Assessment of Factors Influencing the Development of Inland Navigation in Poland

Abstract

The presented article concerns the development of inland navigation in Poland. The aim is to determine and analyse factors that influence the development of this branch of transportation. To determine the parameters that are subject to further analysis, the author analysed publications on the development of inland waterway transport. Then, three groups of respondents (scientists, shipping administration employees, and inland navigation captains) evaluated seven selected factors (demand, operational parameters, ports, service, waterways improvement, fleet, and crew) and their mutual influence. Responses were analysed using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. According to the results, the factors influencing inland navigation to the greatest extent are demand, operational parameters, and waterways improvement; at the same time, all these factors were evaluated as influencing the other factors. This means that by modelling these factors, e.g., through political tools, one can achieve the fastest and greatest impact on the development of inland navigation in Poland. The factors assessed as the most important, at the same time being identified as influencing others (not being their effect), are the factors that should be taken into account first, because they can have the fastest and greatest impact on the development of inland navigation in Poland.

1. Introduction

In recent years, industry and science have put great emphasis on sustainable development and ecological awareness. Part of these activities is broadly understood to be low-emission logistics and low-emission supply chains. Both are strongly connected to transportation. Transporting goods influences the natural environment, and this fact is considered by society and politics. Grzelakowski [1] indicates that increasing the role of inland navigation in freight transport in the European Union, and thus the development of transport based on the assumptions of sustainable mobility and reducing the carbon footprint, is possible only through significant investments in infrastructure resulting from the European policy. Wojewódzka-Król [2] shows the connection between inland navigation and the lives of residents. Ensuring conditions for transporting goods via inland waterways influences the development of the region, improves air quality, and develops water tourism, contributing to the broadly understood quality of life of residents of areas adjacent to waterways. The impact of the development of inland navigation on the natural environment was described in [3] by Maruszczak. The author identifies the main EU programmes aimed at unifying the EU transport space through the development of inland navigation.

The European Union finds the potential to influence global warming in reducing emissions from transport. On a large scale, it can be achieved by transferring cargo from cars to more environmentally friendly branches of transportation. According to the White Paper on Transport [4], the European Union aims to reduce the emissions of harmful substances related to transport through one of its targets. This means that by 2030, at least 30% of cargo transported over distances longer than 300 km (and by 2050, more than 50%) should be transported by rail, sea, or inland waterways. This is intended to reduce the number of vehicles that transport loads over long distances.

Inland waterway transportation is the branch with the lowest emission values of harmful substances into the environment among continental means of transport, but it also has the greatest limitations. Inland navigation uses natural river networks, but it also requires investment to ensure appropriate navigation parameters and maintain a constant depth of transit. It also has the least accessibility to its waterways network. In the case of transport from and to a point not located in the immediate vicinity of a waterway, pickup and delivery operations are necessary. In addition, the course of the waterway often does not coincide exactly with the direction of transport. Therefore, this extends the transport route. It is also important that the average transport speed using this mode of transport is relatively low.

However, relatively low transport costs, low energy consumption, low number of accidents, and the possibility of transporting oversized goods (due to linear dimensions and weight) give inland navigation certain advantages.

The geographical conditions of Poland create favourable conditions for inland navigation. In 2021, the national waterway network covered 3768 km, which gives a relatively high network density rate: there are 12.1 km navigable roads per 1000 km² (in the EU-27, on average, 10.0 km/1000 km²). Unfortunately, however, the national waterway network is characterised by low operational parameters (only 5.5%, 205.9 km of waterways have parameters of classes of international importance). The vast majority of them are roads of regional importance. The inland waterway fleet used in Poland is outdated. Almost 70% of the push boats and all self-propelled barges are over 40 years old. Barges without their own drive systems are over 15 years old and the operation of the rolling stock is possible thanks to its constant modernisation. In 2021, the number of barges without their own drive systems was 174; pushers and tugs, 124; and self-propelled barges, 71 [5]. The structure of transported cargo in 2022 was dominated by goods from the metal ore and other mining and quarrying products group (29.6%), followed by agricultural, hunting, forestry, fishing, and fishing products (14.9%), as well as brown and hard coal, crude oil, and natural gas (12.1%) [6]. These goods belong to the group of bulk goods and such loads constitute the vast majority in Poland. However, there have been attempts (successfully completed) to transport containers on both important rivers in the country (Odra and Vistula).

Inland navigation in postwar Poland had its glory days in the 1980s, and in recent years, its importance has been marginal. In 1980, inland navigation in Poland transported 22 million tons of cargo [7], and in 2022, 2 million tons (the share of inland waterways transport in total cargo transport in Poland in 2022 was 0.09%). In 2022, the average distance of transporting 1 ton in domestic transport was 44.8 km. Taking into account the specificity of this branch of transport, these distances are very short [6]. However, there are groups of scientists, politicians, and entrepreneurs who are aiming to restore the significance of inland waterway transportation in Polish trade transportation.

The aim of this article is to analyse and evaluate the factors influencing the development of inland navigation in Poland. Selecting the most important factors and assessing their impact on the development of inland navigation in Poland will allow us to determine which factors can have the fastest and greatest impact on the development of inland navigation in Poland.

In this article, the author analysed publications on the assessment and development of inland waterway transportation (Section 2), on which factors influenced its development in Poland were selected (Section 3). The selected factors were evaluated by three groups of respondents (scientists, shipping administration employees, inland navigation captains), and their answers were analysed using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method (Section 4). The results of the analyses are discussed in the final part of this publication (Section 5).

The new approach presented in this article is the use of the DEMATEL method to analyse the assessments of various stakeholder groups related to inland navigation in Poland. Gathering different voices on this matter can provide a more complete picture of the topic and allow determining the required courses of action to increase the share of inland navigation in freight transport in Poland.

2. Literature Review

Despite its marginal share in transport volumes in Poland, inland waterway transport is often the subject of analyses and political discussions. Similarly, Polish scientists often discuss this topic. This topic is considered important.

In the scientific literature of the world, gathered in the Web of Science database [8], there are thousands of papers referring to inland waterway transportation.

Table 1. Number of articles on the topic of inland waterways and its development, published in the last ten years. Based on [8], the data collection date is 8 November 2023.

Topic Search	Number of Responses	Reviewed	2013–2023
‘river transport’ + ‘development’	5394	290	179
‘inland navigation’ + ‘development’	260	13	9
‘inland waterway transport’ + ‘development’	248	12	9

presents a number of responses to three different names of this branch of transport plus development. Narrowing the list to peer-reviewed articles published in the last 10 years, 185 responses were received (after removing duplicates).

The published works collected included those whose main topic was not inland navigation. Among the records found in the manner described above, most are related to climate and meteorology (12), hydrology (13), geology (23), riverbed movement and chemistry (49), ecology and environment (62), and other (14). In total, 173 of the 185 papers did not cover the expected topic. The remaining 12 refer to navigation of autonomous ships [9]; other 4.0 technologies applied to inland waterway transport [10]; operating parameters and their influence on environmental issues, specifically: climate changes [11,12,13,14], sustainable development of inland waterway transport [15]; and development of point and linear infrastructure, specifically: waterways [14,16,17,18]; flood risk assessment [19], and collisions of ships with lock gates [20].

