Identifying Strategic Dimensions of Territorial Logistics Management in Turbulent Environments: A Factor-Analytic Model for Smart, Sustainable, and Resilient Supply Chains

Abstract

Background: Territorial logistics management has become increasingly important in turbulent environments, where digitalization, sustainability, resilience, and governance interact to shape regional logistics performance. This study aims to identify the strategic dimensions that structure territorial logistics management. Methods: A sequential mixed-methods design was adopted. First, relevant variables were identified through a structured literature review and expert judgment. Second, a survey of 376 specialists was analyzed using principal component analysis (PCA) to explore the empirical structure of the retained variables. Results: The analysis identified a four-dimensional structure comprising: (1) digital infrastructure and intelligent logistics, (2) sustainability and circular economy, (3) systemic resilience and risk management, and (4) territorial logistics, governance, and accessibility. Together, these dimensions explained more than 70% of the total variance. Conclusions: The findings suggest that territorial logistics management is a multidimensional phenomenon shaped by the interaction of technological, environmental, institutional, and spatial factors. The study provides an empirically grounded exploratory framework for understanding territorial logistics and supporting more integrated strategies for smart, sustainable, and resilient supply chains.

1. Introduction

In recent years, global logistics and supply chains have been increasingly exposed to turbulent environments characterized by disruptions, uncertainty, and structural transformations driven by geopolitical tensions, pandemics, environmental pressures, and rapid technological change. In this context, logistics is no longer conceived merely as an operational function focused on the movement of goods, but rather as a complex, adaptive system that integrates infrastructure, digital technologies, governance, and spatial decision-making processes. Contemporary research highlights that supply chain resilience—understood as the capacity to anticipate, adapt, and recover from disruptions—has become a central strategic capability in such environments [1,2,3]. The COVID-19 pandemic, in particular, marked a turning point by exposing systemic vulnerabilities and accelerating the shift from reactive recovery approaches toward proactive adaptation and long-term viability of supply chains.

At the same time, logistics systems are being reshaped by three major and interrelated transformations. First, digitalization has emerged as a key driver of performance, visibility, and adaptability. The integration of technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, and big data analytics has enabled real-time monitoring, predictive decision-making, and dynamic reconfiguration of supply chain processes [4,5,6]. Second, sustainability and circularity have become increasingly central to logistics and supply chain management due to regulatory pressures, environmental constraints, and stakeholder expectations [7,8,9,10]. Third, resilience-oriented approaches have gained prominence by emphasizing flexibility, redundancy, and risk management capabilities to cope with disruptions [11,12]. Together, these developments reflect a broader shift toward multidimensional logistics systems that must simultaneously address efficiency, sustainability, and adaptability.

Despite these advances, further integration is still needed. Existing research has generated important contributions on digitalization and operational efficiency [4,5,11,13], sustainability and circular logistics [7,8,9,10], resilience and supply chain risk management [1,3,14,15,16,17], multimodal and intermodal logistics systems [18,19,20,21], local supply chain resilience [12], and transport governance [22]. Several studies have also proposed integrative approaches that connect digital transformation, sustainability, resilience, and supply chain performance [23,24,25,26]. However, many of these models remain focused on specific domains, sectors, or network-level supply chain configurations, and their territorial operationalization remains limited. In particular, less attention has been paid to how digital infrastructure, environmental sustainability, resilience, governance, accessibility, and territorial socioeconomic conditions jointly structure logistics management in spatially heterogeneous and disruption-prone contexts. Therefore, the gap addressed in this study is not the absence of integrative logistics models, but the need for an empirically grounded exploratory framework that organizes these dimensions within a territorial logistics perspective under turbulent environments.

This gap is particularly critical in developing regions, where territorial asymmetries, institutional fragmentation, and uneven infrastructure distribution intensify logistical challenges. In such contexts, logistics performance is strongly conditioned by spatial factors, including accessibility, connectivity, governance capacity, and the availability of digital infrastructure. Consequently, understanding logistics as a territorially embedded system becomes essential for designing strategies that enhance competitiveness, resilience, and sustainability.

In response to these limitations, this study aims to identify and empirically structure the strategic dimensions of territorial logistics management that shape logistics performance, supply chain adaptability, and resilience in turbulent environments. By integrating expert judgment and multivariate statistical analysis, the research proposes an empirically grounded model that captures the multidimensional nature of logistics systems and highlights the role of territorial factors in enabling smart, sustainable, and resilient supply chains. Accordingly, the study adopts an exploratory and integrative approach, focusing on identifying latent dimensions rather than testing predefined causal relationships.

2. Theoretical Framework

2.1. Evolution of Logistics Management in Turbulent Environments

Over the past decade, logistics management has undergone a profound transformation driven by technological innovation, environmental pressures, economic uncertainty, and territorial reconfigurations. However, this transformation should not be understood as a rupture with the original foundations of logistics, but rather as an expansion of them. From its classical conception, logistics has been concerned with the coordinated management of material, information, and service flows across interconnected nodes in order to achieve system-wide objectives. In this sense, contemporary logistics extends beyond transportation, warehousing, and inventory control, evolving into a complex socio-technical and spatially embedded system integrated into global supply chains and territorial development processes [19,27].

Recent literature highlights that this transformation has been accelerated by increasingly turbulent environments characterized by disruptions, volatility, and systemic risks. In this context, logistics systems have shifted from reactive models toward predictive, adaptive, and coordinated configurations supported by advanced analytics, risk assessment, and real-time decision-making [1,3,11]. The COVID-19 pandemic acted as a critical inflection point, exposing structural vulnerabilities and accelerating the adoption of resilience-oriented strategies and digital technologies across supply chains. This interpretation is also consistent with the foundational argument of Christopher and Peck [14], who emphasized that turbulent environments require supply chains to be designed not only for efficiency but also for vulnerability reduction, adaptability, and continuity.

At the same time, logistics is increasingly understood as part of interconnected supply chain ecosystems rather than isolated functional processes. Digital platforms, collaborative networks, and data-driven coordination mechanisms have redefined how logistics systems operate across multiple spatial and organizational scales [2,12]. This systemic perspective aligns with recent studies emphasizing that supply chains are complex adaptive systems composed of multiple interdependent actors and layers, and that logistics performance depends on coordination across firms, infrastructures, and territories rather than on isolated operational decisions [23].

A central driver of this transformation is digitalization. Technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, digital twins, and big data analytics have enabled real-time visibility, predictive capabilities, and dynamic reconfiguration of logistics networks [4,5,13]. These technologies not only enhance operational efficiency but also significantly improve resilience and adaptability under uncertainty, as digital transformation strengthens information-processing capacity and recovery capabilities within supply chains. Recent review studies reinforce this view by showing that digital supply chains and smart logistics improve synchronization, monitoring, and intelligent decision-making, while also opening new possibilities for interoperability and system integration [23,24].

Simultaneously, sustainability has emerged as a core dimension of logistics transformation. Green logistics, circular economy principles, and low-emission transport systems are increasingly integrated into logistics strategies due to regulatory pressures and societal expectations [7,8,9,10]. More recent research further demonstrates that digital transformation and sustainability are mutually reinforcing processes that jointly contribute to resilient and adaptive supply chains [25]. In parallel, studies on sustainable supply chain management and circular economy transitions show that reverse flows, resource efficiency, and environmental performance must be treated as structural logistics concerns rather than secondary operational issues [28,29].

A parallel stream of research has emphasized resilience and risk management as indispensable capabilities in turbulent environments. Rather than focusing only on post-disruption recovery, recent studies highlight preparedness, flexibility, redundancy, continuity, and network redesign as central resilience mechanisms [1,3,15]. This broader view is consistent with Christopher and Peck [14], who argued that resilience should be embedded in supply chain design, and with later systematic reviews showing that supply network resilience depends on adaptive capacity, inter-organizational coordination, and strategic risk management across the system [16].

