shipping Connectivity Index LSCI, seaport development,
and seaport connectivity. Ports with a higher level of these factors
have achieved greater levels of development of logistics routes of intermodal transport (Krivokapic, 2022).
  
T
he social and economic development of all maritime states very
much depends on the development and efficiency of seaports.
This also applies to small seaports, due to their flexibility and the
possibility of relatively rapid strategic adjustment. Sustainable, competent, and modern seaport management must overcome various constraints and conflicting relationships in the inner and outer institutional
and operational environment. To succeed in this, small seaports must force push by applying new knowledge, skills, technologies, investments,
strategies, information, business networking, and privatepublic partnership.
They are generally analysed, explained, and researched in the article
on the possibilities of developing small (peripheral) seaports in the process of adapting to the contemporary world trends and finding ways to
overcome their backward status and include them in global trade routes,
with reference to the three selected small Adriatic seaports.
65
Complex relationships, which affect the evolution of small seaports,
are also explained. Various author's suggestions are also listed for improving and developing strategies for the development of small seaports, as
well as concrete ways to implement them. The listed methods, and especially the method of description and analysis, have verified the basic and
auxiliary hypotheses.
66
3. THE ESSENTIAL FACTORS ON THE
COMPETITIVENESS OF SMALL ADRIATIC
SEAPORTS
T
he subject of the paper is to investigate the hypothetical
perceptions of the impact of negative factors on the
competitiveness development of selected small
Adriatic seaports. The aim of the paper is to show that small Adriatic
ports must accept the need for urgent investment in four selected and
researched factors, which according to the respondents' perception are
the key basis for the development of their competitiveness.
In this sense, this paper starts from the basic hypothesis that for
the small Adriatic ports of Koper, Rijeka, and Bar, the elimination of the
deficit in strengthening the aforementioned factors (independent
variables in the offered model) is a key condition for the growth
of their port competitiveness. In other words, the considered factors
represent a serious business barrier, which essentially acts as a
negative externality and a limitation to the development
of competitiveness.
For researching the perception of respondents the multiple linear
regression method is used. In the conclusion, the verification of
the basic hypothesis is stated. In addition, it is concluded that the level
of competitiveness is different in individual selected ports, and that it is
precisely the result of the action of selected influencing factors.
67
S
eaports actively participate in logistics, supply chain management, and economic activities at all levels (national, regional
and international). Irrespective of their size and location, through their transport and service operations, they enable active accessibility
to all essential resources and business interactions (economic, social, informational, energy, technological, work, safety, security, social inclusion,
etc.). On the other hand, their development (especially small seaports) is
subject to many institutional, environmental, legal, operational and market (competitive) pressures and imperatives through growing global and
other interdependence (Gerlitz et al., 2021, p. 1).
The mentioned “vulnerability” is inversely proportional to the size of
the port, primarily due to much smaller capacities (infrastructural, suprastructural, logistical, personnel, organizational (Pupavac et al., 2019), institutional (Draskovic et al., 2020b; Nguen and Nguen, 2021; Tran,
2022), transshipment, security, political, hinterland, etc.). The aforementioned pressure on ports is constantly increasing with the introduction of
new institutional and environmental initiatives, strategies and standards.
P. Coto-Millan et al. (2010, p. 19) believe that for the development of
small seaports it is necessary to increase infrastructure investment 1,
productivity and efficiency. In addition, they suggest:
− strengthening the institutional framework, especially for port operations, changing the role of port authorities under a competitive
and privatized port environment, as well as in relation to port operators and shipping lines,
− rational conceptualisation (regionalisation) of the hinterland and
− overcoming defects and insufficiencies in the economic system,
i.e. factors that determine port development: transport demand,
the structure of trade, transport services, port capacities and development within the maritime system, etc.
In addition, due to various regulatory and technological changes, as
well as an unstable management environment, Cheon et al., 2018) believe that small ports must improve their performance in order to participate (compete) in the global maritime market. But measuring the performance of ports has proven to be complex, especially in the context of a
Current infrastructural bottlenecks arise especially in relation to port capacities (infrastructure).
1
68
supply chain network (among various participants), as shown by J. Lam
and D-W. Song (2013).
It is known that ports can be compared in several ways; by the value
of traffic or transshipment volume, by the number of cruise passengers,
by storage capacity, by port revenues, etc. In 2007, the World Bank introduced the concept of the “Logistics Performance Index” (LPI). That indicator refers to supply chain service delivery. Popularly called "Six Components of Logistics Performance Indicators". Three components refer to areas for policy regulation (inputs: customs, infrastructure and service quality), and three components to service delivery performance customs (time,
cost reliability: timeliness, international shipments and tracking (World
Bank LPI report, 2016) The development of standardization measuring
port efficiency has led to the development of another indicator called
"Trading across border" (TAB).
Booming in international trade has been contributed due to open policies and advanced technology (Bernhofen et al., 2016). As a result, international seaborne trade has increased significantly with a rate of 2,9%
within 20 years (UNCTAD, 2021). After 2006., the highest growth rate of
maritime trade was recorded in 2021 (4.3 per cent). International Seaborne Trade in 2020 reached the amount of. 10.7 billion tons (despite the
fall of -3,8%). Container trade which has expanded fastest with an average
rate of 8.1% annually between 1980 and 2017, which has increased from
50 million TEUs in 1996 to 148 million TEUs in 2017, that is, on 815,6
million TEUs in 2020 (despite the fall -1,2%). UNCTAD (2018) was projected that the global container trade will continuously increase corresponding to the growing global seaborne trade (Krivokapic, 2023).
All seaports and especially small ones have a problem with competitiveness. That is why there is a constant need for them to review all conditions of competition, in order to attract them new business and new investors. D. Hales et al. (2016) have proposed a new model of port competitiveness that simultaneously considers the effect of port strategy on
customers and investors. This model is referred to as the “balanced theory of port competitiveness”. It is based on 10 factors that customers and
investors consider important (Figure 10).
69
Port competitiveness


Volume competitiveness
Investment competitiveness
Location
Facility
Cargo Volume
Service level
Port Fees

Price
Institutional structure
Legal framework
Financial resources
Port reputation

Institutional economic, technological, environmental
and others changes
Figure 10. Conceptual framework for port competitiveness
Source: Adapted to D. Hales et al., 2016, p. 175.
It is clear that seaport managers have to take into account all the
above factors at the same time (Delibasic, 2021). It must be borne in mind
that small ports have numerous limitations in all the mentioned factors.
In order to more simply and easily overcome the mentioned limitations,
we believe that small ports (such as the three Adriatic ports of Bar, Rijeka
and Koper) must be dominantly oriented towards the development of:
−
−
−
−
modern technologies,
public private partnership (PPP),
modern logistics systems, and
the most modern information systems.
Seaports as the open, complex and dynamic systems, must adapt
constantly to contemporary business conditions in order to remain competitive in the global market. In order to stay competitive, seaports increasingly invest in new technologies. That's why they increasingly invest
in new technologies. Seaports and terminals have evolved and have entered into a fifth stage of evolution characterized by their digital transformation and alignment with Industry 4.0 practices (Pavlic-Skender et al.,
2020). Network connection with different logistics and transport operators is the main condition to make higher value for the customers. Modern
70
technologies may improve the safety and efficiency of operations in and
outside seaports.
Due to growth in trade, and the need for efficient operations, seaports
are reaching out for modern technological solutions. She can reduce decision making time, increase the security, and efficiency of business operations, because they have the possibility of product personalization, service orientation, horizontal orientation, vertical orientation, smart product,
real-time capability, virtualization, smart factory, and decentralization
(Ibid.). In small seaports there is an open question: how to implement the
achievements of leading seaports in terms of: Internet of things, Cloud
computing, Big data, Radio Frequency Identification, Location detection
technology, Blockchain technology, Cloud Computing, Cybersecurity, Augmented Reality, Automation, and Industrial Robots, Additive Manufacturing, Simulation and Modeling, Cyber-Physical Systems and Semantic
Technologies.
According to W. Fang (2020), four technologies are crucial in the development of the IoT system: Radio Frequency Identification (RFID), Dedicated Short Range Communication (DSRC), General Packet Radio Services (GRPS), and Wireless Sensor Network (WSN). IoT connects real
(physical) objects with a digital system; in other words, it represents realtime communication of people and machines. The integration of IoT brings
many benefits such as the reduction in delays in data collection, real-time
information, data transparency, better control of the process, and the possibility of predicting future market changes (Haddud et al., 2017).
Due to the lack of many resources, small ports have many limitations
regarding the introduction and application of modern technological innovations.
The many cases illustrate the progressive strategy change in various
regions and ports regarding port infrastructure and services, which were
commonly expected to be provided by public agencies / actors (government / municipalities / regions). There has been a global shift towards
public private partnerships over the last decades involving the private sector, especially in operations and financing of port infrastructure and services. Seaports are one of the public authorities' main areas of responsibility. PPP can bring great added value in terms of efficiency and quality
of service (Draskovic, et al., 2020).
Public Private Partnerships (PPP) have been growing more prevalent
and thus require more knowledge and expertise. A strong legal and
71
regulatory framework and a clear PPP contract that focuses on the outputs, indicators and follow-up will ensure that anti-competitive effects. It
is about such a business arrangement, where allow the government to
keep its primary regulatory role, while the private sector injects investments and expertise into developing infrastructure projects. The main objective of involving the private sector in ports is to enhance a transfer of
risks and responsibilities - among which financing investments in infrastructure and equipment - from public agencies and/or organizations to
private actors.
Port operations and management can be governed by a number of
different models, depending on the level of public and private involvement
in the port (construction – equipment – operation – services). In practice
there are four main models PPP: The Public Service Port, The Tool Port,
The Landlord Port, and The Private Service Port. Of course, the port models most commonly often “mixed”, and the different characteristics of two
or even more models can apply to different port activities or facilities in
the same port. To get a clearer identification of both the PPP sectors’ participation requires a more accurate description of their roles and functions
in each port activity (port ownership, operations, management, etc.).
There are also various types of PPP contracts, and it is gray:
−
−
−
−
−
management contract,
leasing contract,
rehabilitate, operate and transfer,
partial privatization,
privatization, etc. (World Bank Group, 2019).
Let's remind on idea partial business integration (PPP) selected seaports of Eastern Adriatic (Bar, Rijeka and Koper), explained by M.
Draskovic (2019), which assumes а good political will, economic logic and
institutional elaboration (harmonization). It is about the certain redistribution of transport, port and logistics services in the region of Eastern Adriatic, which would strengthen the key competencies of the considered seaports. The implementation of this idea, according to the mentioned author, it should enable a synergistic strengthening of the competitiveness
and key competencies the ports of Koper, Bar and Rijeka by increasing
cargo throughput, as well as their participation in the global flows of integrated marketing logistics.
72
Quality performance and low costs of all logistics activities are essential for the business success of seaports. Modern logistics systems in seaports integrate the organizational functions of marketing and management, through which the process of cargo handling in primary and accompanying logistics flows is carried out (Draskovic, 2019; Pelevic, 2021). It
is particularly closely related to container transport. Next to lack of investment, the main limiting factor for the development sea seaports is underdeveloped intermodal infrastructure (railway and road, and deficit of intermodal terminals, specialized staff and management skills), u J. Baran
and A. Górecka (2019) consider. In addition, limiting factor for the development of small seaports is undeveloped of dry ports in the hinterland of
seaports (Bask et al., 2014).
Figure 11. Role of ports through logistics supply chain
Source: adaptation elaboration
In recent decades, the seaport has become the part logistics network
and supply chain, commonly referred to as the fourth generation of seaports (see Notteboom and Winkelmans, 2001; Rodrigue and Notteboom,
2008). The latest concept of the fifth generation defines the seaport as
the integrated supply chain which has become more independent and active, and the quality of service has adapted to the needs and requirements
of the customers and stakeholders (Lee and Lam, 2014; Hlali and Hammami, 2017). In this sense, a revolutionary progress took place, which
73
turned the seaport from a logistics hub into a logistics community. Figure
11 shows that effective logistics chain constitutes a critical element in the
competitiveness of international shipping operations chain from producer
to the final consumer. Logistic services help to solve various problems to
their customers or stakeholders in order to reduce transport costs, and
the proportion of empty trips.
The integration of container terminals in the global supply chains has
improved the performance and the competitiveness of the container
ports. The logistics integration improved the port efficiency within the
quality services and the value added services. Improvement of logistics
and distribution function (storage, warehousing, logistics service, forwarding services, transport logistics supply, and logistics distribution) the quality increases of logistics, forwarding and transport services. The front results in the generation of a new additional value. Information technology
has become an important part of port management. Proper management
of the port system is essential for more effective and efficient sea
transport processes. That's why movement and container checking systems at the ports are given big priorities. IT systems has an important role
in its duty to process information related the goods from origin to destination. Information technology plays a crucial role in the competitiveness of
ports, as information is one of the key resources of any seaport. The modern transportation and logistics environment requires the implementation
of information systems to maintain effective communication between
members of the port community and improve performance.
Information systems collect, supply, arrange, and use information to
ensure the efficiency and effectiveness of an organisation’s operations.
According to S. Nowduri (2011), information systems enable management to quickly make decisions about different issues in the organisation.
They have become important in logistics service and entail a significant
tool to reduce costs and effectively serve clients through better customisation of the service provided. The role of information systems in enhancing port logistics performance is big.
Quality and modern information system contributes to reducing shipping and trucking costs, improving on-time delivery of goods and services,
increasing trade volume, and enhancing organisational logistics capability. D. Wilson et al. (2015) have shown that are port activities and transportation network operations are inseparable, as good performance of the
port is linked to its information systems usage. The advancement of information systems enables containers’ operators to reduce the manual
74
effort in providing services and facilitating the timely information flow and
enhanced quality control in service and decision making. M. Christopher
(2005) found that logistics performance increase when the members of
the supply chain collaborate using Internet tools, and that poor information systems resource management has repercussions for the performance of the entire supply chain, leading to increased management
costs.
Related to the mentioned theoretical approach, in the implementation
of the empirical model we started from the hypothesis that the mentioned
four factors have a dominant influence on the development of the competitiveness of small ports in general, as well as of the corresponding Adriatic ports that we selected (Bar, Koper and Rijeka). In doing so, we abstracted institutional, political, economic, infrastructural and other significant factors of competitiveness development.
As a methodological framework for the quantitative analysis – a linear
multiple regression model is employed, whereby 150 selected citizens
were surveyed (of which 50 in each country (Slovenia, Croatia and Montenegro) to which a specific seaport belongs. All those surveyed had completed a high background in economics, maritime or logistics, which presupposes their logical thinking at a high level. In addition, the majority of
them are experts in port management. They are asked to assess, based
on their best knowledge, experience and/or intuition, dependent variable
in the model (Y), which is defined as the degree of competitiveness of the
mentioned Adriatic seaports of Koper, Rijeka and Bar (each for the corresponding small port in its country).
In addition, they were asked to estimate the values of four independent variables in the model, which are defined as key obstacles to the development of port competence, and refer to:
−
−
−
−
low level of application of modern technologies (X1),
poorly developed PPP (X2),
unsatisfactory performance of seaports logistics systems (X3) i
insufficiently developed information port systems (X4).
In many scientific works (Misztal, 2010; Gonzalez and Trujillo, 2009;
Del Saz-Salazar and Garcia-Menendez, 2016; Draskovic, 2019) numerous factors, which influence the level of development of seaports, have
been pointed out directly and indirectly. Most of these factors have a negative impact on their competitiveness. Based on the above reasoning, we
75
formulated appropriate research questions in the survey, which refer to
the dependent variable and four independent variables. In this sense, we
defined the appropriate hypothetical research model:
The respondents used a nine-point scale in all cases (1,0; 1,5; 2,0;
2,5; 3,0; 3,5; 4,0; 4,5; 5,0), where is it 1,0 the smallest and 5,0 the biggest influence. When creating the aforementioned survey and analysis,
we defined mathematical model using multiple linear regression analysis,
that is, a functional relationship between the dependent variable (Y) and
independent variables (X1, X2, X3 and X4).
Negative impact of modern technologies - X1

The negative impact of of the existing level of
public private partnership
X2
Level of competitiveness the sea➔ ports of Koper, 
Rijeka and Bar
Y
The negative impact of the existing
level of seaport logistics services
X3

Negative impact of the existing level of information port systems - X4
Figure 12. Hypothetical research model
Source: own
The task is to estimate the expected mean value of the dependent
variable ( Y ), based on individual estimations of the respondents. Since
the respondents have given the estimations based on their own discretion, in line with the requirements of multiple linear regression model, the
coefficients ( b0 , b1 , b2 , b3 ) are to be determined and Y calculated by using
equation (1):
Y =b0 + b1X1+b2X2+b3X3+b4X4 ........................... (1)
Where
−
76
Y - is the mean expected value of the dependent variable;
−
b0 - is Y-axis intercept, determined on the basis of an appropriate
sample;
− b1,b2,b3, and b4 - are coefficients of variables X i , i = 1,4 , respectively, or slopes of the corresponding lines.
This practically means that for any new value of each independent
variable from a predefined interval, one can estimate the value of the dependent variable. It should be said that is average estimated value, because it is the mean value of the probability distribution of possible values
of Y for given: X i , i = 1,4 . To determine is used the least-squares method
(Bertskas and Tsitsiklis., 2008). In fact, our aim here is to determine the
coefficients (b1, b2, b3, and b4), so as to minimize the sum of squared errors (SSE), which is represented by formula (2):
𝑆𝑆𝐸 = ∑𝑛𝑘=1{(𝑌𝑘 } − 𝑌̅𝑘 )2 = ∑𝑛𝑘=1(𝑌𝑘 − (𝑏0 + 𝑏1 𝑋1𝑘 + 𝑏2 𝑋2𝑘 + 𝑏3 𝑋3𝑘 +
2
𝑏4 𝑋4𝑘 )) (2)
Where
Yk - is actual value of the dependent variable, given by the k respondents
( k = 1, n );
Y k - is the estimated value of the dependent variable on the basis of the
model, in the case of k respondents ( k = 1, n );
n – is the total number of respondents (here, per 60 related to the Port of
Bar, Port of Rijeka and Port of Koper), k = 1, n .
Using the least-squares method, in the paper is actually determined a
straight line, which minimizes the sum of vertical differences for each pair
of points (Balakrishnan et al., 2007). In other words, identified is a straight
line that best fits the given set of points, by determining the optimal value
of Y-axis intercept ( b0 ), as well as coefficient ( b1 , b2 , b3 ,b4), in order to obtain a more accurate value of Y for the given values of X i , i = 1,4 and Y
(for k , k = 1, n ).
The realization of multiple linear regression model is very complex,
and therefore it is better to leave it to the computer. For this purpose can
be used SPSS (Coakes, 2013; Pallant, 2011), special Excel VBA tools as
77
Excel Modules Solver, which has been used in this analyzes, while other
similar tools can be used, as well.
In addition to the forecasted average value of the dependent variable
Y and vectors (b0,b1, b2, b3, and b4), based on the model applied, the
following statistical values can be calculated: mean absolute deviation,
mean square error, mean absolute percent error, standard error of regression estimate, correlation coefficient and coefficient of determination. The
formulas used to calculate these values are given below, as well as related brief explanations.
Mean absolute deviation (MAD), indicates the numbers on how much
the value of the dependent variable, obtained through multiple regression
analysis, corresponds to the estimated value by the respondents, or in
other words, to what extent the model reflects the perception of the respondents (3).
Mean square error (MSE) is the mean value of squares of the individual errors of assessment. In other words, if we have n number of respondents, MSE value is calculated using the formula (4).
MSE values expressed deviations.
Mean absolute percent error (MAPE), indicates the error between the
estimated value and value of dependent variable as a percentage, obtained by using the model (5).
The formulas for determining the values of the previously generally described errors in the model are given below:
n
MAD =  Ak − Fk / n … (3)
k =1
n
MSE =  ( Ak − Fk ) / n … (4)
2
k =1
MAPE = 100  Ak − Fk / Ak / n … (5)
n
k =1
Where
A k - is an actual value of a variable (value estimated by respondents),
k = 1, n ;
Fk - is an estimated value (by model), k = 1, n ;
78
n – is a number of respondents (per 50 in the Port of Bar, Port of Rijeka
and Port of Koper).
Standard error of the regression estimate (SE), is also called the standard deviation of regression. This statistical value is suitable for the formation of the so-called confidence intervals around the regression line. It
indicates how much the value of the dependent variable, obtained by
model, can vary numerically (6) Correlation coefficient – r, is used to estimate the strength of linear relationships. Generally, if correlation coefficient is higher than 0.6, it is considered to be a strong linear relation (7)
Coefficient of determination - r2, is a value between 0 and 1, which indicates to what extent (percentage) dependent variable depends on the independent variables included in the model. General formulas for calculating the standard deviation, correlation coefficient and coefficient of determination are given below:
SE =
r=
 (A
n A
− Fk ) / (n − 2) ... (6)
2
k
n Ak Fk −  Ak  Fk
k
2

