Transport Fleet Electrification Development Conditions—Perspective of Transport, Shipping, and Logistics Industry in Poland

Abstract

The development of electric vehicle fleets is an important element of today’s economic, social, and ecological development. This multidimensional sustainable process, although not easy, generates many tangible benefits for various stakeholders, ranging from environmental to financial and competence issues. Despite the fact that the phenomenon of transformation toward rational energy management is gaining momentum on a global scale, there is a significant disproportion in terms of development levels depending on the origin of the economy. The aim of this research article is to identify the key factors affecting the development of fleet electrification in the transport, shipping, and logistics (TSL) sector in Poland. Based on a literature review, a fleet development framework was developed using a PESTEL (political, economic, social, technological, environmental, and legal) analysis and evaluated by TSL companies. According to the conclusions drawn, the most important stimulants are economic factors and technological factors, which limit the development of electrified transport fleets in the TSL industry. Based on this, the authors propose various solutions to improve economic profitability and technological conditions. In addition, it was found that the attitudes of the decision makers at transport companies and cooperation within the TSL sector are also important.

1. Introduction

The issue of fleet electrification has become very popular and, therefore, is eagerly addressed both by theorists [1] and practitioners associated with the transport industry at many conferences [2] and specialized webinars [3]. The importance of the issue is the result of many variables, especially typical environmental reasons, mainly in terms of rationalizing energy consumption levels, which is also related to increasingly restrictive legal requirements, for example, as a consequence of the European Green Deal [4] and the undeniable development of sustainable transport [5]. This includes mainly the commencement of activities to reduce the levels of greenhouse gas emissions [6], as well as typical economic reasons [7], resulting, for example, from the need to rationally manage resources, due primarily to the increasing cost of energy [8]. Because of the multifaceted nature of the transport electrification process, the degree of progress is highly variable, across both European and global country-level analyses.

It is therefore worthwhile investigating the determinants of progress in the electrification of transport according to the adopted criteria.

Understanding the complexity and defining the maturity of the process require a presentation of its ideas and current state, as well as an investigation into the main drivers and barriers to further development. The electrification of fleets is undoubtedly a multistage activity, involving various resources in order to convert the means of transport currently in use from typically gasoline-powered or diesel-powered to battery electric vehicles (BEVs). The choice of transformation strategy depends on many factors, including the policy guidelines of the country or region concerned, the attitudes and awareness of the parties involved, the typical economic considerations related to costs, and the availability of the necessary infrastructure [9]. Worldwide analyses indicate a progressive intensification of the electrification of road transport modes, where the leaders are undeniably China [10] and the USA [11]. Europe also wants to achieve the goals of sustainable transport and is implementing a growing number of initiatives to support the electrification of its fleets. One example is the EU plan for zero-emission vehicles by 2040, as a result of which almost 50% of the EU’s annual fleet mileage will be decarbonized. This should provide significant economic and environmental value—around EUR 330 billion in the cumulative total cost of ownership savings—and a cumulative reduction in CO₂ emissions of more than 1 billion tons by 2030 [12]. In addition to the numerous normative documents regulating and, at the same time, promoting electrification processes in transport, other initiatives are noticeably visibly facilitating the transition. The Alternative Fuels Infrastructure Regulations (AFIR) from 2023 should be mentioned here. Its crucial objectives are primarily related to the provision of a minimum infrastructure to support the required uptake of alternative fuel vehicles across all transport modes and in all EU Member States to meet the EU’s climate objectives. The AFIR also offers a guarantee of full infrastructure interoperability and the provision of comprehensive user information and appropriate payment options via the alternative fuel infrastructure [13]. However, despite these promising trends, the adoption of electric vehicles has not been uniform across Europe, highlighting the need for continued efforts to promote and facilitate sustainable transportation practices in Poland. The key variables driving fleet electrification are shown in

Table 1. Drivers of the electrification of fleets.

Examples of Drivers of Road Transportation Electrification	Source
States’ and cities’ policies	Pereirinhaa, Gonzálezd, Anseánd, Alonsod, Vierad (2018) [14]
Sustainable energy transition	Yuan, Thellufsen, Lund, Liang (2021) [15]
Lower operational costs	Zhang, et al. (2018) [16]
Customers’ intentions	Danhua, et al. (2021) [17]
Enablement of better grid management	Farnsworth, Shipley, Sliger, Lazar (2019) [18]
Climate change and excessive pollution	Harnischmacher (2022) [19]

Source: Authors’ elaboration.