Due to the limited scope of the results obtained from the literature review in terms of inland water transport, the review was expanded to include results containing the terms “transport” and “assessment method” in the category of “topic”. Additionally, due to the research method chosen in this publication, it was decided to add the phrase “DEMATEL” to the search in the “topic” category, to examine publications concerning transportation topic by using this method. This gave 17 responses in the range 2014–2023. The search was repeated for the category “topic” and the terms “DEMATEL” as well as “transport” and “evaluation method”. This resulted in 60 responses between 2010 and 2023. Excluding duplicates, both of these queries resulted in 64 publications.

Described in these publications, the usage of the DEMATEL method included the fields of means of transport (9), infrastructure (9), safety and security (7), service and its quality (21), other (9), and operation and development of system of transport (9). Publications from the last group refer to using the DEMATEL method to analyse transport systems.

More specifically, Rajak, Parthiban, and Dhanalakshmi [21] describe a model that analyses the causes of barriers to sustainable transport in India and the priorities of its development. The model takes into account economic, social–political, environmental, and technical aspects. Of the 22 parameters, the most influential were economic benefits, customer priorities, transport diversity, safety and reliability, and energy security.

Trivedi, Jakhar, and Sinha [22] also refer to India, and based on a literature review, examine 10 barriers to the implementation of inland waterways transport. The analysis results point out the interconnection of rivers, the navigation infrastructure, the cost requirements, and the presence of terminals as the most significant.

A freight transport system performance measurement, based on the evaluation of the performance of seven freight transport companies in Spain, was presented by Yazdani et al. in [23]. The described research points out that the most important criteria are sustainable freight transport and health and safety, followed by personnel training.

An intermodal rail freight transport was examined by Kumar and Anbanandam [24]. The article aims to present 22 inhibitors of intermodal railroad freight transport and the interrelationships between them, in the case of the Indian freight industry. According to research, the inhibitors that have the greatest influence on intermodal railroad freight transport are transport infrastructure, frequency of freight trains, reliability of the mode, competition between transport modes, and a holistic transportation business model.

The next analysed paper refers to smart urban rail transit [25]. Xue et al. analyse the possibility to employ intelligent technologies to urban rail transit industry by analysing 14 indicators specified based on the literature review. The results show that the smart supply chain, the intelligent operation, and the planning of the line network are the key factors for the sustainable development of urban rail transit.

Turan and Ozturkoglu [26] analyse challengers that affect the performance of the sustainable supply chain. The results of this study show that, of the nine factors analysed, the storage specifications and handling practices, transportation and shipping, and traceability are the most important in this case.

The influence of 18 selected factors on a strategic transport management process is described in [27] by Dimić et al. The most important factors, according to experts assessing factors in this study, are organisational restructuring, state project subsidies that support sustainable transport development (in the Republic of Serbia, in this case), and improving market competition.

Stoilova and Kunchev [28] examined intermodal passenger and freight transport. The results of the presented research show that among 11 examined criteria of evaluating transportation, the most important were operational costs for intermodal transportation, duration of transportation, and transport from door to door.

The last paper of this group [29] by Farooq, Xie, Stoilova, and Williams, refers to smart urban mobility. The authors present criteria for the assessment of smart mobility, and out of seven, their results point to mobility, connectivity, and the environment as the most important.

The research described in this paper concerns Poland; therefore, the literature review was extended to include Polish authors in order to address their considerations in the context of the development of inland navigation.

Some of the authors, e.g., Abramowicz-Gerigk, Burciu, and Jachowski [30] are concerned about ships and their innovative technical aspects, influencing the waterways environment. Abramowicz-Gerigk et al. [30] point out problems with waterway operating parameters and propose the solution, helping to navigate in shallow water with respect to the Natura 2000 network (areas with protected environments). Another publication [31] by Załoga and Kuciaba emphasises the fact that insufficient financing of the infrastructure of inland waterways prevents its usage from increasing. Investments in inland waterways do not meet real needs.

The article by Wojewódzka-Król [2] analyses the relationship between the development of inland navigation and socioeconomic aspects. The author points out that the lack of stable navigation conditions is a significant block to the development of inland navigation. Initiatives to include water transport in urban logistics depend on infrastructure investments that are beyond the control of local governments. Therefore, support for the development of inland navigation would be a comprehensive and coherent idea that would cover the entire country and be implemented from above. This publication also draws attention to an important aspect of the use of inland navigation in the transport services of seaports. The next article [32] by Kaizer et al. address the same problem, similarly to Kotowska, Mańkowska, and Pluciński [33]. In addition to noting the benefits of implementing inland navigation to service seaports, the authors emphasise that it is important to properly plan activities and assign their implementation to individual stakeholders. The need to revitalise the waterway infrastructure is also emphasised.

Based on the presented articles, the areas and factors related to transport development that are the most frequently analysed are shown in

Table 2. Factors related to the development of different modes of transport, analysed in the literature.

