# Synergistic Effects of Tech-Enabled Capabilities for Sustainability in Road Freight Transportation

Vitor Furlanetto Barrionuevo, Mario Henrique Callefi, Moacir Godinho Filho, Matthias Thürer, Gilberto Miller Devós Ganga, Marina Ivanova

<figure><img src="image_1.png" /></figure>

## To cite this version:

Vitor Furlanetto Barrionuevo, Mario Henrique Callefi, Moacir Godinho Filho, Matthias Thürer, Gilberto Miller Devós Ganga, et al. Synergistic Effects of Tech-Enabled Capabilities for Sustainability in Road Freight Transportation. Business Strategy and the Environment, 2025, 10.1002/bse.70130. hal-05290558

HAL Id: hal-05290558

https://normandie-univ.hal.science/hal-05290558v1

Submitted on 30 Sep 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH ARTICLE

OPEN ACCESS

# Synergistic Effects of Tech-Enabled Capabilities for Sustainability in Road Freight Transportation

Vitor Furlanetto Barrionuevo<fnref n="1" /> | Mario Henrique Callefi² ID | Gilberto Miller Devós Ganga<fnref n="1" /> ID |

Mario Henrique Callefi² ID

Marina Ivanova²

Moacir Godinho Filho<fnref n="1" />,³ ID

<figure><img src="image_3.png" /></figure>

Matthias Thürer² ID |

**Correspondence:** Mario Henrique Callefi (mariocallefi@gmail.com)

**Received:** 9 August 2024 | **Revised:** 18 July 2025 | **Accepted:** 23 July 2025

**Funding:** This work was supported by Bundesministerium für Wirtschaft und Klimaschutz, 01MK22001B.

**Keywords:** Competitive Advantage | Interpretive Structural Modeling (ISM) | Natural Resource-Based View (NRBV) | Road Freight Transportation | Sustainability | Tech-enabled Capabilities

## ABSTRACT

Road freight transportation is a crucial enabler of the global economy due to its cost and flexibility advantages. However, road freight transportation also significantly impacts the environment, contributing to greenhouse gas emissions, traffic congestion, and pollution. To address this trade-off, this study investigates the synergistic effects of tech-enabled capabilities on the sustainability of road freight transportation. Grounded in the natural resource-based view (NRBV) theory, it identifies tech-enabled capabilities for sustainability, explores their interrelationship, and determines how these capabilities can contribute to a competitive advantage. A mixed-methods approach was employed, comprising five phases: capability identification, expert validation using the Fuzzy Delphi method, development of a structural model via Interpretive Structural Modeling (ISM), analysis of interrelationships using Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), and creation of an Integrated Capabilities Framework for Sustainability in Road Freight Transportation (ICF-SRT). Findings highlight three groups of capabilities—pioneer, integrator, and ancillary—each playing a distinct role in enhancing sustainability and competitive advantage in road freight transportation. The study advances NRBV theory by mapping the interdependencies of tech-enabled capabilities and demonstrating their role in fostering sustainable practices. It provides road freight companies with a strategic framework for integrating and optimizing technologies to improve sustainability efforts, align with environmental and social governance (ESG) standards, and maintain a competitive edge in an eco-conscious market.

## 1 | Introduction

In a global economy, ever more goods are moved from one place to another. In this context, freight transportation plays a critical role as it facilitates the movement of goods from their origin to their destination (Kumar and Anbanandam 2020; Shardeo and Sarkar 2024). Although multiple modes of transportation exist, road transportation is predominantly used in many countries (Flodén et al. 2017; Fu et al. 2020) due to

its advantages in terms of cost and flexibility (Mlepo 2022). However, the advantages of road transportation come with significant environmental and social downsides, including pollutant emissions, traffic congestion, noise pollution, and an increased risk of highway accidents (Dwivedi et al. 2023; Ghisolfi et al. 2024). When considering sustainability, road freight transportation is often singled out as a major source of environmental harm and negative impacts on social well-being, since it is a leading source of greenhouse gas emissions

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Business Strategy and the Environment* published by ERP Environment and John Wiley & Sons Ltd.

<footnote n="1">¹Department of Production Engineering, Federal University of São Carlos, São Carlos, SP, Brazil | ²Chair of Factory Planning and Intralogistics, Chemnitz University of Technology, Chemnitz, Germany | ³Department of Supply Chain Management and Decision Sciences, EM Normandie Business School, Le Havre, France</footnote>
worldwide (Alp et al. 2022; Centobelli et al. 2020). This reality underscores the need to implement measures to enhance this sector's sustainability, in line with the ambitious objectives of the Paris Agreement (Gastón Cedillo-Campos et al. 2024; Pfoser 2022).

Miklautsch and Woschank (2022) argue that technological innovation can play an important role in reducing greenhouse gas emissions. In fact, technology can positively influence the triple bottom line beyond pollution reduction, encompassing social, environmental, and economic dimensions (Meyer et al. 2022). Two main categories of technologies are critical for the sustainability of road freight operations: vehicle technology and Information and Communication Technology (ICT). Vehicle technologies include, for example, electric, autonomous, and hydrogen vehicles, which aim to decrease (or eliminate) fossil fuel dependence and/or promote driving automation (Shah et al. 2021). ICT includes, for example, IoT, cloud computing, and wireless technologies, which offer various supply chain applications, such as monitoring fuel consumption and cargo temperature (Callefi et al. 2022, 2025; Wang et al. 2015). Although there exists research on the role of such technologies in creating sustainable freight transportation (Pathak et al. 2019), discussions often remain focused on specific technology groups, particularly vehicle technologies (Ahmadi 2019; de Mello Bandeira et al. 2019). This technology-centric perspective limits the understanding of the true role of technologies in creating sustainability, as many necessary capabilities are enabled by systems that incorporate various technologies (Callefi et al. 2022); it is the synergistic effect of different technologies that often creates a capability. This requires a system design that is driven by conscious management decisions that balance capabilities and investment in resources.

The resource-based view (RBV) emphasizes the importance of this relationship between resources and capabilities, where businesses must deploy their resources based on the required capabilities to achieve a competitive market advantage (Amit and Schoemaker 1993). Although RBV principles can also be considered in pursuing sustainability (Khanra et al. 2021), the natural resource-based view (NRBV) theory proposed by Hart (1995) extends the RBV by incorporating aspects of sustainable development and pollution prevention. This makes the NRBV specifically suitable for understanding sustainability in road freight transportation. The convergence of capabilities and resources is extensively debated in diverse contexts. Nevertheless, the focused examination of technology-enabled abilities in road freight transportation is a comparatively recent field of study (Callefi et al. 2022; Callefi, Ganga, et al. 2024b).

Despite the growing volume of research on digitalization and sustainability in freight systems, three gaps remain. First, studies often neglect the capability-oriented perspective of NRBV, focusing instead on resources or technologies in isolation. Second, most works lack an analysis of how tech-enabled capabilities interrelate and mutually reinforce each other to support sustainability goals. Third, there is a lack of prescriptive frameworks that guide road freight operators in systematically building these capabilities to achieve a sustainable competitive advantage. Therefore, to the best of our knowledge, there is still

a lack of studies specifically examining the intersection between tech-enabled capabilities and sustainable competitive advantage in this context. This leads to the following research questions that motivate our study:

* Research Question 1. Which tech-enabled capabilities are crucial for supporting sustainability in road freight transportation?

* Research Question 2. What are the interrelationships among these tech-enabled capabilities within the road freight transportation sector?

* Research Question 3. How can these interrelated tech-enabled capabilities systematically contribute to a competitive advantage in road freight transportation?