.

Poland’s situation is not as good as other European countries. Although the transformation processes have begun, the intensity is far greater in other economies. This fact is one of the motivations behind this article. Both the statistics concerning the number of electric cars (by the end of January 2023, 67,097 fully or partially electric vehicles were registered, which is 63% more than a year earlier [20]) and those concerning infrastructure (studies show that there has been a systematic increase in the number of charging stations; by the end of April 2024, there were 6490, which is 8% more than in the same period a year previously [21]) clearly indicate that the dynamics of the development of electric transport in Poland are not as high as expected by stakeholders in the process, including, among others, the government, which assumed that by the end of 2030, there would be 600,000 electric and hybrid vehicles. Therefore, as part of the revision of the National Recovery Plan (NRP) in April 2024, funds for electric car subsidies of EUR 373.75 million, or around PLN 1.6 billion, were included [22]. However, as the results of authors’ analyses show, the barriers/challenges to fleet electrification include not only financial issues but also a number of other variables, such as the energy density and range, charging infrastructure (speed and availability), battery life, recycling and disposal or degradation, safety concerns, technological advancements, and standardization. Examples are shown in

Table 2. Challenges in road transportation electrification.

Challenges in Road Transportation Electrification	Source
The energy storage system	Pereirinhaa, Gonzálezd, Anseánd, Alonsod, Vierad (2018) [14]
Poorly developed eRoad infrastructure	Chen (2015) [23]
Higher costs—Battery costs, learning effects, and maintenance costs	Tamba, Krause, Weitzel, Ioan, Duboz, Grosso, Vandyck (2022) [7]
Increase in operational complexity	Charles River Associates (2023) [24]
The risk of negative attitude toward fleet electrification	Khan, Maoh (2022) [25]
The need to change fleet management	Castillo, Álvarez (2023) [5]

Source: Authors’ elaboration.

.

Such an outline of the issue unambiguously indicates its importance and complexity at the same time. A review of national studies indicates the need for an in-depth analysis of conditions for further development of fleet electrification in the TSL industry in Poland, and in particular the need to identify key factors supporting the transformation. The aim of this research article is to identify the key factors influencing the development of fleet electrification in the TSL sector. The novelty of this work is the attempt to classify them according to the categories from the PESTEL analysis.

2. Materials and Methods

The research procedure was conducted in three stages: the (1) design, (2) data collection, and (3) analysis and conclusions.

At the design stage, preparations were made to conduct an empirical study. The aim of this study was formulated, and then, on the basis of the literature review, a research questionnaire was developed, grouping the variables according to the PESTEL analysis. It studies the key external factors (political, economic, sociological, technological, environmental, and legal) that influence an organization. It can be used in a range of different scenarios and can guide professionals and senior managers in strategic decision making. The target group of enterprises has been precisely defined based on the following criteria:

• Organizations with a minimum of three years of experience in the TSL industry;
• The exclusion of micro-enterprises, including sole proprietorships (a minimum of 11 employees in the organization);
• All companies had to declare that they implement modern solutions in the field of improving digital logistics methods or tools;
• The respondents to the questions had to be people holding middle-level (e.g., distribution logistics manager, supply chain manager, warehouse manager, logistics planning manager, transport project manager, fleet control manager) and high-level (e.g., logistics director, director of supply chain optimization, director of transport and forwarding, director of transport management, director of logistics resources, director of operational control, director of planning and analysis, director of freight forwarding services) positions.

Based on the literature review and the PESTEL method, the authors elaborated on the following six detailed hypotheses (see Figure 1).

Hypothesis 1 (H1).

The political condition has a direct and positive influence on the transport fleet electrification development.

Hypothesis 2 (H2).

The economic condition has a direct and positive influence on the transport fleet electrification development.

Hypothesis 3 (H3).

The social condition has a direct and positive influence on the transport fleet electrification development.

Hypothesis 4 (H4).

The technological condition has a direct and positive influence on the transport fleet electrification development.

Hypothesis 5 (H5).

The environmental condition has a direct and positive influence on the transport fleet electrification development.

Hypothesis 6 (H6).

The legal condition has a direct and positive influence on the transport fleet electrification development.

Furthermore, the authors also posed the following three research questions:

• RQ1: What is the biggest driver for the development of the electrification of the transport fleet in the TSL industry?—PESTEL
• RQ2: What limits the development of the electrification of the transport fleet in the TSL industry to the greatest extent?
• RQ3: What is the importance of the attitude of decision makers?