	Linear Infrastructure	Point Infrastructure	Management of Infrastructure	Operational Parameters	Environmental Factors	Costs of Transportation	New Technology	Safety and Reliability	Fleet and Crew	Comments
K. Wojewódzka-Król, “Inland water transport in the light of contemporary social and economic problems” [2]		x	x	x		x		x
C. Liu et al., “Human–machine cooperation research for navigation of maritime autonomous surface ships: A review and consideration” [9]			x	x			x		x
J. F. Restrepo-Arias et al., “Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review” [10]							x
S. A. Némethy et al., “Environmental Viability Analysis of Connected European Inland–Marine Waterways and Their Services in View of Climate Change” [11]				x	x
V. Steege et al., “Germany’s federal waterways—A linear infrastructure network for nature and transport” [12]					x
G. Luburić, D. Budimir, and I. Bortas, “Transport technology in the function of water transport development in the republic of Croatia” [13]		x	x	x	x
Y. Pokhrel et al., “A review of the integrated effects of changing climate, land use, and dams on Mekong river hydrology” [14]	x	x		x	x
B. R. C. de Barros, E. B. de Carvalho, and A. C. P. Brasil Junior, “Inland waterway transport and the 2030 agenda: Taxonomy of sustainability issues” [15]		x	x		x					12 different issues analysed
F. Bu and H. Nachtmann, “Literature review and comparative analysis of inland waterways transport: ‘Container on Barge’” [16]	x	x							x
B. Duldner-Borca, E. van Hassel, and L. M. Putz-Egger, “Understanding the effects of resolving nautical bottlenecks on the Danube: a KPI-based conceptual framework” [17]	x	x	x
P. Negi, R. Kromanis, A. G. Dorée, and K. M. Wijnberg, “Structural health monitoring of inland navigation structures and ports: a review on developments and challenges” [18]	x	x
F. Perosa, L. F. Seitz, A. Zingraff-Hamed, and M. Disse, “Flood risk management along German rivers—A review of multi-criteria analysis methods and decision-support systems” [19]			x	x				x		16 different aspects analysed
S. Ehlers et al., “A review of technical solutions and simulation approaches for ship collisions with lock gates” [20]	x							x	x
S. Rajak, P. Parthiban, and R. Dhanalakshmi, “Analysing barriers of sustainable transportation systems in India using Grey-DEMATEL approach: A supply chain perspective” [21]			x	x	x			x		different modes of transport
A. Trivedi, S. K. Jakhar, and D. Sinha, “Analyzing barriers to inland waterways as a sustainable transportation mode in India: A dematel-ISM based approach” [22]	x	x	x	x
M. Yazdani, D. Pamucar, P. Chatterjee, and S. Chakraborty, “Development of a decision support framework for sustainable freight transport system evaluation using rough numbers” [23]						x		x	x	different modes of transport
A. Kumar and R. Anbanandam, “Evaluating the interrelationships among inhibitors to intermodal railroad freight transport in emerging economies: A multi-stakeholder perspective” [24]	x	x	x					x		rail
X. Xue et al., “Evaluation on Sustainable Development of Smart Urban Rail Transit” [25]	x		x				x			rail
C. Turan and Y. Ozturkoglu, “Investigating the performance of the sustainable cold supply chain in the pharmaceutical industry” [26]	x		x				x			supply chain
S. Dimić, D. Pamučar, S. Ljubojević, and B. Dorović, “Strategic transport management models-the case study of an oil industry” [27]			x			x				supply chain
S. Stoilova and L. Kunchev, “Study of criteria for evaluation of transportation with intermodal transport” [28]			x			x		x		rail-road
A. Farooq, M. Xie, S. Stoilova, and E. J. Williams, “The Application of Smart Urban Mobility Strategies and Initiatives: Application to Beijing” [29]					x		x			urban mobility
T. Abramowicz-Gerigk, Z. Burciu, and J. Jachowski, “An Innovative Steering System for a River Push Barge Operated in Environmentally Sensitive Areas” [30]	x			x	x		x		x
E. Załoga and E. Kuciaba, “Financing of inland navigation development in Germany and Poland in a context of competitive and resource efficient transport system” [31]	x		x			x
A. Kaizer, M. Winiarska, K. Formela, and T. Neumann, “Inland Navigation as an Opportunity to Increase the Cargo Capacity of the Tri-City Seaports” [32]	x	x
I. Kotowska, M. Mańkowska, and M. Pluciński, “Planning the development of inland shipping in the sea-port-hinterland transport: A case study of the Oder River in Poland” [33]	x	x				x

. If a document referred to a branch other than inland waterway navigation, it was marked in the column ‘comment’. Among the publications that analyse larger factors’ spectra, Table 2 indicates those that, according to the authors of the cited publications, were the most important. The areas and factors identified in the literature were divided into nine groups:

• Linear infrastructure—its condition, costs, and sources of financing;
• Point infrastructure—transshipment infrastructure, ship service areas, transshipment volumes;
• Management of infrastructure–decision makers, management methods, infrastructure efficiency;
• Operational parameters—technical parameters of the linear infrastructure (for inland navigation mainly transit depth), traffic;
• Environmental factors—aspects related to the impact of transport and its infrastructure on the natural environment and vice versa;
• Costs of transportation—aspects related to the profitability of transportation from the point of view of customers and service operators;
• New technology—the use of new technologies for transport (in inland navigation, mainly for navigation purposes and vehicle construction);
• Safety and reliability—aspects related to the safety of supplies and people participating in the process and process failure;
• Fleet and crew—aspects related to the fleet, its condition, number and type, and crews—their availability, qualifications, and costs.

Of the factors included in Table 2, for the 26 analysed publications, infrastructure management appears in 14 of them, linear infrastructure in 13, and point infrastructure in 11. Operational parameters were analysed in nine papers, environmental factors in eight, and safety and reliability in six. The costs of transportation and new technology appear each in six publications and fleet and crew in five. All these factors were the subject of further analysis.

3. Selected Factors That Influence the Development of Inland Navigation in Poland

The areas and factors summarised in Table 2 concern the development of different modes of transport and different geopolitical areas. The planned research was to focus on the Polish issue, so it was necessary to adapt the factors to local conditions. For this purpose, the factors listed in Table 2 were analysed and discussed with scientists, specialists in the field of freight transport using inland navigation in Poland [34].

Based on these conversations, it was decided that “linear infrastructure” would be examined separately as operational parameters of waterways (which indicate the size of ship and size of cargo it can transport), and waterways improvement, due to the significant impact of current navigation conditions on the feasibility of transport and the potential opportunities and benefits resulting from infrastructure renovation. Similarly, the “point infrastructure” was divided into the availability of inland ports with suprastructure and the availability of other ships and crew service areas, due to the different nature of these alignments and the expected different assessment of these factors. “Management of infrastructure” was incorporated into the waterways improvement (plans of it and its realisation) and partially into the operational parameters mentioned before. The ‘operational parameters’ have been respected, but the scope of its understanding has been expanded (to include partially ”linear infrastructure”, ”management of infrastructure”, and ”safety and reliability”). “Environmental factors” were excluded from further analysis due to the inability to quickly influence this parameter (the purpose of the study is, among others, to identify and analyse factors that can influence the development of inland navigation in Poland). The “costs of transportation” factor was extended to demand on transport by inland waterways, and analysed in such a general form. The factor of “new technology” has been narrowed down to issues related to waterways improvement and fleet, due to the very broad scope of this concept and, at the same time, the lack of purposefulness of considering this factor in isolation from the context of its use. Similarly, “safety and reliability” was considered to be partly within operational parameters and waterways improvement; at the same time, there is no point in considering this factor in isolation from the context of its use, but also the relatively high safety of using inland navigation in Poland, and the known, low reliability—closely related to operational parameters. “Fleet and crew” was separated to the availability of a fleet suitable for transporting cargo and a crew with specific qualifications, because it was considered worth analysing these parameters separately.

The scope of understanding of the identified factors was defined more specifically:

(1)

The reported (e.g., by the Polish Central Statistical Office [5,7]) and potential (studied, e.g., by Nowakowski et al. [35], Wojewódzka-Król and Rolbiecki [36], and Pluciński et al. [37]) demand for cargo transport services by inland waterways carried out in Poland (demand).

(2)

Maintaining the operational parameters of the waterways in accordance with the Regulation of the Council of Ministers of 7 May 2002 on the classification of inland waterways [38], for at least 240 days a year. Understood as the ability to ensure adequate damming even during annual floods and droughts, for a minimum of 240 days a year (operational parameters).

(3)

The availability of ports, reloading facilities, storage yards, transshipment facilities, etc., i.e., the infrastructure and suprastructure needed for the logistic handling of cargo, along with connections with other modes of transport (ports).

(4)

The availability of fleet and crew service areas, i.e., the availability of repair yards, fuel, fresh water and supply collection points, waste collection points, and safe parking places (including night parking) (service).

(5)

The improvement of parameters of waterways, understood as visible and consistent actions ensuring—within a specified time frame—increasing the parameters of waterways. Certainty that shipping investments will be implemented in accordance with the plan and that the sections of waterways provided for in these plans will achieve the assumed parameters within a specified period of time (waterways improvement).