To address these research questions, this study adopts a multi-phase methodological approach. It first identifies and catalogues the most relevant tech-enabled capabilities for sustainability in road freight transportation to address Research Question 1. It then examines the structural interdependencies among them using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) to address Research Question 2. Finally, it proposes a strategic framework grounded in NRBV theory to support capability development for sustainable competitive advantage and address Research Question 3. The outcome of this process is the Integrated Capabilities Framework for Sustainability in Road Freight Transportation (ICF-SRT), which systematically leverages the interrelated capabilities to guide sustainability-oriented decision-making in the sector.

Theoretically, this study advances the NRBV by mapping the web of capability interdependencies and their role in fostering sustainable practices within the road freight sector. It challenges and expands upon existing NRBV frameworks by integrating technological innovation as a core component of resource-based competitive strategy, highlighting the dynamic and interconnected nature of resources and capabilities in achieving sustainable outcomes. The development and application of the ICF-SRT within this study also offer a novel contribution to NRBV theory by operationalizing its principles in the specific context of road freight transportation. From a practical perspective, the study offers road freight companies a strategic framework (ICF-SRT) for applying technology to enhance their sustainability efforts. This assists companies in aligning with environmental and social governance (ESG) standards while maintaining a competitive advantage in an increasingly eco-conscious market. The ICF-SRT framework serves as a guide for industry decision-makers, steering them towards sustainable operational practices and strategies that harmonize economic goals with sustainability commitments.

The remainder of the paper is organized as follows. Section 2 discusses the literature to provide the backdrop to our study before the research method employed to answer our research questions is outlined in Section 3. Results are then presented in Section 4 and discussed in Section 5, where our framework will be developed. Section 6 presents the research contributions and implications of this study. Finally, Section 7
summarizes conclusions, limitations, and future research directions.

# 2 | Conceptual Background

## 2.1 | Sustainability of Road Freight Transportation

The number of published studies on sustainability in the context of road freight transportation has increased over the years, as it has in other fields. Three major topics emerge as the most recurrent research trends in the field over the last 5 years. The first focuses on road freight transportation, emissions, and the environment, with particular attention to truck operations, the use of alternative fuels, and environmental assessment (Osorio-Tejada et al. 2024; Wu et al. 2022). The second addresses sustainability and performance indicators applied to logistics and transportation systems, encompassing operational, financial, and technological aspects (Callefi et al. 2022; Parviziomran et al. 2024). The third explores the relationship between economic growth and emissions, highlighting spatial analyses, regional variability, and energy dependence (Shi et al. 2021; Zhao et al. 2023). These topics reflect the increasing integration of environmental sustainability, technological innovation, and economic efficiency in the field of mobility and logistics.

Despite the growing number of studies, most adopt a predominantly descriptive approach. The literature tends to focus on measuring environmental impacts, analyzing performance indicators, and identifying relationships between economic, spatial, and operational variables (Osorio-Tejada et al. 2024; Pecorari and Lima 2021; Wu et al. 2022). Although some studies move towards prescriptive approaches by proposing frameworks or strategies, their number remains limited. Existing frameworks also often lack operational specificity and practical guidance, making implementation difficult. Consequently, a significant need remains for prescriptive research that clearly outlines actionable pathways, policies, or technological solutions to reduce the environmental footprint of road freight operations. There is also limited exploration of tensions between economic performance and environmental goals, an issue that remains central to the sustainability debate in logistics. This limitation is especially apparent when considering the role of technology, which, despite its acknowledged significance, is frequently addressed in general terms, without a thorough analysis of its application or integration into road freight operations.

## 2.2 | The Role of Technologies in Road Freight Transportation Sustainability

Technological advancements have the potential to address sustainability issues, being technological evolution progressively acknowledged as a principal element in reducing the social and environmental repercussions linked to road freight operations (Awan et al. 2022). The pressing need to create sustainability in this sector has led to technology being used in initiatives to diminish emissions, enhance energy efficiency, and curtail the overall environmental impact of freight activities (Acheampong et al. 2022). Furthermore, technological innovations play a pivotal role in economic terms, fostering increased effectiveness

and efficiency within freight operations (Harris et al. 2015). By deploying innovative solutions and sophisticated systems, technology facilitates a harmonious balance between the critical economic role of road freight transportation and the imperative demands for environmental guardianship and social accountability.

In recent years, several studies have examined the interface between technological innovation and sustainability. For example, Kashav and Garg (2025) developed a framework to identify key drivers of sustainable freight innovation, but they did not specify which technologies deliver the most value in practice. Acheampong et al. (2022) examined how technology can reduce CO₂ emissions and energy use, yet their work remains at a conceptual level. Hu et al. (2025) highlighted the potential of underground logistics systems and the need for enabling policies and technologies, but provided only a limited discussion on operational requirements. Other studies, such as those by Dwivedi et al. (2023) and Fulzele and Shankar (2022), emphasize the role of various technological practices, although often without clarifying which specific technologies are most effective or how they should be implemented. Meanwhile, research on individual technologies—such as blockchain (e.g., Callefi, Grzona, et al. 2024; Tian et al. 2021) and electric vehicles (e.g., de Mello Bandeira et al. 2019)—also presents limitations, as these studies often examine technologies in isolation and overlook the broader ecosystem of interdependent tools and systems required for their effective implementation. In practice, critical capabilities in freight transportation are not supported by a single solution, but rather by a combination of technologies that must be integrated across operational processes. Consequently, there is a pressing need to investigate further the role of technologies in enhancing sustainability in road freight transportation, with particular attention to their complementary characteristics, operational requirements, and implementation contexts. Such research should also go beyond high-level theoretical discussions to provide structured, actionable guidance for practitioners.

## 2.3 | The NRBV on Road Freight Transportation Sustainability

According to the RBV, competitive advantage is underpinned by a firm's resources and capabilities (Amit and Schoemaker 1993). Given that technologies constitute resources that a company can leverage to enable specific capabilities essential for sustaining its competitive advantage, it is ineffective to focus on deploying resources without understanding which capabilities need to be developed. This situation becomes even more critical in road freight transportation, where the operational capabilities essential for supporting the operations are enabled by a group of technologies rather than a single technology (Callefi et al. 2022). Therefore, as emphasized by Callefi et al. (2024b), the strategy for technology acquisition in road freight transportation should be defined by an understanding of which operational capabilities are essential. Once road operators identify the necessary capabilities, they can effectively acquire and implement technologies.

Hart (1995) argued that management theory has traditionally ignored natural constraints and proposed NRBV, a theory of competitive advantage based upon a firm's relationship to the
natural environment that is composed of three interconnected strategies: pollution prevention, product stewardship, and sustainable development. Similar criticism holds for the literature on road freight transportation. There is a significant lack of literature on using technology to improve its sustainability. Although certain studies have identified distinct capabilities for sustainable freight operations, their discussions have primarily concentrated on emphasizing one or more capabilities enabled by specific technologies (or clusters of technologies). For example, Sharma et al. (2022) investigated the influence of several technologies, particularly digital ones, on sustainable freight operations, but did not explicitly examine the concept of capability. Similarly, Alp et al. (2022) investigated how technology affects energy consumption and CO₂ emissions, but they did not consider the concept of capabilities. Meanwhile, although Callefi et al. (2022) identified three capabilities relevant to sustainability, the discussions were purely descriptive and did not explore the interconnections between these capabilities. Overall, existing studies tend to treat sustainability as the result of isolated technological practices, rather than as an emergent property of strategically coordinated capabilities, thereby failing to fully engage with the core logic of the NRBV. To the best of our knowledge, only Pålsson and Kovács (2014) have considered the RBV and NRBV, but their study was specifically aimed at understanding why companies prioritize reducing transportation emissions.