Finally, contemporary approaches increasingly incorporate territorial and governance dimensions. Logistics is now recognized as a critical infrastructure that shapes regional competitiveness, spatial connectivity, and economic development [18,19,20,27]. This perspective highlights the need to integrate physical infrastructure, digital networks, and institutional coordination across multiple territorial scales. In addition, governance-oriented approaches show that transport and logistics systems require coherent policy articulation, multi-level coordination, and institutional capacity in order to align logistics flows with broader territorial and development objectives [22].

Taken together, these developments suggest that contemporary logistics research should not be interpreted as a set of disconnected thematic specializations but as a progressively more integrated field in which digitalization, sustainability, resilience, and territorial governance function as complementary and mutually reinforcing dimensions. This integrative perspective provides the conceptual basis for understanding territorial logistics management under turbulent environments and for identifying the strategic dimensions examined in this study.

2.2. Contemporary Approaches to Logistics: A Cluster-Based Perspective

Building on the reviewed literature and following a conceptual-mapping logic comparable to clustering approaches used in systematic and bibliometric reviews, four major paradigms can be identified in contemporary logistics research. These paradigms should not be interpreted as isolated lines of inquiry, but as partially overlapping and mutually reinforcing capabilities that shape logistics and supply chain performance in turbulent environments. Thus, the cluster-based perspective is used here as an analytical device to organize an increasingly interconnected body of literature on digital transformation, sustainability, resilience, and territorial coordination, rather than to fragment the field. Recent review studies on digital supply chains, circular supply chain management, and resilience research support the usefulness of this type of integrative categorization for synthesizing a rapidly expanding body of knowledge [16,23,30].

Cluster 1—Smart and Digital Logistics (Digital Capability). This cluster groups studies that conceptualize logistics through digital transformation and technological integration. Contributions from Wang et al. [4], Rejeb et al. [5], Dallasega et al. [6], Toorajipour et al. [13], and Wamba and Queiroz [2] converge in identifying digitalization as a structural driver of contemporary logistics systems. Technologies such as artificial intelligence, the Internet of Things, blockchain, digital twins, big data analytics, and intelligent autonomous systems are presented as enablers of real-time visibility, traceability, predictive capabilities, and dynamic decision-making. Recent review studies further show that digital supply chains are increasingly built around bundles of interoperable technologies rather than isolated tools, and that the value of digitalization lies in synchronization, information integration, and adaptive coordination across networks [23,24,31].

From an analytical perspective, digital capability refers to the capacity of logistics systems to sense, process, connect, and respond through data-intensive infrastructures. Its relevance lies not only in improving efficiency but also in strengthening responsiveness and resilience under uncertainty. However, much of this literature assumes relatively high levels of technological infrastructure, interoperability, and data availability, thereby underestimating territorial asymmetries, especially in developing or peripheral regions where connectivity gaps, digital exclusion, and infrastructure fragility constrain implementation. This limitation is particularly relevant from a territorial logistics perspective, since the benefits of smart logistics depend not only on firm-level technological adoption, but also on spatially distributed digital readiness.

Cluster 2—Sustainable and Circular Logistics (Sustainability Capability). This cluster focuses on integrating environmental sustainability into logistics systems. Authors such as Ahi and Searcy [8], Ren et al. [7], Farooque et al. [10], and Zhu and Sarkis [9] emphasize that sustainability is no longer a complementary concern, but a structural requirement for logistics and supply chain management. Key elements in this stream include emissions reduction, energy efficiency, reverse logistics, green transport systems, eco-design, and circular economy practices. In this literature, logistics is understood not merely as a support function for production and distribution, but as a critical arena for reducing environmental externalities and enabling circular flows of materials and resources.

Recent reviews reinforce this view by showing that circular economy and sustainable supply chain management are increasingly convergent fields. Reverse logistics, resource recovery, remanufacturing, and closed-loop supply chains are now treated as central mechanisms for the transition toward more sustainable production and consumption systems [28,30,32]. At the same time, newer contributions suggest that sustainability is increasingly intertwined with digitalization, as digital technologies improve environmental monitoring, route optimization, asset utilization, and circular coordination across supply networks. Even so, important limitations remain: many frameworks are still conceptual or sector-specific, and empirical validation at the territorial level is limited. Therefore, although sustainability capability is well established as a core logistics dimension, its territorial operationalization remains insufficiently developed.

Cluster 3—Resilience and Risk Management (Resilience Capability). This cluster includes research focused on resilience, continuity, and risk management in logistics systems. Contributions from Ivanov [1], Dolgui and Ivanov [3], Wieland and Durach [11], and Elluru et al. [15] provide analytical frameworks for anticipating, absorbing, and mitigating disruptions through redundancy, flexibility, recovery planning, scenario analysis, and adaptive reconfiguration. This literature shows that disruptions such as pandemics, geopolitical conflict, climate shocks, infrastructure breakdowns, and supplier failures can generate cascading effects across supply chains, making resilience an indispensable strategic capability rather than a secondary operational concern.

The field has also been strengthened by foundational and review studies. Christopher and Peck [14] argued that resilience must be built into supply chain design through vulnerability reduction and adaptive capacity, while Datta [16] showed that resilience depends on the fit between uncertain events, organizational mechanisms, and network responses. Likewise, Gurtu and Johny [17] systematized the risk management literature by identifying key supply chain risk categories, mitigation approaches, and research trends, reinforcing the need to integrate risk assessment with resilience-oriented design and decision-making. Taken together, this cluster defines resilience capability as the capacity of logistics systems to prepare for, respond to, and recover from disruptions without losing core functionality. Recent evidence from risk-aware planning in forest-to-bioenergy supply chains further reinforces the importance of flexibility as a resilience mechanism under environmental disruptions. Gomes et al. [33] show that wildfire disturbances can substantially affect supply chain continuity and that adaptive planning approaches are necessary to support more robust and flexible logistics configurations under climate-related risk conditions.

However, much of this literature emphasizes global supply chain risks or firm-level resilience strategies, while giving less attention to localized territorial risks such as rural accessibility deficits, subnational infrastructure fragility, institutional response asymmetries, or place-specific climate vulnerability. This omission is especially problematic in studies concerned with territorial logistics in emerging or unevenly developed regions.

Cluster 4—Territorial Logistics, Infrastructure, and Governance (Territorial Capability). The fourth cluster addresses logistics from a spatial, infrastructural, and governance perspective. Authors such as Notteboom and Pallis [27], Bešinović [18], Rodrigue et al. [19], Kurniawan [20], and McDougall and Davis [12] conceptualize logistics as a critical territorial infrastructure that structures connectivity, accessibility, regional competitiveness, and development opportunities. This cluster emphasizes variables such as multimodal connectivity, accessibility, logistics integration, institutional coordination, spatial organization, and the territorial effects of infrastructure investment.

Additional contributions reinforce the centrality of governance in this cluster. Jaimurzina [22], from a transport governance perspective, argues that logistics and transport performance depend not only on physical assets but also on regulatory coherence, inter-institutional coordination, and multi-level policy articulation. Likewise, research on logistics integration highlights that coordinated logistics processes require relational and organizational alignment among actors, not merely the provision of infrastructure [34]. In this sense, territorial capability refers to the capacity of places and regions to organize logistics flows through the combined effect of infrastructure, governance, accessibility, and institutional integration.

Despite its relevance, this stream also presents limitations. A large share of the literature focuses on urban systems, port regions, intermodal corridors, or major logistics hubs, while peripheral, rural, border, and low-density territories remain comparatively underexplored. Research on inclusive logistics, first-mile conditions, and spatial justice remains less consolidated than research on hubs and freight competitiveness. As a result, territorial logistics is often examined from the perspective of major nodes rather than from the realities of uneven territorial development.