The next stage of this empirical study was data collection. This study was entrusted to an external research company, which conducted the research using the CATI method on a sample of 100 entities in the second quarter of 2024. The questions concerned the development of the fleet as well as motivators and barriers to its development, which were firstly tested for validity and reliability (pilot survey).

The companies surveyed represented different groups of entities in terms of the number of employees, capital of origin, market served, and type of fleet.

In the sample surveyed, more than half of the enterprises were enterprises employing from 11 to 50 people (54 entities). A group of enterprises employing from 51 to 249 people from 30 companies were analyzed. The smallest group (16 entities) were large enterprises, with more than 500 employees. The majority of the group were therefore entities from the SME sector. Among the surveyed group of entities, enterprises with domestic (67.00%), mixed (23%), and foreign (10.00%) capital dominated. Regarding the type of fleet and market served, the authors analyzed all three groups of main transport vehicles, important in the development of electric vehicle fleets from the perspective of companies from the TSL industry, which include forklifts, trucks, and passenger cars. More detailed data are presented in Figure 2.

The last stage of empirical research was the analysis and conclusions. The quantitative method was used to analyze the collected data. As a result, a model was built, and the following hypotheses were investigated. Based on the analysis, main conclusions were elaborated on.

3. Results

Based on the literature review, the following framework (

Table 3. Framework of various conditions to transport fleet development based on PESTEL analysis.

Type of Conditions	Variables
Political	P1: Electrification is a global trend—fleet ecosystem P2: Pressures from the automotive sector (e.g., competitors, financial institutions, etc.) require the company to electrify its fleet
	P3: Geopolitical conditions of the automotive industry (e.g., problems with the availability of parts, interruption of supply chains, etc.)
Economic	EC1: company optimizes the costs of fleet management (e.g., optimizes the costs of energy consumption) EC2: As part of the electrification of the fleet, the company is entitled to discounts EC3: As part of the electrification of the fleet, there are subsidy systems EC4: Ensure cost-effective use EC5: The need to incur high financial outlays for the purchase of electric vehicles EC6: The need to invest in charging station management software
Social	S1: Estimates by industry specialists in the field of reducing the cost of acquiring electric vehicles S2: Increase in demand for services using electric vehicles S3: Personal attitude toward fleet electrification is positive S4: Increase in the number of fleet electrification specialists S5: The need to reorganize workplaces and infrastructure in the field of fleet maintenance S6: The need for new fleet maintenance skills
Technological	T1: Development of fleet management software T2: Development of fleet management infrastructure T3: Intensification of R+D activities to increase the reliability of the electric fleet T4: Technological development in the production of batteries used in electric vehicles T5: The need for an electrical infrastructure in the company T6: Vehicles currently available on the market have a limited range T7: Vehicles currently available on the market have a limited load capacity T8: The charging infrastructure (charging stations) currently available on the market has a limited range T9: Vehicles currently available on the market are highly defective
Environmental	EL1: The company’s environmental awareness is growing EL2: Advancing eco-innovation EL3: Focus on the life cycle analysis of the use of an electric vehicle EL4: Development of design concepts for recovery EL5: Problems with the disposal of electric vehicles currently available on the market
Legal	L1: Legal requirements for the conversion of diesel transport to electric transport L2: Normative requirements for fleet electrification L3: Legal conditions of the automotive industry for fleet electrification

Source: Authors’ elaboration.

) was elaborated.

The reliability of the measures in this study was assessed by two commonly used measures: Cronbach’s alpha and composite reliability (CR) [26]. Cronbach’s alpha, α (or coefficient alpha), developed by Lee Cronbach in 1951, measures reliability, or internal consistency. Cronbach’s alpha tests to see if multiple-question Likert scale surveys are reliable. Composite reliability (sometimes called construct reliability) is a measure of internal consistency in a scale item. Using both reliability criteria (

Table 4. Theoretical Model Variables.