Our study sets out to fill this important research gap. While capabilities can encompass a wide range of factors—including innovation readiness, digital maturity, and process alignment—this study focuses specifically on tech-enabled operational capabilities. These refer to the intangible abilities needed to carry out freight-related tasks, such as cargo status monitoring, real-time data processing, and automated operational decisions (Callefi, Ganga, et al. 2024b).

## 3 | Research Method

This research uses a mixed-methods approach that integrates both qualitative and quantitative methods (Bryman 2006) into five phases: (1) identification of the preliminary list of capabilities through a literature review; (2) validation of the list of capabilities with experts; (3) elaboration of the structural model; (4) development of a cluster diagram that identifies the driver and dependence power; and (5) construction of the ICF-SRT framework. It is summarized in Figure 1.

### 3.1 | Rationale for Selecting FD, ISM, and Fuzzy MICMAC Methods

As highlighted by Farooque et al. (2019) and Callefi et al. (2024b), the selection of multiple criteria decision-making (MCDM) methods should be directly aligned with the nature of the research questions being addressed. In this context, the integrated use of FD, Interpretive Structural Modeling (ISM), and Fuzzy MICMAC was guided by the conceptual character of this study and its specific goals of identifying, structuring, and analyzing interrelationships among tech-enabled capabilities for sustainability in road freight transportation.

The FD Method was applied to consolidate expert opinions regarding which capabilities should be retained for analysis. Compared to the classical Delphi method, the fuzzy version enhances the treatment of uncertainty in linguistic assessments and reduces cognitive bias during consensus-building, making it more suitable when expert judgment involves imprecision (Ishikawa et al. 1993). In line with Rathore et al. (2022), FD ensures methodological transparency while enhancing reliability in filtering the most relevant constructs before modeling.

To structure the interrelationships among the selected capabilities, we employed the ISM method, which enables the construction of a hierarchical model based on expert inputs and logical rules (Warfield 1974). ISM has been widely used in operations and supply chain management to model complex interdependencies, including in studies on Lean, sustainability, and logistics (Bianco et al. 2023; Callefi, Grzona, et al. 2024; Godinho Filho et al. 2025). Its step-by-step process, from expert selection and SSIM construction to reachability matrix analysis, makes it well suited for studies that aim to develop conceptual models from structured expert knowledge. Moreover, its clarity and low implementation cost favor managerial understanding and engagement (Attri et al. 2013; Bianco et al. 2023). However, a known limitation of ISM is its binary treatment of relationships, which does not account for varying degrees of influence among elements. To overcome this, we integrated the Fuzzy MICMAC technique, which enhances the ISM output by classifying variables according to their driving and dependence power, utilizing fuzzy logic to capture uncertainty in expert assessments more effectively (Callefi, Grzona, et al. 2024). As discussed by Bianco et al. (2023), the Fuzzy MICMAC method adds methodological rigor by enabling a more nuanced and flexible classification of variables within the modeled system. Alternative methods such as AHP or DEMATEL could have been considered; however, these require experts to assign precise numerical weights or strength levels to a large number of variable pairs, in this case, 153 for 18 variables, which increases respondent fatigue and may reduce accuracy. In contrast, ISM only requires experts to indicate whether a relationship exists or not, reducing cognitive overload while preserving interpretability. As highlighted by Bianco et al. (2023), this simplicity is particularly important in studies with a large number of interrelated variables.

### 3.2 | Phase 1: Identification of the Preliminary List of Capabilities

The initial list of tech-enabled capabilities was obtained through a literature review. A total of 23 capabilities were identified, consisting of 21 proposed by Callefi et al. (2024b) and Callefi et al. (2022), and one additional capability—computational modeling—proposed by Agnusdei et al. (2022). We did not conduct a systematic literature review since this was only recently accomplished by Callefi et al. (2024b) and Callefi et al. (2022). We rather scanned the literature for any additional capabilities. Given that the reviewed studies covered broad aspects of road freight transportation, it was important to adapt the 22 identified capabilities to the sustainability context. For this, the principles of sustainable development theory (Steer and Wadegery 1993) were considered, and the title and description of the 22 tech-enabled capabilities were clearly defined. To guarantee
<figure><img src="image_4.png" /><figcaption>FIGURE 1 | Research method.</figcaption></figure>

the reliability of the proposed list of capabilities, the authors conducted several meetings to discuss if the titles and descriptions of the capabilities were relevant to the topic of sustainable road freight transportation, as well as the initially proposed capabilities.

## 3.3 | Phase 2: Validation of the List of Capabilities

The Delphi method was originally developed by Dalkey and Helmer (1963) to forecast future events. Since then, it has proven effective in building consensus among experts for identifying and scrutinizing relevant factors in multi-criteria decision-making (Hsu et al. 2013). While the method offers significant benefits, concerns about the vagueness and uncertainty of expert responses were raised (Hsu et al. 2010). Addressing these issues, Murray et al. (1985) improved the method by integrating fuzzy set theory with the traditional Delphi process, thus creating the FD method. Over time, the FD method has been applied across various domains to validate factors, serving primarily as a preliminary step for

employing other multi-criteria decision-making techniques such as AHP, fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), and ISM (Rathore et al. 2022; Virmani et al. 2024). Therefore, the FD method was chosen to structure the capabilities validation process. It includes three main steps: (1) selection of experts, (2) consultation with experts, and (3) analysis of expert responses.

According to Clayton (1997), a heterogeneous panel of experts for the Delphi method should include at least five individuals with substantial knowledge and experience in the subject under study. In our research, we defined an expert in road freight transportation or logistics as someone with at least 5 years of professional or academic experience in the field (Callefi et al. 2022). The sampling process for the FD method followed a strategy based on theoretical saturation, allowing for the inclusion of new participants until the responses became redundant and no additional insights emerged. This strategy enhances the reliability of qualitative assessments by ensuring that expert input is both comprehensive and stabilized (Francis et al. 2010). To implement this, data collection
<table><caption>**TABLE 1** | Detail of expert sample to the fuzzy Delphi method.</caption><thead><tr><th>Expert</th><th>Position</th><th>Academic background</th><th>Years of experience</th></tr></thead><tbody><tr><td>1</td><td>Manager</td><td>Specialization</td><td>26</td></tr><tr><td>2</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>16</td></tr><tr><td>3</td><td>Researcher/ Consultant</td><td>Master's degree</td><td>5</td></tr><tr><td>4</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>5</td></tr><tr><td>5</td><td>Manager</td><td>Specialization</td><td>12</td></tr><tr><td>6</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>20</td></tr><tr><td>7</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>5</td></tr><tr><td>8</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>20</td></tr><tr><td>9</td><td>Researcher/ Consultant</td><td>Master's degree</td><td>10</td></tr><tr><td>10</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>25</td></tr><tr><td>11</td><td>Researcher/ Consultant</td><td>Master's degree</td><td>5</td></tr><tr><td>12</td><td>Researcher/ Consultant</td><td>Master's degree</td><td>5</td></tr></tbody></table>

<table><caption>**TABLE 2** | Fuzzy Delphi scale.</caption><thead><tr><th>Linguistic variable</th><th>Value</th><th>Corresponding TFN</th></tr></thead><tbody><tr><td>Extremely unimportant</td><td>1</td><td>(0.1, 0.1, 0.3)</td></tr><tr><td>Unimportant</td><td>2</td><td>(0.1, 0.3, 0.5)</td></tr><tr><td>Normal</td><td>3</td><td>(0.3, 0.5, 0.7)</td></tr><tr><td>Important</td><td>4</td><td>(0.5, 0.7, 0.9)</td></tr><tr><td>Extremely important</td><td>5</td><td>(0.7, 0.9, 0.9)</td></tr></tbody></table>

Source: Singh and Sarkar (2020).

and analysis were carried out in parallel, and saturation was considered reached when subsequent expert evaluations no longer contributed new arguments or modifications to the capability list.