(6)

The availability (on Polish waterways) of a fleet suitable for transporting cargo, in navigation conditions consistent with the Regulation of the Council of Ministers of 7 May 2002 on the classification of inland waterways [38] (fleet).

(7)

The availability of crews with qualifications consistent with demand (e.g., authorisation to transport dangerous goods) (crew).

The above factors were collected in a matrix and analysed using the DEMATEL method. The description of the study is included in the next chapter.

4. Analysis of Selected Factors Using the DEMATEL Method

To conduct the research, the DEMATEL method was used. This method was chosen because it allows the examination of cause-and-effect relationships between the factors under study. This allows the identification of key elements that have the greatest impact on the overall system, which may be difficult to achieve using other methods. Additionally, DEMATEL allows for using subjective data, i.e., expert opinions. This is particularly useful in a selected area (development of inland navigation in Poland), where hard quantitative data is lacking and it is difficult to compare the factors due to their heterogeneous nature.

Other methods from the Multi-Criteria Decision-Making (MCDM) group have other areas of application. These include the following: (i) TOPSIS—based on quantitative data and requires precise definition of criteria and alternatives; working with subjective data is more limited; it focusses on ranking alternatives in relation to ideal solutions but does not analyse direct relationships between criteria and does not offer tools to reduce system complexity. (ii) AHP—handles subjective data well, using pairwise comparisons and expert opinions to assess the validity of criteria; effective in analysing hierarchical problem structures, but less effective in the case of complex dependencies between criteria; it does not provide direct tools for cause-and-effect analysis. (iii) ANP—deals with subjective data, but the evaluation process can be more complicated due to the network structure of relationships; it enables the analysis of dependencies, but is less intuitive in identifying cause-and-effect relationships compared to DEMATEL.

The DEMATEL shortcut stands for DEcision MAking Trial and Evaluation Laboratory. It is a multiple-criteria decision-making method first developed in Switzerland for the Science and Human Affairs Programme. The main goal of the procedure was to define the cause-and-effect relationships between selected factors, the degree of influence of the factors, and to identify the factors that cause other problems to occur [39]. Since 1970, the method has also been in constant use outside the socioeconomic field (e.g., in the transportation supply chain [21,22,23,24,25,26,27,28]).

The DEMATEL method, to express the direct impact in relation to a pair of elements, uses a matrix. Subsequent numbers represent the intensity of influence of the i-th element on the j-th element (the author of the research used a scale of 0 to 3, understood as 0—lack of influence, 1—small influence, 2—medium influence, 3—large influence), marked with the symbol x*ij, where: i, j = 1, 2…n, where n is the number of system elements (examined factors, i.e., n = 7).

The $X^{*}$ matrix shows the direct impact structure.

$$X^{*}=\left[\begin{array}{ccccc}0& x_{12}^{*}& x_{13}^{*}& \dots & x_{1n}^{*}\\ x_{21}^{*}& 0& x_{23}^{*}& \dots & x_{2n}^{*}\\ x_{31}^{*}& x_{32}^{*}& 0& \dots & x_{3n}^{*}\\ ⋮& ⋮& ⋮& \ddots & x_{n-1n}^{*}\\ x_{n1}^{*}& x_{n2}^{*}& x_{n3}^{*}& \dots & 0\end{array}\right]$$

(1)

The transformed standardised matrix of direct impact $X$ is then calculated using a maximum sum of rows of elements of the $X^{*}$ matrix.

$$X={\displaystyle \frac{1}{\max \left\{{\displaystyle \sum _{j=1}^n{x}_{ij}^{*}}\right\}}}\cdot X^{*}$$

(2)

The total impact structure is determined by the total impact matrix T = X(I − X)⁻¹, and I is a unit matrix consistent with $X$.

On the basis of those calculations, two important indicators accrue: s⁺ and s⁻, calculated as a row and column sums of the matrix T. The s⁺ indicator represents the overall influence and expresses the importance of the element, while s⁻ is a net influence and determines if the factor is considered causal (s⁻ > 0) or effect (s⁻ < 0).

The selected factors (described in Section 3) and their influence on each other were evaluated by three groups of respondents: (1) inland navigation captains (professionally active for more than 15 years, professionally educated in Poland, and familiar with the situation of Polish inland navigation), (2) shipping administration employees (employed for more than 15 years in government administration bodies—Polish Waters, Inland Navigation Offices—in management positions related to inland navigation and/or waterway navigability), and (3) scientists (publishing and conducting research in the field of inland navigation for more than 15 years, with at least a Ph.D. degree, currently employed by Polish universities). Selected groups of respondents were assessed as related to the research topic and having appropriate knowledge. Additionally, the comparison of opinions of these three groups allows for obtaining results assessed from different perspectives—users, managers, and researchers.

The survey was conducted online. Links to participate were sent directly to specific groups of respondents, keeping them separate (10 links were sent to representatives of each group). Participation was voluntary and anonymous.

The survey began with presenting the purpose of the study (determining the relationship between selected factors affecting the development of inland navigation in Poland), information about the voluntary and anonymous nature of the survey, the expected time to complete the form, and developing descriptions of the factors used (as in Section 3 of this article). To eliminate errors related to determining whether factor (1) affects factor (2) or factor (2) affects (1), which could appear when filling out the matrix, the study divided the questions into seven groups, determining the impact of each factor on the remaining factors.

In each group, respondents assessed the impact of the selected parameter (each of the seven in turn) on the other six (a total of 42 assessments were made), selecting the strength of their influence from four possible answers: 0—lack of influence, 1—small influence, 2—medium influence, 3—large influence.

Due to the fact that the survey was conducted in Polish, the author does not include screenshots, but a translated sample of the survey is presented in

Table 3. A translated sample of the conducted survey. Source: own work.

Please Determine the Impact of (1) Demand on:	0—Lack of Influence	1—Small Influence	2—Medium Influence	3—Large Influence
(2) operational parameters	o	o	o	o
(3) ports	o	o	o	o
(4) service	o	o	o	o
(5) waterways improvement	o	o	o	o
(6) fleet	o	o	o	o
(7) crew	o	o	o	o

. The form allowed selection of only one influence value in each row.