Construct	Question	Correlation of Positions in Total	Factor Loading	Cronbach’s Alpha	CR	AVE
Political	P2	0.711687	0.808	0.812	0.819	0.601
	P1	0.704051	0.791
	P3	0.575333	0.725
Economic	EC1	0.705398	0.752	0.876	0.878	0.594
	EC2	0.741481	0.868
	EC3	0.732607	0.850
	EC4	0.714772	0.760
	EC6	0.653905	0.594
Social	S1	0.700904	0.750	0.887	0.888	0.664
	S2	0.724816	0.776
	S3	0.717367	0.833
	S4	0.741412	0.893
Technological	T1	0.679985	0.864	0.903	0.904	0.609
	T2	0.757319	0.859
	T3	0.729290	0.884
	T4	0.741213	0.701
	T5	0.721195	0.677
	T6	0.759543	0.661
Ecological	EL1	0.778079	0.853	0.921	0.923	0.669
	EL2	0.768260	0.775
	EL3	0.834296	0.878
	EL4	0.844375	0.885
	EL5	0.801479	0.827
	EL6	0.620444	0.671
Legal	L1	0.762410	0.821	0.871	0.876	0.701
	L2	0.807796	0.916
	L3	0.694331	0.768
Fleet development			1.000	1.000	1.000	1.000

Source: Authors’ elaboration, N = 100.

), it can be seen that all constructs meet typical requirements—values greater than 0.7 are suggested [26,27]. For each construct, the average variance extracted (AVE) is greater than 0.5 [26,27]; consequently, the convergent validity can be confirmed (see Table 4).

Concerning the discriminant validity (see

Table 5. Discriminant validity.

	Ecological	Economical	Political	Social	Technological	Legal	Fleet Development
Ecological	0.818
Economical	0.965 **	0.756
Political	0.912 **	0.958 **	0.775
Social	0.940 **	0.903 **	0.918 **	0.815
Technological	0.975 **	0.963 **	0.918 **	0.919 **	0.738
Legal	0.971 **	0.944 **	0.861 **	0.886 **	0.920 **	0.740 **
Fleet Development	0.669 **	0.673 **	0.674 **	0.676 **	0.638 **	0.838 **	1.000

** Correlation is significant at the level of 0.01 (2-tailed). Source: Authors’ elaboration.

), the Fornell–Larcker criterion suggests that the root square of each construct’s AVE should be higher than the correlation with any other construct [28].

Numbers on the diagonal are square roots of AVE for constructs; numbers off-diagonal are correlations between them.

Finally, the results show that the fit index for the measurement model is an approximate fit in

Table 6. Model fit.

	Estimated Model	Decision
Chi-square	3.129
Number of model parameters	8.000
Number of observations	100.000
Degrees of freedom	2.000
p value	0.209	>0.05—confirm the model
ChiSqr/df	1.564	<2—good fit
RMSEA (Root Mean Square Error of Approximation )	0.075	≤0.08—good fit
RMSEA LOW 90% Confidence Interval	0.000
RMSEA HIGH 90% Confidence Interval	0.226
GFI (Goodness of Fit Index)	0.986	>0.9—good fit
AGFI (Adjusted Goodness of fit Index)	0.928	>0.9—good fit
PGFI (Parsimony Goodness-of-Fit Index)	0.197
SRMR (Standardized Root Mean Square Residual)	0.026	≤0.08—good fit
NFI (Normed Fit Index)	0.980	>0.9—good fit
TLI (Tucker-Lewis index)	0.978	>0.9—good fit
CFI (Comparative Fit Index)	0.993	>0.9—good fit
AIC (Akaike Information Criterion)	19.129
BIC (Bayesian Information Criterion)	39.970

Source: Authors’ elaboration.

; then, it can be claimed that the model fits approximately well [29].

Hypothesis 1 (H1).

The political condition has a direct and positive influence on the transport fleet electrification development.

The political condition has a direct and positive influence on the transport fleet electrification development (β = 0.663; p < 0.0001) (see Figure 3,

Table 7. H1 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path Coefficients (Standardized)
Political condition influence on the transport fleet electrification	0.918	0.142	6.473	0.000	0.663

Source: Authors’ elaboration.

and

Table 8. Detailed model data (H1).

	Estimated Model	Decision
Chi-square	3.129
Number of model parameters	8.000
Number of observations	100.000
Degrees of freedom	2.000
p value	0.209	>0.05—confirm the model
ChiSqr/df	1.564	<2—good fit
RMSEA	0.075	≤0.08—good fit
RMSEA LOW 90% CI	0.000
RMSEA HIGH 90% CI	0.226
GFI	0.986	>0.9—good fit
AGFI	0.928	>0.9—good fit
PGFI	0.197
SRMR	0.026	≤0.08—good fit
NFI	0.980	>0.9—good fit
TLI	0.978	>0.9—good fit
CFI	0.993	>0.9—good fit
AIC	19.129
BIC	39.970

Source: Authors’ elaboration.