Based on these criteria, 12 experts were selected, as detailed in Table 1. This number exceeds the minimum requirement suggested by Clayton (1997) and ensures sufficient validation power. The panel composition is also heterogeneous, including professionals from academia and industry, with backgrounds ranging from logistics management to supply chain consulting and technology, as well as varying academic degrees and years of experience. This diversity strengthens the relevance, credibility, and generalizability of the validation process outcomes.

A questionnaire was emailed to the 12 selected experts, which contained details about their capabilities, including their titles and descriptions. The experts were then requested to evaluate their level of agreement on a scale (see Table 2) ranging from 1 (extremely unimportant) to 5 (extremely important) regarding the alignment of the assessed criterion with at least two aspects of the triple bottom line in the context of road freight transportation. Once the expert agreed that the analyzed capability was related to at least two of the three aspects, it was possible to guarantee a link between capability and the theme of sustainability.

After gathering the responses, precise aggregation and subsequent defuzzification were conducted using the center of gravity procedure. This allowed for the identification of the weight obtained by each criterion. The calculations were carried out following the guidelines presented by Rathore et al. (2022) and Ishikawa et al. (1993). After calculating the defuzzified weight of each capability, it was necessary to compare the weights obtained against a threshold value (α). The chosen value for α was 0.6, based on the guidelines from Kumar et al. (2017).

## 3.4 | Phase 3: Elaboration of the Structural Model

The ISM approach, a widely used method in freight transportation and logistics research (Rathore et al. 2022; S. Singh et al. 2021), was chosen for the development of the structural model. ISM offers a more efficient and accurate approach to structural model construction. According to Raj et al. (2008), the ISM method comprises the following steps: (1) creating a structural self-interaction matrix (SSIM); (2) forming an initial reachability matrix (IRM); (3) developing a final reachability matrix (FRM); (4) conducting level partitions; and (5) deriving the ISM-based model.

For the development of the structural model, a separate sampling process was conducted to select the expert panel. While the Fuzzy Delphi focused on evaluating and filtering the most relevant capabilities, ISM required a broader analysis of the interrelationships among the validated capabilities, and therefore demanded a larger pool of informed experts capable of assessing such systemic interactions. A total of 16 experts were selected for the ISM phase, as shown in Table 3. This number exceeds the size of expert panels used in previous ISM studies—such as Nazarian-Jashnabadi et al. (2023), who employed four experts; Chen et al. (2022), who involved nine; and Sherif et al. (2022), who worked with eight—and ensures a solid basis for achieving consensus and reliability in structuring the relationships among capabilities. As in the FD phase, the ISM panel was constructed using a saturation-based approach, whereby experts were progressively consulted until the evaluations converged and no new substantial insights emerged from additional responses. This theoretical saturation approach guarantees the stability of the relationship matrix and reinforces the robustness of the resulting structural model. The final panel also maintained heterogeneity in professional profiles, combining academics, consultants, and logistics managers, with a wide range of years of experience and educational backgrounds. This ensures that the analysis reflects both conceptual rigor and practical relevance, which is essential for developing a robust and generalizable structural framework.
<table><caption>TABLE 3 | Detail of expert sample to the ISM method.</caption><thead><tr><th>Expert</th><th>Position</th><th>Academic background</th><th>Years of experience</th></tr></thead><tbody><tr><td>1</td><td>Researcher/Consultant</td><td>Specialization</td><td>15</td></tr><tr><td>2</td><td>Manager</td><td>Specialization</td><td>14</td></tr><tr><td>3</td><td>Researcher/Consultant</td><td>Master's degree</td><td>5</td></tr><tr><td>4</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>10</td></tr><tr><td>5</td><td>Researcher/Consultant</td><td>Master's degree</td><td>5</td></tr><tr><td>6</td><td>Researcher/Consultant</td><td>Specialization</td><td>5</td></tr><tr><td>7</td><td>Manager</td><td>Specialization</td><td>12</td></tr><tr><td>8</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>20</td></tr><tr><td>9</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>9</td></tr><tr><td>10</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>20</td></tr><tr><td>11</td><td>Researcher/Consultant</td><td>Master's degree</td><td>9</td></tr><tr><td>12</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>10</td></tr><tr><td>13</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>21</td></tr><tr><td>14</td><td>Researcher/Consultant</td><td>Master's degree</td><td>7</td></tr><tr><td>15</td><td>Researcher/Consultant</td><td>Master's degree</td><td>10</td></tr><tr><td>16</td><td>Professor</td><td>Doctorate/Post Doctorate</td><td>20</td></tr></tbody></table>

We distributed a questionnaire to these experts to rigorously evaluate pairwise relationships between capabilities. The questionnaire utilized specific symbols to denote the nature of interactions between the capabilities: (i) V: capability i positively affects capability j; (ii) A: capability j positively affects capability i; (iii) X: capabilities i and j will help achieve each other; (iv) O: there is no relationship between (i, j) capabilities. The IRM was then constructed by converting the SSIM into a binary format using the rules set forth by Watson (1978). The conversion rules are:

* If the (i, j) element of SSIM is V, then in the IRM, the (i, j) element is 1, and the (j, i) element is 0;

* If the (i, j) element of SSIM is A, then in the IRM, the (i, j) element is 0, and the (j, i) element is 1;

* If the (i, j) element of SSIM is X, then in the IRM, the (i, j) element is 1, and the (j, i) element is 1;

* If the (i, j) element of SSIM is 0, then in the IRM, the (i, j) element is 0, and the (j, i) element is 0;

* Diagonal elements are set to 1 since both i and j are the same.

We subsequently developed the Final Reachability Matrix (FRM) while considering transitivity. This condition stipulates that if capability A influences capability B and capability B influences capability C, it is inferred that capability A is indirectly related to capability C. This indirect relationship among the capabilities is denoted with a “1*” in the FRM (Watson 1978).

Finally, level partitions were executed based on the completed FRM. This entailed systematically identifying the reachability and antecedent sets for each capability following the method proposed by Ruiz-Benitez et al. (2017). These sets determine the direct and indirect influences among capabilities. In addition, the final step involved constructing the structural model from the level partitions and the FRM. This model demonstrates relationships between capabilities through arrows indicating directional influences from one capability to another.

## 3.5 | Phase 4: Development of a Cluster Diagram

According to Singh and Agrawal (2022) and Singh and Gupta (2020), ISM and Fuzzy MICMAC are complementary methodologies. While ISM provides insights into the hierarchical relationships among the studied factors without addressing the degree or intensity of those relationships, fuzzy MICMAC allows for a more comprehensive understanding of each factor's role by calculating its driving and dependence power. It has consequently been widely used to complement the weaknesses of ISM (Patel et al. 2021). In our study, the fuzzy MICMAC was used for the development of the cluster diagram. Considering the guidelines proposed by Bhosale and Kant (2016), fuzzy MICMAC includes the following steps: (1) development of binary direct reachability matrix (BDRM); (2) construction of fuzzy direct reachability matrix (FDRM); (3) development of Fuzzy MICMAC stabilized matrix (FMSM); and (4) derivation of cluster diagram.

The BDRM is obtained by replacing the diagonal elements of the IRM with zeros. In addition, BDRM does not cover the transitivity instances. A fuzzy scale, which rates the intensity of the relationship between the analyzed elements, was then considered for constructing the FDRM (see Table 4).