Taken together, these four clusters suggest that contemporary logistics research is moving toward an integrated understanding of logistics as a multidimensional system. Digitalization enhances intelligence and coordination; sustainability redefines the environmental logic of flows; resilience strengthens continuity under disruption; and territorial governance embeds logistics within the spatial and institutional realities of regions. Accordingly, these clusters should be understood not as separate explanatory models, but as complementary analytical dimensions whose interaction provides the conceptual basis for the empirical structure examined in this study.

2.3. Implications for Territorial Logistics Systems Under Turbulence

The integration of the four capabilities identified in the previous section—digitalization, sustainability, resilience, and governance—suggests that logistics must be understood as a territorially embedded system operating amid uncertainty, interdependence, and disruption. Rather than representing separate domains, these capabilities function as complementary and mutually reinforcing dimensions that jointly shape logistics performance, adaptability, and long-term viability. From this perspective, territorial logistics cannot be reduced to transport efficiency or infrastructure provision alone; instead, it must be analyzed as a multidimensional configuration in which technological, environmental, institutional, and spatial factors converge [19,22,27].

First, territorial planning must incorporate logistics as a strategic dimension, since flows of goods, information, services, and mobility directly influence spatial organization, accessibility patterns, productive integration, and regional competitiveness [12,19]. This implies moving beyond a narrow operational view of logistics toward a territorial perspective in which logistics infrastructures and services are treated as enabling conditions for balanced development, economic articulation, and functional connectivity across regions. In particular, the territorial distribution of logistics assets affects not only market efficiency but also the inclusion or exclusion of peripheral spaces within broader supply and distribution systems [20,22].

Second, the literature indicates that integrating physical and digital infrastructure is essential to enabling smart logistics systems. Digital technologies such as artificial intelligence, the Internet of Things, blockchain, digital twins, and predictive analytics do not operate in isolation; their effectiveness depends on connectivity, interoperability, data-sharing capacity, and institutional readiness across territories [1,23,24]. As a result, digital transformation should be interpreted not only as a firm-level technological process, but also as a territorially conditioned capability. In regions where digital infrastructure is weak or unevenly distributed, the benefits of smart logistics may remain concentrated in major hubs, thereby reinforcing rather than reducing territorial asymmetries [23,31].

Third, sustainability must be embedded into territorial logistics planning frameworks. The reviewed literature shows that green logistics, circular economy practices, reverse logistics, and low-emission mobility systems are increasingly central to logistics system design [7,10,28]. Therefore, environmental criteria should be incorporated into infrastructure planning, land-use regulation, route configuration, energy systems, and logistics operations. Failure to do so may intensify environmental externalities, increase dependence on carbon-intensive options, and reproduce territorial inequalities in access to cleaner, more efficient logistics solutions. In this sense, sustainability is not simply an environmental add-on, but a structuring principle for territorial logistics development [8,30,32].

Fourth, resilience must be addressed at the territorial level. Existing research on supply chain resilience has demonstrated the importance of flexibility, redundancy, preparedness, continuity planning, and adaptive capacity in the face of disruptions [1,3,14,16]. However, from a territorial perspective, resilience also depends on the characteristics of places, including infrastructure robustness, climate exposure, accessibility, governance quality, and the capacity of institutions to coordinate responses among multiple actors. This implies moving beyond firm-centered resilience toward systemic territorial resilience, where regions develop anticipatory capacities through scenario-based planning, strategic investment, and inter-institutional coordination [15,18].

Finally, governance emerges as a critical enabling condition for articulating the previous dimensions. Effective territorial logistics systems require coordination among public institutions, private firms, infrastructure operators, and local communities. Without coherent governance structures, logistics strategies may become fragmented, sectorally disconnected, or territorially unbalanced. Governance, therefore, plays a dual role: it enables coordination across actors and scales, and it aligns logistics development with broader economic, social, and environmental objectives [22,34]. This is especially important in emerging and territorially heterogeneous contexts, where institutional fragmentation often constitutes a major barrier to integrated logistics management.

Taken together, these implications suggest that territorial logistics systems must evolve toward integrated models that combine digitalization, sustainability, resilience, governance, and territorial equity. Such models are necessary not only to improve operational performance but also to strengthen the capacity of territories to adapt to turbulence, reduce structural vulnerabilities, and support more balanced and competitive regional development. From this perspective, logistics becomes not merely a support function for economic activity, but a strategic lever for territorial transformation under conditions of uncertainty.

3. Materials and Methods

Territorial logistics management is a complex, multidimensional, and interdependent construct in which technological, environmental, operational, institutional, and spatial variables interact dynamically. Because this phenomenon is not directly observable and its relevant dimensions are theoretically dispersed across different streams of literature, a qualitative phase was necessary to support their prior identification, conceptual refinement, and contextual interpretation before quantitative structuring. At the same time, a purely qualitative approach would not have allowed the empirical examination of whether these variables formed a stable latent structure. For this reason, the study adopted a sequential mixed-methods design. This design added value by linking theory-driven variable identification with data-driven exploratory dimensional assessment: the qualitative phase supported variable identification and contextualization, while the quantitative phase examined their empirical grouping through Principal Component Analysis (PCA). Accordingly, the mixed-methods approach was selected because it provides both conceptual depth and methodological rigor for the exploratory validation of a multidimensional territorial phenomenon.

The methodological procedure is structured in two complementary phases: a qualitative exploratory phase aimed at identifying and conceptualizing key variables, and a quantitative phase focused on validating the latent dimensional structure of these variables through multivariate analysis.

3.1. Phase 1: Qualitative Exploratory Analysis

This phase was conducted in two sequential stages.

3.1.1. Stage 1: Literature-Based Variable Identification

First, an extensive review of the literature on logistics transformation was conducted, focusing on four major paradigms identified in the theoretical framework: smart and digital logistics, sustainable and circular logistics, resilience and risk management, and territorial logistics and governance. These perspectives enabled the identification of explicit and implicit variables that shape contemporary logistics systems and provided the conceptual basis for the preliminary specification of the analytical dimensions examined in this study.

To ensure transparency and methodological rigor, the review followed a structured exploratory framework. Searches were conducted in Scopus, Web of Science, and Google Scholar, complemented, when necessary, by institutional and academic sources of recognized relevance. The search strategy combined terms such as territorial logistics, logistics management, smart logistics, digital supply chain, sustainable logistics, circular economy, supply chain resilience, risk management, logistics governance, accessibility, and territorial development. The review primarily covered peer-reviewed journal articles published between 2015 and 2025 to capture recent developments in logistics in turbulent environments, while also retaining seminal and foundational works published earlier when necessary for conceptual grounding. In addition, selected academic books and institutional reports were included when they directly contributed to defining the analytical dimensions and governance-related variables.

The review process served a dual purpose. First, it structured the theoretical framework by organizing the literature into four complementary capability domains. Second, it supported the preliminary identification of variables later subjected to expert validation. Rather than deriving variables arbitrarily, the study extracted recurrent constructs, dimensions, and operational concerns from the literature, including digitalization, operational efficiency, environmental sustainability, circularity, systemic resilience, governance structures, accessibility, multimodal connectivity, critical infrastructure, and territorial socioeconomic effects.

Table 1. Literature-based support for the preliminary identification of variables in Stage 1.