Twenty-three responses were received. After removing answers considered invalid (due to the same answers to all questions) the remaining 20 were the basis of DEMATEL analysis. The arithmetic averages of those answers were calculated in each field of the X* matrix. The matrixes built based on the responses of the respondents are presented in

Table 4. The direct impact matrix for selected factors for the development of inland navigation in Poland assessed by inland navigation captains. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0	3	2.75	2.5	2.75	2.25	1.875
(2) operational parameters	2.125	0	2.75	2.125	2.625	2.125	1.625
(3) ports	2.25	2.375	0	2.125	2.375	1.875	1.25
(4) service	1.875	1.875	2	0	1.875	1.875	1.125
(5) waterways improvement	2.625	2.875	2.625	2.125	0	2.375	1.375
(6) fleet	2.75	2	2	2	2.125	0	1.75
(7) crew	1.625	1.75	1.5	1.625	1.75	2.125	0

for the inland navigation captains,

Table 5. The direct impact matrix for selected factors for the development of inland navigation in Poland assessed by employees of the shipping administration. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0	2.625	2.375	2.25	2.5	2.25	1.75
(2) operational parameters	2.625	0	2.5	2.625	2.5	1.5	1.375
(3) ports	2.25	2.25	0	2.25	1.75	1.75	1.25
(4) service	2	2.125	1.625	0	1.625	2.625	1.75
(5) waterways improvement	2.125	2.5	2.375	2.375	0	2.5	1.875
(6) fleet	2.25	1.625	1.875	2.125	1.375	0	2
(7) crew	1.875	1.375	1.5	1.5	1.25	2.375	0

for the shipping administration employees,

Table 6. The direct impact matrix for selected factors for the development of inland navigation in Poland assessed by scientists. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0	2.25	3	2	2.5	2.5	2.25
(2) operational parameters	2.75	0	2.5	2	1.25	2	1.75
(3) ports	2.5	2.5	0	2.25	1.5	1.25	1.25
(4) service	1.25	1.25	1.25	0	1	2	1.25
(5) waterways improvement	2.5	1.5	2	1.75	0	2	1.75
(6) fleet	2	1.5	1.75	1.5	1.75	0	2.25
(7) crew	1.5	0.75	0.75	0.75	0.75	2.25	0

for scientists, and

Table 7. The direct impact matrix for selected factors of the development of inland navigation in Poland evaluated by all groups of responders. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0	2.625	2.708	2.250	2.583	2.333	1.958
(2) operational parameters	2.5	0	2.583	2.250	2.125	1.875	1.583
(3) ports	2.333	2.375	0	2.208	1.875	1.625	1.250
(4) service	1.708	1.75	1.625	0	1.5	2.167	1.375
(5) waterways improvement	2.417	2.292	2.333	2.083	0	2.292	1.667
(6) fleet	2.333	1.708	1.875	1.875	1.750	0	2
(7) crew	1.667	1.292	1.25	1.292	1.25	2.25	0

for all groups of respondents.

As can be seen, there is one answer rated 3 by all responders. In the opinion of all responders, operation parameters have a large influence on demand for inland waterway transportation. The lowest marks are (4) service on (7) crew (1.125), (3) ports on (7) crew (1.25), and (5) waterways improvement on (7) crew (1.375). In fact, all the influence of the parameters on (7) crew was rated by the captains of the ships below 2. And the influence of (7) crew on other parameters was also rated below 2, except the (6) fleet (2.125). This leads to conclusion that ship captains do not rate like they would have a large influence on examined parameters, and the parameters do not have a large influence on them. The highest rates had the influence of (1) demand (except (1) demand on (7) crew) and the influence of (5) waterways improvement (except influence on (7) crew).

Shipping administration employees gave the highest (2.625) rates to the influence of (1) demand on (2) operational parameters; the (2) operational parameters on (1) demand and on (4) service; and (4) service on (6) fleet. Similarly, as inland navigation captains, shipping administration employees rated low (below 2) the influence of (7) crew and on (7) crew (except (7) crew on (6) fleet: 2.375).

Scientists generally gave the lowest rates, which may be interpreted as that they see the lowest correlation between examined parameters. All scientists gave the highest rate (3) to influence (1) demand on (3) ports, and the second highest rate (2,75) got the influence of (2) operational parameters on (1) demand. Rates below 1 (meaning that some of the responders gave rate 0) were given to the influence of (7) crew on (2) operational parameters, (3) ports, (4) service and (5) waterways improvement. Low rates were also given to the influence of (4) service (1.25, except influence on (5) waterways improvement: 1, and (6) fleet: 2).

All respondent groups rated the highest influence of (1) demand on (3) ports 2.708, on (2) operational parameters 2.625, and on (5) waterways improvement 2.583). Furthermore, the influence of (2) operational parameters on (3) ports was rated above 2.5 (2.583). Likewise, in particular groups, the influence of (7) crew got low rates (below 2, except the influence on (6) fleet 2.25), and the influence on (7) crew also got low rates (below 2, except (6) fleet 2). Also, the influence of (4) service on other parameters was low-rated (below 2, except (6) fleet 2.167).

The transformed standardised matrixes X of direct impact, calculated using the equation (2) for separated groups, and all groups together are presented in

Table 8. The standardised direct impact matrix for selected factors of the development of inland navigation in Poland evaluated by inland navigation captains. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0.000	0.198	0.182	0.165	0.182	0.149	0.124
(2) operational parameters	0.140	0.000	0.182	0.140	0.174	0.140	0.107
(3) ports	0.149	0.157	0.000	0.140	0.157	0.124	0.083
(4) service	0.124	0.124	0.132	0.000	0.124	0.124	0.074
(5) waterways improvement	0.174	0.190	0.174	0.140	0.000	0.157	0.091
(6) fleet	0.182	0.132	0.132	0.132	0.140	0.000	0.116
(7) crew	0.107	0.116	0.099	0.107	0.116	0.140	0.000

,

Table 9. The standardised direct impact matrix for selected factors of the development of inland navigation in Poland evaluated by employees of the shipping administration. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0.000	0.191	0.173	0.164	0.182	0.164	0.127
(2) operational parameters	0.191	0.000	0.182	0.191	0.182	0.109	0.100
(3) ports	0.164	0.164	0.000	0.164	0.127	0.127	0.091
(4) service	0.145	0.155	0.118	0.000	0.118	0.191	0.127
(5) waterways improvement	0.155	0.182	0.173	0.173	0.000	0.182	0.136
(6) fleet	0.164	0.118	0.136	0.155	0.100	0.000	0.145
(7) crew	0.136	0.100	0.109	0.109	0.091	0.173	0.000

,

Table 10. The standardised direct impact matrix for selected factors of the development of inland navigation in Poland evaluated by scientists. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0.000	0.155	0.207	0.138	0.172	0.172	0.155
(2) operational parameters	0.190	0.000	0.172	0.138	0.086	0.138	0.121
(3) ports	0.172	0.172	0.000	0.155	0.103	0.086	0.086
(4) service	0.086	0.086	0.086	0.000	0.069	0.138	0.086
(5) waterways improvement	0.172	0.103	0.138	0.121	0.000	0.138	0.121
(6) fleet	0.138	0.103	0.121	0.103	0.121	0.000	0.155
(7) crew	0.103	0.052	0.052	0.052	0.052	0.155	0.000

and

Table 11. The standardised direct impact matrix for selected factors of the development of inland navigation in Poland evaluated by all groups of responders. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	0.000	0.177	0.183	0.152	0.174	0.157	0.157
(2) operational parameters	0.169	0.000	0.174	0.152	0.143	0.126	0.126
(3) ports	0.157	0.160	0.000	0.149	0.126	0.110	0.110
(4) service	0.115	0.118	0.110	0.000	0.101	0.146	0.146
(5) waterways improvement	0.163	0.155	0.157	0.140	0.000	0.155	0.155
(6) fleet	0.157	0.115	0.126	0.126	0.118	0.000	0.135
(7) crew	0.112	0.087	0.084	0.087	0.084	0.152	0.000

.