BDRM stabilization was then achieved. The literature describes several fuzzy composition procedures, each with its advantages and disadvantages (e.g., max-product, max-average, and max-min). The max-min operator proposed by Kandasamy et al. (2007) was chosen in our study since it is considered more appropriate for the fuzzy MICMAC context (Callefi, Grzona, et al. 2024). The calculations for the transition to the FMSM matrix were executed in MATLAB.

Together with the FMSM identification, the driving and dependence power levels of each element were also determined. The driving power is determined by the sum of all values in
<table><caption>TABLE 4 | Linguistic terms for the relationship evaluation.</caption><thead><tr><th>Strength</th><th>Value assigned</th><th>Several experts agreed that the factor i drive factor j</th></tr></thead><tbody><tr><td>Null</td><td>0</td><td>0</td></tr><tr><td>Low</td><td>0.25</td><td>1–5</td></tr><tr><td>Medium</td><td>0.50</td><td>6–9</td></tr><tr><td>Strong</td><td>0.75</td><td>10–16</td></tr><tr><td>Very strong</td><td>1</td><td>16</td></tr></tbody></table>

Source: Adapted from Mohanty (2018).

the respective row, while the sum of all values in the respective column determines the dependence power. Finally, the cluster diagram was constructed. This diagram visually represents the capabilities, categorized based on their calculated driving and dependence power values. It helps visually assess which capabilities are most influential, which are most dependent, and how they interrelate within the overall system.

## 3.6 | Phase 5: Construction of the ICF-SRT

The ICF-SRT is developed by analyzing the results of the ISM method and the fuzzy MICMAC. This framework adopts an inductive perspective, grounded in the principles of the NRBV theory, and categorizes capabilities into distinct yet interdependent groups.

## 4 | Results

### 4.1 | Validated List of Capabilities for Road Freight Transportation Sustainability

From the literature review, 23 capabilities were identified, which were adapted to the reality of sustainability in road freight transportation. They are summarized in Table 5.

The FD methodology was then employed for validation. Results are summarized in Table 6. Of all the capabilities assessed, 16 have an acceptance threshold above 0.6 (Kumar et al. 2017), while seven were rejected. The capabilities that were rejected include the following: eco-compliant driving data assurance (C07), eco-friendly freight oversight (C08), green route projection for drivers (C14), renewable mobility monitoring (C16), responsible freight data management (C18), sustainable terms enforcement (C21), and vehicular collision avoidance (C23). Although these capabilities relate to the sustainability of road freight transportation, the experts' evaluations revealed that they had a weak connection with at least two of the three triple bottom-line dimensions (environmental, social, and economic).

### 4.2 | ISM Results

Individual results for each step executed to assess the relationships between validated capabilities are summarized in an

Appendix. Initially, the SSIM was developed (see Table A1), followed by the construction of the IRM (see Table A2) and the FRM (see Table A3). Subsequently, the level partitioning of each capability was determined (see Table A4). The level partitioning analysis involved checking if the analyzed factors had matching sets of reachability and intersection. A total of three iterations were performed. Once the stopping criterion was satisfied, it was confirmed that all factors were classified at a specific level. In total, eight iterations were carried out. Ultimately, the ISM-based model was constructed using the results from the level partitioning, as shown in Figure 2.

The ISM model has eight levels of hierarchy, as can be observed from Figure 2, each representing different tiers of capabilities within freight transportation operations. The first level with one capability (C17), the second level with four capabilities (C02, C04, C09, C15), the third level with one capability (C19), the fourth level with three capabilities (C01, C10, C11), the fifth level with three capabilities (C03, C06, C12), the sixth level with one capability (C13), the seventh level with one capability (C22), and the eighth level with two capabilities (C05, C20). This multi-level structure allows for a clear delineation of a capabilities' roles and dependencies.

### 4.3 | Fuzzy MICMAC Results

Individual results for each step are again summarized in an Appendix. The BDRM was initially developed using the fuzzy MICMAC approach (Table A5). Subsequently, the FDRM (Table A6) and the FMSM (Table A7) were constructed. The 16 capabilities were then categorized into three clusters by considering the driving power and dependence power values, as shown in Figure 3.

Figure 3 shows three distinct clusters of capabilities. The first includes five capabilities (C02, C04, C09, C15, and C17) that are named driving factors due to their high driving power and low dependence. The second, the largest, comprises seven capabilities (C01, C03, C06, C10, C11, C12, and C19) that are named autonomous factors because they exhibit low driving and dependence powers. It is crucial to note that despite their autonomous nature, these capabilities maintain a certain degree of dependence on driving factors and influence the dependent factors. The third consists of four capabilities (C05, C13, C20, C22) that are categorized as dependent factors because they demonstrate high dependence power but low driving power. These capabilities require support from those with high driving power to be activated.

The analysis of ISM and fuzzy MICMAC results revealed complementary findings. The initial two levels of the ISM model covered the first cluster, while levels III, IV, and V encompassed the second cluster, and levels VI, VII, and VIII included the third cluster. These findings provide a logical understanding of capability prioritization, which will be further discussed in the following section.

## 5 | Discussion and Framework Development

Following the results of the previous section, and considering the literature on freight transportation and NRBV theory, we
<table><caption>TABLE 5 | Tech-enabled capabilities for road freight transportation sustainability.</caption><thead><tr><td>Cod.</td><td>Capability</td><td>Description</td></tr></thead><tbody><tr><td>C01</td><td>Autonomous eco-driving</td><td>It ensures the reduction or elimination of the need for driver control to enhance sustainable practices in vehicle operation.</td></tr><tr><td>C02</td><td>Cargo data integrity to minimize environmental impact</td><td>It ensures the integrity and reliability of cargo data, such as position and condition, to optimize efficiency and minimize environmental impact.</td></tr><tr><td>C03</td><td>Cargo smart control for green freight</td><td>It allows for a proactive approach through IoT systems to identify and communicate cargo safety and integrity issues. This smart monitoring ensures early detection of problems such as high temperatures, preventing environmental hazards.</td></tr><tr><td>C04</td><td>Computational modeling towards sustainable improving</td><td>It involves the use of computational systems to simulate and improve scenarios for more sustainable road freight activities.</td></tr><tr><td>C05</td><td>Decision support for sustainable freight transportation</td><td>It ensures the management and analysis of road freight data to support decision-making prioritizing sustainability.</td></tr><tr><td>C06</td><td>Driver well-being monitoring</td><td>It provides real-time monitoring of the driver's health to ensure its well-being.</td></tr><tr><td>C07</td><td>Eco-compliant driving data assurance</td><td>It ensures the reliability of driving data to guarantee eco-driving initiatives, which is pivotal in validating measures to reduce emissions and augment fuel economy.</td></tr><tr><td>C08</td><td>Eco-friendly freight oversight</td><td>It continuously monitors the location of vehicles and cargo, enhancing the ability to conduct transportation in a more environmentally responsible manner.</td></tr><tr><td>C09</td><td>Eco-performance monitoring of vehicles</td><td>It involves continuously monitoring vehicle component operating parameters, such as the engine, tires, and suspension systems. Thus, ensuring that each component functions within optimal parameters and significantly contributes to sustainable transportation practices is possible.</td></tr><tr><td>C10</td><td>Eco-responsible accountability</td><td>It provides transparency in operations by tracking who is performing actions and contributing to responsible and sustainable logistics.</td></tr><tr><td>C11</td><td>Emission management</td><td>It provides management and analysis of data regarding pollutant emissions, focusing on reducing the environmental impact of freight operations.</td></tr><tr><td>C12</td><td>Fuel consumption optimization</td><td>It aims to minimize fuel consumption by vehicles, thereby reducing the carbon footprint of transportation.</td></tr><tr><td>C13</td><td>Green fleet coordination</td><td>It ensures real-time coordination and control of a group of vehicles, aiming for reduced emissions and improved fuel efficiency.</td></tr><tr><td>C14</td><td>Green route projection for drivers</td><td>It enables the virtual projection of essential real-time information to drivers, such as eco-friendly routes and schedules, with a target of minimizing environmental impact.</td></tr><tr><td>C15</td><td>Real-time cargo condition monitoring</td><td>It enables real-time monitoring of cargo conditions, ensuring sustainable preservation and transport of goods.</td></tr></tbody><tfoot><tr><td colspan="3" style="text-align: right;">(Continues)</td></tr></tfoot></table>
<table><caption>TABLE 5 | (Continued)</caption><thead><tr><td>Cod.</td><td>Capability</td><td>Description</td></tr></thead><tbody><tr><td>C16</td><td>Renewable mobility monitoring</td><td>It provides monitoring of vehicle parameters in real-time, such as speed and driving time, to ensure optimal environmental performance throughout the transportation process.</td></tr><tr><td>C17</td><td>Responsible freight data exchange</td><td>It ensures instant data exchange on transportation activities to improve sustainability through better resource management.</td></tr><tr><td>C18</td><td>Responsible freight data management</td><td>It ensures that essential freight data is accessible only to stakeholders while supporting the confidentiality and integrity of information in sustainable supply chains.</td></tr><tr><td>C19</td><td>Routing optimization</td><td>It provides the most sustainable routes, considering the shortest distance and minimal environmental impact.</td></tr><tr><td>C20</td><td>Sustainable freight booking</td><td>It enables a direct interface between shippers and carriers to promote sustainable freight services.</td></tr><tr><td>C21</td><td>Sustainable terms enforcement smart</td><td>It involves the use of smart contracts encoded in computational codes to automatically enforce the environmentally focused terms and conditions of transportation agreements.</td></tr><tr><td>C22</td><td>Transport decarburization</td><td>It involves using green energy sources, like electricity or hydrogen, to transport goods to reduce carbon emissions.</td></tr><tr><td>C23</td><td>Vehicular collision avoidance</td><td>It provides information for drivers of potential collision risks, promoting safer, more efficient driving and reducing the chance of accidents that could harm the environment.</td></tr></tbody></table>