− ( Ak ) n Fk − ( Fk )
2
2
2


n Ak Fk −  Ak  Fk

2
r =
2
2
2
2

(
)
(
)
n
A
−
A
n
F
−
F




k
k
k
k



... (7)
2


 ... (8)



Where
A k - is an actual value of a variable ( k = 1, n );
Fk - is an estimated value ( k = 1, n );
n – is a number of respondents (per 50 in the Port of Bar, Port of Rijeka
and Port of Koper).
The respondents, namely per 50 experts for port management in Montenegro (Port of Bar), Croatia (Port of Rijeka) and Slovenia (Port of Koper)
were asked to estimate the dependent (Y) and three independent variable
in the model (X1, X2, X3 and X4), each with a number on a scale from 0.5
to 5.0. In fact, respondents were supposed to estimate the level of competitiveness of the port (dependent variable), as well as the extent to
which the following independent variables:
79
−
−
−
−
low level of application modern technologies (X1,
(ii) poorly developed PPP (X2),
(iii) unsatisfactory performance seaports logistics systems (X3),
(iv) insufficiently developed information port systems (X4) and - affect the dependent one.
Also, the values of statistical parameters, described in the previous
section, have been determined in order to analyse the reliability of the
proposed predictive model. Using Excel Modules Solver are obtained the
results of multiple regression analysis, for all respondents, for each of the
analysed ports. In fact, determined are coefficients in a function of the
dependent variable, that is, the slice on the Y-axis ( b 0 ) and coefficients
(b1, b2, b3, and b4) which correspond to the independent variables, 𝑋𝑖 , i =
̅̅̅̅
1,4 seriatim.
Table 5. Mean values of the dependent and independent variables in the
case of Port of Bar, Port of Rijeka and Port of Koper
X1
X2
X3
X4
Y
Port of Bar
4,30
4,45
3,52
2,71
approx. 2,13
Port of Rijeka
3,04
2,96
3,41
2,53
approx. 2,57
Port of Koper
2,48
3,46
2,95
2,98
approx. 3,22
Source: own
Based on these values and average values, estimated by the respondents, for each of the independent variables, are calculated average values
of the dependent variable Y . These values are shown in Table 5.
Using model are obtained the approximate values: 2,65 for all 3 ports,
and 2,13, 2,57, 3,22 respectively for the case of Port of Bar, Port of Rijeka, and Port of Koper (Table 5). By taking into account that the participants have evaluated the level of economic and logistic development of
the analysed ports by one number on a scale of 0.5 to 5.0, these are relatively low levels.
80
Table 6 contains numerical values: mean absolute deviation (MAD),
mean square error (MSE), mean absolute percent error (MAPE), standard
error of the regression estimate (SE), correlation coefficient (r), and coefficient of determination (r2) for the analysed sets of respondents’ estimations per each of the considered ports.
Table 6. Errors, coefficients of correlation and determination
MAD
MSE
MAPE
SE
r
r2
P –value
Port of Bar
0,342
0,208
16,49%
0,480
0,729
0,659
0,000
Port of Rijeka
0,198
0,085
7,99%
0,306
0,882
0,777
0,000
Port of Koper
0,337
0,189
10,55%
0,431
0,691
0,597
0,000
Source: own
The obtained results of the regression analysis shown for each observed port in Table 6 show that it is a well-established model. The values
of the correlation coefficient (r) are high: Port of Bar r=0.729, Port of Rijeka r=0.882, and Port of Koper r=0.691. The coefficient of determination
(r2) shows that the model is of good quality in each port and that there is
a high functional connection between the level of competitiveness of ports
and the explanatory variables in all observed ports: Lila Bar r2 = 0.659,
Rijeka Port r2 = 0.777, and Koper Port r2 = 0.597. Therefore, the independent variables to a high extent explain the dependent variable - level
of competitiveness of port in the case of each port. As we assumed, other
factors also affect the level of competitiveness, and the selected ones
have a dominant influence. p-values are less than 0.05 for all ports, indicating that the variables are statistically significant.
According to the mean values of the independent variables (Table 6),
the following can be concluded:
− The biggest limitations regarding the possible level of competitiveness of the port characterize the seaport of Bar. This is understandable, given that the negative influence of the observed factors (independent variables) is most pronounced, where it is most
81
pronounced in the part of (i) low level of application of modern
technologies (X1), (ii) poorly developed PPP (X2), the unsatisfactory
seaports logistics services (X3) is moderately expressed, and the
impact of insufficiently developed information port systems (X4) is
least pronounced.
− At the seaport of Koper, the smallest perceptual limitations were
identified in terms of the possible level of competitiveness of the
port, i.e. the greatest perceptual possibilities. The former resulted
from the fact that it is characterized by the smallest negative influences in most of the independent variables. And yet, it is noticeable that the greatest negative impact is manifested in poorly developed PPP (X2).
− At the seaport of Rijeka, moderate perceptual negative influences
were manifested in terms of the possible level of competitiveness
of the port. They are significantly smaller (more favourable) compared to the seaport of Bar, but they are also partially larger (less
favourable) compared to the seaport of Koper. At the same time,
the findings showed that the biggest (but also approximate) negative impact was manifested in the independent variable unsatisfactory performance seaports logistics system (X3).

T
he paper presents a theoretical and methodological framework
for the quantitative modelling of the possible level of competitiveness of port (dependent variable), as well as the extent to
which the following independent variables: (i) low level of application modern technologies (X1), (ii) poorly developed PPP (X2), (iii) unsatisfactory performance seaports logistics systems (X3), and (iv) insufficiently developed
information port systems (X4) - affect the dependent one. The research is
based on the analysis conducted among the highly educated people from
Montenegro, Croatia, and Slovenia (50 respondents per state) in the field
of economics, maritime and logistics As a methodological framework for
the quantitative analysis – a linear multiple regression model is employed,
The functional dependencies between level of competitiveness of
port, as the dependent variable and the above-mentioned factors cause
82
and deepen it, as the independent variables, were established. Based on
the numerical analysis in Excel and SPSS, it was found that the level of
economic and logistic development of port in all three of the above-mentioned states was estimated as medium (2,65), with smaller or larger variations. Also, the average value of independent variables influence to the
dependant variable has been calculated on the basis of each of the analysed sample from the three observed ports. In addition, the other parameters of regression analysis was calculated, and they approved the starting hypothesis. Coefficients of determination are 0,659, 0,777, 0,597 respectively for the case of Port of Bar, Port of Rijeka, and Port of Koper.
Further research should be carried out in the direction of the possible
inclusion of additional independent variables, or the establishment of different, more complex aspects of functional dependence among the variables. In addition, the questionnaires should be replaced by in-depth interviews and in such way, it would become possible to determine the reasons
for the respondents' commitment to a certain linguistic/quantitative value
of the degree of influence of the certain social disturbance(s) on the destructive institutional violence phenomenon (Krivokapic, 2023).
83
84
Part 2
CHANGING THE STRATEGIC ROLE
OF SMALL SEAPORTS
Milica Delibasic
85
86
1. STRATEGIC ADAPTATION OF SEAPORTS
T
he subject of the paper is to consider some basic perspectives
for the development of seaport strategies, i.e. their strategic
adaptation to modern technological, commercial,
and environmental trends.
The aims of the paper are:
a) to explore the motives and possibilities of peripheral ports in the
process of adapting to the concentration of container traffic and finding
ways and strategies to overcome their peripheral status and include
global trade routes, with reference to the Montenegrin port of Bar,
and
b) to contribute to the understanding of complex relationships, which affect the complex evolution of seaports. It starts with the basic hypothesis that the adaptation of peripheral seaports requires a broader
systemic approach, which consists of institutional, functional,
commercial, strategic, and environmental frameworks
and determinants.
The auxiliary hypothesis is that peripheral ports strive to adapt their
development to the dominant world maritime trends through the
application of various proactive and reactive strategies in a complex
87
institutional environment.
In terms of methodology, in addition to abstraction, concretization,
induction, deduction, and description, the paper uses qualitative and
quantitative analysis.
In conclusion, it is stated that the description and analysis of the
available literature verified the correctness of the hypotheses.
In addition, it is emphasized that seaport management needs to
overcome various constraints and conflicts of the economic,
institutional, and ecological environment by
applying new strategies, knowledge, skills, technologies,
and information
T
wo and a half decades ago, the world port economy underwent
substantial changes, which significantly influenced the evolution
of port systems. These changes are the result of globalization of
economic flows, technological development, internationalization of production and exchange, concentration of capital in some significant entities of port activity (especially in logistics operators), new organizational
solutions and increasing demands (restrictions) of the ecological environment. L. Wang et al. (2017, p. 1) statement as follows:
„In 1992, the United Nations introduced the concept of 'sustainable
development' in the environment and development conference. Since
then, all port stakeholders discussed the port sustainable development
issues. In principle, a port is a complex system. Port is not only closely linked with socio-economic and government policy, but also directly impacts
on the ecological environment and resources. The concept of port sustainability includes three main perspectives:
− an economic perspective including returns on investment, efficiency of the use of the port area, and provision of facilities for companies to maximize their performance;
− a social scope such as the direct contribution to employment in
port companies and activities connecting to the port (indirect
employment, the interaction and relationship between port and
88
city, the contribution to knowledge development and education,
and the livability of the area surrounding the port); and
− environmental performance and management including noise
pollution, air quality, dredging operations, and dredging disposal“.
The ultimate goal of the evolution of the seaport functional purpose
was to form new competitive advantages. According to competent world
experts in this field, several basic stages of this evolution are evident (Table 1), noting that the fourth evolutionary stage is already in sight, which
is the creation of clusters in seaports, based on modern principles of logistics and organizational functionality.
Table 1. Evolutions of the functional purpose of seaports
Stage
I
II
III
Purpose
Commercial center of transport services (3, 4 or 5 Party
Logistics)
−
−
−
−
−
technical port services for all forms of transport
storage, processing, distribution
information service
freight forwarding service,
logistics service
− environmental and other services
IV
Source: according to Titov, 2009, p. 10.
Advances in shipbuilding, propulsion, smart shipping, advanced materials, big data and analytics, robotics, sensors and communications in
conjunction with an increasingly skilled workforce are all having monumental shifts in how the maritime industry are approaching new challenges and opportunities. New technologies must be in balance with commercial and environmental requirements in order to maintain the
89
maritime transport system (Delibasic, 2017b). According to the Global
Marine Technology Report 2030, two technology landscapes will shape
the future of commercial shipping with a significant impact on ship design
and ship operation: one is industrial (Shipbuilding, Propulsion and Powering). It refers to technology sophistication and operational efficiency. Others relate to: Smart Shipping, Big Data and Analytics, Advanced Materials,
Robotics, Communications and Sensors (Delibasic, 2021).
Structural changes in international trade and the evolution of maritime transport have a direct impact on port growth and expansion. Clearly,
seaport strategies need to adapt to the dominant trends of increased vertical and horizontal concentration (mergers and acquisitions), which has
been prominent in the container-shipping sector. The effects of container
shipping concentration on ports are daunting and blackmailing. Carriers
are not only merging (horizontal integration), but also incorporating other
parts of the freight transport chain (vertical integration), in particular the
container terminal industry. Port development predominantly depends on
the investments of multinational companies in the industry and the logistics sector. In that sense, the port authorities strive to create the most
competitive business environment for these investments.
The recent years have seen growing interest in the environmental
impact of port operations and development due to pressing global issues
such as climate change and energy consumption. The port industry is facing increasing challenges since because it must be harmonized of environmental regulatory. Glavna environmental issues related are to at the
level of vessel and cargo handling operations, port extension projects and
hinterland accessibility. In addition, providing adequate capacity, quality
services and cost-effective solutions are essential. In this sense, devising
a green port strategy fulfilling both economic and environmental objectives would be desirable and contribute to the ports sustainable growth and
development (Bauk and Delibasic, 2017; Delibasic, 2017, 2017a).
It is considered (Wilmsmeier and Notteboom, 2011) that port selection can be based on several criteria, from physical characteristics and
geographical location to port efficiency, strategic carrier considerations
and hinterland access. On the other hand, M. Magala and A. Sammons
(2008) argued that port choice is a by-product of the choice of logistics
pathway (function of the overall network cost and performance) – Delibasic, 2021.
90
With the changes of the complexity of the international environment,
the traditional strategies and theory cannot meet the requirements of
sustainable port development any more. The competition among the port
enterprises is not only the competition of the core competence of the port,
the port industry chain or the port supply chain, but also the competition
of the port business ecosystem. The strategy of the port business ecosystem includes horizontal collaboration, vertical collaboration, and collaboration between enterprises and the environment. Horizontal collaboration in the port business ecosystem mainly refers to the cooperation
between the port enterprise, which is at the core of the ecosystem, and
other port enterprises to achieve horizontal collaboration of interests by
complementing resources and preventing vicious competition. The vertical collaboration in the port business ecosystem mainly refers to the vertical benefit collaboration formed by the core enterprise with upstream
and downstream enterprises in intensive operation and risk-sharing (Ibid.).
Strategic management concept is relevant for ports because they are
can be considered business networks in an analogy with the business ecosystem (Pettit and Beresford, 2017). Ports are confronted with a closer
integration in the maritime and shipping industries. The cooperation agreements taking place in the maritime and shipping industries can take several forms, such as alliances and mergers among shipping lines, conferences, the involvement of shipping companies in terminal management,
and extending interests in inland transport of shipping companies (Van de
Voorde and Vanelslander, 2014).
The content of the fourth generation seaport (evolutionary stage) is
based on the mutual capabilities and competencies of the seaport, various companies and participants in the logistics chain of cargo delivery. A
seaport is increasingly becoming a cluster which contains the environment of the port industrial-logistics zone, port terminals, transport-logistics and distribution centers, and cargo handling complex, with numerous
service complexes, specialized storage facilities, and intermediary agencies. The functional environment of the port cluster was modeled by Titov
(Ibid., p. 11) as shown in Figure 1.
The port cluster is formed in order to increase the competitiveness of
all transport nodes, which is based on volume effects, stimulating innovation in the environment of transshipment and terminal port complexes,
developing new clusters and creating synergies between them.
91
The various clusters are interconnected by information, material and
financial flows, transport and storage infrastructure, and the basic cell of
the port cluster, which consists of various steward terminal functions
(loading and unloading operations). Customs, freight forwarding, service,
brokerage, overhaul, production, inspection, and other port functions are
networked in the mentioned system. All this is linked with the appropriate
information support.
Figure 1. Functional environment of the port cluster
Source: Ibid., p. 11; Delibasic, 2021.
92
In this way, the seaport appears as a concentrator of cargo flows, connected to transport-technological supply chains. It forms around itself various branch economic activities and activities in the hinterland. In that
way, it creates a cluster structure, whose basic cell is the port infrastructure. The port cluster is formed in order to increase the competitiveness
of transport hubs, which is based on the effects of volume, stimulating
innovation in the environment of transshipment and terminal port complexes, developing new clusters, and creating synergies between them.
In the institutional evolution of seaports, the main change was the involvement of private operators as intermediaries between seaports,
freight forwarders, and shipping companies. In this sense, in addition to
state bodies and port authorities, the institutional structure was supplemented by private operators, who are responsible for the development of
superstructure, management strategies, and modern technological equipment (Grgurevic, 2020, p. 235).
Figure 2 conditionally shows the basic flows that connect the various
levels and segments of decision-making which are relevant to port governance. As shown, the front covers various levels and environments: international, market (commercial), management, strategic, technological,
organizational, institutional, operational, and functional.
R. Robinson (2002) explained the role of seaports2 in the modern environment, which has been significantly changed and restructured by logistics. Previous research has viewed ports as (Ibid.):
− places for handling ships and cargo,
− operating systems that deal with efficiency,
− economic units that operate according to appropriate principles,
and
− administrative units dealing with management, organization, and
business policy.
“Seaports are interfaces between several modes of transport, and thus they are centers for combined transport. Furthermore, they are multi-functional markets and industrial areas where goods are not only in transit, but they are also sorted, manufactured
and distributed. As a matter of fact, seaports are multi-dimensional systems, which must
be integrated within logistic chains to fulfil properly their functions. An efficient seaport
requires, besides infrastructure, superstructure and equipment, adequate connections
to other transport modes, a motivated management, and sufficiently qualified employees” (UNCTAD definition).
2
93
Figure 2. The decision flows for port governance
Source: Brooks and Cullinane, 2007.
Factors affecting port performances su: infrastructural, network and
connectivity, operational, sustainability and environmental (Delibasic,
94
2017b). The new port environment has been significantly changed and
restructured under the influence of globalization, technological revolution,
corporatization, privatization, integration, intermodalism, containerization, digitalization, and intensified market competition. Seaports have become functional and networked parts of complex logistics systems and
supply chains. Today, seaports are hubs that share maritime transport
(distribution) and land distribution, whose levels of functionality (services,
integration, and operability) are constantly evolving and improving. No
matter how many new principles and criteria emerge, economies of scale
remain dominant. As a result, seaports no longer compete with each other
individually, but supply chains do (Delibasic, 2021). T. Notteboom and J.P. Rodruque (2004) conclude the following: “Observed logistics integration and network orientation in the port and maritime industry have redefined the functional role of ports in value chains and have generated new
patterns of freight distribution and new approaches to port hierarchy”.
Seaports that prefer efficiency, competitiveness and development must
accept new freight distribution paradigm. She implies a ‘regionalization’
phase in port and port system development. Contributed to this: containerization, structural changes in logistics and development of global supply
chains. Port Regionalization is closely related to Port Terminal Systems
and Logistics Integration (Rodruque, 2004; Onwuegbuchunam, 2018).
Ports are complex organizations in which many entities (Figure 3) institutions, and processes interact at different levels. Within a port, two
different sets of people act as stakeholders: those who directly use, regulate, maintain, and police the port, and those who indirectly benefit or are
otherwise affected by the activities of the port. Luka is a mix and crossover of economic, social, political, legal environmental/ecological, and
technical impacts. In order to be efficient and competitive in the market,
it must successfully integrate with the supply chain network, hinterland/intermodal transport network, liner shipping network, and urban network. One of the central tasks of supply chain management is the coordination of relations within it. This includes assessing the position of each
supplier in the chain and assessing its importance, i.e. contributions to
the overall success of the cooperation. At the same time, each organizational link in that chain strives to maximize its own performance and profit.
Seaports are actively involved in these supply chains through various adaptation strategies, depending on their own capabilities, conditions and
constraints. Figure 3 provides an example of some of the stakeholders in
the analysis that may influence or exert pressure on. Port performance
95
must be analyzed by keeping in mind both the stakeholders inside and
outside the port environmental.
Figure 3. Outside stakeholders in the port
Soure: Adapted to Gharehgozli et al., 2017; Delibasic, 2021.
96
In maritime logistics, there are three types of actors in cargo handling;
port authorities, shipping lines with terminal operations, and independent
container terminal management companies. Their action depends on
global supply chains (Heaver, 2006). The structures of port administration/port authority could be illustrated by the landlord model, in terms of
its ownership and the operating structures of the port. The port could be
separated into infrastructure and superstructure, whereby the infrastructure is administrated by the governance and the superstructure is operated by private companie (Delibasic, 2021).
Studying ports in the network context would be even more beneficial
to capture the complexity needed to understand port performance and its
interaction with various stakeholders. Unable to define unified framework
for analysing port’s integration in global freight supply chains including
shipping line networks, especially from the hinterland aspect and intermodal transport network. Because, such a framework encompasses embraces a wider group of stakeholders involved (terminal operators, port
authorities, shippers, shipping companies, inland transport providers,
freight forwarders/logistics service providers, and other ports).
Ways and modalities of connection and integration can be various.
They must be based on the specifics and actual goals of the port. They
very much depend on the geographical, location, infrastructural, suprastructural, and logistical characteristics of the port, the economic development of the port hinterland and the home country, its political relations,
and integrations in the region. All this needs to be networked, as well as
local port operation management with a global manufacturing supply
chain in various fields such as service, organization, value-adding, and
flow (information, material, and financial). Above all, it is necessary to valorize and harmonize different institutional levels and relations.
A. Pallis and A. Ng (2010) schematically proposed a way to manage
(functional and strategic), governance3 and institutional reforms in a seaport (Figure 4). They prefer the method of adjusting all port structures in
a time when downtime, changing conditions, and various scenarios can
occur. Figure 3 explains that institutional frameworks play a significant
role in the functional and strategic adaptation of seaports.
The logistics strategy of seaports is a long-term and hierarchical direction of logistics
development in the form and means of its implementation (logistics technologies, logistics operations and functions, information system), formulated by port authorities in accordance with strategic goals.
3
97
Figure 4. The road to institutional reforms, management reforms, and
governance
Source: Pallis and Ng, 2010; Delibasic, 2021.
The transition from the transport functions of to the logistics functions
essentially means a change in the character of the seaport functional purpose. The seaport goals are increasingly identified with the basic goals of
logistics. It means optimal utilization of bandwidth, high flexibility in the
production industry, rapid response to customer requirements, willingness to provide complex services in the package, security in the execution
of services, short deadlines for service delivery, cost reduction, continu98
ous and comprehensive customer support, etc. The realization of these
goals leads to an increase in the competitiveness of seaports.
At the same time, there is a necessary change in the port infrastructure, because transport and logistics centers are being formed on the seaport territory, while industrial and logistics zones are being formed in the
hinterland. These are realistic conditions for the realization of new logistics port functions, which enable seaports to be included in logistics supply chains. Thereby, it is considered (Titov, Ibid., p. 7) that seaports can
participate in logistics supply chains as organizers of cargo delivery at various stages of their movement, or as logistical elements at the joints of
system transport components.
Table 2. Basic logistics strategies of seaports
Form of strategy
minimization of total
logistics costs
quality improvement
of logistics services
investment minimization in logistics
infrastructure
outsourcing
Participation in the
supply chain
Method of realization
reduction of operational logistics costs in certain logistics functions, optimization of decisions in certain logistics functions
improving the quality of certain logistics operations and functions, value-added logistics services, improving information support and customer requests, quick response to customer
requests, quality control certification, use of
benchmarking, etc.
direct delivery of cargo to customers, without
storage, use of the concept "just in time", optimal dislocation of logistics infrastructure, etc.
orientation of the port to key competencies,
adjustment to the 2PL, 3PL or 4PL concept (depending on the level of development),
optimization of the choice of external investors
(providers), optimization of the number of logistics intermediaries
supply chain choice optimization (good positioning)
Source: Adapting to Titov, 2009, p. 8.
99
The logistics strategy of seaports is a long-term and hierarchical direction of logistics development in the form and means of its implementation
(logistics technologies, logistics operations and functions, information
system), formulated by port authorities in accordance with strategic goals.
As distributing centres of trade and transportation network, port Bar
play an important role in the development of the montenegrin economy.
With the changing of the global business environment, the port enterprise
operation environment is continuously changing. Growing problems are
becoming increasingly complex. Challenges, uncertainties, and risks alternate. The development of the port of Bar will have to respect environmental requirements (restrictions).
The growth of the global economy, production, and trade over the last
25 years, and new marketing techniques that generate greater demand
have increased the need for efficient transportation infrastructure and
services. However, this has led to a growing gap with the environment.
Efficient transport and logistics are essential for the business due to production expansion around the world using cheap labor and other benefits
and incentives. This requires frequent deliveries, precise planning, and
efficient logistics, because it is a condition for connecting numerous components, assembling parts, and regular delivery of finished products to
the desired destinations.
In this context, inland intermodal facilities or dry ports are attracting
attention because of the potential that they offer to improve transport efficiency and meet supply chain. Efficient transport providers (especially
seaports) will always attract larger amounts of foreign direct investment.
To achieve this, significant effort is required that requires strategic
coordination within a wider international framework and involves a
greater number of stakeholders in both the public and private sectors.
Undoubtedly, a strong institutional framework is a prerequisite for any
major investment. Institutional strength consists of two core dimensions:
enforcement (the degree to which rules are complied with in practice) and
stability (institutions are stable when they survive not only the passage of
time but also changes in the conditions – Levitsky and Murillo, 2009). In
Montenegro, institutional instability is directly related to political and economic instability. Therefore, the creation of a stable and efficient institutional environment in Montenegro is a condition for all other reforms and
prosperity. This condition allows to reduce uncertainty and develop stable
expectations business entities about others’ behaviour.
100
It is considered that in the transport sector, there seems to be overwhelming consensus that there is no one single recipe or template for
institutional arrangements for dry port development. The correct choice
depends on a number of factors and policies, among which are: 101ocation, port policy, government policy, land policy, investment policy, environmental policy, infrastructure policy, logistics policy, transport policy, legal relief, etc.
Figure 5 shows a conditional model of the port-industrial terminal in
the port special free economic zone. Substantial evolutionary progress is
visible in the part of the port industrial zone, which contains the transport
and logistics center, in which the added value of the newly produced port
services is created to the processing part, and to the part of creating new
qualities. All these activities relate to input and output material flows, as
well as to all forms of transport.
Raising the port cluster to the level of a special free economic zone
enables the seaport to become a catalyst for the process of creating production systems around the basic port cluster. However, all cluster-concentrated activities are based on modern principles of logistical and organizational functionality. Port clusters are integrated on the basis of port
industrial terminals in the port special free economic zone. The practice
of advanced world ports has shown the justification for the formation of
transport logistics centers within the port free economic zones.
The port of Bar must attract foreign investors, shippers, large logistics
providers, bankers, and other important business entities in order to expand spatially, increase its competitiveness and economies of scale, complete and modernize infrastructurally, increase the depth of its draft for
receiving the largest ships, build intermodal logistics and distribution centers in the closer and wider hinterland, train personnel management and
institutionally adapt to world. The only way for the port of Bar to adapt to
changes in the global maritime market is to increase the size of the infrastructural and supra structural capacity, to increase technological and information advancement, to cooperate with logistics providers, and to integrate its logistics functions (Delibasic, 2021).
101
Figure 5. Hypothetical model of future development of Luka Bar
Source: Own creation
102
In modern business conditions, according to V. Draskovic and M.
Draskovic (2012), the advanced seaports tend to integrate all functional
areas of logistics to the greatest possible extent, in order to significantly
shorten the time of executing orders of port services, accelerate and
streamline logistics flows, reduce total logistics costs, reduce the time of
logistics operations and achieve the appropriate complete and quality
customer satisfaction in the part of the port logistics services. Tu praksu
mora da slijedi Luka Bar. This practice must be followed by the Port of Bar.
In maritime industry, there are requirements for the purchase of new
ships that will perform container service between the Port of Bar and a
transhipment centre. For entering into long term agreements with the parties interested in the transhipment of goods from a wider gravitation area
of the Port of Bar, it is necessary to introduce the most updated logistics
forms and create a single transport chain, which would include various
forms of transport. Through organizational, management and functional
transformation, and subsequent privatization of the Port of Bar, it is necessary to create infrastructural prerequisites for raising the attractiveness
and optimal positioning of the Port of Bar on the market of transport. This
will facilitate the attraction of foreign capital to be invested in operational
activities and other development projects of the Port of Bar.
These are all strategic movements toward the creation of conditions
for providing the Port of Bar with regional significance. This primarily refers
to the finding of strategic partners (Port, shipowner and global logistics
provider). It is necessary to improve the port infrastructure, provide a
deeper draft and updated technology for the transhipment of containers
and general cargo. As unused opportunities, there are modalities for the
activation of 7.8 ha of the port aquatorium, which is aimed at developing
production and trading activities. This also anticipates the procurement of
modern mechanization (mobile port cranes, loading bridges with deadweight of 12 tons, etc…).
The hinterland of the Port of Bar can be adjusted to the development
of assembly industries and distribution centres for export to European
countries, banking services and insurance, ecotourism and organic food
production for the needs of tourism and export. The development of operational port and logistic functions, associated with the formation of large
distribution centres, modern warehouses and port terminals (in the very
Port of Bar and its hinterland) can be put in the function of the future free
zone, which would be oriented toward the entire territory of Montenegro.
In this part, the transport logistics is of special importance. It also antici103
pates the development of inspections, quarantines, industrial and economic administration, tax authorities and banks, insurance and telecommunications companies, liberalized legislation in the field of investment,
low taxation and profit repatriation.
  