Variable	Synthesized Meaning	Main Supporting References
Digital infrastructure and connectivity	Digital networks and data exchange capacity for logistics coordination	Büyüközkan and Göçer [23]; Wamba and Queiroz [1]; Rejeb et al. [5]; Zhang et al. [31]
Technological integration (AI, IoT, blockchain, digital twins)	Use of enabling digital technologies in logistics processes and decisions	Wang et al. [4]; Toorajipour et al. [13]; Feng and Ye [24]; Rejeb et al. [5]
Operational efficiency	Process performance, responsiveness, and cost optimization	Wang et al. [4]; Feng and Ye [24]; Karia and Wong [21]
Logistics networks and interoperability	Integration and coordination of actors, systems, and flows	Wamba and Queiroz [1]; Büyüközkan and Göçer [23]; Kim et al. [34]
Emerging technologies	Advanced intelligent and autonomous logistics tools	Toorajipour et al. [13]; Feng and Ye [24]; Zhang et al. [31]
Environmental sustainability	Ecological criteria in logistics and supply chain design	Ren et al. [7]; Ahi and Searcy [8]; Zhu and Sarkis [9]
Circular economy and reverse logistics	Recovery, recirculation, and reverse material flows	Farooque et al. [10]; Genovese et al. [28]; Lengyel et al. [29]; Salas-Navarro et al. [32]
Energy efficiency and low-emission logistics	Reduction of emissions and energy intensity	Ren et al. [7]; Genovese et al. [28]; Theeraworawit et al. [30]
Sustainable mobility/green corridors	Environmentally oriented freight and mobility solutions	Ren et al. [7]; Hasani Goodarzi et al. [21]; Kurniawan [20]
Logistics resilience	Capacity to absorb, adapt to, and recover from disruption	Ivanov [1]; Wieland and Durach [11]; Christopher & Peck [14]; Datta [16]
Risk management and continuity	Anticipation, mitigation, and continuity planning under uncertainty	Dolgui and Ivanov [3]; Elluru et al. [15]; Gurtu and Johny [17]
Critical infrastructure fragility	Vulnerability of logistics performance to infrastructure failure	Bešinović [18]; Elluru et al. [15]; OECD [35]
Climate and water vulnerability/extreme events	Exposure to climate-related and disruptive shocks	Elluru et al. [15]; Ivanov [1]; OECD [35]
Logistics security	Protection against operational and systemic threats	Gurtu and Johny [17]; Elluru et al. [15]
Physical logistics infrastructure	Material base for transport, storage, and connectivity	Rodrigue et al. [19]; Notteboom et al. [27]; Kurniawan [20]
Multimodal connectivity	Integration of transport modes across territories	Rodrigue et al. [19]; Kurniawan [20]; Hasani Goodarzi et al. [26]
Territorial accessibility	Spatial access to logistics nodes, services, and markets	McDougall and Davis [12]; Rodrigue et al. [19]; Jaimurzina [22]
Governance and institutional coordination	Multi-level articulation of actors, rules, and policies	Jaimurzina [22]; Notteboom et al. [27]; Kim et al. [34]
Interorganizational collaboration	Cooperation among firms, institutions, and logistics actors	McDougall and Davis [12]; Kim et al. [34]; Wamba and Queiroz [2]
Strategic logistics investment	Long-term investment in logistics capabilities and infrastructure	Notteboom et al. [27]; Rodrigue et al. [19]; OECD [35]
Territorial socioeconomic impact	Effects on competitiveness, inclusion, and regional development	McDougall and Davis [12]; Jaimurzina [22]; Kurniawan [20]
Productive location/relocation	Spatial configuration of production shaped by logistics conditions	Rodrigue et al. [19]; Notteboom et al. [27]
Territorial logistics costs	Spatially differentiated logistics burdens and competitiveness effects	Karia and Wong [21]; McDougall & Davis [12]; OECD [35]

summarizes the main variables identified in Stage 1 and the principal references that supported their inclusion.

3.1.2. Stage 2: Expert Validation and Variable Consolidation

Based on the theoretical review, a preliminary list of variables was developed, ensuring representation across the four conceptual clusters identified in the theoretical framework.

A semi-structured questionnaire was then administered to a panel of 18 experts in logistics, territorial planning, transportation systems, governance, and digital systems. The experts were purposively selected according to three complementary criteria: (i) advanced academic training in relevant fields; (ii) professional, institutional, or research experience directly related to logistics and territorial systems; and (iii) demonstrated familiarity with at least one of the four analytical domains addressed in the study. All participants had relevant academic qualifications and professional or research trajectories in logistics-related fields.

To strengthen the rigor of the selection process, expert inclusion was also supported by a competence coefficient greater than 0.75. Following established expert-judgment methodology, this coefficient was calculated as K = 0.5 (Kc + Ka), where Kc represents the expert’s self-assessed knowledge of the topic and Ka represents the degree of argumentation supporting that knowledge, based on the expert’s reported sources of expertise. This procedure was used to reduce arbitrariness in panel selection and to ensure that participants had sufficient thematic competence for the exploratory stage.

The participation of 18 experts was considered methodologically adequate for this phase of the study. In expert-based exploratory research, panels of approximately 10 to 20 specialists are commonly regarded as sufficient when participants are intentionally selected and possess a high degree of expertise. In this study, the panel size was considered appropriate to ensure informed judgment, thematic diversity, and analytical feasibility during variable refinement and consolidation prior to the quantitative phase.

This stage yielded a refined set of 43 variables, including emerging factors not explicitly identified in the literature, such as the territorial digital divide, logistics system interoperability, local climate vulnerability, and institutional response capacity.

To improve the transparency of the expert validation stage and reduce potential sectoral or territorial bias,

Table 2. Profile of experts participating in Phase 1 (n = 18).

Variable	Category	n	Variable	Category	n
Sector	Academia	6	Years of experience	5–10	2
	Public sector	3		11–15	4
	Private sector	5		16–20	6
	International cooperation	1		21–25	4
	Consulting	3		>25	2
Institutional background	University/research center	6	Field of expertise	Logistics and supply chain	4
	Central government	1		Transport and mobility	3
	GAD/local government	1		Territorial planning	2
	Public enterprise	1		Infrastructure	1
	Logistics operator	2		Public governance	1
	Industrial/commercial firm	1		Sustainability/circular economy	1
	Port/airport/customs authority	1		Risk and resilience	1
	Business association	1		Digital systems/smart logistics	2
	International organization	1		Trade and customs	1
	Consulting firm	3		Regional development	2
Highest Academic degree	Bachelor’s	2	Geographic location	Highlands	8
	Master’s	10		Coast	7
	PhD	6		Amazon Region	3

summarizes the profile of the 18 experts participating in Phase 1. In addition to meeting the competence threshold (K > 0.75), the panel was intentionally composed to ensure diversity in institutional background, academic qualification, field of expertise, professional experience, and territorial perspective.

The experts represented academia (n = 6), the private sector (n = 5), the public sector (n = 3), consulting (n = 3), and international cooperation (n = 1). Their institutional backgrounds included universities and research centers, central and local government bodies, public enterprises, logistics operators, industrial or commercial firms, port, airport, or customs authorities, business associations, international organizations, and consulting firms. In academic terms, the panel included experts with bachelor’s degrees (n = 2), master’s degrees (n = 10), and PhDs (n = 6). The experts also represented a range of professional trajectories, with most reporting between 11 and 25 years of experience, and covered multiple areas of specialization, including logistics and supply chain management, transport and mobility, territorial planning, sustainability, digital systems, governance, and regional development. Geographically, the panel included experts from the Highlands (n = 8), the Coast (n = 7), and the Amazon Region (n = 3). This heterogeneity was considered important because territorial logistics is embedded in diverse spatial, institutional, and infrastructural realities, which may shape how strategic priorities and constraints are interpreted.

3.2. Phase 2: Quantitative Analysis and Dimensional Validation

The second phase aimed to explore the empirical structure of the variables retained after the qualitative stage through multivariate analysis. In this study, Principal Component Analysis (PCA) was used as an exploratory dimensional reduction technique to identify coherent covariance-based groupings among variables previously selected through literature review and expert validation. Accordingly, the purpose of this phase was not to confirm a latent measurement model in the strict psychometric sense, but rather to determine whether the retained variables exhibited an interpretable component structure that could support an initial multidimensional representation of territorial logistics management.

A structured questionnaire was administered during the quantitative phase using a non-probabilistic purposive sampling strategy. The target population was defined as professionals with relevant knowledge or experience in logistics, territorial development, transport systems, infrastructure management, governance, and related technological fields. This population included academics, government officials, logistics managers, infrastructure operators, and specialists in advanced logistics technologies.