suggest that three capability groups support sustainability in road freight transportation:

* Pioneer: This group of capabilities is fundamental in supporting companies to leverage the capabilities of the other two groups. This characteristic is due to the high driving power of these capabilities, meaning they have a significant positive influence on the different capabilities.

* Integrator: This group of capabilities is vital in bridging pioneer and ancillary capabilities. Although the integrator capabilities have a moderate level of dependency on the pioneer capabilities, their dependence is not as high, allowing them some autonomy. Simultaneously, they exert a significant, yet not overwhelming, influence on the ancillary capabilities. This intermediate position of these capabilities and their relationship with the other two capability groups underscores their role as integrators.

* Ancillary: This group of capabilities is characterized by a high level of dependence. They play a more auxiliary role. Other groups of capabilities should therefore be developed first to leverage the sustainability of road freight transportation.

Figure 4 illustrates these three groups of capabilities and the relationship between them. It is possible to observe that pioneer capabilities have the power to drive ancillary and integrator capabilities. This driving power is high on ancillary

capability but low on integrator capabilities. Integrator capabilities also have a driving power on ancillary capabilities but with a low intensity.

## 5.1 | Pioneer Capabilities

The pioneer capabilities group comprises five key capabilities: CO2, CO4, CO9, C15, and C17. These capabilities are crucial as they demonstrate high driving and low dependence power. Companies aiming to achieve a competitive advantage through the sustainability of their operations should thus prioritize them. A significant characteristic of the pioneer capabilities is that they relate to different application contexts. CO2 is linked to data reliability, CO4 to operations simulation, CO9 and C15 to cargo and truck parameters sensing, and C17 to data transmission infrastructure. This diverse nature of the capabilities shows that road operators seeking a competitive edge through sustainability must invest in, and adopt, technologies pertinent to various aspects of freight transportation operations. Furthermore, all five capabilities in this group are enabled by ICTs, indicating that while current literature on road freight transportation sustainability primarily focuses on the adoption of less polluting vehicles (Müller 2024; Ueckerdt et al. 2021), ICTs must also be considered to make road freight transportation operations more sustainable. This finding aligns with Baffour Gyau et al. (2024), who, using the Panel Quantile Autoregressive Distributed Lag (PQARDL) method, highlight ICTs as crucial in mitigating transportation emissions.
<table><caption>TABLE 6 | Fuzzy Delphi (FD) results.</caption><thead><tr><th>Capability</th><th>Fuzzy weights</th><th>Defuzzification value</th><th>Result</th></tr></thead><tbody><tr><td>C01</td><td>(0.3,0.77,0.9)</td><td>0.66</td><td>Accepted</td></tr><tr><td>C02</td><td>(0.3,0.75,0.9)</td><td>0.65</td><td>Accepted</td></tr><tr><td>C03</td><td>(0.3,0.76,0.9)</td><td>0.65</td><td>Accepted</td></tr><tr><td>C04</td><td>(0.3,0.80,0.9)</td><td>0.67</td><td>Accepted</td></tr><tr><td>C05</td><td>(0.3,0.78,0.9)</td><td>0.66</td><td>Accepted</td></tr><tr><td>C06</td><td>(0.3,0.79,0.9)</td><td>0.66</td><td>Accepted</td></tr><tr><td>C07</td><td>(0.1,0.64,0.9)</td><td>0.55</td><td>Rejected</td></tr><tr><td>C08</td><td>(0.1,0.73,0.9)</td><td>0.58</td><td>Rejected</td></tr><tr><td>C09</td><td>(0.3,0.74,0.9)</td><td>0.65</td><td>Accepted</td></tr><tr><td>C10</td><td>(0.5,0.83,0.9)</td><td>0.74</td><td>Accepted</td></tr><tr><td>C11</td><td>(0.3,0.81,0.9)</td><td>0.67</td><td>Accepted</td></tr><tr><td>C12</td><td>(0.5,0.83,0.9)</td><td>0.74</td><td>Accepted</td></tr><tr><td>C13</td><td>(0.5,0.83,0.9)</td><td>0.74</td><td>Accepted</td></tr><tr><td>C14</td><td>(0.1,0.65,0.9)</td><td>0.55</td><td>Rejected</td></tr><tr><td>C15</td><td>(0.3,0.78,0.9)</td><td>0.66</td><td>Accepted</td></tr><tr><td>C16</td><td>(0.1,0.70,0.9)</td><td>0.57</td><td>Rejected</td></tr><tr><td>C17</td><td>(0.5,0.82,0.9)</td><td>0.74</td><td>Accepted</td></tr><tr><td>C18</td><td>(0.1,0.74,0.9)</td><td>0.58</td><td>Rejected</td></tr><tr><td>C19</td><td>(0.3,0.80,0.9)</td><td>0.67</td><td>Accepted</td></tr><tr><td>C20</td><td>(0.5,0.79,0.9)</td><td>0.73</td><td>Accepted</td></tr><tr><td>C21</td><td>(0.1,0.76,0.9)</td><td>0.59</td><td>Rejected</td></tr><tr><td>C22</td><td>(0.3,0.76,0.9)</td><td>0.65</td><td>Accepted</td></tr><tr><td>C23</td><td>(0.1,0.65,0.9)</td><td>0.55</td><td>Rejected</td></tr><tr><td>Threshold</td><td></td><td>0.6</td><td></td></tr></tbody></table>