). However, the model explains 44% of political variation.

Hypothesis 2 (H2).

The economic condition has a direct and positive influence on the transport fleet electrification development.

Economics conditions have a direct and positive influence on the transport fleet electrification development (β = 0.673; p < 0.0001) (see Figure 4,

Table 9. Detailed model data (H2).

	Estimated Model	Decision
Chi-square	14.095
Number of model parameters	12.000
Number of observations	100.000
Degrees of freedom	9.000
p value	0.119	>0.05—confirm the model
ChiSqr/df	1.566	<2—good fit
RMSEA	0.075	≤0.08—good fit
RMSEA LOW 90% CI	0.000
RMSEA HIGH 90% CI	0.147
GFI	0.956	>0.9—good fit
AGFI	0.898	≈0.9—good fit
PGFI	0.410
SRMR	0.034	≤0.08—good fit
NFI	0.956	>0.9—good fit
TLI	0.972	>0.9—good fit
CFI	0.983	>0.9—good fit
AIC	38.095
BIC	69.357

Source: Authors’ elaboration.

and

Table 10. H2 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path Coefficients (Standardized)
Economics conditions’ influence on the transport fleet electrification	1.050	0.158	6.636	0.000	0.673

Source: Authors’ elaboration.

). However, the model explains 45.3% of economics variation.

Hypothesis 3 (H3).

The social condition has a direct and positive influence on the transport fleet electrification development.

The social condition has a direct and positive influence on the transport fleet electrification development (β = 0.683; p < 0.0001) (see Figure 5,

Table 11. Detailed model data (H3).

	Estimated Model	Decision
Chi-square	18.664
Number of model parameters	10.000
Number of observations	100.000
Degrees of freedom	5.000
p value	0.002	<0.05—reject the model
ChiSqr/df	3.733	<2—good fit
RMSEA	0.165	>0.08—poor fit
RMSEA LOW 90% CI	0.090
RMSEA HIGH 90% CI	0.248
GFI	0.935	>0.9—good fit
AGFI	0.896	≈0.9—good fit
PGFI	0.312
SRMR	0.041	≤0.08—good fit
NFI	0.937	>0.9—good fit
TLI	0.904	>0.9—good fit
CFI	0.952	>0.9—good fit
AIC	38.664
BIC	64.715

Source: Authors’ elaboration.

and

Table 12. H3 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path Coefficients (Standardized)
Social condition influence on the transport fleet electrification	1.053	0.161	6.536	0.000	0.683

Source: Authors’ elaboration.

). First of all, the model explains 46.7% of social variation. The indicators show that the model is reasonably fit. Inflated RMSE values indicate that the model does not have predictive capabilities, which is a major limitation.

Hypothesis 4 (H4).

The technological condition has a direct and positive influence on the transport fleet electrification development.

The indicators shown demonstrate that the model is reasonably fit. Inflated RMSE values and low GFI and AGFI values indicate that the model does not have predictive capabilities, which is a major limitation. The technological condition has a direct and positive influence on the transport fleet electrification development (β = 0.633; p < 0.0001) (see Figure 6,

Table 13. Detailed model data (H4).

	Estimated Model	Decision
Chi-square	38.902
Number of model parameters	12.000
Number of observations	100.000
Degrees of freedom	9.000
p value	0.000	<0.05—reject the model
ChiSqr/df	4.322	<5—good fit
RMSEA	0.182	>0.08—poor fit
RMSEA LOW 90% CI	0.126
RMSEA HIGH 90% CI	0.243
GFI	0.875	≈0.9—good fit
AGFI	0.809	<0.9—poor fit
PGFI	0.375
SRMR	0.069	≤0.08—good fit
NFI	0.900	≈0.9—good fit
TLI	0.866	≈0.9—good fit
CFI	0.920	>0.9—good fit
AIC	62.902
BIC	94.164

Source: Authors’ elaboration.

and

Table 14. H4 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path Coefficients (Standardized)
Social condition influence on the transport fleet electrification	0.774	0.108	7.153	0.000	0.633

Source: Authors’ elaboration.

). The model explains 40.0% of technological variation.

Hypothesis 5 (H5).

The environmental condition has a direct and positive influence on the transport fleet electrification development.

The ecological condition has a direct and positive influence on the transport fleet electrification development (β = 0.668; p < 0.0001) (see Figure 7,

Table 15. Detailed model data (H5).