The total impact matrices T for the separated groups and all the groups together are presented in

Table 12. The total impact matrix for selected factors of the development of inland navigation in Poland evaluated by inland navigation captains. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	Sum:
(1) demand	0.800	1.002	0.981	0.902	0.969	0.888	0.670	6.213
(2) operational param.	0.843	0.751	0.897	0.806	0.881	0.805	0.600	5.583
(3) ports	0.800	0.836	0.693	0.760	0.818	0.746	0.546	5.198
(4) service	0.699	0.725	0.724	0.559	0.710	0.668	0.482	4.567
(5) waterways improve.	0.903	0.948	0.928	0.839	0.769	0.850	0.612	5.848
(6) fleet	0.841	0.834	0.825	0.769	0.822	0.651	0.585	5.328
(7) crew	0.668	0.698	0.678	0.638	0.684	0.665	0.400	4.431
Sum:	5.554	5.794	5.726	5.273	5.652	5.273	3.895

,

Table 13. The total impact matrix for selected factors of the development of inland navigation in Poland evaluated by employees of the shipping administration. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	Sum:
(1) demand	1.197	1.313	1.276	1.343	1.187	1.319	1.052	8.687
(2) operational param.	1.318	1.117	1.247	1.324	1.155	1.241	0.999	8.401
(3) ports	1.169	1.131	0.970	1.174	1.001	1.125	0.891	7.460
(4) service	1.162	1.127	1.081	1.038	0.997	1.181	0.926	7.510
(5) waterways improve.	1.324	1.298	1.269	1.342	1.025	1.326	1.054	8.639
(6) fleet	1.133	1.060	1.054	1.128	0.946	0.979	0.906	7.206
(7) crew	1.002	0.938	0.929	0.983	0.843	1.018	0.694	6.406
Sum:	8.306	7.983	7.825	8.331	7.154	8.189	6.521

,

Table 14. The total impact matrix for selected factors of the development of inland navigation in Poland evaluated by scientists. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	Sum:
(1) demand	0.474	0.521	0.609	0.520	0.499	0.603	0.543	3.769
(2) operational param.	0.572	0.339	0.530	0.470	0.388	0.519	0.464	3.283
(3) ports	0.533	0.464	0.359	0.462	0.380	0.453	0.412	3.064
(4) service	0.354	0.303	0.332	0.231	0.270	0.388	0.319	2.197
(5) waterways improve.	0.536	0.411	0.480	0.435	0.291	0.498	0.445	3.095
(6) fleet	0.479	0.385	0.437	0.395	0.374	0.349	0.447	2.867
(7) crew	0.330	0.244	0.271	0.250	0.230	0.368	0.210	1.902
Sum:	3.278	2.667	3.018	2.764	2.432	3.178	2.840

and

Table 15. The total impact matrix for selected factors of the development of inland navigation in Poland evaluated by all groups of responders. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	Sum:
(1) demand	0.695	0.804	0.824	0.780	0.756	0.811	0.799	5.468
(2) operational param.	0.775	0.594	0.756	0.721	0.678	0.725	0.714	4.963
(3) ports	0.719	0.687	0.562	0.675	0.623	0.666	0.656	4.588
(4) service	0.626	0.595	0.600	0.487	0.549	0.637	0.627	4.121
(5) waterways’ improve.	0.788	0.742	0.759	0.727	0.567	0.764	0.752	5.100
(6) fleet	0.692	0.627	0.648	0.631	0.593	0.542	0.651	4.384
(7) crew	0.545	0.497	0.505	0.494	0.466	0.564	0.424	3.494
Sum:	4.841	4.545	4.655	4.515	4.232	4.707	4.623

.

Based on presented data, the final results of DEMATEL were calculated. The significance values and the relationship of the indicators are presented in

Table 16. The significance and relation indicators values for selected factors of the development of inland navigation in Poland assessed by inland navigation captains. Source: own work.

	Significance Indicator s+	Relation Indicator s−	Cause or Effect
(1) demand	11.766	0.659	Cause
(2) operational parameters	11.378	−0.211	Effect
(3) ports	10.923	−0.528	Effect
(4) service	9.840	−0.706	Effect
(5) waterways improvement	11.501	0.196	Cause
(6) fleet	10.601	0.055	Cause
(7) crew	8.326	0.535	Cause

for the captains of inland navigation,

Table 17. The significance and relation indicators values for selected factors of inland navigation development in Poland assessed by the employees of the shipping administration. Source: own work.

	Significance Indicator s+	Relation Indicator s−	Cause or Effect
(1) demand	16.992	0.381	Cause
(2) operational parameters	16.384	0.417	Cause
(3) ports	15.286	−0.365	Effect
(4) service	15.841	−0.821	Effect
(5) waterways improvement	15.793	1.486	Cause
(6) fleet	15.395	−0.983	Effect
(7) crew	12.927	−0.115	Effect

for the employees of the shipping administration, and

Table 18. The significance and relation indicators values for selected factors of the development of inland navigation in Poland assessed by scientists. Source: own work.

	Significance Indicator s+	Relation Indicator s−	Cause or Effect
(1) demand	7.047	0.491	Cause
(2) operational parameters	5.950	0.616	Cause
(3) ports	6.082	0.046	Cause
(4) service	4.961	−0.568	Effect
(5) waterways improvement	5.527	0.663	Cause
(6) fleet	6.045	−0.311	Effect
(7) crew	4.742	−0.938	Effect

for the scientists. The results of all three groups are presented in

Table 19. The significance and relation indicators values for selected factors in the development of inland navigation in Poland assessed by all response groups. Source: own work.

	Significance indicator s+	Relation indicator s−	Cause or Effect
(1) demand	10.075	0.505	Cause
(2) operational parameters	9.330	0.298	Cause
(3) ports	9.050	−0.200	Effect
(4) service	8.335	−0.629	Effect
(5) waterways improvement	9.063	0.667	Cause
(6) fleet	8.650	−0.022	Effect
(7) crew	6.847	−0.619	Effect

.

The response analysis of the group of captains indicates that they consider (1) demand, (5) waterways improvement and (2) operational parameters to be of the most importance, and (7) crew and (4) service received the lowest significance indicator s⁺. From the factors analysed, (2) operational parameters, (3) ports, and (4) service have been recognised as effects, and (1) demand, (5) waterways improvement, (6) fleet, and (7) crew as causes. Nevertheless, it is worth paying attention to the fact that the relation indicators s⁻ for all factors were relatively low, so none of them is strongly attached to the role of cause or effect.

The significance indicator s⁺, based on responses from shipping administration employees, was the highest for (1) demand and (2) operational parameters. The lowest value was for (7) crew. Ratings vary from previous responder groups, but the final answer was similar. Factors considered as causes are (1) demand, (2) operational parameters, and (5) waterways improvement. Factor (5) waterways improvement has the highest rate. Effects are (3) ports, (4) service, (6) fleet, and (7) crew. The most significant scores were for (6) fleet and (4) service.