M
any entities participate in the market of maritime services, in
addition to seaports. They face various constraints, which
need to be overcome by proper seaport management
through applying new strategies and knowledge, skills, technologies, and
information. It is the only path that ensures the quality, timeliness, and
safety of maritime services. In addition to this, the success of maritime
companies depends on their competitiveness and competitive advantage.
Therefore, seaport management is considered to be the main organizational resource of maritime companies. The totality of management methods enables their efficient application in everyday business practice.
With the advancement of transport technologies, logistics principles,
and network connection, the movement of cargo in the port, the speed of
delivery, the quality of logistics services have been improved, storage and
terminal spaces are used more efficiently, energy is saved, environmental
standards are applied to a greater extent. Long-term business, logistics,
development, and other crisis processes in the Montenegrin port of Bar
require the application of a maritime integration strategy into international
supply chains in the near future. This is probably the only rational way to
put the port of Bar on a sound business and competitive basis, increase
its port and logistics capacities, improve its work skills, activate its narrower and wider hinterland, as well as the long-wanted idea of a free zone.
This positioning implies radical changes in traditional management structures, which are characterized by inefficient bureaucratic control and deficient knowledge.
Strategic alliances between port operators and shipping lines have
both exhibited strong concentration processes and increasing vertical integration. These developments have significantly influenced the development of ports and their dependence on the network strategies of global
players.
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2. INSTITUTIONAL IMBALANCE OF INTERESTS
IN MARITIME TRANSPORT AND SUSTAINABLE
DEVELOPMENT
T
he subject of the paper is a critique of ineffective institutions in the
field of maritime transport. The aim of the paper is to explain the
basic reasons for the existence of an institutional imbalance between the interests of maritime transport and sustainable
development, i.e. continuous institutional erosion in this sector at the
global level, which leads to its impasse and jeopardizing
sustainability. It starts with one basic and two auxiliary hypotheses.
A basic hypothesis is that the maritime industry sustainability implies a
balance between conflicting interests: social legitimacy (including
ecology - Glonti et al., 2020) and economic efficiency.
Auxiliary hypotheses are:
a) in the near future, the institutional logic of sustainability must
dominate in relation to business-as-usual, and
b) the existence of two competing institutional logics in maritime
transport causes its reduced efficiency, i.e. long-term crisis operations.
The paper uses the basic methods of economics: generalization,
abstraction, description, analysis, synthesis, and graphical modeling.
The conclusion states the verification of all hypotheses, as well as the
fact that in the field of maritime transport there is a long-term
105
institutional stagnation, which inevitably leads to loss of confidence
in the system, various stagnant processes and institutional fiasco
in regulating GHG emissions.
T
he global maritime industry efficiently and reliably handles over
90% of the world's long-distance freight transport (Yang, 2017).
Despite its efficiency, the shipping industry is responsible for the
emission of harmful air pollutants that accelerate the effect of global
warming and lead to serious human health and environmental consequences (Streimikiene, 2021). The above contradiction has been present
for a long time. It has been virtually maintained and it represents an increasing problem due to the diametrically opposed interests of the ecological and economic environment (Roman, 2021) and the inability of the
institutional environment to harmonize them. However, even this institutional imbalance of interests has its origin in its very being. Namely, shipping’s institutional environment is influenced by a range of different factors: regulatory pressures, institutional pressures being exerted from both
within the shipping companies themselves and from external groups such
as the public and professional bodies.
In recent decades, economic development, regional and global integrations (industrial, business, environmental, institutional, and other)
have been closely linked to the development of institutions at all levels,
on the one hand, and the transformation of the physical freight transport
movement by sea. This fact increases the significance of the mentioned
gap between different institutional interests. Institutions are “the rules of
the game in a society or, more formally human the humanly devised constraints that shape human interaction” (North, 1990, p. 3).
They structure interactions and human stimuli in all areas (political,
economic, social and other). D. Acemoglu et al. (2004) have proved that
the company will prosper from the economic institutions that facilitate the
accumulation of factors of innovation and the efficient allocation of resources, and vice versa. A distinction is made between formal institutions
such as laws, policies, and regulations, and informal institutions such as
conventions, norms, and understandings (North, 1991).
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The boom of global maritime transport was achieved with the development of container transport in the 1950s. This initiated the globalization of trade and enabled the geographical redistribution and redefinition
of the global maritime logistics system. M. Höllerer et al. (2017) define
globalization from an institutionalist perspective as “the intensifying of
worldwide exchanges and flows, which are driven by isomorphic pressures emanating from a shared cultural core, as well as the consolidation
of the world into an ‘imagined community’”.
Two facts emphasize the undertaking of urgent holistic action at the
highest institutional level in order to address the maritime sector level on
an ongoing basis. The first is that maritime transport in 2018 produced
over a billion tons of greenhouse gas (GHG) emissions (which accounted
for almost 3% of global anthropogenic GHG emissions). The second is the
very pessimistic forecast of the International Maritime Organization (IMO,
2020), which predicts that the said emission will have a 50% increase by
2050. While emissions tends to be the main environmental issue discussed, there are many other environmental challenges at sea, including
accidents, oil spills, and water pollution from ballast water. At the port
level, environmental problems include noise, dust, waste, and water pollution (Bergqvist and Monios, 2019).
In the field of business and institutional creation, global maritime policymaking has long been alarming. Competing institutional strategies focus on the sustainability of the sector, IMO, as the key regulator attempts
to bring in stricter environmental legislation (Csikosova et al., 2021). Sustainable shipping is a term used to describe the societal and environmental improvement focus of the global shipping industry. It is a term which
recognizes a key role in business not only economic drivers, but also environmental and societal factors; politicians, regulators, civil servants, and
pressure groups seemingly (but without success) make great efforts to
improve the situation. Whereas, the IMO attempts to bring in stricter environmental legislation, but the dominant logic will not allow these developments. Therefore, J. Monios & A. Ng (2021, p. 1) correctly stated that “this
leads to an ongoing erosion of the legitimacy of the institution of maritime
transport governance and a state of inertia with no new institution able
to emerge”.
This state of stagnation and the institutional gap between business
and environmental interests undermines trust in maritime institutions.
This further contributes to the blockade of possible institutional change in
this area. The foregoing was metaphorically criticized by M. Roe (2013, p.
107
395) with the following statement: “Seas continue to be polluted, administrations continue to argue, regulations are unenforced, security continues to be breached, and people continue to die”. He thinks (Ibid., p. 168)
that governance failure in the maritime sector is “evidenced by the inadequacies of shipping or ports policy to address the problems of environmental, security, safety, and economic concerns”.
Neo-institutional economic theories suggest the need to apply positive
and real institutional changes. In addition to intellectual capital, innovation, and information technology, efficient institutional changes are also
needed (the so-called “model 4i” – M. Delibasic and N. Grgurevic, 2013,
p. 66). Through the establishment of rules of conduct, norms, mechanisms, and sanctions, institutions represent the continuous intermediaries between the collective pattern and the behavior of individuals. Institutions organize, regulate, limit, stimulate, guarantee contract execution
and reduce risk.
O. Williamson (1985, p. 16; 1995, p. 175) explained in detail the interaction between the institutional environment, management structures
and individuals from the aspect of transaction cost theory. All business
transactions (primary and feedback) are harmonized and regulated in the
field of management structures. Aforesaid Williamson's concept is hypothetically presented in Figure 6, showing the interaction and relationship
between individuals (first level) and institutions of various types: those
that represent institutional arrangements (second level) and those that
are integral parts of the institutional environment (third level).
Institutional arrangements are voluntarily established rules of exchange between economic subjects, rules of market operations, rules of
reciprocal effects between organizations (hierarchical structure) and various hybrid forms of institutional arrangements, which contain signs of the
market (contractual) and hierarchical relations (Delibasic, 2014, p. 16).
Figure 6 (Ibid., p. 17) shows seven types of mutual effects: first, the
impact of individuals on the institutional arrangements, second, the impact of institutional arrangements on the institutional environment, third,
the impact of the institutional environment on the institutional arrangements, fourth, the impact of institutional arrangements on individuals,
fifth, the impact of institutional arrangements on each other, sixth, the
impact of individuals on the institutional environment, and seventh, the
impact of the institutional environment on individuals (Delibasic, 2022).
108
Figure 6. The interaction of individuals and institutions (enviroments and
agreements)
Source: Delibasic, 2014, according to Williamson, 1995.
INSTITUTIONAL ENVIRONMENT
restriction of
opportunistic →
behavior
Institutional change
(architecture: agencies and
relationship)
 Agents
coordination