	Estimated Model	Decision
Chi-square	16.920
Number of model parameters	14.000
Number of observations	100.000
Degrees of freedom	14.000
p value	0.260	>0.05—confirm the model
ChiSqr/df	1.209	<2—good fit
RMSEA	0.046	≤0.08—good fit
RMSEA LOW 90% CI	0.000
RMSEA HIGH 90% CI	0.112
GFI	0.957	>0.9—good fit
AGFI	0.914	>0.9—good fit
PGFI	0.478
SRMR	0.028	≤0.08—good fit
NFI	0.966	>0.9—good fit
TLI	0.991	>0.9—good fit
CFI	0.994	>0.9—good fit
AIC	44.920
BIC	81.393

Source: Authors’ elaboration.

and

Table 16. H5 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path Coefficients (Standardized)
Ecological conditions’ influence on the transport fleet electrification	0.876	0.121	7.219	0.000	0.668

Source: Authors’ elaboration.

). The indicators show that the model is a good fit. The model explains 447% of ecological variation.

Hypothesis 6 (H6).

The legal condition has a direct and positive influence on the transport fleet electrification development.

The legal condition has a direct and positive influence on the transport fleet electrification development (β = 0.663; p < 0.0001) (see

Table 17. H6 verification.

	Parameter Estimates	Standard Errors	T Values	p Values	Path (Standardized)
Legal condition influence on the transport fleet electrification	0.918	0.142	6.473	0.000	0.663

Source: Authors’ elaboration.

). The indicators shown demonstrate that the model is a good fit.

The authors also asked a question regarding the personal attitude of TSL companies’ decision makers.

To answer the question of whether attitude matters, the authors asked the decision makers of analyzed companies about their perspective. Among the group of representatives were, e.g., directors, high- and middle-level managers, and people influencing the development of fleet electrification. To verify the correlation, the Spearman rank order was used. According to our results, it is positive and statistically significant (

Table 18. Personal attitude of TSL companies’ decision makers—summary of number of responses.

Decision Makers’ Personal Attitude Toward Fleet Electrification Is Positive	Stage of Development of Fleet Electrification Importance
	Meaningless	Little	Moderate	Important	Very Important	Total
Meaningless	11	2	3	1	1	18
Of little importance	5	2	4	0	1	12
Moderate importance	7	2	7	5	3	24
Important	0	3	5	17	6	31
Very important	1	1	5	4	4	15
Total	24	10	24	27	15	100

Source: Authors’ elaboration.

). That means that higher values in one question indicate higher values on average in the other question. The more positive the attitude toward the electrification of the fleet of decision makers, the more the company develops it.

Table 19. Personal attitude of TSL companies’ decision makers—correlation verification.

Variable	Spearman Rank Order Correlation Tagged Correlations Are Significant with p < 0.05000
	Stage of Development of Fleet Electrification Importance	Decision Makers’ Personal Attitude toward Fleet Electrification Is Positive
Stage of development of fleet electrification importance	1	0.518120
Decision makers’ personal attitude toward fleet electrification is positive	0.518120	1

Source: Authors’ elaboration.

shows the correlation of Spearman’s rank order. According to the results, it is positive and statistically significant, which means that higher values in one question indicate higher values on average in the other question. Consequently, decision makers’ personal attitude toward fleet electrification is important.

The most significant drivers and barriers are as follows.

The next step in the analysis of the results was to verify the variables in terms of the importance of drivers and barriers to the development of fleet electrification in the group of companies studied.

According to the analysis of descriptive statistics, the respondents rated all drivers similarly (Figure 8). The median indication for all is 3, which means that 50% of the respondents gave at least a value of 3. It can be noted that grades 3 and 4 were the most frequently indicated. Danew indicates the lack of significant differences in the assessment of the importance of drivers, although it is noticeable that for variable EC1 (the company optimizes the costs of fleet management (e.g., optimizes the costs of energy consumption)), values of 1 (meaningless) and 2 (little importance) were indicated less frequently than in the other barriers. In addition, significantly higher marks for variable EC4 (ensure cost-effective use) occur mainly in the group of large enterprises in relation to the group of small- and medium-sized enterprises.

According to the analysis of descriptive statistics in terms of barriers to the development of fleet electrification (Figure 9), the largest of them have been identified, which include the following:

• Vehicles currently available on the market have a limited range (T6).
• Vehicles currently available on the market have a limited load capacity (T7).
• The need to incur high financial outlays for the purchase of electric vehicles (EC5).
• The need to reorganize workplaces and infrastructure in the field of fleet maintenance (S5).
• The need to invest in charging station management software (EC6).