Several real-world cases illustrate how firms are deploying pioneer capabilities in ways that align with the core tenets of the NRBV, leveraging technological resources to build sustainability-oriented competitive advantages. For example, Einride's implementation of its Saga digital platform and autonomous electric trucks at GE Appliances' U.S. operations reduced loading times by up to 80% and eliminated tailpipe emissions, showcasing how integrated data infrastructure (C17) and simulation capabilities (C04) can support pollution prevention and operational efficiency simultaneously (Einride 2023). Tive's use of 5G-enabled IoT trackers in cold chain logistics—monitoring cargo conditions such as temperature, humidity, and shock in real time—has enabled clients to recover high-value shipments and prevent spoilage, thereby strengthening eco-performance monitoring (C09) and real-time cargo condition visibility (C15) (FreightWaves 2025). Similarly, Biocair's adoption of sensor-based cargo integrity systems for pharmaceutical and cell therapy shipments has reduced environmental risk while ensuring patient safety, illustrating how secure data integrity (C02) and smart monitoring

(C15) create organizational routines that support product stewardship and stakeholder trust (Biocair 2025). Collectively, these cases illustrate how ICT-enabled capabilities can be integrated and packaged into a firm's logistics systems to improve sustainability performance, thereby converting resources into strategic, capability-based advantages, as envisioned by NRBV.

To simplify the visualization of the impact of each capability within each group, the presentation is organized in descending order of driving power. This choice is justified by the fact that driving power provides a quantitative measure of how strongly a given capability influences others. The first capability in the pioneer group, C17 (Responsible freight data exchange), involves adopting mobile and wireless technologies to facilitate an infrastructure for sending and receiving data through road freight operations (Harris et al. 2015). This capability is highlighted as the foundational component in the ISM model (Figure 2) and possesses the greatest driving power in the fuzzy MICMAC cluster diagram (Figure 3). This
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Developed ISM model.</figcaption></figure>

primary role of the capability can be explained by the neces-
sity for communication infrastructure to enable data visibility
in road freight transportation (Callefi, Ganga, et al. 2024a).
Furthermore, regarding sustainability, it is crucial to empha-
size that this capability enables access to precise and up-to-
date information on the location and condition of cargo. This
enables the optimization of routes and reduction of unneces-
sary trips, thereby decreasing fuel consumption and pollutant
emissions (Schröder and Cabral 2019).

The second capability, C15 (Real-time cargo condition mon-
itoring), involves using sensor networks to monitor various
cargo parameters, such as temperature, humidity, etc. This ca-
pability can be implemented using technologies ranging from
mature to emerging technologies (Núñez-Merino et al. 2020).
Among the mature technologies are radio frequency identifica-
tion (RFID) and wireless sensor networks (WSN) (Jedermann
et al. 2006), while the emerging technologies include IoT sys-
tems (Bumblauskas et al. 2020) and smart shipping management

systems (Baygin et al. 2022). Adopting these technologies not
only enhances the efficiency of cargo monitoring but also plays a
crucial role in realizing sustainability. By leveraging data-driven
decision-making models, these technologies enable smart
real-time decisions in freight transportation, which can opti-
mize resource use and reduce environmental impact (Kumar
et al. 2019).

The third capability is C04 (Computational Modeling for Sustainable Improvement), which is related to information systems that allow for decision-making and scenario planning within road freight transportation operations. This capability uses the generated data for identifying and optimizing critical areas, such as routes and transportation schedules, to improve operational efficiency and effectiveness (Melnyk et al. 2024). In the context of sustainability, this capability is primarily linked to transportation planning, where, for example, environmental performance can be assessed based on the measurement of energy consumption rates and overall
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Cluster diagram from the Fuzzy MICMAC.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | A framework with the capability groups that support sustainability in road freight transportation. Note: C01—Autonomous eco-driving; C02—Cargo data integrity to minimize environmental impact; C03—Cargo smart control for green freight; C04—Computational modeling towards sustainable improving; C05—Decision support for sustainable freight transportation; C06—Driver well-being monitoring; C09—Eco-performance monitoring of vehicles; C10—Eco-responsible accountability; C11—Emission management; C12—Fuel consumption optimization; C13—Green fleet coordination; C15—Real-time cargo condition monitoring; C17—Responsible freight data exchange; C19—Routing optimization; C20—Sustainable freight booking; C22—Transport decarbonization.</figcaption></figure>
environmental impact (Kelle et al. 2019). Consequently, this
capability enables companies to better understand their oper-
ations in ways that can reduce the environmental footprint of
road transportation activities.

The fourth capability is C02 (Cargo data integrity to minimize environmental impact), essential for ensuring the accuracy and credibility of data generated through road freight transportation operations. This, in turn, is important to ensure data visibility (Callefi, Ganga, et al. 2024a). One key technology for allowing this capability is, for example, blockchain, which guarantees that recorded data cannot be modified unless all members of the decentralized system agree (Kshetri 2018; Risso et al. 2023). Blockchain technologies ensure the reliability of cargo transportation data, allowing enterprises to monitor and improve their operations to minimize environmental impact. This aligns with sustainability objectives and regulatory requirements.

Finally, the fifth capability is C09 (Eco-performance monitoring of vehicles), which entails continuously monitoring operating parameters for various vehicle components, including the engine, tires, and suspension systems. Huang et al. (2023) underscored that eco-driving can be achieved through automated driving systems, which allow for the real-time analysis and control of multiple parameters. This facilitates a reduction in fuel consumption and vehicle emissions. Additionally, this capability enhances driver safety by enabling the sensing of various vehicle operational parameters, allowing for the early detection of potential defects before accidents occur. Thus, freight operators can promote environmental sustainability and contribute to safer driving conditions through this proactive vehicle monitoring approach.

From a NRBV theory perspective, which considers sustainability a
critical factor for competitive advantage, capabilities such as C02,
C15, and C17 cover data monitoring, data transfer, and data reli-
ability in road freight operations, thus supporting road operators
in complying with environmental regulations and improving re-
source utilization efficiency. In sectors like the agri-food industry,
where transparency and data reliability throughout the product
supply chain are essential, these capabilities ensure enhanced
security for end customers and that the products are transported
safely, adhering to strict quality standards (Giganti et al. 2024).
Similarly, conclusions can be inferred for other contexts.

Regarding capability C09, it is also important to note that inte-
grating advanced ICT systems enables a more precise analysis
of fuel consumption and CO2 emissions (Li et al. 2023). Thus,
road operators that develop this capability are aligned with the
pursuit of sustainable competitive advantage, as they can ensure
the reduction of environmental impacts through their activities.
This capability is increasingly important as companies must
align with ESG principles (Nenavani et al. 2024). Finally, com-
putational modeling related to capability C04 can enable road
operators to identify optimization opportunities that reduce
unnecessary resource usage and promote cleaner, more envi-
ronmentally friendly practices (Boonpanya and Masui 2021;
Crainic et al. 2018). This approach helps align the organization's
strategy with environmental guidelines, enhancing the compa-
ny's resilience and adaptive capacity in a market that is increas-
ingly focused on sustainability.