regulation

Institution

stimulating
of legitimate
behavior
Reduc-tion
of
→
transaction
costs
→
INSTITUTIONAL ENVIRONMENT
Figure 7. Logic of institutional changes
Source: Own creation
In their analysis of different institutional levels and change M. Hobley
and D. Shields (2000, p. 15) also distinguish between three levels. They
have different implications in terms of the degree of change to be promoted and the means by which support is provided. The first level is institutional change (architecture: agencies and relationship). The second
109
level is organizational change (capacity building, repositioning within
agencies). The third level is process change (reengineering).
Viewed through this prism, the logic of institutional change (Figure 7)
implies their active influence on agents, who operate in the institutional
environment through their preferences and technologies. Institutions perform coordination, regulation, and other functions related to the behavior
of agents. Their main goal is to limit the opportunistic behavior of agents
and to stimulate their legal behavior, in order to reduce transaction
cost.The practice of maritime transport has shown that in order to maintain competitiveness in the market, the institutions accept and legitimize
certain behaviors developed by other organizations in the field, which is
known as “institutional isomorphism” (DiMaggio and Powell, 1983). This
concept (process of homogenization) refers to the homogenization in maritime organizations, which are forced to resemble to others involved in the
same environment. In this sense, there are four types of institutional
structuring of maritime organizations:
− The increase in interaction,
− The emergence of rapidly defined inter-institutional structures
with coalition patterns,
− The increase in the flow of information, and
− The development of mutual knowledge.
Maritime organizations are oriented towards globalized organizational
behavior that is appropriate and legitimate within their traditional institutional field. In this context, i.e. in such a homogeneous (in principle: status
quo) institutional environment, it is very difficult to implement significant
(strategic) institutional changes, such as those related to the regulation
of GHG emissions (Delibasic, 2022). In economic terms, institutions are
focused primarily on solving collective action problems and reducing
transaction costs. No matter how stable, balanced, and efficient institutions are, in practice they are prone to collapse. Institutions are always
incomplete, constantly being remade through the actions, strategies, and
routines of various actors, shared beliefs, cultural frameworks, and social
norms. Practice acknowledges many examples of so-called “institutional
erosion” or deinstitutionalization (“the processes by which institutions
weaken and disappear” – W. Scott, 2001, p. 182). This phenomenon can
in a way be called “institutional opportu-nism”. However, institutional opportunism cannot be compared to the so-called “institutional nihilism”, a
phenomenon mentioned by V. Draskovic et al. (2019). Complexity and
110
“erosion” increases when institutions are multi-level, as is the case in environmental governance. They arise due to various endogenous and exogenous influences (political, social, functional, etc. – C. Oliver, 1991). “Institutional erosion” can be phenomenologically explained by O. Williamson’s interpretation of two basic assumptions of economic behavior: limited (institutional) rationality and opportunistic behavior.
These behaviors are common in the transactional environment, which
consists of the market, the characteristics of goods and services, and the
rule of law. Essentially, “institutional erosion” is possible in the conditions
of inefficient institutional structure (Draskovic et al., 2020b), which represents a mutually arranged set of formal and informal institutions, that
forms the matrix of social and economic behavior and determine the limits
of social and economic subjects. In such conditions, it is not possible to
ensure efficient order or reduce the uncertainty in the relationship between people and organization. Ignoring the institutional environment is
an important source of transaction cost (Delibasic, 2022).
When the institutional environment is predominantly focused on promoting market effects, it loses its pluralistic institutional capacities (for
example: a sense of ecology) as well as some of its vital elements (good
case law, rule of law, Pareto optimum etc.).
Aside from that, all institutional changes occur as a result of external
shocks and disruptions as well as endogenous processes (Maguire and
Hardy, 2009). Institutional changes (especially those in the function of
seaport integration) must respect complex socio-political and institutional
re-ordering. Supply chain integration has structurally changed the competitive landscape in which individual ports and port actors operate. If institutions are viewed as regulatory external structures, whose rules of the
game the actors adhere to, the ability and possibility of the actors to undertake certain institutional changes over time must be recognized.
Such an institutional perspective in the part of maritime transport implies the realization of the process of designing and redesigning the rules
of the game at sea with the purpose of coordinating the mutual actions of
the subjects of integration, regionalization, or other forms of cooperation
and interaction. Formal and informal institutional changes can advance
or disrupt these processes. In this sense, W. Jacobs and T. Noteboom
(2010) view port regionalization as the corresponding new phase in the
traditional (spatial and functional) institutional environment. However, it
is obvious that the mentioned institutional change was not enough to
111
substantially change the behavior of maritime entities towards dangerous
environmental challenges. These challenges require the acceptance of
radical institutional arrangements and contradict the current profit motives of participants in the existing institutional environment.
A. Ng et al. (2019) analyzed the institutional action (essentially: adaptation) of seaports to various environmental changes. They showed that:
− reliance on informal institutions with the so-called “lower representation” reduces the strength of existing institutions,
− polycentric management hinders institutional dependence on existing hierarchical planning systems, which reduces responsibilities for solving current problems, and
− old governance institutions fail and change slowly (renew).
The specified situation was named “institutional erosion.” This erosion (preventing the emergence of new institutions) in the maritime
transport system is significantly contributed by:
− various conflicts of interest between individual actors of the institutional environment (key suppliers, resource and product consumers, regulatory agencies, and other organizations that produce similar services or products),
− through alteration of the interaction patterns and power balances
among them, and
− technologies, routines, working traditions, skill profiles and competencies, actors, formal arrangements, rules and social positions.
E. Buitelaar et al (2007, p.) believe that institutional change can be
realized in practice as a combination of evolution and deliberate design
(Figure 8). They start from the existing institutional arrangement (which is
based on hegemonic discourse) at that under pressure of both external
societal developments and institutional reflections by the actors involved
creates a first window of opportunity for change. This conceptual model
provides “a better understanding of how actions aiming at institutional
design are positioned within a perspective of institutional evolution” (Ibid.,
p. 897). Societies are faced with aggravating environmental challenge. To
respond to these challenges with desired institutional changes, we need
to understand the processes of institutional stability and change. What
matters is how discourses materialize, i.e. are institutionalized. M. Hajer
(1995) distinguishes between a) discourse structuration, when they be112
come widely accepted and influences the way a broad set of actors conceive particular problems, and b) discourse institutionalisation, when
structured discourses become increasingly stabilized, routinised and
eventually fully embedded and institutionalized in rules, resources, and
actor constellations.
Figure 8. Conceptual model of institutional changes in seaports
Source: Adapted to Buitelaar et al., 2007; Jacobs & Noteboom, 2010;
Delibasic, 2022).
No matter how much the actors are aware of the importance of the
burning problem (e.g. environmental), clearly, in the first case (under a)
they can behave institutionally opportunistically (not to change basic
113
values and beliefs), i.e. that for profit motives, business efficiency, and
investment deficit is supported by the so-called “institutional erosion”. “Institutional erosion” appears to manifest in practice through the effects of
institutional inertia, which operates in both the upper and lower fields (Figure 3). It can be assumed that the stated effect of institutional inertia will
weaken over time in the lower field, under the influence of strong pressures from global institutions and the danger of environmental pollution.
R. Whittington (2006, p. 619) correctly observed the practice of following common routines of behavior in institutional subjects, through
which they successfully translate exogenous events into engogenic ones.
Translated into maritime organizations (seaports, shipping companies, logistics operators, etc.) and their relationship through ecology, it can be
said that this is a specific discursive hegemony as a form of routine opportunistic behavior towards environmental problems and global institutional solutions.
UNCTAD’s (2018) identifies seven key contemporary trends in the maritime transport:
− rising protectionism,
− digitalization and e-commerce,
− excessive new capacity, which will have repercussions on freight-rate
levels and volatility, transport cost, as well as earnings,
− increased liner shipping consolidation through mergers and alliances,
− change of the relationship between ports and container shipping
lines,
− technological advances is as increasingly important, and
− great efforts to curb the carbon footprint and improve the environmental performance of international shipping.
The key trends in the development of the maritime transport industry
predetermine the prioritizing of its sustainability as a whole, as well as all
its subsystems. This highlights the priority of institutional solutions at all
levels.
J. Monios and G. Wilmsmeier (2015) suggest the following: “Liner
shipping strategies based on hub-and-spoke and hierarchical network
structures have led to a concentration of container traffic at selected
ports”. They point out that all attempts to strengthen certain strategic locations through the methods of centralization, mediation, and development of greenfield investments have failed. For, local and regional actors
114
were afraid that they would remain on the margins of concentration. Because of all this, the evolution of mobile maritime networks has been
forced to adapt to the proactive and reactive strategy of seaports (which
are immobile), in which different actors remain within a complex institutional environment. In this way, the illusion was created that seaports are
simply physical spaces. In fact, they are complex organizations with varying levels of public4 and private ownership, goals and responsibilities
(Ibid.). In global maritime networks there is a significant problem of unproductive and induced mobility of cargo, which reduces competitiveness.
These are:
− empty movements,
− ship capacity utilization, and
− two types of cargo transshipments, which are associated with their
expansion (hub-and-spoke strategy, where cargo from smaller
ports is transported to large hubs and transshipped onto very large
vessels that then traverse the main line route between the hub
ports), and by simplification (feeder services).
Port system evolution is extremely important for the infrastructural
and institutional development of maritime networks. It was very complex,
and consists of port expansion and upgrading of berthing and handling
facilities, by strengthening port competition through hinterland accessibility and port regionalization processes, to the development of intermediate
transshipment hubs, terminal activity, and the maritime service structure.
Over time, all this has led to tracks the geographical concentration of
cargo flows at hub ports and a later trend of geographical deconcentration
as other ports grow and compete. Secondary ports as a key factor of port
system evolution have also emerged.
This has led to a significant port and logistical reorganization. Small
seaports access global trade via large hub ports. In contrast, some medium-sized ports attempt to insert themselves as intermediate hubs. In
these cases small feeder vessels connect small and medium ports. Large
The state is important institutional factor. This is particularly true in the regulation of
pollution management, an externality which firms and the market are typically poor at
dealing with. The state is primarily concerned with the “regulation of individual or organisational activities using a command-and-control framework based on law and bureaucratic hierarchy” (Lee and Lounsbury, 2015). From the perspective of this thesis, this reaffirms the importance of state regulation in shaping the emission reducing activities of
the shipping industry.
4
115
feeder vessels connect medium and hub ports. In addition, the new generation of ultra-large container vessels connect the hub ports. On the
other hand, the centralization of cargo in huge terminals has also caused
institutional, business, and other forms of disconnection between the seaport and the city.
In the last fifteen years, shipping lines and major port terminal operators have consolidated and integrated their portfolios through mergers
and acquisitions. This has led to a reduction in the number of dominant
and large companies. They have therefore benefited significantly from the
use of economies of scale and scope. Also, they have enabled the provision of seamless intermodal transport services from port to port. The main
goal was to strengthen port competition and to occupy as much hinterland
as possible.
Seaports (Juscius et al., 2020) have sought to expand their institutional capacity in various ways. They did so primarily through institutional
adaptation, which included privatization, corporatisation, strengthening
the competencies of port authority (concept of institutional plasticity – S.
Strambach, 2010), and restructuring business models through processses. These are all basic ways (among others) of institutional mobility of
seaports, which corresponds to their fear of business and geographical
marginalization in the global system of synchronizing maritime networks
and demand.
  
D
espite the fact that business interests of maritime transport
are institutionally inconsistent with the needs of sustainable
development, their relationship in the future must be addressed through the prism of the fact that maritime transport is crucial for
the global economy. Indisputably, in the field of maritime transport there
is a long-term institutional stagnation, which inevitably leads to loss of
confidence in the system, various stagnant processes, and institutional
fiasco in regulating GHG emissions. However, it is currently the most en116
vironmentally friendly form of mass transport. In economic terms it brings
jobs, earnings, and profits to a large number of companies and a huge
number of employees.
World trade and maritime transport are a condition for economic
growth and spreading prosperity at the global level. Its environmental consequences are no less significant. Therefore, they are institutionally regulated in a number of global, regional, and local strategies, policies, actions, and regulatory frameworks. However, the imbalance between the
interests of maritime transport and sustainable development is narrowing
slowly and gradually, which is worrying and requires urgent and decisive
institutional action. Yet, the character of that action is contrary to the interests of numerous maritime business entities, which therefore resort to
various forms of opportunistic behavior.
Practice has shown that the regulatory regime developed by the IMO,
which provides a solid institutional framework, i.e. basic and general institutional guidelines for countries to develop their maritime transport infrastructure in a safe, efficient, and environmentally sound manner, is insufficient. However, its application in practice often fails.
In line with the conclusions of prominent representatives of neo-institutional economic theories (DiMaggio and Powell, 1983; Oliver, 1991),
and in fear of their own business and institutional marginalization (uncertainty), seaports have long resorted to homogenizing strategies: competitive system rationality that emphasizes market competition, niche change
and fitness measures, and institutional isomorphism (political power and
institutional legitimacy, for social as well as economic fitness). All these
strategies represent institutional opportunism regarding the global problem of environmental protection.
The development strategies of maritime shipping have the same character, because they give priority to short-term profit motives in relation to
long-term extremely unfavorable and dangerous environmental effects.
The paper verifies the basic hypothesis that the sustainability of maritime transport must include a balance between conflicting interests: social legitimacy (including ecology) and economic efficiency. Two initial hypotheses were also verified in a descriptive way: that in the future the institutional logic of sustainability must dominate business-as-usual, and
that the existence of two competing institutional logics in maritime
transport causes its mutually reduced efficiency, i.e. long-term crisis business.
117
118
3. INSTITUTIONAL CHANGES AS A FRAMEWORK
OF THE MARITIME DEVELOPMENT
S
. Kuznets wrote that structural changes are the central element
in the process of development and the most important part of
the growth model.
They can hinder the growth, if carried out too slowly or inefficiently, but
they can also promote economic growth if the distribution of resources
gets better.
The article has presented the authors' attempts to present new approaches to modeling the institutional behavior of economic agents.
Different approaches and views on economic growth and economic
development in their application to institutional changes are considered.
The descriptive method explains the hypothesis of the dominant role of
social innovations (primarily institutional changes) in social and
economic development.
Significance of sociocultural capital in the context of contemporary
knowledge economy is potentiated.
The conclusion is that transition countries must use exemplary models
and civilization achievements of institutional changes
in developed countries.
119
T
he opening note of this article clearly shows that most of S. Kuznets’ economic ideas concerned economic growth. However,
here we still need to keep in mind some other statements of the
same author (e.g., Kuznets, 1996, p. 445): without political democracy
and civil freedoms implementation of real institutional changes will be impossible. This obviously shows that the overall situation around economic
growth is not simple and depends on many factors at the same time.
Moreover, dynamic interdependencies here exact not only between
the factors of economic growth but also between its key elements. We can
present economic development as a sum of: economic growth, long-term
prospective, structural changes, institutional changes and environmental
sustainability. And interrelation of structural and institutional changes
should be considered exactly in this context. Apart from this, in this article
we stem from the popular today view of the neoinstitutionalists (for example, North, 2005) that institutional changes must be in priority to structural changes.
On the top of that, we will be operating S. Kuznets’ ideas concerning
the role of structural changes and also his rather smart idea that without
the human factor/capital and the processes of its reproduction economic
development simply cannot be modelled or tracked. On the ideas of Kuznets we can track the evolution in understanding the importance of social
sphere (social structures) which essentially requires institutes and also
takes part in their formation, so that later institutes can serve as regulators, coordinators and limiters of human and organizational behavior. Besides that, institutes are forming a flexible supporting structure (or set of
structures) for further interaction of individuals joined into special-interest
groups (M. Draskovic et al., 2017).
Kuznets’ views have been widely discussed, supported and/or opposed, in literature, especially his idea to treat economic development by
certain periods. These periods are connected to economic & technological
cycles which Kuznets himself named “long swings” (Kuznets, 1958). Later
on, another famous economist and the Nobel prize winner of 1979 U.A.
Lewis called those „Kuznets cycles“ already (according to: Abramovitz,
1966, p. 520). Here we also would like to quote another of his rather interesting thoughts, namely, that „contemporary economic growth forces
us to take action so that to solve the emering conflicts and also in relation
to always newly created changes in economy and social structure... Continuity of technological innovative features in today's economic growth
and in social innovations eases the necessary adaption and is probably
120
the most important factor impacting economic and social structure”
(translated from Russian, quote from Nobel Laureates in Economics,
2007, p. 98).
Thus, we can state that Simon Kuznets was among the first (or maybe
even the very first) to use the term „social innovations“. And in our article
here we will try to analyze those so that to determine how these social
innovations are related to institutional innovations. This is especially relevant and highly important for the post-socialistic countries, most of which
are still having troubles with reaching a decent rate of economic growth
and/or economic development. In other words, studying similarities and
differences between thes two notions we would like to try to explain the
key reasons behind the obvious failures of post-socialistic reforms. At this,
we initially assume that one of the key reasons for these failures is the
deficit of institutional changes (institutional innovations), to which – probably – also belong social innovations. This deficit has led to many contradictial or even paradoxal phenomena and also so-called pseudo-innovations (which proved to be not temporary, as it was believed earlier (V. Polterovich (2012), moreover, they directed the transition process into a
long-term and complex crisis).
Many authors are applying the institutional approach when studying
social capital. In this way they are able to discuss means and methods
with which formal and informal institutes influence social capital accumulation in the society or a social group. Studies of many authors have already proved that sustainable economic development is possible only under close partnership and cooperation of private business, society and the
state. At this, the state is not only creating social benefits but it also helps
forming long-term and efficient alliances of various social groups and
strata. In the countries with well-developed institutional pluralism there is
always strong social consensus which is essentially the balance of interests of various social classes and groups when it comes to social product
distribution, setting the minimum wage, transfer payments’ allocation etc.
This social consensus guarantees there exists a certain level of social welfare which further promotes investments’ growth, better investment climate, high rate of economic growth etc. (M. Draskovic et al., 2017).
And again, S. Kuznetz (1995) studied the relation between economic
growth and social inequality. While studying this relation it is important to
pose the following questions: what is the role of the state (represented by
political and tax authorities) in wealth distribution? And what is the mission of social capital in this regard? There can be many answers to that,
121
actually. Some researchers outline four vital elements in these relations:
social networks, general norms, values and trust. Some other scientists
are of the opinion that three elements are needed for institutional relations – social networks, general norms and convictions, and also two factors more are necessary for social capital formation – trust and rules (at
all social levels). Another additional factor of important influence is also
experience (see Figure 9).
Figure 9. Social capital components
Source: V. Draskovic, et al., 2013, p.
As of today there is a general consensus in scientific circles concerning the overall importance of institutes and their leading role in the development of national economy. But what is exactly their role and how relevant it can be in relation to different levels and rates of economic development – this still needs to be studied in more detail, since despite quite
intensive theoretical research and avalaibility of substantial empirical material, there is still no clear understanding why institutes are so important
for economic growth. One of the possible reasons for this gap in the related research is lack of theoretical basis for application of institutional
notions and also the impossibility of measuring exactly (that is, calculating) the influence of institutes. As well noted by D. Acemoglu (2009) it can
be partially explained by the fact that very notion of institute is defined in
122
literature (see, for example, Greif, 2006; Hodgson, 2006) as too broad,
and the range of institutional forms and manifestations is also too broad,
thus, it is rather difficult to allocate them a specific place as applied to
economic results.
Institutional changes and economic growth are, most probably, the
most important components and preconditions for economic development. However, this leads us logically to a question regarding casual relations here: what is causing what in these relations, what is the cause, and
what is the consequence – institutes or development? These questions
remain open for quite a while already. However, there is a popular opinion
that institutional shifts go first, then follows development. Another, and
also popular, opinion is that development determines the institutes (which
are in some sort of compliance with the actual level of economic system
development).
Both options are equally convincing on paper (Chang, 2005, 2011).
The very history of many developed countries supports the second variant,
actually: in these countries the achievements of economic development
opened the way for further modernization of many institutes and formation of the institutional system as such, and the latter quickly became
relevant not as much to the developed economy, but more to the very
strive for further economic development, thus forming the need for further
economic growth (Yerznkyan, 2013). Taking this into account, we also
need to note that the view on high and stable rates of economic growth
as the key factor of general social welfare increase and stabilization is not
only popular but also has solid empirical support. Besides, this view also
complies with the central ideas of Kuznets' teachings.
Representatives of the new institutional economic theory (North,
1981, 1990) have already provided enough proof that pluralistic, politically desirable and legally protected institutional environment predetermined long-term economic growth. In other words, stably high rates of
economic growth are not cauasing institutional changes, on the opposite
– they are the consequences of these changes. Rapid changes in economic realia of the two recent decades, predetermined by exponential
technological and organizational changes and also by some of the global
processes, have lead to paradigmal modifications in the economic growth
model.
The neoclassical model of growth by R. Solow (1956) emphasized the
key role of technological progress, and its author added to the already
123
existing models (which used to emphasize the role of physical capital and
laborforce) the technological factor. According to his calculations, technological factor predetermined about 4/5 of growth in case of American
economy when calculated per one worker/employee. R. Lucas (1988) emphasized the role of human capital, while R. Barro and Sala-i-Martin
(1997) described the meaning and role of technological diffusion. R. Barro
(1990) also studied the role of social infrastructure, while P. Romer
(1990) described the role of innovation stimuli etc.
The contemporary theories of economic growth mention the following
key decisive factors: institutes, innovations, information (and some other)
technologies and intellectual/human capital. This means that here we can
talk about the „model of four“ which reveals the causes of economic
growth (Delibasic, 2014, p. 9).
As presented by D. North (1997, p. 17), institutes are the „game rules“
for the society. Or, speaking more formally, they are the limiting framework
established by the people so that to organize and regulate the relations
between themselves. Therefore, these rules/frames are shaping all further incentives for human interaction in any field – politics, social life or
economic activities. In other words, this capacity of the institutes to shape
and structure social stimuli tells us, simply speaking, that any possibility
of free individual choice is, to a large extent, predetermined and limited
by institutes. Social (inlcuding political, traditional, moral, cultural and
other) as well as economic institutes are directly and indirectly influencing
on the structure of economic incentives inside the society. D. North assumes that interpretation of institutes can be actually two ways: institutes
as informal limitations in a society (generally accepted norms, rules and
certain code of conduct) or institutes as formal (that is, created and introduced by people) rules (M. Draskovic et al., 2017).
Informal institutes have the determining influence on our behavior. As
same D. North one wrote, the latter is to a great extent determined by the
unwritten codes, norms and formalities (Ibid., p. 56) which usually emerge
from the information transmitted via certain social mechanisms and are
part of that legacy which we call culture (Ibid., p. 57). Cultures as our heritage are able to explain why formal institues, under different circumstances, can lead to rather different results. Formal institutes (or, as D.
North calls them – rules) include political (and legal), economic rules and
contracts. These rules have their own hierarchy – from constituions to
statutes, then to legislative acts and regular laws, then go decrees and
orders, and finally individual contracts and agreements. This overall
124
hierarchy of rules poses both general and specific limitations (Ibid., p. 68).
D. Acemoglu, S. Johnson and J. Robinson (2004) proved, in this regard,
that societies with the economic institutes which promote the
accumulation of innovation factors and increase the efficiency of
resources' distribution, have more chances to reach prosperity, and vica
verse (M. Draskovic et al., 2017).
Human is a social being and an inseparable element of social environment. This is why any human is motivated not only by his/her own interests but also by habits, traditions and rituals, changes in the society
etc. Institutes are also important components of the same social environment.
The concept of sociocultural capital (sociocultural factors and social
relations) explains the role of individual and organizational social relations, collective actions and social integration as applied to development.
Sociocultural capital as a set of mostly informal institutes and social habits (which are certain ethical, cultural, religious and civilizational values)
predetermines all social changes – and thus, also predetermines social
and economic development (Acemoglu et al., 2003; North et al., 2009;
Fukuyama, 2004, p. 21; Patnem, 1996, pp. 207-209). However, if this
sociocultural capital is under the dominating influence of public authorities – it can quickly become its own complete opposite – the barrier to
development, because economy and politics are closely interconnected in
real life and tend to absord the sociocultural capital, as stated in (Fukuyama, 1995, 2001).
Sociocultural capital includes knowledge, with its complex of normative means for knowledge integration and identification, development, education, organization, communications etc. Sociocultural capital has the
capacity to mobilize and combine the capacities of individual and collective subjects. In its traditional meaning, sociocultural capital is defined by
the following factors: morale, ideology, culture, religion, political regime,
authority and trust to authorities, history of institutional changes, social
connections, knowledge and investment in knowledge (and/or in human
capital) etc. This set of inmaterial social resoures is essentially the environment surrounding and connecting both formal and informal institutes.
Such comprehensive understanding of the notion „sociocultural capital“ may serve as a methodological and analytical connection between the
notion „social innovations“ and „institutional innovations“ (which are essentially institutional changes). Disregarding all their similarities and dif125
ferences, we are of the opinion that the deficit of all these innovations has
lead to evolutionary crisis during the transition phase in the post-socialistic countries and thus has also lead to restoration of some sort of quasiinstitutional monism (which is also quasi-market-oriented and quasi-liberal) - M. Draskovic et al., 2017.
Due to mostly neglected role of sociocultural capital during the whole
transition stage in the post-socialistic countries, their economies and societies found themselves following some sort of anti-development model
which is essentially very paradoxal (Draskovich et al., 2016, pp. 103-111).
Why did this happen? Because the quality of sociocultural capital determines the level of real institutional changes. It is sociocultural capital that
provides sustainability to all institutes and development overall. Institutes
as the standards and regulators of individual behavior are determining
the general direction of socioeconomic development. Together with people, institutes are important components of social environment. There is
enough empirical material and detailed research to prove that in the majority of post-socialistic countries institutes have been developing very
slowly, often insufficiently and also illogically, moreover, they were often
under the influence of alternative institutes.
Many authors, including (Draskovic, 2014; Draskovic et al., 2015;
Delibasic, 2016; Draskovic, Draskovic et al., 2016) see the major precondition for such development in the anti-productive and anti-civilizational
development of sociocultural capital as well as in parallel domination of
alternative (shadow) institutes, the latter getting only stronger due to
growing dominance of narrow, personal incentives over the truly social interests. Therefore, we have no doubts that the deficit of institutes is the
major limiting factor in the potential development of sociocultural capital
in these countries, thus, it is also a limiting factor for their socioeconomic
development. Subjective (alternative, and in some radical cases – simply
criminal) institutes tend to ignore institutional norms of behavior and all
institutional changes as such. Dominance of these alternative institutes
in the society proves there can be institutional irrationality (Draskovic,
2014; Delibasic, 2014).
So, the key question is: who is to be blamed for this deformation of institutional structure and/or for institutional underdevelopment which hinders all further development? Different authors suggest different answers
to this question, however, the most known and acknowledged economists
(e.g., North, 1981; Denzau and North, 1994; Friedman et al., 1998; Acemoglu et al., 2003; North et al., 2009) mention in this regard the de126
structive nature of institutional imitations and some sort of “government
improvisations”.
Most of these authors are of the opinion that it is necessary to reduce
and control the dominating influence of politics over economy, moreover,
institutes are supposed to dominate both – political and economic life. It
would be quite appropriate to mention here M. Mann (2014) who proved
that actions of central authorities in underdeveloped countries are predetermined by an intricate combination of political, economic and ideological sources. So, how did this happen? Public authorities indeed have been
using the neoliberal model, but only with the ideological purposes! Economic neoliberalism has made the institute of public regulation the key
enemy of the society. Economic radicalism is now being implemented under the slogan of “minimum intrusion of the state”. In fact, the market is
mostly being ignored, except for those cases when specific interests of
some small privileged groups must be followed (M. Draskovic et al.,
2017).
Market today is being substituted by the distributive coalitions and
raider ideologies of quasi-neoliberalism (Draskovic, 2010, 2014; Jovovic,
2012; Draskovich et al., 2016). And these small groups of the most privileged are parasiting on public policies’ use in their own interests, substituting the real market mechanisms by the monopolistic quasi-competition
and semi-legal acquisition of public property. Thus, these small groups are
using non-market methods for own enrichment, and this lead the majority
to perceive public authorities, all public policies and actions as the acts of
“predatory state” (Marcouiller and Young, 1995).
This perverted individualism of the few most priviliged has quickly
found its own placed as a socially approved norm. This, in turn, has lead
to quick spread of opportunistic behavior, network corruption and other
forms of alternative social institutes. Some authors (e.g., Landes, 1998,
p. 516; Draskovic, 2014, p. 22) explain these trends by the underdevelopment of sociocultural capital. Moreover, further spread of these alternative institutes has led to further erosion of sociocultural capital and as
a result – to constant reproduction of economic and social crises, with
already typical accompanying consequences: value crisis in the society,
dogmatism, negative selection, poverty, inequality and unfair selection,
neglection of legislation, inflexibility of public bodies to change etc.
127
Within the „knowledge economy“ (also known as „new economy“) the
role of intellectual component of capital is growing every day. Information
as the most obvious intangible factor today predetermines the use of new
communications and their convergence. This, in turn, leads to society consolidation in many fields of economic and social activities. The growth of
information economy has its own, new organizational logic which is preconditioned by the ongoing process of technological change. Introduction
and full functioning of knowledge economy has certain preconditions,
namely, there must be national guarantees for social freedom, and also
well-developed system of education, high quality of institutional environment overall and very specific rules of doing business, as well as reasonable balance between state control and market freedom. Lack or underdevelopment of any of these components make „knowledge economy“
just a vox, and nothing else.
Today, in the era of knowledge economy, any national economic policy
must rest on the following core principles: a) development of sciences and
technologies must be the core factor of economic growth; b) favourable
investment climate must attract investments, and mainly into the top-priority high-tech sectors; c) institutional environment must be flexible
enough in all sectors of the economy (the so-called institutional pluralism),
especially when it comes to national regulation which must be always ready to respond promptly and adequatly to market failures, especially when
these failures are somehow related to education and science; d) competitiveness of production capacities must be supported by means of stimulating innovations related to higher performance and/or labor productivity; e) human resources at key productions must be timely and adequatly
retrained so that to be ready to respond to risks, sudden changes and
crisis; f) all new organizational changes must positively contribute to the
economy, social life and/or legal field.
The overall structure of knowledge economy consists of human capital, information & communication technologies, innovations and some
other components (for more details – see Figure 10). Factors with dominating influence on economic development (according to our literature review) are presented in Figure 10. As we can see, stable development depends on a whole range of driving forces, including: access to human capital, quality and rate of innovations, availability of soft and hard infrastructure, current rate of welfare, institutional structures and finally, entrepreneural activity (Cornett, 2009; Naudé et al. 2008; Audretsch and Keilbach, 2004).
128
Figure 10. The model of economic growth and economic development
under „knowledge economy“
Sources: various.
129
Assuming that the following statements are true:
− institutional development has its positive influence on economic
growth and economic development;
− economic development directly (through better motivation and
more active investment in education and sciency) as well as indirectly (through creation of better living conditions: high wages for
researchers, scientists and engineers; better communications and
more access to information and statistics etc.) influences the
growth of expert knowledge and innovations,
we can make a conclusion that there exist indeed strong interdependencies and feedback relations, as demonstrated in Figure 11
(social innovations are shown with curved arrows).
Figure 11. Social innovations and the development formula within
knowledge economy
Source: V. Draskovic, et al., 2013, p. 17
The dependence „institutes – economic development – investments
in knowledge – innovations – increased level of knowledge“ can be analyzed and interpreted in many different ways, however, their mutual dependence is already real and meaningul in today's economic reality, and
for this matter we are indeed living in the times of „knowledge economy“.
130
The conceptual frameworks of GEM have been defined in 1999 (Figure 4), and unlike more traditional models of national economic growth,
the former clearly shows that national economy's growth is the results of
human efforts and availability of opporunities to take these efforts. It also
shows that this process is taking place in the interaction with the environment.
Figure 12. GEM's conceptual model of economic growth
Source: Reynolds, 1999, pp. 9-10; Global Entrepreneurship Monitor
2014 Global Report, pp. 13-14.
131
  