The initial pool of 410 invited participants comprised all eligible professionals identified through academic, institutional, and professional networks, in accordance with the study’s inclusion criteria. After data collection and screening, 376 valid responses were retained for analysis.

The final sample comprised respondents with diverse yet relevant professional profiles in logistics, territorial development, infrastructure, governance, and related technological fields. To improve transparency,

Table 3. Profile of respondents participating in Phase 2 (n = 376).

Variable	Category	n	%	Variable	Category	n	%
Sector	Academia	56	14.9	Years of experience	5–10	94	25.0
	Public sector	98	26.0		11–15	186	49.5
	Private sector	157	41.7		16–20	63	16.8
	International cooperation	24	6.5		21–25	22	5.8
	Consulting	41	10.9		>25	11	2.9
Institutional background	University/research center	56	14.9	Field of expertise	Logistics and supply chain	96	25.5
	Central government	13	3.4		Transport and mobility	45	12.0
	GAD/local government	28	7.4		Territorial planning	22	5.8
	Public enterprise	57	15.2		Infrastructure	64	17.0
	Logistics operator	84	22.4		Public governance	12	3.3
	Industrial/commercial firm	63	16.7		Sustainability/circular economy	59	15.7
	Customs authority	5	1.4		Risk and resilience	43	11.4
	Business association	16	4.2		Digital systems/smart logistics	12	3.3
	International organization	13	3.5		Trade and customs	7	1.8
	Consulting firm	41	10.9		Regional development	16	4.2
Highest academic degree	Bachelor’s	266	70.8	Geographic location	Highlands	156	41.5
	Master’s	85	22.6		Coast	143	38.1
	PhD	16	4.2		Amazon Region	56	14.9
	Other/not reported	9	2.4		National scope	21	5.5

summarizes the main characteristics of the 376 participants, including their sectoral affiliation, institutional background, field of expertise, years of experience, academic degree, and geographic location.

Sectorally, the sample was composed primarily of respondents from the private sector (n = 157), followed by the public sector (n = 98), academia (n = 56), consulting (n = 41), and international cooperation (n = 24). In terms of professional experience, most participants reported 11–15 years (n = 186), followed by 5–10 years (n = 94) and 16–20 years (n = 63), indicating a respondent pool with substantial professional maturity. The participants also represented a range of substantive areas, particularly logistics and supply chain management (n = 96), infrastructure (n = 64), sustainability and circular economy (n = 59), transport and mobility (n = 45), and risk and resilience (n = 43), along with other fields relevant to territorial logistics analysis. Regarding academic qualifications, most respondents held bachelor’s degrees (n = 266), followed by master’s degrees (n = 85), PhDs (n = 16), and a small number of other or unreported qualifications (n = 9). Geographically, the sample included participants from the Highlands (n = 156), the Coast (n = 143), the Amazon Region (n = 56), and a smaller group with national or multi-territorial scope (n = 21). This descriptive profile helps contextualize the respondent pool and supports interpretation of the factor-analytic model as an exploratory structure derived from a heterogeneous yet specialized population.

Although the sample was not designed for probabilistic generalization, its size was considered adequate for the exploratory multivariate analysis applied in this study. In particular, the final number of valid cases exceeded commonly accepted recommendations for PCA, both in absolute terms and relative to the number of variables examined, thereby providing sufficient empirical support for dimensionality reduction and exploratory structural identification.

Before conducting the PCA, the variables identified in the qualitative phase were screened using expert-based prioritization to retain only those deemed sufficiently relevant for the quantitative stage. Following the methodological criterion proposed by Noda Hernández et al. [36], only variables mentioned by at least 80% of experts were retained, resulting in a final set of 32 essential variables.

This screening criterion was intended to retain variables that the expert panel perceived as core and recurrent structural components of territorial logistics management across contexts. Therefore, variables that did not reach the 80% consensus threshold were not interpreted as conceptually irrelevant, but rather as less stable or less widely shared dimensions for inclusion in the exploratory component model. In particular, items such as extreme events, social disruptions, and labor availability may represent important manifestations or triggers of turbulence in specific territorial or crisis contexts, but they did not achieve sufficient agreement to be treated as consensus-based structural variables in the PCA. In this sense, the model captures turbulent environments primarily through variables associated with resilience, risk management, continuity, climate vulnerability, infrastructure fragility, logistics security, and institutional response capacity, which reflect the enduring capabilities that enable territorial logistics systems to confront disruption.

Table 4. Variables retained for quantitative analysis after expert-based prioritization.

Variable	%	Variable	%
Physical logistics infrastructure	100	Energy transition and clean mobility	96
Digital infrastructure and connectivity	100	Logistics security	90
Technological integration (AI, IoT, blockchain)	100	Governance and institutional coordination	100
Operational efficiency	96	Territorial accessibility	96
Environmental sustainability and circularity	100	Multimodal connectivity	100
Sustainable mobility/green corridors	96	Institutional response capacity	90
Reverse logistics	90	Territorial digital divide	84
Emissions and energy efficiency	100	Spatial justice and logistics equity	84
Logistics resilience	96	Interorganizational collaboration	96
Risk management and continuity	96	Logistics networks and interoperability	100
Critical infrastructure fragility	96	Territorial socioeconomic impact	100
Strategic logistics investment	100	Emerging technologies (drones, autonomous vehicles)	90
Production location/relocation	100	Climate and water vulnerability	84
Changes in logistics demand	96	Territorial logistics costs	90
Institutional innovation	90	Logistics centers and distribution platforms	96
Territorial economic conditions	100	E-commerce and last-mile logistics	84

presents only the variables retained after this screening procedure, together with the percentage of expert agreement for each item. This step provided a structured basis for variable retention and helped ensure consistency between the qualitative and quantitative phases of the study. Importantly, it did not define the final dimensional structure a priori; rather, it delimited the set of variables that was subsequently examined empirically through PCA.

Under this criterion, variables such as extreme events (climatic, sanitary) (18%), barriers to digitalization (48%), labor availability and territorial migration (30%), and social disruptions and conflicts (24%) were excluded from the quantitative phase and are therefore not included in Table 4.

After variable screening, the data were analyzed using PCA, a multivariate technique appropriate for exploratory contexts aimed at reducing dimensionality and detecting empirical patterns of association among variables. PCA was selected because the study sought to organize a broad set of theoretically and experientially derived variables into interpretable dimensions, rather than to test a previously specified latent-construct model. After extraction, an orthogonal Varimax rotation was applied to improve the interpretability of the component structure and facilitate the identification of dominant loadings.

The reliability and adequacy of the data for exploratory factor structuring were assessed using Cronbach’s alpha, the Kaiser–Meyer–Olkin (KMO) coefficient, and Bartlett’s test of sphericity. These indicators provided complementary evidence regarding internal consistency and the suitability of the correlation matrix for component extraction.

As shown later in the Section 4, the PCA revealed a four-dimensional structure broadly consistent with the thematic domains identified in the theoretical review: (1) smart logistics and digital infrastructure, (2) sustainable and circular logistics, (3) resilience and risk management, and (4) territorial logistics, governance, and accessibility. The total explained variance exceeded 71%, supporting the empirical relevance and interpretability of the exploratory structure. Similar sequential procedures—combining qualitative screening with multivariate dimensional analysis—have been used in recent logistics and sustainability research to examine multidimensional constructs.

To facilitate understanding of the research design, Figure 1 summarizes the sequential mixed-methods procedure adopted in this study, showing the progression from literature-based variable identification and expert validation to the quantitative survey and exploratory dimensional assessment through PCA.

4. Results

According to the results presented in

Table 5. Results of the factor analysis for variables influencing territorial logistics management.