Figure 9. The most significant challenges to the transport fleet electrification development. Source: Authors’ elaboration.

Figure 9. The most significant challenges to the transport fleet electrification development. Source: Authors’ elaboration.

In summary, according to the respondents of this study, the most important conditions for the development of fleet electrification are economic factors (in terms of stimulators) and technological factors (in terms of limitations). Therefore, it is worth considering the use of various solutions in this area.

4. Discussion

The obtained results, both the theoretical analysis of the issue under consideration and the conclusions drawn from the research section, constitute an important stimulus for a multilevel, interdisciplinary discussion. It is evident that the development of fleet electrification in the Polish TSL industry is the result of many factors, both internal and external. Therefore, there is not one obvious dimension that determines the level of maturity of fleet transformation. This proves the complexity of the issue and the need to understand it in order to manage it skillfully.

The first group of factors that have a significant impact on the examined process are variables related to the broadly understood political context of a given industry or entity. Government regulations; available grants; dedicated programs such as, for example, the Federal Energy Management Program in the U.S.; or specially established units supporting electrification in transport are just examples of activities that can improve the transition [30]. Other factors dictating the pace of change include the geopolitical situation, which, as the results show, has significantly influenced changes in the transport sector, mainly in the functioning of the automotive industry. The COVID-19 pandemic and the repercussions of ongoing wars have noticeably changed the conditions for the functioning of supply chains, which have significantly lost their stability and predictability [31], which resulted in increased interest in alternative solutions, including electrification [32]. Moreover, stakeholder pressure within the industry has highlighted and popularized the trend of the gradual transition from combustion vehicles to hybrid or fully electric vehicles, thus becoming one of the key strategies in the sustainable energy transition [15].

The results of the conducted research clearly indicate that the most important stimulators of decisions regarding fleet electrification are economic factors. The implemented innovations should be linked here with the need for optimal energy management and the development of fleet management ecosystem mechanisms [33]. In addition, financial indicators are an effective incentive for entities from the TSL industry, showing measurable and real large financial benefits of the transition, albeit within a specific time frame [34]. To show long-term financial benefits, numerous applications have been created to calculate the required investment costs with the option of referring to the savings generated (for example, the tool Advanced Fleet Conversion Savings Estimator and Charging Planner) [35]. Examples of proposed solutions to eliminate financial problems are presented in the conclusion.

The third distinguished group is related to the sociological conditions of the functioning of the TSL industry. The transformation of the transport industry requires significant support, primarily from the human factor. This applies both to the required new competencies and to the awareness of the validity of using other solutions and the belief in their validity [36]. Decision makers play an important role in this by building relationships and thus weakening or strengthening the staff’s attitude regarding changes toward fleet electrification [37]. Broadly understood social acceptance also significantly determines the level of advancement in transport electrification. Although this is still a niche issue, the literature on the subject already contains comprehensive analyses that show that social acceptance of increasingly widespread electrification depends on many factors, including the driving range, charging location and duration, charger type, charging rate, willingness to pay, and environmental factors [38]. It should be added that social acceptance of these new transport technologies is often overlooked in the framework of energy system optimization modeling (ESOM), especially concerning the decarbonization of the transport sector [39]. Public opinion on electromobility has changed significantly over the past 10 years. The higher level of acceptance is probably due to numerous educational initiatives and the effective reduction in barriers and potential threats related to the transformation undertaken by society [40].

Technological factors, on the one hand, constitute the specter of profitable investment in innovative solutions that support the competitive potential of entities, but on the other hand, they raise many questions and doubts in the context of the correctness of the decision to switch to an electric fleet. Concerns regarding the efficiency of electric vehicles and their load capacity and failure rate are fully justified and result from the desire to minimize the multidimensional risk associated with managing an electric vehicle fleet [41]. An example tool for minimizing the identified barriers may be various types of solutions in the field of telematics and other smart technologies, which will significantly improve the safety, efficiency, and affordability of electric vehicles [42].