T
he article has presented the authors' attempt to find new approaches to modelling the institutional behavior of economic
agents. For this, various approaches and views on economic
growth and economic development have been considered in their application to institutional changes.
It is important to emphasize here that many theoretical and empirical
studies have already discovered and proved there exists a direct correlation between institutional development of a country and its economic development (D. North, D. Acemoglu et al.) as well as between the level of
knowledge and economic development. On these grounds we can assume
that it would be logical to unite the cause-effect relations here: development based on knowledge (and investments in knowledge), institutes and
their changes, economic development and economic growth – and this
article we presented this graphically, inter alia.
This constant interconnection of various spheres – institutional, economic, social, cognitive etc. – is the necessary precondition for the formation of some sort of platform for further modelling of institutional behavior of economic agents. This modelling is supposed to be adequate to
the actual real situation, specifically – in the countries of post-Soviet and
post-socialistics space. At the same time, such modelling attempts must
not be limited to economies in transition only. We assume that modelling
of institutional behavior of the economies would have some common features, regardless the history of a country and the level of its economic
development.
132
Part 3
LOGISTICS DEVELOPMENT
OF SMALL SEAPORTS
Mimo Draskovic
133
134
1. EMPLOYMENT IN THE MARITIME TRANSPORT
T
he basic objective of this study is to examine the state of employment and employment trends in the maritime transport system of
the European Union and the Republic of Croatia.
The purpose of this article is to estimate the number of employed in
the maritime transport system of the Republic of Croatia by 2025.
It will look into interdependence of the number of employees in the
national maritime transport system and the number of passenger
and cargo ships, static and dynamic indicators of labour and the
movement of GDP.
The method of descriptive statistics and statistical methods
of correlation and regression analysis were used to examine
norms and regularities in relations between the mentioned
stochastic phenomena.
The obtained findings contribute to a scientifically based
perspective regarding direct contribution of maritime transport to total
employment within the transport sector and the national economy,
as well as the factors determining the number of employed persons
in maritime transport.
The main finding of this paper points to the fact that the number
of employees in the national maritime transport system is positively
and statistically significantly correlated with labor
market indicators in freight transport.
135
E
urope is one of the leading maritime centres in the world: with
329 key seaports along its coastline, and controlling around one
third of the world's merchant fleet. Ports are vital gateways, linking European transport corridors to the rest of the world. As 75% of European external trade transits through EU ports, the shipping sector plays a
major role in connecting the European market with its trade partners
(https://ec.europa.eu/transport). Sea transport has always been the
dominant support of global trade (Rodrigue, 2017). The global maritime
shipping industry is serviced by about 100,000 commercial vessels. With
a dynamic short-sea shipping sector, the European maritime sector contributes to the development of a competitive and resource efficient
transport system in the EU. Today, shipping accounts for around a third of
intra-EU exchanges, and annually 400 million passengers embark and disembark at EU ports (https://ec.europa.eu/transport). Overall, maritime industries are an important source of employment and income for the European economy. On a global supply estimated worldwide at 1 371 000 seafarers, EU plus Norway represents about 18,50% of the total workforce,
23% of the officers and near 15% of the ratings (European Comision,
2011). Although EU is an entity for employment, there is a strong imbalance of situation for seafarers between Western and Eastern Member
States. From ISF/BIMCO data, the total number of seafarers in Western
Europe countries (EU plus Norway) is 146 231 (81 652 officers and 64
579 ratings) and the number from East Europe 107 988 (62 315 officers
and 45 673 ratings). The total number of seafarers in Croatia is 18 658
(11 704 officers and 6 954 ratings). The country was ranked eighth in the
number of officers and sixth in ratings within the EU. Currently, there are
about 22 thousand seafarers in Croatia (www.mmpi.hr). Croatian seafarers are trained in eight naval maritime schools, four maritime university
colleges and 22 specialized maritime collegesv (Pupavac et al., 2019).
Thus, the educational system of the Croatia directly caters the global
maritime labour market where Croatian seaman is an "export product" in
extreme demand. Croatian seafarers are still competitive on the world
seafarer market with their education and training, competence and expertise, as well as with labor price (Marinov et al., 2005). In 2016 there were
only 3 429 people employed in the Croatian national maritime transport
system, or 4.4 times less than during the pre-transitional period. Left without large shipping companies such as Jugolinija/Croatia Line and Losinj
Navigation, the Croatian maritime transport system became wholly dependent on human capital, and that capital needs to be put to service as
soon as possible and to the fullest extent to affirm Croatia as a maritime
136
country. Research results in this study are organized in five logically related parts. After introduction, the problem of research is presented in the
second part, followed by analysis of employment in the maritime transport
of the Croatia and EU-28+Norway. The third part encompasses secondary
data that was used as the subject of analysis, and the presentation of
scientific methodology used. After the results of the research and discussion in the fourth part of the study, the conclusion is reached in the fifthv
(Pupavac et al., 2019).
Croatia has entered a vicious circle of unemployment and dislocation
(Jurcic, 2017). The trend of emigration is increasing, which turns human
capital into scarce resource and limiting factor of domestic production
growth. The number of employed persons in the Republic of Croatia in
2015 has decreased by about 200,000 compared to 2008 (Pupavac,
2017). In the same period, there was also a negative relation of immigration with respect to emigration. The average number of emigrants in 2015
and 2016 has exceeded 30,000. Negative economic trends had a negative effect on Croatian transport sector. Reduced volume of transport operations measured by static and dynamic indicators, as a result of economic crisis, negatively affected employment trends in the transport sector as well. The average number of employees in the transport sector in
2014 decreased by 12.25% compared to 2008. In comparison, the total
employment decreased by 13.7% in the same period, and the real gross
domestic product by 12.02% (Pupavac and Bakovic, 2017). The number
of employees in the maritime transport system of the Croatia decreased
by as much as 21.1% in the same period. This relatively large decrease in
the number of employees in the maritime transport system of the Republic
of Croatia is a consequence of problems in the national economic system,
but also of the great global financial crisis and the difficult period in world
shipping. Negative economic trends and reduction in real income and purchasing power had a negative effect on Croatian transport sector.
Decrease in passenger transportation in all forms of transport is a
continuation of the negative trend started in 2009. In 2014, there were
38.7% less passengers carried compared to 2008. An increase in the
number of passengers carried was recorded only by maritime and coastal
transport, which in 2014 carried 1.3 percent more passengers than in
2008. This shift is a result of increased number of tourist arrivals and the
fact that maritime and coastal transport cannot be replaced by other types
of transport and are therefore less sensitive to change. In addition to
137
passengers, in 2014 there was also a decrease in the amount of goods
carried (36,4%) compared to 2008 (Pupavac et al., 2019).
The amount of transported goods has decreased in all types of transportation. The biggest drop of 40.3% was registered in road transport, the
most important mode of transport in goods transportation, but also the
most important when it comes to employment. Maritime transport, as the
second greatest goods transporter, had 34% lower volume of goods carried than in 2008. The decrease in goods transport is mostly related to
negative trends in processing industry and trade (Pupavac and Draskovic,
2015). Reduced volume of both passenger and goods transport as a consequence of economic crisis has negatively affected employment trends
in the transport sector, thus confirming that the demand for transport services is a derived demand, i.e. economic growth induces higher demand
for transport services and vice versa (Pupavac and Zelenika, 2004). In the
period from 2008 to 2014 there were 9 944 jobs lost within the transport
system of the Republic of Croatia, of which 873 or 8.78% within maritime
traffic (Table 1).
Table 1. Employees in Croatian transport system and maritime traffic,
2008-2014
Year
Maritime
traffic
Index
2008.
2009.
2010.
2011.
2012.
2013.
2014.
4154
3862
3870
3830
4018
3397
3281
100
92,97
93,16
92,20
96,72
81,77
78,98
Croatian
transport
system
81220
80733
76486
75827
76085
74882
71276
Index
100
99,4
94,2
93,4
93,7
92,2
87,7
Source: The Statistical Yearbook of the Republic of Croatia 2015, (online
data at www.dzs.hr, PCAxis) (access: 5/12/2018.
Based on data from Table 1, it is evident that the number of employees in Croatian transport system decreased by 12.3% in 2014 compared
to 2008, while the number of employees in maritime traffic decreased by
138
21.02%. The relative share of the number of maritime traffic employees
in the total number of people employed in transport has been in steady
decline for the last three decades. For example, in 1988 it was 11.85%,
in 1998 it was 7.05%, in 2008 it was 5.11% and in 2014 it was only 4.6%.
This information is particularly worrying given the fact that the number of
employees in Croatian transport system has shown a tendency of steady
decline since 1987 (see Figure 1).
In 1987, Croatian transport system employed a record-breaking 128
400 workers (Pupavac, 2009), while the number of maritime traffic employees was 15,000. This means that there was one maritime traffic
worker per 8,56 employees in the transport system. In the forthcoming
period, this ratio will increasingly deteriorate to the detriment of maritime
traffic employees (see Figure 2) - Pupavac et al., 2019.
Figure 1. Maritime traffic employees and transport system employees
ratio, 1987-2015
Source: authors’ calculation
139
Figure 2. Employment trends in transport system of the Croatia,
1983-2015
Source: The Statistical Yearbook of the Republic of Croatia 2015. (online data at
www.dzs.hr, PCAxis)
140
Table 2. Descriptive statistics on employment in maritime transport in EU23, 2014.
Column1
Mean
Standard Error
Median
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Confidence Level(95,0%)
7,87
2,15
2,14
10,31
106,2
2
6,44
2,24
44,31
0,17
44,49
181,0
23,00
4,46
Source: own
The EU-28 maritime transport system in 2014 employed 181 017
workers or 1.7% of the total number of employees in the EU-28 transport
system. Only five European Union countries - Czechoslovakia, Luxembourg, Hungary, Austria and Slovakia - do not keep records of the number
of employees in maritime traffic, since these countries do not have sea
access anyway. Below is a brief overview of employment in the EU-23 maritime traffic based on the method of descriptive statistics (cf. table 2).
Based on data from Table 2, it is evident that average employment
per member country is 7,870 people. Large variation range and standard
deviation (SD = 10.31) show great dispersions. The largest number of
people is employed by the maritime transport system of Italy - 44 490,
while the least people are employed by Slovenia - 174. Italy is followed by
Denmark with 21 292 employees in the maritime system, Germany with
16 642, UK with 16 130 and Greece with 13 997. Thus according to the
number of maritime transport workers, the Republic of Croatia is below
the EU-23 average (Pupavac et al., 2019).
141
Figure 3. Employment trends in maritime transport system of Croatia (000)
Source: prepared by the authors according to Statistical Yearbook of the
Croatia, different years.
Employment trends within the Croatian maritime transport system
over the past three decades is shown in Figure 3. However, Croatia is
among the top EU countries if the share of seafarers in the total population is observed (cf. Figure 4). Based on Figure 4, it is evident that there
are only 253 inhabitants per one seafarer in Croatia, whereas, for example, in Belgium, there are 18 983. In the observed EU-23 countries there
is an average of one seafarer per 1 767 inhabitants. When the share of
seafarers in the total number of inhabitants is considered, Luxembourg is
the leading country with only 113 inhabitants per one seafarer, although
it has no sea access. However, in 2010, there were 106 ships sailing under their flag. During the same year, the largest number of ships was sailing under the flag of Malta - 1552, then of Norway with 1381 ships,
Greece with 1305, Italy with 990 and United Kingdom with 944. Accordingly, based on the data from the table 3 we made the correlation analysis
between the number of seafarers in individual EU countries, the gross domestic product per capita and the number of vessels flying their flag (cf.
table 4) - Pupavac et al., 2019.
142
Figure 4. Number of inhabitants per one seafarer in EU countries
Source: prepared by the authors
143
Table 3. Number of seafarers, GDP/p.c. and number of ships,
EU-25+Norway
Belgia
Bulgaria
Cuprys
Denmark
Estonia
Finland
France
Germany
Greece
Ireland
Italy
Latvia
Lithuania
Luxemburg
Malta
Netherlands
Norway
Poland
Portugal
Romania
Slovakia
Slovenia
Spain
Sweden
UK
Croatia
SeaNumber of
GDP/p.c.
farers
ships
590
32700
99
33269
4800
67
3421
21000
855
3938
42600
409
9000
10700
35
4200
33300
176
13696
29900
299
10253
30500
663
12963
19600
1305
3112
35000
49
20950
25700
990
7892
8600
33
5395
8900
58
4436
78600
106
2436
15200
1552
3574
23382
22669
2221
24343
576
644
7043
10923
231193
18658
35400
77500
9200
16200
5800
12100
17400
22800
37300
27500
10152
827
1381
72
64
34
0
0
116
356
944
194
Source: prepared by the authors according to European Commisssion
(2011), Study on EU Seafarers Employment and Regional GDP per capita
in the EU in 2010: eight capital regions in the ten first places
144
Table 4. Correlations between the number of seafarers, GDP/p.c. and
number of ships, EU25+Norway
Correlations (Spreadsheet1) Marked correlations are significant at p < ,05000 N=26
(Casewise deletion of missing data)
Means
Std.Dev.
S
GDP/p.c.
NS
Seafarers
18491,42 44275,49 1,000000
-0,005885 0,239260
GDP/p.c.
25709,69 18869,66 -0,005885 1,000000
0,293969
Number of Ships 410,92
484,31
0,239260
0,293969
1,000000
Based on data from Table 4, there is evidently no correlation between
the number of seafarers in certain European countries and their GDP /
p.c. (r = -0.005; p <0.05), i.e. there is a poor correlation between the number of seafarers in certain European countries and the number of ships
under their flag (r = 0.29, p <0.05). Accordingly, it seems more appropriate to investigate dependence of the number of seafarers on GDP trends
individually at the national level. Data about employment in maritime
transport (NE), waterway fleet in seawater and coastal transport (passenger ships-PS and cargo ships-CS), seawater and coastal transport of passenger (passenger carried-PC and passenger miles-PM) and goods (goods
carried-GC and tonne-miles-TM) are taken from the Croatian Bureau of
Statistics, while data regarding the GDP in constant prices is the result of
author's calculations (table 5) - Pupavac et al., 2019.
In order to determine the factors that directly and essentially influence
employment in the national maritime transport system, first a correlation
analysis will be carried out. After that, with the help of regression analysis,
it will be attempted to formulate a suitable mathematical model that can
be used for objective estimate of the number of employees in the maritime transport system by 2025. In order to make an objective forecast
regarding the number of employees in maritime transport in Croatia, a
theoretical model should be defined first. This study investigates the dependance of the number of employees in maritime transport (NE) as the
dependent variable and passenger ships, cargo ships, passengers carried, passenger miles, goods carried, tonne-miles and gross domestic
product (GDP) as independent variables. Accordingly, a model to estimate
the number of employees in maritime transport can be written as a function (Pupavac et al., 2019):
NE = f (PS, CS, PC, GC, PM, TM, GDP)
(1)
145
Table 5. Movement of employment in sea transport, passenger ships,
cargo ships, passengers carried, passenger miles, goods carried,
tonne-miles and GDP
NE
PM in mln
PS
TM in
mln
CS
PC in
000
GC in
000t
2007
4290
265
91
74230
67
12723
32420
GDP Constant price in
HRK
323522,76
2008
4154
265
88
77199
68
12861
30768
331155,41
2009
3862
263
88
74160
64
12550
31371
308305,68
2010
3870
266
85
87878
68
12506
31948
301214,65
2011
3830
315
80
83929
67
12926
30348
301214,65
2012
4018
325
91
67861
64
12474
25636
295190,36
2013
3397
331
85
68727
46
12770
24744
292238,45
2014
3281
335
84
58158
45
13029
20335
290777,26
2015
3427
337
84
65995
43
13082
21376
295430,00
2016
3429
352
86
61071
41
13525
20951
303997,47
Source: The Statistical Yearbook of the Republic of Croatia 2017.
Supposing that the number of employees in maritime tranport depends on passenger ships, cargo ships, passengers carried, passenger
miles, goods carried, tonne-miles and gross domestic product, its linear
form would be as following:
NE= b0 + b1PS + b2CS + b3PC + b4GC + b5PM + b6TM + + b7GDP
(2)
bi – (i=0,1,2,3,4,5,6,7) = model parameters.
Based on data given in Table 5, correlation analysis was conducted
(Table 6). It shows a strong and positive interdependence between the
number of employyed in the maritime transport and number of cargo
ships (r=0,90; p<0,05), goods carried (r=0,84; p<0,05), GDP (r=0,75;
p<0,05) and strong and negative interdependence between the number
of employed in maritime transport and passenger miles (r=-0,78; r<0,05).
Connections between the number of employees in maritime traffic and
the number of passenger ships (r = 0.59, r <0.05) and realized tonne
miles (r = 0.61; r <0.05) are positive and of medium strength, and the one
between the number of employed in maritime transport and the number
of passengers carried (r = -0.57; r <0.05) is negative and of medium
strength. The existence of a negative correlation between the number of
146
employ-yees in maritime transport and indicators of labour in passenger
traffic can be explainned by an insufficient utilization of the existing capacities, especially off season (Pupavac et al., 2019).
Since there was a strong or relatively strong interdependence between
the number of employed in maritime transport and independent variables,
regres-sion analysis was also conducted using data from Table 5.
Calculations conducted to determine the value of parameters of the
function in the form (2) yielded no conclusive regression models. After
many trial and errors procedures, a simpler model has been developed to
estimate the future number of employees in the maritime transport of the
Republic of Croatia.
NE = f (CS)
(3)
NE = f (GC)
(4)
Regression analysis between the number of employees in maritime
transport and number of cargo ships (CS) and goods carried (GC) has
resulted with the following models of linear regression:
NE = 2215,045 + 26,889CS (R=0,90; F(1,8)=35,723; p<0,01) (5)
NE = 2131,707 + 0,060GC
(R=0,84; F(1,8)=19,587; p<0,01) (6)
As there is a positive and firm link between the number of cargo ships
and tonnes of goods carried (r = 0.92, p <0.05), it seems more appropriate
to choose only one model for predicting the number of employees in the
national maritime system. This is also supported by the fact that between
2007 and 2016 the number of ships in freight maritime transport
decreased by 38.8%, while freight maritime transport measured in tonnes
of goods carried decreased by 35.4%.
We have decided to relate the number of employees in maritime
transport to tonnes of goods carried, so in order to establish the most
accurate trends, the following is an overview of tonnes of goods carried in
the Republic of Croatia from 1996 to 2016 (Figure 5).
147
Table 6. Interdependence of the number of employed in maritime
transport and passenger ships, cargo ships, passengers carried,
passenger miles, goods carried, tonne-miles and gross domestic
product
Table 7. Pessimistic estimates for employment in the maritime transport
of Croatia 2025.
Predicting Values for variable: NE
b-Weight Value
b-Weight*Value
G
0,60175 16536,40 995,070
Intercept
2131,707
Predicted
3126,777
-95,0%CL
2767,826
+95%CL
3485,729
Source: own
If the observed negative trend were to continue in the future, the
number of transported tonnes of goods in maritime transport in 2025
would be only 16.5 million tonnes, or 21.07% less than in 2016. Such a
reduction in the volume of cargo in maritime transport would result in the
loss of another 303 jobs, i.e. a significant reduction of employment in
maritime transport by 8.83% compared to 2016 (table 6).
148
Figure 5. Goods carried in sea water and coastal transport (000t)
However, as the Croatian economy shows signs of recovery, i.e. positive growth rates have been achieved since 2015, it is expected that the
observed trend in the maritime transport of goods will be reversed. The
Strategy of the Transport Development of the Republic of Croatia 20172030 seems to support this. It defines the following specific objectives for
maritime transport (Strategy, 197):
149
− To encourage development and enhance the competitiveness of the
port of Rijeka as the main Croatian seaport;
− To reduce the impact of maritime transport on the environment (fleet
development, prevention and mitigation measures for pollution by
naval facilities, environmental protection);
− To increase the distribution of freight transport on oversea Adriatic and
coastal routes in favour of maritime transport,
− To increase reliability of maritime transport (public transport and
supply chains) in aggravating weather conditions;
− To improve the efficiency and cost-effectiveness of the maritime
transport system;
− To improve maritime transport security,
− To improve the integration of ports into the local transport system
(passenger and freight). According to defined goals and planned
measures towards achieving them, it is expected that all indicators of
labour in maritime transport will be increased.
The initial assumption for predicting the number of employees in the
national maritime transport system is that by 2025, tonnes of goods
carried in maritime transport will reach the average level of tonnes of
goods carried for the period from 1996 to 2016 (Table 8).
Table 8. Descriptive statistics for goods carried, 1996-2016.
Mean
Standard Error
Median
Standard Deviation
Sample Variance
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count
Source: own
150
30361,24
1114,043
31371
5105,188
26062948
0,013715
-0,64488
18309
20335
38644
637586
21
If this estimate would have been realized, it would result in an increase
in the number of employed in the maritime transport system for around
530 people (table 9).
Table 9. Optimistic estimates for employment in the maritime transport of
Croatia in 2025.
Predicting Values for variable: NE
b-Weight Value
b-Weight*Value
G
0,60175
30361,40
1826,983
Intercept
2131,707
Predicted
3958,691
-95,0%CL
3778,129
+95%CL
4139,252
There is a 95% reliability that the number of employees in Croatian
maritime transport in 2025 would move within the range of 3778 - 4139
employees.
  