Reliability and Validity Analysis
Cronbach’s alpha coefficient:			0.893
Kaiser–Meyer–Olkin (KMO) coefficient:			0.881
Bartlett’s test of sphericity:			p = 0.000
Factor extraction results
Variables	Component 1	Component 2	Component 3	Component 4
Eigenvalues	7.45	5.51	4.56	3.87
Variance explained	24.80%	18.30%	15.10%	12.90%
Cumulative variance	24.80%	43.10%	58.20%	71.10%
Factor loadings
Variables	C1	C2	C3	C4
Digital infrastructure and connectivity	0.962	0.041	0.032	0.018
Technological integration (AI, IoT, blockchain)	0.954	0.027	0.036	0.012
Operational efficiency	0.901	0.066	0.101	0.024
Territorial digital divide	0.874	0.041	0.012	0.021
Emerging technologies (drones, autonomous vehicles)	0.913	0.022	0.036	0.041
Institutional innovation	0.911	0.034	0.055	0.029
E-commerce and last-mile logistics	0.889	0.047	0.020	0.058
Environmental sustainability and circularity	0.029	0.967	0.042	0.015
Reverse logistics	0.047	0.945	0.032	0.017
Emissions and energy efficiency	0.034	0.954	0.018	0.012
Sustainable mobility/green corridors	0.042	0.918	0.071	0.044
Logistics resilience	0.031	0.042	0.983	0.018
Risk management and continuity	0.028	0.034	0.979	0.011
Critical infrastructure fragility	0.024	0.021	0.961	0.052
Climate and water vulnerability	0.036	0.075	0.944	0.023
Logistics security	0.033	0.046	0.901	0.021
Physical logistics infrastructure	0.012	0.008	0.044	0.947
Governance and institutional coordination	0.021	0.018	0.027	0.951
Territorial accessibility	0.030	0.012	0.044	0.955
Multimodal connectivity	0.028	0.033	0.017	0.962
Interorganizational collaboration	0.033	0.044	0.022	0.947
Territorial socioeconomic impact	0.039	0.033	0.054	0.978
Strategic logistics investment	0.051	0.042	0.031	0.965
Production location/relocation	0.036	0.028	0.040	0.957
Territorial logistics costs	0.029	0.068	0.048	0.921

Note: Factor loadings greater than 0.40 are shown in bold to facilitate identification of the dominant component for each variable and the visual detection of potential cross-loadings.

, the factor analysis supports the technique’s adequacy for identifying the empirical structure of the variables examined in this study.

The four extracted components jointly explain 71.1% of the total variance, indicating substantial explanatory power for a complex, multidimensional construct such as territorial logistics management, particularly in the context of social and organizational research. More specifically, the first component—associated with digital infrastructure and smart logistics processes—accounts for 24.8% of the total variance. The second component, related to environmental sustainability and circular economy practices, raises the cumulative explained variance to 43.1%. The third component, linked to systemic resilience and risk management, increases the cumulative explained variance to 58.2%. Finally, the fourth component—associated with territorial logistics, governance, and accessibility—brings the cumulative explained variance to 71.1%.

These results confirm the robustness of the extracted factor structure and indicate that the identified components capture the underlying logic of territorial logistics systems. From an analytical perspective, the four dimensions can be interpreted as complementary capabilities that enable logistics performance, supply chain adaptability, and resilience under turbulent conditions.

4.1. Factorial Structure and Dimensional Interpretation

Figure 2 presents the factorial plane defined by components I and II, in which distinct groupings of variables are clearly identifiable. These groupings reflect the underlying latent structure of territorial logistics management and provide empirical support for the four-dimensional model identified through principal component analysis.

In the upper-right quadrant, Dimension 1 (Digital Infrastructure and Smart Logistics) emerges as a dominant cluster, grouping variables related to digital connectivity, technological integration, operational efficiency, institutional innovation, and the adoption of emerging technologies. This configuration indicates that digitalization serves as a central enabling capability for logistics performance, facilitating real-time coordination, process optimization, and adaptive decision-making under uncertainty.

In the upper-left quadrant, Dimension 2 (Sustainability and Circular Economy) is identified, integrating variables associated with environmental efficiency, sustainable mobility, circular practices, and reverse logistics. This clustering reflects the structural role of sustainability in shaping logistics systems, highlighting its contribution to long-term viability and alignment with environmental and regulatory constraints.

In the lower-left quadrant, Dimension 3 (Systemic Resilience and Risk Management) groups variables related to climate vulnerability, critical infrastructure fragility, logistics security, and risk management and continuity. This dimension captures the ability of logistics systems to absorb, adapt to, and recover from disruptions, reinforcing the importance of resilience as a core capability in turbulent environments.

Finally, in the lower-right quadrant, Dimension 4 (Territorial Logistics, Governance, and Accessibility) integrates variables such as physical infrastructure, multimodal connectivity, territorial accessibility, governance and institutional coordination, and territorial socioeconomic impact. This dimension reflects the spatial and institutional embedding of logistics systems, demonstrating how territorial conditions shape connectivity, inclusion, and overall system performance.

The clear separation of variables across quadrants confirms the coherence of the factor structure, as variables with high loadings tend to cluster around their corresponding components. This pattern is consistent with the principles of factor analysis, where variables strongly associated with a factor exhibit high loadings and contribute significantly to the interpretation of latent dimensions. The high loadings observed for several variables suggest a clearly differentiated exploratory component structure.

4.2. Reliability and Internal Consistency

To assess the internal consistency of the identified dimensions, Cronbach’s alpha coefficients were calculated for the set of variables included in the factorial solution. The overall coefficient reached α = 0.893, indicating a high level of internal consistency and supporting the reliability of the measurement instrument. In applied social and management research, values above 0.80 are generally considered indicative of robust reliability.

In addition to Cronbach’s alpha, the adequacy of the data for factor analysis was examined through the Kaiser–Meyer–Olkin (KMO) coefficient and Bartlett’s test of sphericity. The KMO value of 0.881 indicates high sampling adequacy, suggesting that the correlation structure among variables was appropriate for dimensional reduction. Likewise, Bartlett’s test was statistically significant (p = 0.000), confirming that the correlation matrix was not an identity matrix and was therefore suitable for factor extraction.

Taken together, these indicators provide complementary evidence of the robustness of the factorial structure and support the validity of the multidimensional model proposed for territorial logistics management. The combination of high internal consistency, satisfactory sampling adequacy, and a significant Bartlett’s test reinforces the stability and interpretability of the extracted dimensions.

4.3. Conceptual Model of Territorial Logistics Management

Figure 3 presents an interpretive conceptual framework based on the four exploratory dimensions identified in the study: (1) smart logistics and digital infrastructure, (2) sustainability and circular logistics, (3) resilience and risk management, and (4) territorial logistics, governance, and accessibility. Although these dimensions were empirically identified through Principal Component Analysis, the broader framework shown in the figure also incorporates contextual and theoretical elements from the literature to illustrate how these dimensions may be situated within a wider territorial logistics perspective. Accordingly, the figure should be interpreted as a conceptual synthesis rather than a direct empirical validation of all the relationships and elements it represents.

Elements such as territorial interests, migration trends, and logistics outcomes are included as interpretive contextual components and not as variables directly validated in the PCA. Rather than representing independent components, these dimensions operate as interdependent capabilities that jointly shape logistics performance and supply chain adaptability. Their interaction reflects the systemic nature of territorial logistics, where technological, environmental, institutional, and spatial factors converge to influence decision-making and operational outcomes.

The model adopts a prospective perspective, incorporating temporal dynamics and external conditions such as technological disruption, environmental pressures, and policy frameworks. These elements interact with internal territorial capacities—including infrastructure, governance, and resilience—to generate logistics trajectories that affect regional competitiveness and development.

From this perspective, logistics mobility—understood as the flow of goods, services, information, and energy—plays a central role in maintaining system balance. Disruptions such as congestion, climate events, infrastructure failures, or connectivity gaps can destabilize this balance, generating bottlenecks, inefficiencies, or systemic failures.

The model therefore highlights the need for integrated strategies that combine infrastructure planning, digital transformation, sustainability policies, risk management, and multi-level governance. These strategies enable territories to anticipate disruptions, optimize resource allocation, and transition toward more resilient, efficient, and sustainable logistics systems.