The remaining two groups of factors—environmental and legal—also transpired to be important determinants for the entities studied. Typically, environmental reasons, related to the sustainable development strategy, the implementation of eco-innovation assumptions, or building an eco-image, are for many entities an important expected benefit and at the same time an argument in favor of fleet electrification [43]. In the environmental sphere, there are also certain issues related to having an electric fleet, which raise concerns and may constitute an obstacle to making the final decision. Namely, it regards analyzing the life cycle of electric vehicles and the possibility of minimizing negative environmental aspects at each stage, and in particular at the final stage—the decline phase [44]. The problem concerns primarily the need to dispose of batteries, which may be toxic to the environment. Fortunately, policymakers recognize this challenge and are developing numerous normative solutions in this area [45]. Therefore, it is to be hoped that soon the implementation of reverse logistics assumptions in the electric vehicle industry will be as effective and efficient as in other industries, and subsequent regulations in the form of legal acts, standards, or other types of documents will facilitate the understanding of both the advantages and disadvantages of the fleet electrification process.

5. Conclusions

This study shows that the dynamically developing topic of fleet electrification is conditioned by political, economic, social, technological, environmental, and legal factors. The authors illustrated key factors influencing the development of fleet electrification in the TSL sector. Additionally, key factors were identified that influence the development of fleet electrification in the TSL industry, namely the economic and technological conditions. Solutions proposed by the authors could be beneficial not only to TSL sector representatives but also people from the public sector.

Firstly, both the literature on the subject and the empirical study results underline the role of the electric vehicle fleet’s economic profitability. It can be increased through various solutions. These include the optimization of energy consumption (through efficient charging, e.g., at night or during off-peak periods) and range management (planning routes in a way that minimizes energy consumption, using eco-driving techniques and regular monitoring of battery status). Reduction in operating costs can be achieved through regular maintenance and regular technical inspections to prevent breakdowns and to extend the life of vehicles, as well as by minimizing insurance costs, i.e., by searching for more favorable insurance offers and by reducing rates as a result of incorporating telematics systems to monitor and improve the driving style of drivers. Companies have the right to use grants, reliefs, and subsidies related to the investment in the electrification of the fleet, including the necessary infrastructure in the form of the installation of charging stations at the company’s headquarters or in strategic locations. Related to economic factors, it is also important to pay attention to the optimization of the vehicle life cycle, the management of buy-back and resale, and battery recycling. The role of the economic education of the staff is also important.

Secondly, the authors proposed various solutions referring to the technological conditions, which according to the research results mostly limit the development of the transport fleet electrification in the Polish TSL industry. The cooperation of entities in conducting multidimensional research in the development of new technologies is of great importance. Standardization and its promotion are also crucial in order to integrate the developed solutions and interoperability between systems on a local, national, and international scale. Companies should provide appropriate training and education for fleet employees in the operation, maintenance, and use of electric vehicles. Partnership cooperation in the TSL sector in this area is crucial.

Based on the conclusions of this empirical study, it is clear that the attitude of the decision makers of TSL companies is of great importance in the development of fleet electrification. Therefore, it is worthwhile focusing on solutions that support decision making, which include the ability to conduct a cost–benefit analysis related to the implementation, operation, and life cycle of electric vehicles and the necessary infrastructure. It is worth defining a purchasing strategy in this area, developing a system for measuring the effectiveness of the changes being introduced, as well as the monitoring and control tools, and conducting additional support activities in the form of training, introducing a program to encourage employees to use electric vehicles.

The topic of the development of fleet electrification, noticeable in the literature and in the economic practice, is very actual and discussed. It determines various directions for further research. In light of the results of this article, all technical solutions (e.g., research into new battery technologies such as solid batteries, and increasing energy density or faster charging to improve the efficiency and range of electric vehicles; research into intelligent fleet management systems using telemetry data to optimize routes, and charging and maintenance vehicles) are of great importance.

Moreover, further research is also focused on the development of autonomous electric vehicles that can be safer and more efficient. In the area of fleet management, research into the development of V2X (Vehicle-to-Everything) technology and 5G networks to improve communication between vehicles, infrastructure, and other road users is important. The authors of this article are particularly interested in linking the subject of electric vehicles with aspects of environmental protection.

Research could also investigate the integration of electric vehicles into renewable energy systems, such as solar or wind, to increase fleet sustainability. By continuing research in these areas, it will be possible to further improve the electric vehicle fleet, increase efficiency, and reduce environmental impact.

The authors plan to conduct research on the comprehensive life cycle of electric vehicles, from production to use to recovery, in order to minimize the impact on the environment.

The authors conducted this research with great care and diligence but unfortunately this work is not free from limitations. One such limitation is certainly the fact that the research perspective concerns only one country. It is necessary therefore for the authors to undertake a comparative analysis of the situation in other countries. A repetition of the research in the long term is also being considered, as this market is developing dynamically.