T
he main finding of this study is that the number of people
employed in the maritime transport in the Republic of Croatia is
steadily falling, and so the maritime transport seems to be
heading in the same direction as the entire transport system where some
57,000 jobs were lost in the last three decades. Maritime transport has
lost 11.7 thousand workplaces in the past three decades, or 20.6% of
total workplaces in transport. The ratio between the total number of those
employed in transport and the number of employed in maritime transport
is steadily worsening to the detriment of maritime transport. The Republic
of Croatia is below the EU average considering the number of maritime
transport workers. Among the EU countries with records of employment in
151
maritime transport, Italy has the largest number of employees, and
Slovenia the smallest. However, if the number of seafarers is observed,
then the situation is significantly different. With one seafarer per 263
inhabitants, the Republic of Croatia is at the very top of the EU. As for the
number of seafarers in individual EU countries and their GDP/p.c., no
statistically significant relationship was established. Such analyzes should
better be conducted at individual national levels.
The correlation analysis between employment trends in Croatian
maritime traffic on one hand and GDP trends, the number of passenger
and cargo ships and the static and dynamic indicators of transport work
for the period 2007-2016 on the other hand, confirmed the existence of
a solid and positive relation only between employment trends in maritime
transport and the number of cargo ships, goods carried and GDP. To
estimate the number of employees in the maritime transport system in
2025, a simple linear regression model was chosen where the number of
employees in the maritime transport system is related to tonnes of goods
carried. Further continuation of the observed negative trends will result in
reduction in maritime transport employment (Pupavac et al., 2019).
Since this scenario is unrealistic for a country that is so maritime
inclined, the starting point for this study is a scientifically based
assumption that the observed trend would be reversed, and that tonnes
of goods carried in maritime transport of the Republic of Croatia by 2025
will reach at least the average tonnes of goods carried for the period from
1996 to 2016. In this case, the number of employed in the maritime
transport system of the Republic of Croatia by 2025 would increase by
15.4% compared to 2016. In support of this estimate are the following
facts: the end of a difficult period in world shipping, the recovery of
Croatian economy, and the adoption of the Transport Development
Strategy of the Republic of Croatia for the period 2017-2030.
The main limitation of this study stems from the fact that employment
in maritime transport is seen as a dependent variable of only one
independent variable - goods carried. In future researches the model for
estimating the number of employees in maritime transport should include
the greater number of variables.
152
2. SPREADSHEETS IN FUNCTION OF
OPTIMISATION OF LOGISTICS NETWORK
S
tudy below discusses how estimated spreadsheets functions in logistics networks optimization.
The basic point for efficacy of estimated spreadsheets in designing
logistics networks is n and a practical example. In this way the given
model can be applied to all logistics networks of similar problem capacity.
Logistics network model confronting estimated spreadsheets
present a real world at a level needed for understanding the problem of optimization of logistic networks.
Applied scientific research is based on analysis and synthesis method, mathematical method and information
modelling method.
T
he development of network of national, regional and global economies provides possibilities for taking advantages of volume
economies, i.e. development of greater number of logistic operators which will, besides the services provided within national networks,
offer them on regional level and ultimately, on global level. Spreading of
logistics network leads to rationali-sation in transport network, distribution network and decrease of stock within unique global logistics network.
Technological break-throughs, that form part new technology paradigm,

Co-authorship with D. Pupavac
153
offer the possibility of creating different structure of global logistics networks, which can be entirely optimised by use of information technologies.
Therefore, the following hypothesis has been set: Calculation tables
form representative model in logistics networks' optimisation, i.e. model
once created for a certain problem can be used for solving problems in all
logistics networks of similar problem area. Scientific research applied for
proving the hypothesis is based on methods of mathematical and information modelling.
Multiple networks between companies are becoming more of a rule
than an exception nowadays. The world where single companies are competing among themselves for profit, in a kind of interpersonal market,
does not actually exist. The world of modern business is characterised by
networks of social and exchange relations between companies and surrounding factors. Companies choose cooperation as one of the ways of
achieving competitiveness; enter different kinds of supply chains or logistics networks. In this way, multiple networking is being created which has
marked modern global economy, and has made difficult drawing a line
between cooperation and competition.
Table 10. Type of network
Type of
network
Nodes
Arcs
Flow
Communicaton networks
O-D pairs for
massages
Transmission
lines
Message routing
Computer
networks
Storage device or
computers
Transmission
lines
Data, messages
Railway networks
Yard and junction
pts.
Tracks
Trains
Logistics
networks
Plants, warehouses, ...
Highways, railway tracks etc.
Trucks, trains,
etc
Sources: various
154
Complex inter-connected processes (networks) can be found in almost every kind of human activity, especially transport, logistics and economy. A network is made up of nodes and directed arcs connecting pairs
of nodes. Networks can take all sorts of forms (Table 10).
Companies develop logistics networks in order to obtain information,
resources, markets and technologies, or in order to achieve economy effects of size and range. Logistics networks represent ultimate achievement of inter-logistic management or logistic chains management. In logistics terms network is the collection of locations and routes along which
a product can be shipped.
For example, a company needs to decide whether to ship products
directly to customers or to use a series of distribution layers. Quick response to changes in demand requires effective solutions by all participants along the logistics network (Figure 6).
Logistics network in figure 6. is made of four objective layers. Process
of produc-tion is taking place downstream from the production supplier,
from production plant to distribution centres and from distribution centres
to market. Logistics network can have any given number of objective layers. Furthermore, production layers sometimes take place downstream
even when semi-products or parts of products are being returned to production plants for finishing or when the products not intended for further
sale are being returned from retail locations to distribution centres for recycling. In this way the there is no competition between single companies
but between entire networks, and the prize goes to the company that has
created a better network.
Principle of operation is very simple: create a solid network of relations with key elements, aided by logistics operator as optimisation factor
for logistic activity along the network, and the profit will follow.
The network being created by global logistics operator between global
producer and buyer can be viewed as follows (Figure 8).
155
Figure 6. Logistics network
156
Retail
Trade
Production
Suppliers
Transport
→
Storage
Space
Transport
→
 