The highest significance indicator of the analysis of the responses of the scientists was given to (1) demand, the lowest (7) crew, and (4) service—similar to other groups of respondents. Causes were considered to be (1) demand, (2) operational parameters, (3) ports, and (5) waterways improvement. Effects were (4) service, (6) fleet, and (7) crew. However, similarly to the results of Table 16, all of the relation indicators s⁻ were relatively low.

The results of the analysis of all groups of responses show that the most significant factor was (1) demand; the least significant factor was (7) crew. As causes of the development of inland navigation in Poland, the following were pointed out: (1) demand, (2) operational parameters, and (5) waterways improvement; and the following pointed out as effects: (3) ports, (4) service, (6) fleet, and (7) crew. However, also in this case, the relation indicators s⁻ have relatively low values.

To verify the reliability of the results obtained, a sensitivity analysis was performed. For this purpose, it was checked at what change in the value of the influence of individual factors on other factors the effect of the analysis would change. In particular, it was checked whether, as a result of changes in parameter values, the significance indicator s⁺ would change and whether the sign of the relation indicator s⁻ would change, which is tantamount to a change in the factor assessment from cause to effect or vice versa.

The calculations were carried out by changing the influence values of individual parameters on other parameters from 10% to 190% in 10% increments. Analysis was performed for the values obtained for all combined study groups, so each value in Table 7 was varied within this specific range and compared with the values shown in Table 19. The results of the analysis are presented in

Table 20. Values of the direct impact multiplier for (1) demand at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	x	0.4	0.4	0.2	0.4	0.3
(2) operational parameters	x	1.4
(3) ports	x		0.7
(4) service	x			0.1
(5) waterways improvement	x
(6) fleet	x					0.9
(7) crew	x						0.1

,

Table 21. Values of the direct impact multiplier for (2) operational parameters at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	1.7	x
(2) operational parameters	0.6	x	0.6	0.5	0.5	0.5	0.2
(3) ports		x	0.7
(4) service		x		0.1
(5) waterways improvement		x
(6) fleet	0.3	x			0.1	0.9
(7) crew		x

,

Table 22. Values of the direct impact multiplier for (3) ports at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	1.7		x
(2) operational parameters		1.5	x
(3) ports	1.3	1.4	x	1.3	1.4	1.4	1.7
(4) service			x	0.1
(5) waterways improvement			x
(6) fleet			x
(7) crew			x

,

Table 23. Values of the direct impact multiplier for (4) service at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	1.9			x
(2) operational parameters		1.6		x
(3) ports				x
(4) service				x		1.9
(5) waterways improvement				x
(6) fleet				x
(7) crew				x

,

Table 24. Values of the direct impact multiplier for (5) waterways improvement at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	1.7				x
(2) operational parameters		1.5			x
(3) ports			0.7		x
(4) service					x
(5) waterways improvement	0.2	0.1	0.1		x	0.1
(6) fleet	0.2				x	0.9
(7) crew					x

,

Table 25. Values of the direct impact multiplier for (6) fleet at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand	1.7					x
(2) operational parameters		1.6				x
(3) ports			0.6			x
(4) service						x
(5) waterways improvement						x
(6) fleet	1.1	1.1	1.1	1.1	1.1	x	1.1
(7) crew						x

and

Table 26. Values of the direct impact multiplier for (7) crew at which the relation indicator s⁻ changes sign for individual factors. Source: own work.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) demand							x
(2) operational parameters							x
(3) ports			0.4				x
(4) service							x
(5) waterways improvement							x
(6) fleet						0.9	x
(7) crew							x

.

The results presented in Table 20 show for which values of the direct impact multiplier presented in Table 7 the sign of relation indicator s⁻ will change. It can be seen that the assessment of (1) demand changes from cause to effect, with a change in the value of the direct impact of (1) demand on (2) operational parameters, (3) ports, and (5) waterways improvement of 40% of the value resulting from the respondents’ answers; (1) demand on (6) fleet for 30%, and on (4) service for 20%. It will also change (2) operational parameters from cause to effect for 140% of the base value of the influence of (1) demand on (2) operational parameters.

The changes from effect to cause, when checking different values of (1) demand, are: when changing the direct impact value of (1) demand on (3) ports of 70%, then the relation indicator s⁻ changes for (3) ports from effect to cause. When changing the influence of (1) demand to (4) service at the level 10%, the relation indicator s⁻ changes for (4) service, with a change in the influence of (1) demand on (6) fleet at a level of 90% for (6) fleet and with a change in the influence of (1) demand on (7) crew at a level of 10% for (7) crew.

Furthermore, when changing the value of the direct impact of (1) demand on (3) ports and on (5) waterways improvement up to a value equal to 10% of the base value, the dominant parameter, i.e., significance indicator s⁺, changes. For such values, the parameter (2) operational parameters has the highest value of the significance indicator s⁺.

The presented results show that changes in the value of the direct impact of (1) demand have the fastest impact on (6) fleet, the parameter which, given the basic results, received the absolute value of the coefficient closest to 0. The relation indicator s⁻ changes from effect to cause. At the same time, the significance indicator s⁺ for (6) fleet remained in the same place (5th out of 7 parameters).

The data presented in Table 21 show that changes in the magnitude of the direct impact of factor (2) operational parameters on the remaining parameters cause changes in the results for (6) fleet at the same level (90% when changing the influence of (2) operational parameters on (6) fleet), and similarly for (3) ports (70% with the influence of (2) operational parameters on (3) ports). Similarly to (1) demand, increasing the magnitude of the direct impact of this parameter affects the results to a small extent.

Changes in the direct impact value for (3) ports are different from the previous ones. The results summarized in Table 22 indicate that changes in the results occur when the parameter multiplier increases and does not decrease (from 130% for (3) ports to (1) demand (change of the result for (3) ports) and (3) ports to (4) service (change of the result for (3) ports); 140% for (3) ports to (2) operational parameters (change of the result for (3) ports) and (3) ports to (5) waterways improvement and (6) fleet (change of the result for (3) ports); for (3) ports to (2) operational parameters (change of the result for (2) operational parameters); and 170% of (3) ports to (1) demand (change of the result of (1) demand) and (3) ports to (7) crew (change of the result of (3) ports)).

In addition, in this case, within the scope examined, there were no changes in the scores in the first and last place in the significance indicator s⁺.

The results summarized in Table 23 show that the changes in factor (4) within the assumed range have little impact on the results of the analysis. Only increasing factor (4) to 160% of the influence on (2) changes the relation indicator s⁻ for (2), and changing the influence of (1) to 190% changes the relation indicator s⁻ for (1) and changing the influence of (4) the relation indicator s⁻ for (6).

Table 24 shows that both increasing and decreasing the direct impact of factor (5) waterways improvement changes the results of the analysis. With small changes, the direct impact of factor (5) waterways improvement at the level of 90% of the impact on (6) fleet changes the result of (6) fleet, with 70% of the impact on (3) ports; it changes the result of (3) ports. When increasing the influence of (5) waterways improvement to 150% on (2) operational parameters, it changes the result of (2) operational parameters, and to 170% on (1) demand changes the result of (1) demand.