5. Discussion

5.1. Key Findings and Their Theoretical Interpretation

The findings confirm that territorial logistics management is a complex and multidimensional phenomenon that cannot be adequately explained through isolated operational or sector-specific perspectives. The identification of four core dimensions—smart logistics and digital infrastructure, sustainability and circular logistics, systemic resilience and risk management, and territorial logistics with governance and accessibility—shows that territorial logistics performance depends on the simultaneous interaction of technological, environmental, institutional, and spatial factors. This multidimensional structure is consistent with recent studies showing that digital transformation, resilience, and sustainability increasingly function as mutually reinforcing capabilities in contemporary supply chains rather than as separate managerial domains [1,3,10,12,25].

These findings do not imply that previous logistics and supply chain models have ignored integration. Rather, they suggest that existing integrative approaches often privilege specific analytical levels—such as firm-level supply chain resilience, digital supply chain transformation, intermodal transport systems, or sustainability-oriented supply chain design—while providing less empirical evidence on how these dimensions are jointly configured at the territorial level. The contribution of this study, therefore, lies in organizing these dimensions into an exploratory territorial logistics framework that explicitly incorporates digital, environmental, resilience-related, governance, accessibility, and socioeconomic components.

The factorial structure further suggests that these dimensions should be interpreted as interdependent capabilities within territorial logistics systems. The concentration of digitalization-related variables in the first dimension confirms that connectivity, technological integration, operational efficiency, and institutional innovation are central enablers of logistics adaptability. This result is consistent with the literature on smart logistics and digital supply chains, which highlights the role of AI, IoT, blockchain, and analytics in improving visibility, coordination, and responsiveness under uncertainty [4,5,6,13]. At the same time, the present findings extend this line of research by showing that these benefits are territorially conditioned: digital capability depends not only on firm-level technology adoption, but also on infrastructure availability, digital connectivity, and institutional readiness across space [2,34].

The second dimension confirms that sustainability and circularity have moved from being complementary concerns to becoming structural pillars of logistics management. The grouping of variables related to energy efficiency, sustainable mobility, reverse logistics, and environmental performance supports the view that green logistics and circular economy practices are increasingly embedded in logistics strategy [7,8,9,10]. This finding is also in line with more recent syntheses showing that sustainability outcomes improve when ecological objectives are integrated with digital and resilient supply chain capabilities rather than treated as stand-alone initiatives [25]. From a territorial perspective, this means that sustainability must be addressed through infrastructure planning, mobility systems, land-use coordination, and low-carbon logistics policies.

The third dimension, focused on systemic resilience and risk management, constitutes one of the study’s most relevant contributions. This interpretation is also supported by recent work on risk-aware forest-to-bioenergy supply chains, which demonstrates that wildfire disturbances require logistics systems to incorporate flexible planning, adaptive configuration, and disruption-sensitive decision-making mechanisms [33]. The strong association between climate vulnerability, critical infrastructure fragility, logistics security, and continuity capacity indicates that resilience should be understood not as a reactive organizational response, but as a structural property of territorial logistics systems. This interpretation is consistent with Ivanov’s work on disruption propagation and adaptive supply chains, as well as with later studies showing that resilience depends on anticipating shocks, managing interdependencies, and strengthening redundancy and coordination mechanisms [1,3,11,28]. It also aligns with recent international evidence emphasizing that resilient supply chains require agility, adaptability, and alignment across infrastructure, governance, and logistics services [35].

The fourth dimension highlights the spatial and institutional foundations of logistics performance. The strong loadings associated with accessibility, multimodal connectivity, interorganizational cooperation, socioeconomic impact, and strategic investment indicate that logistics systems are shaped not only by flows and infrastructure, but also by governance arrangements and territorial organization. This result reinforces the arguments advanced by Notteboom and Pallis [27], Bešinović [18], Rodrigue et al. [19], Kurniawan [20], and McDougall and Davis [12], who stress that logistics performance is deeply embedded in network coordination, planning institutions, and spatial accessibility. Recent work on intermodal and resilient transport systems points in the same direction, showing that multimodal integration and coordinated infrastructure governance are central to sustainable and disruption-tolerant logistics performance [26].

Taken together, these findings suggest that the four dimensions should not be interpreted as isolated factors, but as complementary territorial capabilities for logistics and supply chain management in turbulent environments. Their value lies in providing an integrative framework that links logistics performance to adaptability, sustainability, and resilience at the territorial scale. This is particularly relevant in developing contexts, where regional asymmetries, uneven digitalization, environmental exposure, and fragmented governance intensify logistics challenges. In that sense, the study contributes to the literature by moving beyond a narrow operational conception of logistics and positioning it instead as a territorially embedded system of strategic capabilities.

Although the present study does not test causal relationships among the four dimensions, the findings and the reviewed literature suggest that they should be interpreted as interdependent territorial capabilities rather than as isolated domains. Digitalization may strengthen resilience by improving visibility, coordination, and adaptive response capacity across logistics networks. In turn, sustainability-oriented logistics strategies may contribute to resilience by reducing resource dependence, improving efficiency, and supporting longer-term system robustness. At the same time, territorial governance and accessibility appear to condition the extent to which digital and sustainability capabilities can be effectively translated into resilient logistics performance. From this perspective, the four dimensions identified in the study are better understood as mutually reinforcing components of an integrated territorial logistics system, even though the direction and strength of these relationships remain to be tested in future explanatory research.

From a practical standpoint, the results indicate that improving territorial logistics management requires coordinated interventions rather than isolated investments. Digitalization strategies should be accompanied by infrastructure expansion and institutional upgrading; sustainability goals should be embedded in mobility and logistics planning; resilience should be incorporated into infrastructure design and continuity planning; and territorial competitiveness should be pursued through multilevel governance and stakeholder coordination. This interpretation is consistent with recent policy-oriented analyses that emphasize the need to combine digitalization, sustainability, and governance to strengthen logistics systems under uncertainty [29,35].

Overall, the discussion confirms that territorial logistics is more than a technical subsystem. It is a strategic field through which territories manage connectivity, sustainability, resilience, and competitiveness amid uncertainty. By empirically identifying four interdependent dimensions, this study provides a structured basis for both future research and policy design to build smarter, greener, and more resilient territorial logistics systems.

5.2. Limitations and Future Research

Despite its contributions, this study presents several limitations that should be acknowledged. First, the quantitative phase relied on Principal Component Analysis (PCA), which was appropriate for the exploratory purpose of the research but should not be interpreted as equivalent to a full latent variable validation strategy. PCA identifies covariance-based component structures and supports dimensional reduction, but it does not confirm a measurement model in the strict psychometric sense, nor does it allow causal testing of how the identified dimensions interact with one another. Therefore, although the study provides an empirically grounded exploratory framework, it does not explain the direction, strength, or mechanisms by which digitalization, sustainability, resilience, and territorial governance may influence one another. Therefore, the four-dimensional structure proposed in this study should be understood as an empirically grounded exploratory framework rather than as a definitively validated latent construct model.

Second, although the sample size was adequate for exploratory multivariate analysis, the quantitative phase was based on a non-probabilistic purposive sample of specialized respondents. Accordingly, the findings should be interpreted as analytically informative rather than statistically generalizable to all territorial contexts. Although the heterogeneity of the participant pool strengthens the interpretive robustness of the exploratory factor structure, future research should test the model’s stability in larger, probabilistic, and territorially stratified samples.

Third, the study relies on expert-based and perception-driven data, which may introduce subjective bias. Both the initial selection of variables and their perceived relevance were influenced by the experience, institutional background, and territorial context of the experts and respondents. Although this is consistent with the exploratory nature of the study, it may limit the transferability of the findings to other territorial or institutional environments with different logistics realities.

Fourth, the cross-sectional nature of the data restricts the ability to capture dynamic changes in territorial logistics systems over time. Territorial logistics is inherently evolutionary, shaped by technological change, policy adjustments, environmental pressures, and market transformations. As a result, the model provides a static representation of a phenomenon that is, in practice, highly dynamic.