Basic
Storage
Space
 
Accessory
Storage
Space

→
Final
Custo-mers

Whole-sale
Trade
Figure 7. Logistics network in industrial firm
Source: own
Figure 8. Global logistics network
Source: Prepared author according: Wiegmans et. al., 1999.
157
By connecting supply and demand, i.e. production and consumption,
logistics operators are creating national, regional and global logistics network which can provide following advantages to participants in global logistics chains:
− decrease of costs (labour, taxes, customs and other duties),
− improvement of effects for all the participants in supply chain that
has been formed,
− higher quality production inputs, and especially higher quality logistics services,
− opening of new and more distant markets, and
− improvement of own performance through development of partner
relations with other participants of the chain.
In the realm of accounitng jargon a «spread sheet» or spreadsheet was
and is a large sheet of paper with columns and rows that organizes data
about transactions for a business person to examine. An electronic
spreadsheet organizes information into software defined columns and
rows. The data can then be “added up” by a formula to give a total or sum.
The market for electronic spreadsheet software was growing rapidly in the
early 1980s and VisiCalc stakeholders were slow to respond to the introduction of the IBM PC that used an Intel computer chip. During this period,
Mitch Kapor developed Lotus and his spreadheets program quickly became the new industry spreadsheet standard. In 1983, Lotus' first year of
operations, the company reported revenues of $53 Million and had a successful public offering. In 1984, Lotus tripled in revenue to $156 Million
(Power, 2004).
The next milestone was the Microsoft Excel spreadsheet. Excel was
originally written for the 512K Apple Macintosh in 1984-1985. Excel was
one of the first spreadsheets to use a graphical interface with pull down
menus and a point and click capability using a mouse pointing device.
When Microsoft launched the Windows operating system in 1987, Excel
was one of the first application products released for it. When Windows
finally gained wide acceptance with Version 3.0 in late 1989 Excel was
Microsoft's flagship product. For nearly 3 years, Excel remained the only
Windows spreadsheet program and it has only received competition from
other spreadsheet products since the summer of 1992.
Definition of a calculation table within new condition of technological
paradigm is being transferred to functional nature of calculation tables
from the system transition application state viewpoint (Vukmirovic et al.,
158
2004). In such paradigm a calculation table is being observed as an entirety made of four main components saved in address lines of lines, columns and matrixes. Such observation is pointed to calculation table as
function of computer supported complex mathematical operation combined with matrix-network modelling. Such approach leads to new definition of calculation table: calculation table is collection of functions and
formulas which, when inter-connected, can support the logic of data flows
and establish development of complex computer supported mathematical
algorithms to support quantity modelling of entire and complex problems.
Following expenses can be the object of optimisation on a logistics
network (Pupavac and Zelenika, 2004):
−
−
−
−
−
−
−
material cost,
acquisition costs,
investment costs,
production costs,
costs of distribution centres,
costs of keeping stock,
costs of internal and outbound transport.
Execution of optimisation methods by use of calculation tables has
the advantage in possibility of physical integration of programmed routines into self-generated applications. Computer supported optimisation
methods are created in a manner that allows them to be parallel used in
other relevant applications, to the point that they can be physically incorporated into them. Such methods fall under category of computer-integrated tools of applied mathematics. After programme execution the data
remains permanently saved in template form, which is the basis for development of model base in logistics networks optimisation.
In order to illustrate the part of calculation tables in logistics network
optimisation we will further on deliberate on logistics network which has
“i” production plants, “j” distribution centres and "k" consumer points (Figure 9). Production plants P1, P2 and P3 produce same goods during the
period in question in quantities p1, p2 and p3. B1 and B2 are consumer
points of the same goods with quantities b1 and b2. Every unit of goods is
being transported from producer to consumer via one of distribution centres D1 and D2 which have capacities of c1 and c2. We will mark cij the cost
of transport per unit from producer P1 to distribution centre DCj, and cjk as
cost of transport from distribution centre DCj to buyer Bk. This is a classic
two-layer transport problem (Pasagic, 2003, pp. 161-162) because the
159
transport is done from the place of production to the place of consumption
through distribution centres.
Figure 9. Crossdocking
One can ask which are the reasons that speak in favour of distribution
centres in a logistics network. The reasons are many (Barkovic et al.,
1986), and we will state only three:
− decrease distribution costs (degression effect of cost from producer to distribution centres due to quantities being transported),
− decrease of delivery time (from distribution centre to buyer due to
stock),
− possibility of combining shipments for one buyer with the possibility of reduction of transport costs.
As the costs of shipments' processing in distribution centres are not
an issue of this scientific debate, the total function of transport costs to
be minimised on the suggested logistics network is (Pasagic, et al., 2004,
p. 431):
160
C=
m
r
i =1
j =1
r
n
j =1
k =1
  c x +  c x
ij ij
jk jk
→ min
(1)
Production centres produce one type of goods in quantities p1 =
200000 t, p2 = 300000 t and p3 = 100000 t. Demand for such goods is
b1 = 400000 t and b2 = 180000 t. Only 200000 t can be distributed from
each production centre to each distribution centre, and the same can be
done from each distribution centre to each consumer. Transport costs differ and are shown in table 11 and table 12.
Table 11. Transportation costs - Plant to DC (€ 000 t)
Plant to
DC
Plant 1
Plant 2
Plant 3
DC 1
5
1
1
DC 2
5
1
0,5
Table 12. Transportation costs - DC to Customer (€ 000 t)
DC to Customer
Customer 1
Customer 2
DC 1
2
12
DC 2
2
12
In table 11 we have set solution for minimum cost network flow problem by us of Excel calculation table, or its add-in Solver.
Firstly, single transport costs from production centres to distribution
centres and from distribution centres to consumer centres (upper left part
of the table) are entered into table 11, followed by information on
transport capacities and distribution centres capacities (upper right part
of the table). The decision variables represent quantities being transported from production centres to distribution centres and from distribution centres to consumer centres (lower left part of the table). Transport
costs from production centres to distribution centres, from distribution
161
centres to consumer centres, as well as total transport costs are shown in
lower right part of the table.
Variables: $C$17:$D$19;$C$23:$D$24
Constraints:
Do not exceed supply at the plants
$E$17:$E$19  $F$17:$F$19
Meet customer demand
$E$23:$E$24  $F$23:$F$24
Do not exceed shipping capacity
$C$17:$D$19  $K$6:$L$8
$C$23:$D$24  $K$11:$L$12
Flow conservation at the DCs
$C$28:$D$28 = 0
After formulating the model in this manner in Solver Parameters, click
on Solve which activates the Solver programme calculating the value of
variables in address sequence $C$17:$D$19 and $C$23:$D$24. table
Decision variables calculated in address sequence $C$17:$D$19
and $C$23:$D$24 define the optimum solution. Table 12 show the optimal solution to the problem by use of calculation table MS Excel.
Based on the information from the table 12 it is clear that 180 t of
goods should be shipped from the first production centre to first distribution centre, 200 tons of goods from second production to first distribution
centre and 100 tons of goods to second distribution centre. 100 tons of
goods should be shipped from third production centre to second distribution centre.
Therefore, 380 tons of goods will be shipped through first distribution
centre as follows: to first consumer centre 200 tons of goods and 180
tons to second consumer centre. Second distribution centre will have
shipped 200 tons of goods to first consumer centre. Minimum cost of
such shippment amount to 4 210 000€ and are 390 000€ or 9,26% more
favourable from the least acceptable solution obtained when the function
is resolved by maximum.
Modern supply chains represent dynamic, flexible and responsive networks operating on “predict and process” principle, which is opposed to
traditional approach “produce then sell”. Quick response to changes in
demand requires effective solution in all stages of supply chain: production, acquisition, stocking, transport and distribution. Lower number of
162
participants, but also the domination of logistics operator characterize
modern logistics network.
Logistics operator is a factor, which successfully designs and optimises the logistics network, which is more and more integrated into national, regional and/or global economic system. This is the main reason
for transformation of traditional forwarders into logistic operators offering
not only transport, but also warehousing, information technology, and
even production and global approach.
The use of computer and computer applications has become basic
tool in logistics network optimisation process. This is especially important
because logistics network management represents new management
concept that is trying to manage resources on the entire logistics network.
In order for participant to complete their tasks it is necessary to have the
logistics network competitively profiled. This is done through improvement
of at least one of following three dimensions: service, speed and property.
When solving the problems on the network, user orientation of calculation
tables has been proved, as it is not necessary to use programming methods, or writing of programming instructions. In the example shown for use
of calculation table in network problem solving it is clear that all the activities are automated by use of functions and formulas in preparing the table through user application Solver.
A Solver Model is build in this way: Objective: Minimize $K$28
163
Table 13. Minimum cost network flow problem
164
Table 14. Optimal minimum cost network flow problem solution by use
of calculation table
165
166
3. NEGATIVE EXTERNALITIES AND LOGISTICS
DEVELOPMENT OF ADRIATIC SEAPORTS
T
he subject of the paper is to investigate the
hypothetical percep-tions of the impact of negative
externalities on the expansion and development of
selected Adriatic seaports.
The aim of the paper is to show that Adriatic seaports must
accept and apply the integration strategy for as a key
business and logistic competence, which can be the basis
their expansion and development.
Therefore, this paper starts with the basic hypothesis
that a partner business performance and cooperation
between the Adriatic seaports of Koper, Rijeka, and Bar
is a crucial condition for easier finding of large foreign
investors and global logistics providers.
It also starts with the auxiliary hypothesis that it is necessary to overcome many business barriers, which are treated
as negative externalities.
For researching the perception of the impact of negative
externalities, the multiple linear regression method is used.
It is concluded that the level of selected negative
externalities is different in individual
se-lected ports, but also between them.
The research results verified the initial hypothesis.
167
S
eaports are constantly adapting to the changes in the world
maritime market in several ways: increasing the size of their infrastructure and suprastructural capacities, technological and
information improvement, cooperation with logistics providers and integrating their logistics functions. It is indisputable that investments played
a major role in their modernization. Given the long-standing crisis situation, as well as the need to increase business efficiency, the development
of logistics services (in terms of marketing logistics and transport logistics
in order to achieve a satisfactory degree of integration), outsourcing (Bilan
et al., 2017), and regional competitiveness, strategy formulation of Adriatic seaports of Koper, Rijeka, and Bar (the sequence is in terms of development) in the near future should focus on three basic (general) development directions:
− attracting FDI and engaging a well-known global logistics provider
as a key and long-term strategic partner,
− building an efficient logistics and information system and outsourcing, and
− wider and greater connection with the hinterland, with the possible
organization of free zones and logistics-distribution centers in the
wider Montenegrin area.
Bearing in mind the extremely favorable geographic and strategic position of Adriatic seaports, with a high level of safety, it can be assumed
that the implementation of the partial business integration strategy will
significantly contribute not only to the realization of the aforementioned
basic (general) relevant development goals, but also to the following:
− increase the level of quality, the supply universality of their port
services and competitiveness in relation to other relatively close
seaports (Marlow and Paixao, 2003, p. 195),
− better and advanced logistic and transportation links between the
Adriatic seaports, as well as the links with European and world
seaports (UNCTAD, 2009; Draskovic, 2013);
− stability and profitability of all their port operations in the long run,
− sustainable development in the considered Adriatic seaports,
which implies concern for the natural environment (UN, 2015;
Zuzeviciute et al., 2017; Mikalauskiene et al., 2018),
− increase the employment and living standards of the population in
the wider area, which gravitate towards the mentioned seaports,
168
− strengthen and improve the overall institutional environment in
the countries to which the seaports belong (Delibasic, 2016; Popov et al., 2016: Yerznkyan et al., 2017, Draskovic, 2017,
Draskovic et al., 2017), and
− greater overall economic and other benefits for the countries to
which the seaports belong.
It is implied that the realization of the stated goals would not only increase the port traffic, but also a certain redistribution of transport and
port services in the region (primarily referring to the considered Adriatic
seaports of Rijeka, Koper, and Bar), strengthening their key competences
in terms of transport and logistics performance. It is assumed that this
would overload the freight transport corridors in some parts of Europe.
This is particularly relevant for goods of Chinese and Korean origin, hence
it would be logical to employ well known global logistics providers as strategic business partners and investors, mostly from China and South Korea.
It has allready been conceptually and hypothetically explained
(Draskovic, 2013) that realization of the considered idea implies large foreign investments, which should be directed to deepening and leveling the
sea gauge (especially in the port of Bar). This would lead to a reduction in
and/or significant elimination of the existing feeder service, which significantly increases the total transport of container cargo cost towards the
Adriatic seaports, epsecially the port of Bar, which gets a significant portion of container cargo from the seaports of Rijeka and Koper.
Implementation of this idea also includes a significant degree of partnership cooperation, and the related long-term forms of partial business
integration between these ports. It is a necessary condition for overcoming many political, economic, and other problems that objectively exist between the countries belonging to the considered seaports. Achieving such
a partnership agreement would enable the synergistic strengthening of
the competitiveness and key competencies of all these ports, as well as
the consequent increase of their involvement in the global flows of integrated marketing logistics.
Development and implementation of discussed ideas must be seen
at the practical regional level (political, economic, and institutional level),
with the wider participation and co-operation of all interested regional
partners (govenrmental entities, mentioned Adriatic ports, and the selected global logistics provider). It is also necessary to bear in mind the
169
theoretical model, proposed by A. Montwiłł (2014, p. 260) in accordance
with UNCTAD recommendations (2004). It implies the compulsory (minor
or greater) integration of particular operating port functions with city and
regional functions (i.e. "objective functions" with "spatial functions") in order to build and strengthen logistics centers in the seaport and its hinterland (Figure 10).
Figure 10. Possibilities of logistical and economic development of sea
ports
Source: adapted from UNCTAD, 2004; Montwiłł, 2014.
This idea could highly correlate with the activation of the wider hinterland of the listed Adriatic seaports (regardless of the existing degree of
their infrastructure, logistics, and traffic development). The hinterland of
Adriatic seaports can be adjusted to the development of assembly industries and distribution centers for exporting to European countries, banking
services and insurance, ecotourism and organic food production for the
needs of tourism and export. It also suggests the development of industrial and economic administrations, inspections, quarantines, tax authorities and banks, telecommunications and insurance companies, low taxation and profit repatriation.
170
The implementation of the partial business integration requires maximal respect for regonal, economic, and institutional harmonization, given
the specific, complex, crisis and disruptive (mainly inherited) political and
economic conditions that still exist to a significant extent in the observed
region between the countries in which the said seaports operate. In this
respect, we consider that implementation of the discussed idea of expansion, development, and partial business integration of Adriatic seaports
and their possible future partnership and cooperation requires the elimination of several obstacles that objectively exist. In the past, these obstacles have created a specific braking mechanism, made of several negative externalities, among which the following are the main ones:
− Insufficiently developed mutual political relations between the
countries belonging to the selected seaports, and relatively weak
consequent regional economic cooperation, with the presence of
suspicion and distrust due to unfavorable war events and other
political conflicts in the recent past;
− Differences in the institutional development of the countries in
which the discussed Adriatic seaports are located (according to the
indicators noted by A. Denzau and D. North, 1994; G. Hodgson,
2006; D. Acemoglou and J. Robinson, 2012; B. Yerznkyan, 2012;
O. Williamson, 2014; M. Delibasic, 2014, 2016);
− Underdeveloped system of port infrastructure and port superstructure, in accordance with the criteria stated by K. Misztal, 2010; S.
Markusik (2009), and A. Grzelakowski and M. Matczak (2012), as
well as underdeveloped system of port logistics, in accordance
with the criteria stated by UNCTAD (2009), K. Bichou and R. Gray
(2004);
− Poor seaport performance indicators, in accordance with the criteria stated by P. Marlow and Paixao (2003), K. Bichou (2006), S.
Esmer (2008), M. González and L. Trujillo (2009), P. De Langen
and K. Sharypova (2013) and UNCTAD (2016).
As a methodological framework for the quantitative analysis - a linear
multiple regression model was used, with 180 selected citizens surveyed
(60 respondents in each country to which a specific seaport belongs - Slovenia, Croatia, and Montenegro). All respondents had a high education in
the field of economics or logistics, which assumes that their logical thinking was at a high level. In addition, most of them were experts in the port
management. They were asked to evaluate, based on their best know171
ledge, experience and/or intuition, the dependent variable in the model,
defined as the degree of economic and logistic development of the selected Adriatic seaports of Koper, Rijeka, and Bar (each respondent for
the corresponding seaport in his/her own country). They were also asked
to evaluate the values of three independent variables in the model, defined as the key obstacles (i.e. negative externalities) for the implementation of the considered idea of business cooperation and integration of selected seaports, which related to:
− differences in institutional development of the observed countries,
− underdeveloped system of port infrastructure, port superstructure, and port logistics, and
− poor seaport performance indicators. In all cases, respondents
used a scale (1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0), where 1.0
was the lowest impact, and 5.0 was the highest impact.
Designing the survey and the analysis took into consideration the existing under-developed mutual political and economic relations between
the countries where the said seaports are located. It is assumed that their
improvement is a condition for the future business economic and logistic
cooperation.
The idea is to create a mathematical model using multiple linear regression analysis, that is, a functional relationship between the dependent variable (Y): level of economic and logistic development of port and
independent variables (X1, X2 and X3): (i) institutional development of port,
(ii) port infrastructural, suprastructural and logistic development, and (iii)
port perfor-mance indicators.
The task is to estimate the expected mean value of the dependent
variable ( Y ), based on individual estimations of the respondents. Since
the respondents have given the estimations based on their own discretion, in line with the requirements of multiple linear regression model, the
coefficients ( b0 , b1 , b2 , b3 ) are to be determined and Y calculated by using
equation (1):
Y = b0 + b1 X 1 + b2 X 2 + b3 X 3 … (1),
Where
Y - is the mean expected value of the dependent variable;
172
b0 - is Y-axis intercept, determined on the basis of an appropriate sample;
b1 , b2 , b3 - are coefficients of variables X i , i = 1,3 , respectively, or slopes of
the corresponding lines.
This practically means that for any new value of each independent
variable from a predefined interval, one can estimate the value of the dependent variable. It should be said that Y is average estimated value, because it is the mean value of the probability distribution of pos-sible values of Y for given values X i , i = 1,3 . To determine Y is used the leastsquares method (Bertskas et al., 2008). In fact, our aim here is to determine the coefficients ( b0 , b1 , b2 , b3 ), so as to minimize the sum of squared
errors (SSE), which is represented by formula (2):
n
(
SSE =  Yk − Y k
k =1
) = (Y
2
n
k =1
k
2
− (b0 + b1 X 1k + b2 X 2 k + b3 X 3k )) ... (2)
Where
Yk - is actual value of the dependent variable, given by the k respondents
( k = 1, n );
Y k - is the estimated value of the dependent variable on the basis of the
model, in the case of k respondents ( k = 1, n );
n – is the total number of respondents (here, per 60 related to the Port of
Bar, Port of Rijeka and Port of Koper), k = 1, n .
Using the least-squares method, in the paper is actually determined a
straight line, which minimizes the sum of vertical differences for each pair
of points (Balakrishnan et al., 2007). In other words, identified is a straight
line that best fits the given set of points, by determining the optimal value
of Y-axis inter-cept ( b0 ), as well as coefficient ( b1 , b2 , b3 ), in order to obtain
a more accurate value of Y for the given values of X i , i = 1,3 and Y (for
k , k = 1, n ).
The realization of multiple linear regression model is very complex,
and therefore it is better to leave it to the computer. For this purpose can
be used SPSS (Sheridan and Coakes, 2013; Pallant, 2011), special Excel
173
VBA tools as Excel Modules Solver, which has been used in this analyzes,
while other similar tools can be used, as well.
In addition to the forecasted average value of the dependent variable
Y and vectors ( b0 , b1 , b2 , b3 ), based on the model applied, the following statistical values can be calculated: mean absolute deviation, mean square
error, mean absolute percent error, standard error of regression estimate,
correlation coefficient and coefficient of determination. The formulas
used to calculate these values are given below, as well as related brief
explanations.
Mean absolute deviation (MAD), indicates the numbers on how much
the value of the dependent variable, obtained through multiple regression
analysis, corresponds to the estimated value by the respondents, or in
other words, to what extent the model reflects the perception of the respondents (3). Mean square error (MSE) is the mean value of squares of
the individual errors of assessment. In other words, if we have n number
of respondents, MSE value is calculated using the formula (4). MSE values
expressed deviations.
Mean absolute percent error (MAPE), indicates the error between the
estimated value and value of dependent variable as a percentage, obtained by using the model (5).
The formulas for determining the values of the previously generally described errors in the model are given below:
n
MAD =  Ak − Fk / n … (3)
k =1
n
MSE =  ( Ak − Fk ) / n … (4)
2
k =1
MAPE = 100  Ak − Fk / Ak / n … (5)
n
k =1
Where
A k - is an actual value of a variable (value estimated by respondents),
k = 1, n ;
Fk - is an estimated value (by model), k = 1, n ;
174
n – is a number of respondents (per 60 in the Port of Bar, Port of Rijeka
and Port of Koper).
Standard error of the regression estimate (SE), is also called the standard deviation of regression. This statistical value is suitable for the formation of the so-called confidence intervals around the regression line. It
indicates how much the value of the dependent variable, obtained by
model, can vary numerically (6).
Correlation coefficient – r, is used to estimate the strength of linear
relationships. Ge-nerally, if correlation coefficient is higher than 0.6, it is
considered to be a strong linear relation (7).
Coefficient of determination - r2, is a value between 0 and 1, which
indicates to what extent (percentage) dependent variable depends on the
independent variables included in the model (8).
General formulas for calculating the standard deviation, correlation coefficient and coef-ficient of determination are given below:
SE =
r=
 (A
n A
− Fk ) / (n − 2) ... (6)
2
k
n Ak Fk −  Ak  Fk
k
2