Table 25 refers to (6) fleet. As a result of the analysis, this factor received a relationship indicator s⁻ value closest to 0, which means that its changes at the lowest level may influence the change in the nature of its assessment, and, according to this assumption, with an increase in the influence of (6) fleet at a level of 110% on other factors, the results (6) fleet changes from effect to reason.

Due to the significance indicator s⁺ for the (7) crew being significantly lower than others, changes in this factor had a minor impact on the remaining factors. Table 26 shows that changing the influence of (7) crew on (6) fleet at a level of 90% changes the result for (6). Additionally, changing the influence of (7) crew on (3) ports at a level of 40% changes the result for (3).

Analysing Table 20, Table 21, Table 22, Table 23, Table 24, Table 25 and Table 26 collectively, it can be seen that changes in the values of the direct impact of individual factors in most cases affect the change in the character of the result of the parameter that was changed, which does not raise any suspicions.

Additionally, the nature of parameter (6) fleet changed most often (4 out of 7 results, already at the level of 90%). This was because this parameter had an absolute value closest to 0. Similarly, influencing parameter (2) by 140–160% also resulted in a change in its result (in 5 cases to 7). And influencing the parameter (3) ports at the level of 60–70% changed the result (4 of 7, and once at 40%).

Taking into account that the dependency matrix had 42 fields of influence, the nature of the described changes and the moment of their occurrence are beyond doubt. The results of this analysis confirm that the nature of the impact of (6) fleet is on the border between effect and cause, and the assessment of other parameters is stable.

5. Discussion and Conclusions

The article analyses the factors influencing the development of inland navigation. The literature review described in Section 2 indicates that scientists most often analyse issues related to infrastructure, followed by navigation conditions and navigability. Among the articles cited, only Trivedi et al. [22] present a similar approach to the author of this paper to the topic. In [22] by Trivedi et al., based on the literature, ten factors influencing the development of inland navigation in India were identified. Six of them could be compared to the four groups selected in this publication ((2) operational parameters, (3) ports, (4) service, and (5) waterways improvement). However, the assessment of these parameters differs from the results of this analysis. The reason for that may be primarily the different scope of definitions of individual factors and the fact that the analysis concerns a different country. However, in Trivedi et al. [22], the highest-rated parameters are related to the infrastructure, its parameters, and its development, which is similar to the results of this analysis.

The research work of Kotowska et al. [33] concerns the Polish reality and the hierarchy of activities aimed at developing inland navigation. However, it focusses on infrastructure investments and does not cover other issues.

On this basis, it can be concluded that the approach presented in this work is original. It shows the selection of factors based on a review of the literature and discussions with scientists Wojewódzka-Król, Załoga, Tubis, Pluciński, and Jankowski [34] and their assessment by various stakeholder groups.

The analyses presented in the article show the impact of selected parameters of the development of inland navigation in Poland as assessed by captains, shipping administration employees, and scientists. The results of the analyses, limited to the parameters with the highest and lowest rating by individual groups and all groups together, are presented in Figure 1. The graph does not present specific values to avoid difficulties in reading the graph, and because correlation between the factors is more important.

Figure 1 presents a graph of selected significance s⁺ and relation s⁻ indicators for factors of development of inland navigation in Poland, divided into respondent groups, including the factors that received the highest and lowest ratings in particular respondent groups. The data presented in the graph show that:

• The scientists rated the factors the lowest of the responders groups, and the administration gave the highest rates;
• In all groups, (1) demand has the highest significance indicator and was specified as the cause of other factors influence;
• In all groups, (7) crew has the lowest significance indicator, and was specified as effect by all the group expect captains, who assessed it as a cause;
• The factor rated the highest as cause was (5) waterways improvement, except captains’ answers, which rated (1) demand as the strongest cause;
• The lowest relations indicator (meaning: effect) differs; it was (7) crew for scientists, (6) fleet for administration, and (4) service for captains and all groups together.

Also:

• The availability of fleet (4) service was rated as an effect by all groups;
• The (5) waterways improvement was rated as a cause by all groups;
• Regarding members of the crew, the captains’ rate examined factors like their ((7) crew) having a large influence on the parameters, and parameters not having a large influence on them;
• The relation indicators s⁻ were relatively low (only one was rated above 1: (5) waterways improvement, 1.486, rated by shipping administration employees), so the examined factors were not clearly included in a category of cause or effect.

From the presented conclusions, the most important is that, among the examined factors ((1) demand, (2) operational parameters, (3) ports, (4) service, (5) waterways improvement, (6) fleet, and (7) crew), it is difficult to strongly state whether (7) crew is a cause or effect, because the value of its relation indicator s⁻ is very close to 0, and the variability analysis showed that even minor changes in other parameters influence the change in the character of its relation indicator s⁻. The remaining factors, despite relatively low values, change their character only when the influence of the factors itself changes; in the remaining cases, in the vast majority of cases, they remain at the original level.

If it comes to developing inland waterways navigation in Poland, the respondents to the DEMATEL method stated that the most important are (1) demand and (2) operational parameters. Both factors (and additionally (5) waterways improvement) were classified as causes, influencing (3) ports, (4) service, (6) fleet, and (7) crew. This is consistent with the cases from other countries described in the literature in Section 2. The least important factor examined by all groups is (7) crew. This factor was not widely described and taken into account in the literature, but in the opinion of the author, it is strongly underestimated, due to the fact that the job requires specific qualifications, and in the labour market, we observe difficulties in filling the positions of drivers and operators [40].

Further similar research is advisable due to changes taking place in the economy. It can be expected that the results of similar research will vary over the decades, but their aim is to determine the directions of action and priorities for the development of particular fields—in this case, inland navigation, as part of the supply chain. It may also be advisable to expand the scope of research to include aspects, e.g., strictly logistical and environmental ones, to determine the impact of the broadly understood environment on the tested parameters. In addition, the aspect of the demand for inland shipping services requires further in-depth research to indicate more precisely what is the reason for the low share of inland shipping in freight transport in Poland and what actions can stimulate the demand most quickly and effectively, taking into account the growing demand for freight services in general. Aspects that can be considered as influencing the demand for inland waterway services, while requiring stimulation in Polish conditions, are (i) increasing awareness of the possibilities of using inland waterway transport in Poland; (ii) promoting the benefits of inland navigation, such as reduced transport costs over long distances and lower environmental impact; (iii) creating financial incentives (subsidies, tax breaks, or other forms of support) for companies offering shipping services, but also for companies using these services; iv) government support by introducing favourable regulations and policies supporting the development of water transport (e.g., simplifying administrative procedures and increasing public investments).

According to the results of this research, the factors influencing inland navigation to the greatest extent are (1) demand, (2) operational parameters, and (5) waterways improvement; at the same time, all these factors were evaluated as influencing the others and not being the result of their influence. This means that by modelling these factors, e.g., through political tools, the fastest and greatest impact on the development of inland navigation in Poland can be achieved.