Fifth, some variables that are highly relevant in specific crisis contexts—such as extreme events, social disruptions, and labor availability—did not reach the consensus threshold required for inclusion in the PCA. Although they were not retained in the exploratory dimensional structure, this should not be interpreted as evidence of low conceptual importance. Rather, it suggests that their salience may be more context-dependent, episodic, or territorially uneven than that of the variables ultimately retained. Future research should examine these factors more directly through scenario-based, longitudinal, or crisis-focused designs in order to better capture how distinct forms of turbulence reshape territorial logistics systems.

Another limitation concerns the scope of the variables included in the exploratory model. Although the study incorporated variables related to territorial socioeconomic impact, territorial economic conditions, climate and water vulnerability, governance, accessibility, and changes in logistics demand, these dimensions were assessed through expert judgment and survey-based perceptions rather than through objective territorial indicators. Therefore, the model does not directly include external socioeconomic variables such as gross regional product, population size, employment structure, education level, income distribution, productive specialization, or regional competitiveness. Nor does it fully incorporate geographic and climatic indicators, political and regulatory variables, or detailed measures of transportation demand. Future studies should address this limitation by integrating survey-based measures with secondary territorial data, spatial indicators, and demand-based logistics metrics. This would allow a more comprehensive assessment of territorial logistics sustainability and would help determine how socioeconomic structure, geography, climate exposure, political conditions, and transport demand shape logistics performance and resilience across different regions.

Finally, the research was conducted within a specific contextual framework, with a strong emphasis on territorial logistics in developing or emerging contexts. While this provides valuable insights into settings characterized by uneven infrastructure, institutional fragmentation, and territorial asymmetries, it may limit the direct applicability of the findings to highly developed logistics systems with different technological, institutional, and infrastructural conditions.

Building on these limitations, several avenues for future research can be identified. First, comparative studies across different types of territories—urban, rural, border, and remote regions such as Amazonian areas—should be conducted. Incorporating longitudinal designs would enable analysis of how digitalization, sustainability, resilience, and governance evolve over time, thereby identifying territorial trajectories and persistent structural gaps.

Second, future studies should test the stability and construct validity of the proposed dimensional structure using more rigorous procedures, such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) with split-sample validation, or structural equation modeling applied to broader, more rigorously designed datasets. This would help determine whether the four-dimensional structure identified here remains robust under alternative modeling strategies.

Third, future research should incorporate advanced analytical tools, including digital twins, machine learning models, optimization techniques, and spatial analytics. These approaches could enhance the predictive and simulation capabilities of territorial logistics models by enabling the evaluation of alternative scenarios under different technological, climatic, and economic conditions.

Fourth, it is necessary to complement quantitative approaches with participatory and qualitative methods, including Delphi techniques, interviews, focus groups, and participatory mapping. Because exploratory multivariate analysis alone cannot fully capture the complexity of territorial, institutional, and governance dynamics, integrating stakeholder perspectives would improve the contextual validity and interpretive richness of the identified dimensions.

Fifth, future studies should move beyond exploratory dimensional identification and test the relationships among the identified dimensions using structural equation modeling, system dynamics, longitudinal analysis, or other explanatory approaches. This would allow a deeper understanding of how digitalization, sustainability, resilience, and governance interact and jointly influence logistics performance, territorial adaptability, and regional development outcomes.

In addition, future research should incorporate objective socioeconomic, demographic, geographic, climatic, political, and transportation-demand variables into the analysis. Indicators such as gross regional product, population density, education level, employment structure, productive specialization, infrastructure coverage, climate-risk exposure, regulatory quality, public investment, freight demand, passenger–freight interaction, and origin–destination flows could provide a more complete basis for explaining territorial logistics sustainability. Combining these indicators with the perceptual and expert-based dimensions identified in this study would make it possible to develop more robust territorial models and to compare logistics sustainability across regions with different structural conditions.

The high loadings observed for several variables suggest a clearly differentiated exploratory component structure, but they should be interpreted with caution. In studies of this nature, high loadings may reflect strong conceptual coherence among retained variables, particularly when these have been previously refined through literature review and expert screening. At the same time, such results do not by themselves rule out possible conceptual proximity among some indicators. To improve transparency, the survey instrument used in the quantitative phase is provided in Appendix A, allowing readers to assess the wording and distinctiveness of the items. Future research should complement this exploratory evidence with additional psychometric procedures to examine redundancy, discriminant validity, and construct stability.

Finally, further research should examine the role of logistics in promoting territorial equity, sustainable mobility, and access to critical infrastructure. This line of inquiry would strengthen the connection among territorial logistics, ecological transition, and inclusive regional development, positioning logistics as a key lever to address contemporary challenges such as climate change, spatial inequality, and systemic competitiveness.

6. Conclusions

This study demonstrates that territorial logistics management constitutes a structurally multidimensional phenomenon shaped by the interaction of technological, environmental, institutional, and spatial factors. The theoretical review confirms that contemporary logistics systems extend beyond transport and distribution functions, evolving into strategic enablers of territorial development that integrate digitalization, sustainability, resilience, and governance within a systemic and adaptive framework.

The mixed-methods approach—combining qualitative identification of variables with quantitative validation through Principal Component Analysis—proved effective for capturing the latent structure of territorial logistics. The high internal consistency (α > 0.80) and strong sampling adequacy (KMO > 0.80) support the reliability of the measurement instrument, while the factorial structure provides a statistically coherent representation of the phenomenon. In line with the nature of PCA, the model explains variance patterns rather than causal relationships, enabling dimensional reduction while preserving the complexity of the system.

The empirical findings reveal that territorial logistics is organized around four core dimensions: (1) digital infrastructure and intelligent logistics, (2) sustainability and circular economy, (3) systemic resilience and risk management, and (4) territorial logistics, governance, and accessibility. Together, these dimensions explain more than 70% of the total variance, indicating the presence of stable, interpretable structural patterns. This reinforces the value of multivariate techniques for identifying latent constructs in complex logistics systems.

The factorial configuration and the conceptual model derived from it highlight the interdependence between dimensions, showing that improvements in logistics performance cannot be achieved through isolated interventions. Instead, effective territorial logistics strategies require the simultaneous alignment of technological innovation, environmental sustainability, institutional coordination, and spatial planning. This reinforces the study’s theoretical contribution: territorial logistics management should be understood as a multidimensional system of interacting capabilities rather than as a collection of separate operational functions. In this sense, the study contributes an empirically grounded framework that helps bridge fragmented streams of logistics research by integrating digitalization, sustainability, resilience, and territorial governance into a single analytical structure.

From a practical standpoint, the findings support several concrete recommendations. First, policymakers and territorial planners should coordinate investments in digital and physical logistics infrastructure, since the effectiveness of smart logistics depends on both technological adoption and territorial connectivity. Second, sustainability criteria should be more systematically embedded in logistics planning through low-emission mobility strategies, reverse logistics systems, and energy-efficient infrastructure. Third, resilience-building measures should be incorporated into territorial logistics strategies, including continuity planning, climate-sensitive infrastructure design, and risk preparedness mechanisms. Fourth, governance arrangements should be strengthened through multilevel coordination and stakeholder collaboration to ensure that logistics decisions are better aligned with regional development objectives and territorial equity concerns.

Overall, the proposed framework advances the theoretical understanding of territorial logistics under turbulent conditions while also offering a practical basis for designing more integrated logistics policies and development strategies. By identifying four interdependent dimensions, the study provides a structured reference for future research, infrastructure planning, and territorial decision-making aimed at building smarter, greener, and more resilient logistics systems.

Future research should extend this framework by incorporating objective socioeconomic, demographic, geographic, climatic, political, and transportation-demand indicators, thereby enabling a more comprehensive assessment of territorial logistics sustainability across diverse regional contexts.