− ( Ak ) n Fk − ( Fk )
2
2
2


n Ak Fk −  Ak  Fk

2
r =
2
2
2
2

 n Ak − ( Ak ) n Fk − ( Fk )


... (7)
2


 ... (8)



Where
A k - is an actual value of a variable ( k = 1, n );
Fk - is an estimated value ( k = 1, n );
n – is a number of respondents (per 60 in the Port of Bar, Port of Rijeka
and Port of Koper).
The respondents, namely per 60 experts for port management in Montenegro (Port of Bar), Croatia (Port of Rijeka) and Slovenia (Port of Koper)
were asked to estimate the dependent (Y) and three independent variable
in the model (X1, X2 and X3), each with a number on a scale from 0.5 to
5.0. In fact, respondents were supposed to estimate the level of economic
175
and logistic development of port (dependent variable), as well as the extent to which the following independent variables: (i) institutional development of port, (ii) port infrastructure, supra-structure and logistic development, and (iii) port performance indicators - affect the dependent one.
Also, the values of statistical parameters, described in the previous section, have been determined in order to analyze the reliability of the proposed predictive model.
Using Excel Modules Solver are obtained the results of multiple regression analysis, for all respondents, for each of the analyzed ports. In
fact, determined are coefficients in a function of the dependent variable,
that is, the slice on the Y-axis ( b 0 ) and coefficients ( b1 , b2 , b3 ) which correspond to the independent variables, X i , i = 1,3 seriatim.
Based on these values and average values, estimated by the respondents, for each of the independent variables, are calculated average values
of the dependent variable Y . These values are shown in Table 15.
Using model are obtained the approximate values: 1.25; 1.50 and
2.25, respectively for the case of Port of Bar, Port of Rijeka, and Port of
Koper (Table 16). By taking into account that the participants have evaluated the level of economic and logistic development of the analyzed ports
by one number on a scale of 0.5 to 5.0, these are relatively low levels.
Table 16 contains numerical values: mean absolute deviation (MAD),
mean square error (MSE), mean absolute percent error (MAPE), standard
error of the regression estimate (SE), correlation coefficient (r), and coefficient of determination (r2) for the analyzed sets of respondents’ estimations per each of the considered ports.
Table 15. Mean values of the dependent variable Y in the case of Port of
Bar, Port of Rijeka and Port of Koper
Port of Bar
1.302
Port of Rijeka
1.789
Port of Koper
1.393
b1
b2
b3
-0.030
-0.079
0.005
-0.064
- 0.098
0.166
0.007
0.028
0.159
Y
approx. 1.25
approx. 1.50
approx. 2.25
b0
176
Table 16. Errors, coefficients of correlation and determination
Port of Bar
MAD
MSE
MAPE
SE
r
r2
0.383
0.198
42.92%
0.461
0.091
0.008
Port of Rijeka
0.326
0.162
23.97%
0.417
0.159
0.025
Port of Koper
0.315
0.152
15.00%
0.404
0.309
0.095
Source: own
Following are the graphs (Figures 11-13) showing the actual values of
the dependent variable Y, determined on the basis of subjective estimation of 3x60 respondents – port management experts from Montenegro
(Port of Bar), Croatia (Port of Rijeka) and Slovenia (Port of Koper), as well
as those calculated by the model, i.e. Y .
177
Actual
Forecast
R
R
R
R
Respondent
R
R
R
R
R
R
R
R
R
R
R
R
R
R
47
45
43
41
39
37
35
33
31
29
27
25
23
21
19
17
15
13
11
R
R
R
R
R
9
7
5
3
1
0.0
R
49
0.5
R
51
1.0
R
53
1.5
R
55
2.0
R
57
2.5
R
59
Actual vs. Forecast
R
Level
Figure 11. The values of the dependent variables, estimated by respond
ents and those determined by the model, in the case of Port of
Bar
178
Actual
Forecast
R
R
R
Respondent
R
R
R
R
R
R
R
R
R
R
R
R
R
R
45
43
41
39
37
35
33
31
29
27
25
23
21
19
17
15
13
11
R
R
R
R
R
9
7
5
3
1
0.0
R
47
0.5
R
49
1.0
R
51
1.5
R
53
2.0
R
55
2.5
R
57
3.0
R
59
Actual vs. Forecast
R
Level
Figure 12. The values of the dependent variables, estimated by respond
ents and those determined by the model, in the case of Port of
Rijeka
179
Actual
Forecast
R
R
Respondent
R
R
R
R
R
R
R
R
R
R
R
R
R
R
43
41
39
37
35
33
31
29
27
25
23
21
19
17
15
13
11
R
R
R
R
R
9
7
5
3
1
0.0
R
45
0.5
R
47
1.0
R
49
1.5
R
51
2.0
R
53
2.5
R
55
3.0
R
57
Actual vs. Forecast
R
59
3.5
R
Level
Figure 13. The values of the dependent variables, estimated by respond
ents and those determined by the model, in the case of Port of
Koper
180
  
O
n the basis of statistical modeling it has been shown that mean
expected values of the dependent variable are: 1.25; 1.50; and
2.25 in the cases of Port of Bar (Montenegro), Port of Rijeka
(Croatia) and Port of Koper (Slovenia), respectively. Analysis are done over
the re-presentative set of input data composed of the truthful responds of
a large number of the experts in the field.
Linear functional dependence in all three considered case show acceptable level of consistency, with mean absolute percentage errors of:
42% (Port of Bar); 23% (Port of Rijeka); and 15% (Port of Koper).
The proposed regression model can be eventually refined by introducing additional independent variables. Also, lager parent population, or input data set of experts’ responds, might be considered in the future research work.
181
182
4. BUSINESS COOPERATION IN THE
ADRIATIC SEAPORTS
T
he subject of this paper is the analysis of perceptions of 210 respondents about the level of possible business cooperation between the
three Adriatic seaports (Bar, Koper and Rijeka).
The aim of the research is to assess the degree of negative impact
of individual key influencing factors which are grouped in the model
according to the principle of compatibility into three independent variables
at the level of possible business cooperation
(as dependent variables).
It starts from the basic hypothesis that further
development of these seaports, among other things, should be based on their
greater business cooperation, because it can significantly increase their
competitiveness (Juscius et al., 2020) in the wider region through better
business and logistics competencies.
The authors have also defined the auxiliary hypothesis that in order
to establish the mentioned business cooperation, it is necessary to overcome
many economic, political, institutional and other barriers (Gupta et al.
2019; Panikarova et al, 2020; Draskovic et al., 2020b).
The paper uses the method of multiple linear
regression to determine the relationship between the dependent
and the independent variables.
183
I
n this paper, the economic modeling was applied to the three selected Adriatic seaports (Koper, Rijeka and Bar), in which we conducted a field survey (the samples of 70 respondents in each of
these seaports). The aim of the survey was to obtain valid answers, based
on the perception of 210 respondents about the level of possible business
cooperation between the selected Adriatic seaports of Koper, Rijeka and
Bar, as well as about the possible negative impacts of the selected factors. In this sense, we have defined four basic research questions
(Draskovic et al., 2020a):
− What is the level of possible business cooperation between the
seaports of Koper, Rijeka and Bar? - the dependent variable in the
model;
− What is the negative impact of institutional, infrastructural, suprastructural and corporate factors on the establishment of business
cooperation in these seaports? - the first independent variable in
the model;
− What is the negative impact of the applied level of logistics services
on the establishment of business cooperation? – the second independent variable in the model;
− What is the negative impact of political and economic barriers on
the establishment of business cooperation? - the third independent variable in the model.
To verify the initial and auxiliary hypotheses, in addition to theoretical
considerations, we have used numerical tables, graphical and statistical
analysis and regression multiple linear analysis on the data obtained in
the course of surveying 210 respondents. 1. Theoretical approach Many
scientific papers (Misztal, 2010; Gonzalez & Trujillo, 2009; Del Saz-Salazar and Garcia-Menendez, 2016; Draskovic, 2019) have directly and indirectly pointed out a number of factors that affect the level of development
of seaports.
Most of these factors negatively affect the possibilities of their regional connection, i.e. efficient business cooperation. A number authors
pointed out infrastructure as the main constrain for establishing the high
level of seaport cooperation. Baran and Górecka (2019) admitted that ineffective road and rails transportation in Croatia is a big constraint to a
possible business model between seaports in Koper, Rijeka and Bar.
Study of Vlahinić-Lenz et al., (2019) shows bad status of the transport
infrastructure in Central and Eastern E.U. member states have negative
184
effect on economic growth in these countries and consequently on seaports development.
Many studies indicate the special role of infrastructure in locating foreign direct investments (FDI) (Brodzicki et al., 2018; Dorożyński et al.,
2018; Vu and Ho, 2020). Entrepreneurial zones, established through public-private initiative and funded by EU funds attract more foreign direct
investments (FDI) and that money can be used to improve the poor infrastructure of the Rijeka port (Kontosic-Pamic & Belullo, 2018). Based on
the above research questions, which reflect the dependent variable and
the three independent variables in the hypothetical model, we defined the
research framework (Figure 14).
Figure 14. Research framework of the proposed hypothetical model
Source: own creation
The initial research model connects three independent variables with
one dependent variable. For the realization of multiple linear regression
analysis, we used Modules Solver and SPSS (Coakes, 2013; Pallant,
2011; Teles and Schachtebeck, 2019, Bayraktar, 2019). In addition, for
185
simple mathematical statistical modeling, we used the works of N. Balakrishnan et al. (2007); D. Bertskas and J. Tsitsiklis (2008). The constructs
used in this study were measured on a Likert scale from 1 to 5, where 1
means the least impact, and 5 the greatest impact. Multiple regression
analysis was applied to the results of the respondents' perceptions obtained through the survey, for cases of specified ports.
Through the quantitative part of the research, the focus was on data
collecting, processing, and explaining. According to the purpose defined
in the hypothesis of work, descriptive statistics, the data analysis, correlation analysis, and multi-correlation method, were used for approval of it.
The multiple linear regression model was applied after (the method of
least square), as well as hierarchical multiple regression model.
Descriptive analysis of the obtained data at the level of perception of
the respondents in the observed seaports showed that the conditions of
normality and linearity for multiple regression were met. This essentially
justifies the use of regression analysis of the first-order model. All extremes and atypical points were also checked. All of them satisfy the preconditions for the application of the multiple linear regression model for
determining the linear relationship between the dependent variable and
the independent variables (Draskovic et al, 2020a).
It is important to note that the correlation coefficient (r) and the determination coefficient (r2) are quite large (Table 1). In this sense, they also
justify the use of multiple linear regression models. Our goal was to determine the functional dependence between the levels of possible business
cooperation (as a dependent variable - DV) and three independent variables (IV1, IV2, IV3) in the model: institutional, infrastructural, csuprastructural and corporate factors (IV1), applied level of logistics services (IV2),
and political and economic barriers (NP3), respectively.
In addition, our goal was to determine the mean expected value of the
dependent variable based on individual estimates of the respondents.
Since the subjects evaluated the dependent DV and independent variables (IV1, IV2, IV3) according to their subjective judgment (perception),
our task was to determine the coefficients: B0, B1, B2 and B3, as well as
to calculate using the next expression:
186
where is / are:
- DV k - the mean expected value of the dependent variable;
- B0 – section on the ordinate, determined on the basis of ascending data;
- B1, B2 and B3 – coefficients with the independent variables IV1, IV2 and
IV3, which in fact represent the slopes of the corresponding lines on the
abscissa,
- n – is the total number of respondents (70 each from the ports of Bar,
Koper and Rijeka).
Based on these values, the value of the dependent variable can be
calculated for each new value of the independent variable. It can be said
that the mean value is based on the values of IV1, IV2, and IV3. The least
squares method was used to determine (Bertskas et al., 2008). a line
minimizes the sum of the vertical differences for each pair of points by
which those lines are determined In fact, our effort was to determine the
coefficients: B0, B1, B2 and B3, in order to minimize the sum of the
squares of the error (SSE).
The advanced statistical software SPSS 23.0 was used for the realization of multiple linear regression analysis. In addition to the coefficients:
B0, B1, B2, and B3, the following statistical indicators were determined:
Mean absolute deviation (MAD), correlation coefficient r, coefficient of determination (r2), Mean square error (MSE), Mean absolute percent error
(MAPE) and Standard error of the regression estimate (SE). The obtained
results are shown in Table 17. The following is a description of the statistical indicators shown in Table 17 (Draskovic et al, 2020a).
187
Table 17. Key parameters and statistical indicators in multiple linear
regression model
Source: own creation
Mean absolute deviation (MAD) shows the extent to which the value
of the dependent variable, obtained by multiple linear regression analysis,
corresponds to the subjects' estimates. In other words, this statistical indicator speaks to the extent to which the model reflects respondents ’estimates. The specific values of the mean absolute deviation over the samples from the Port of Bar, the Port of Koper and the Port of Rijeka, are:
0,254, 0,337, 0,199 respectively.
These values indicate a high correspondence of the model and the
assessment of the respondents.
Mean square error (MSE) is the mean value of the square of the individual errors in the estimate. This is a deviation of: 0,176, 0,189, and
0,085 respectively, in the case of the analyzed samples from the Port of
Bar, the Port of Koper and the Port of Rijeka. These numerous values also
speak in favor of satisfactory compliance of the model with real data, collected through questionnaires.
Mean absolute percent error (MAPE) is the percentage of error in estimating the value of a dependent variable by respondents and based on
the model. It is the simplest statistical quantity in terms of interpretation.
In the case of our research, it takes the values: 16,49 %, 10,55 %,
10,23%, and 10.23%, respectively, for the cases of the analyzed questionnaires from the Port of Bar, the Port of Koper and the Port of Rijeka.
188
These values also indicate an appropriate level of correspondence between models and respondents' estimates (Ibid.).
The standard error of the regression estimate (SE) is also called the
standard deviation of the regression. This statistical quantity is suitable
for the formation of the so-called confidence interval around the regression line. It shows how much the dependent variable, obtained by the
model, can vary. In our study, the standard deviation has the following
values: in the case of the sample from the Port of Bare 381, the Port of
Koper. 0,306, and the Port of Rijeka 0,345.
According to the data given in Table 1, the lines representing the functional dependence between the dependent and independent variables
(IV1-3) are given below.
The expected mean value of the dependent variable was calculated
based on the previous equations and the following results were obtained:
Port Bar
2,61
Port Kopar
3,07
Port Rijeka
3,56
189
According to the analysis of the linear dependence between the dependent variable and the mean values of the independent variables at the
level of all ports (Figure 15), it is clear that the variable IV1 has the most
pronounced influence on the dependent variable. Then variable IV2, and
then IV3. These analyzes were done over the entire sample. The rank of
influence in individual ports is the similar. Based on the analysis of the
individual sample in the considered seaports, the rank of the influence of
the independent on the dependent variable given in Table 2 was obtained.
Figure 15. Dependencies between the dependent variable and the mean
values of the independent variables at the level of all observed
Adriatic seaports
Source: own creation
190
In accordance with the mean values of the independent variables (Table 18), it can be concluded that the greatest limitations in terms of the
level of possible business-partnership cooperation characterize the seaport of Bar. This is understandable, given that the most pronounced negative impact of the observed factors (independent variables), where it is
most pronounced in the institutional, infrastructural, suprastructural and
corporate factors (IV1), as well as the negative impact of the existing level
of logistics services (IV2), while the negative impact of political and economic barriers (IV3) is moderate (Ibid.).
At the seaport of Koper, the smallest perceptual limitations in terms
of business-partnership cooperation were identified, ie the greatest perceptual possibilities. The pre-performance resulted from the fact that it is
characterized by the least negative influences on all tree independent variables. Nevertheless, it is noticeable that the greatest negative impact was
shown in infrastucture (IV1) and logistic (IV2), and the least in political and
economic barriers (IV3).
Table 18. Mean values of independent variables (I1-3) and the rank of
their impact on the dependent variable (DV)
Source: own
On the other hand, at the seaport of Rijeka, moderate perceptual negative influences were manifested in terms of the level of possible business-partnership cooperation. They are significantly smaller (more favorable) in relation to the seaport of Bar, but they are also partially larger
(less favorable) in relation to the seaport of Koper (Ibid.).
  
191
In the theoretical part of this paper, three factors have been identified,
which have a dominant negative impact on the establishment of business
cooperation between the considered Adriatic seaports of Bar, Koper and
Rijeka. In the empirical part of the paper, a strong connection was found
between the independent variables and the dependent variable.
The results of the research and their analysis confirmed that their influence is different in the considered seaports, but that in all of them at a
certain level it has a negative effect on the dependent variable.
The statistical analysis of the data showed that there is a linear relationship between the dependent variable and the independent variables.
Multiple linear regression analysis determined the functional relationship
between the dependent variable and the three independent variables.
The set relationship model enables the prediction of a change in the
level of business cooperation if some or all of the independent variables
change. It was confirmed that the independent variables largely explain
the relatively low level of possible business cooperation between the considered seaports. Thus, the perception of the respondents and the results
obtained after statistical data processing show the correctness of the initial hypothesis of the work, as well as the auxiliary hypothesis.
Empirical hypothetical research, conducted through a survey of 210
respondents (70 in each of these seaports) showed that the greatest limitations in terms of the level of possible business-partnership cooperation
characterize the seaport of Bar. This is due to the fact that it has the most
pronounced negative impact of the two observed factors (independent
variables),
At the seaport of Koper, survey research and analysis identified the
smallest perceptual limitations in terms of business-partnership cooperation, i.e. the greatest perceptual possibilities. It is anterior because it is
characterized by the smallest negative influences on all four independent
variables.
At the seaport of Rijeka, moderate perceptual negative influences
were manifested in terms of the level of possible business-partnership cooperation. They are significantly smaller (more favorable) in relation to the
seaport of Bar, but they are also partially larger (less favorable) in relation
to the seaport of Koper. The analyzes showed that the largest (but also
approximate) negative impact was shown by the infrastructural factors
(IV1).
192
These conclusions point to the general conclusion that in all the considered Adriatic seaports it is necessary to invest large investment, organizational and other efforts in order to improve certain factors influencing
the dependent variable. This is especially true of the Montenegrin seaport
of Bar.
In the theoretical part of this paper, three factors have been identified,
which have a dominant negative impact on the establishment of business
cooperation between the considered Adriatic seaports of Bar, Koper and
Rijeka. In the empirical part of the paper, a strong connection was found
between the independent variables and the dependent variable. The results of the research and their analysis confirmed that their influence is
different in the considered marine figures, but that in all of them at a certain level it has a negative effect on the dependent variable.
193
194
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Essays on small seaports: contemporary changes, tendenciesand
perspectives for the development
Monograph
Authors
© Assistant Prof. Dr. Ranka Krivokapic, Maritime Faculty of Kotor, University of Montenegro, Podgorica Montenegro
Assistant Prof. Dr. Milica Delibasic, Faculty of Business Studies, Podgorica, Mediterranean University, Podgorica, Montenegro; Maritime Faculty of
Kotor, University of Montenegro, Podgorica Montenegro; Faculty of Mediterranean Business Studies Tivat, Niksic study centre
Prof. Dr. Mimo Draskovic, Maritime Faculty of Kotor, University of Montenegro, Podgorica Montenegro
Reviewers:
Prof. Dr. Borut Jereb
University of Maribor, Faculty of Logistics, Celje, Slovenia
Prof. Dr. Drago Pupavac
University of Rijeka, Croatia
215
Author is responsible for content and language qualities of the text. The
publication is protected by copyright. Any reproduction of this work is possible only with the agreement of the copyright holder. All rights reserved.
1st Edition
© SPH - Scientific Publishing Hub, Celje 2023
Suggested citation:
Krivokapic, R, Delibasic, M., Draskovic, M. (2023), Essays on small seaports: contemporary changes, tendencies and perspectives for the development, SPH - Scientific Publishing Hub, Celje.
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