5.2 | Integrator Capabilities

The integrator capabilities group comprises seven capabilities: CO1, CO3, C06, C10, C11, C12, and C19. These capabilities exhibit a moderating characteristic, as they are influenced by the pioneer capabilities and influence the ancillary capabilities. This means that the effect of the pioneer capabilities on the ancillary capabilities indirectly depends on the integrator capabilities. Real-world examples underscore their strategic role. UPS's ORION platform (C19) has reduced fuel consumption by 10 million gallons and CO2 emissions by 100,000 metric tons annually, generating up to $400 million in savings (BSR 2016). Similarly, TuSimple's autonomous driving technology (C01) has shown 10%-20% fuel efficiency gains in collaboration with UC San Diego (Fleet Owner 2019). These cases exemplify how integrator capabilities transform ICT-based resources into embedded organizational routines that support pollution prevention, cost reduction, and competitive resilience, core pillars of the NRBV.

The first capability in the integrator group is C19 (Routing optimization), which involves the use of information systems that enable optimized routing of deliveries to reduce fuel consumption (Kermanshah et al. 2020). Routing is also essential when using electric vehicles, as it may be necessary to include stops for battery recharges on the same delivery route (Çatay and Sadati 2023).

The second capability is C01 (Autonomous eco-driving), which uses autonomous vehicles to reduce or eliminate human dependence on vehicle driving. This capability can potentially increase freight transportation safety, primarily by reducing accidents and creating safer working conditions for drivers (Bagloee et al. 2016).

The third capability, C10 (Eco-responsible accountability), allows for easy identification of those responsible for environmental issues along the supply chain, thereby promoting greater transparency regarding the environmental impacts generated by freight transportation activities. One means is using blockchain technologies to identify individuals or companies responsible for each stage of freight transportation (Callefi, Ganga, et al. 2024a; Kshetri 2018).

The fourth capability, C11 (Emission management), is directly related to reducing environmental impact and involves using information systems to manage the impact generated by freight transportation activities (Fanti et al. 2017). The fifth capability, C03 (Cargo smart control for green freight), adopts IoT systems to enable an autonomous infrastructure for sensing and decision-making in the context of road freight operators (Callefi et al. 2022). In other words, a network of IoT sensors can verify various cargo safety parameters in real-time, quickly identify potential problems that could impact the safety of the cargo and the driver, and prevent accidents that may affect other individuals and the environment.

The sixth capability, C06 (Driver well-being monitoring), uses sensors to determine if the delivery vehicle driver can continue driving (Coughlin et al. 2011). This capability can reduce the possibility of accidents, which can harm the driver and other persons, damage the cargo, and, in specific cases, cause environmental problems. Finally, the seventh capability, C12 (Fuel consumption optimization), encompasses the use of technologies that reduce fuel consumption in road freight operations (Kermanshah et al. 2020).
From the perspective of the NRBV theory, integrator capabilities are essential for strategically orchestrating technological resources to maximize environmental efficiency. The NRBV underscores that companies can achieve sustainable competitive advantages by managing natural resources strategically. Therefore, by implementing capabilities that enable the efficient operationalization of these resources, road freight companies adhere to more sustainable practices and strengthen their market position in response to increasing regulatory demands and societal expectations for eco-efficient operations (Nenavani et al. 2024).

## 5.3 | Ancillary Capabilities

The ancillary capabilities group comprises four capabilities: C05 (Decision support for sustainable freight transportation), C13 (Green fleet coordination), C20 (Sustainable freight booking), and C22 (Transport decarbonization). These capabilities are highly dependent on pioneer capabilities and low on Integrator Capabilities. Despite this seemingly secondary and dependent nature, these capabilities are far from superfluous, as they present a degree of importance that enables road operators to achieve a competitive advantage through sustainability.

While the ISM and Fuzzy MICMAC methods focus on mapping the dependency hierarchy rather than assessing each capability's intrinsic importance, this approach should not undermine the critical value of ancillary capabilities. For example, capabilities such as transport decarbonization (C20) may rely heavily on other capabilities for activation. Yet, they play a crucial role in directly mitigating the environmental footprint of freight operations (Taefi et al. 2016). Thus, ancillary capabilities are vital, contributing uniquely to sustainability objectives and serving as essential components that bolster the effectiveness of a comprehensive sustainability strategy implemented by road freight operators. Recognizing their contributions enhances our understanding of how each capability is interwoven and supports the overarching goal of minimizing environmental impacts within the transportation sector.

Real-world implementations confirm the strategic value of these dependent capabilities. For instance, Flexport's Sustainability Dashboard (C05, C10) enables shippers to estimate and offset CO₂ emissions before booking, offering structured decision support integrated with environmental accountability (Flexport 2025). Similarly, Amazon's intermodal booking solutions have achieved up to 50% carbon reductions by consolidating freight and optimizing modal choices, illustrating green fleet coordination (C13) and decarbonization (C22) in action (Amazon Freight 2024a). In parallel, Amazon's broader strategy, such as the deployment of 3000 compressed natural gas (CNG) trucks and targeted electrification under the Climate Pledge (Amazon 2025; Amazon Freight 2024b), demonstrates how ancillary capabilities can evolve into resource-based strategic assets when enabled by upstream technological infrastructures. These examples align with NRBV's logic by showing how organizations can integrate and scale dependent capabilities to deliver environmental performance while enhancing competitive positioning.

# 6 | Research Contributions and Implications

Investigating the interdependence of tech-enabled capabilities using a structured and theory-driven framework enhances academic understanding and provides valuable insights for freight transportation management. This study consequently contributes to theoretical development, management practice, and public policy.

## 6.1 | Theoretical Contributions and Implications

This study advances NRBV theory by detailing how specific tech-enabled capabilities can systematically contribute to sustainable competitive advantages in road freight operations. It extends existing models by incorporating a structured analysis of interdependencies, enriching our understanding of how technological and operational enhancements can align with sustainable development principles.

While previous studies, such as Callefi et al. (2024b) and Callefi et al. (2022), have identified technology-enabled capabilities relevant to freight operations, their discussions on sustainability have remained primarily descriptive and fragmented. Meanwhile, Sharma et al. (2022) and Alp et al. (2022) recognized the potential of digital technologies to improve environmental performance in transportation, but they did not explore how such capabilities interact or how they can be systematically leveraged to build competitive advantage based on sustainability. Moreover, these contributions did not explicitly adopt the NRBV framework as a theoretical foundation for analyzing capability development in sustainable logistics.

The proposed ICF-SRT framework addresses this gap by introducing a novel, theory-driven perspective that not only classifies capabilities into pioneer, integrator, and ancillary groups but also structures their relationships hierarchically using ISM and Fuzzy MICMAC. This framework reveals the influence and dependency patterns among capabilities, offering insights into how companies can strategically prioritize investments in technologies that support sustainability goals. By integrating methodological rigor with theoretical depth, this study contributes to the literature by applying NRBV in a sector where it has rarely been explored, operationalizing its principles through a multi-method analysis, and providing a structured model to understand how tech-enabled capabilities interact to generate sustainable value. This contribution provides a solid foundation for future research on strategic technology deployment in logistics and other resource-intensive sectors, which face increasing pressure to decarbonize.

## 6.2 | Management Contributions and Implications

This study provides road freight companies and managers with a clear and actionable guide to build the capabilities required to enhance sustainability. The proposed ICF-SRT could be applied as a strategic tool that enables decision-makers to prioritize investments in technologies that offer the greatest environmental and operational benefits. By distinguishing between pioneer, integrator, and ancillary capabilities, the framework helps managers understand which capabilities should be developed first
to enable and sustain others, thereby maximizing the impact of digital transformation initiatives.

In practical terms, the framework enables managers to assess their current level of technological maturity and develop a structured roadmap for implementation. This roadmap can be operationalized in four steps. First, managers should determine the current maturity of sustainability-related capabilities within their organization. Second, based on the ICF-SRT model, they should prioritize investments in pioneer capabilities, those with the highest driving power, such as cargo data integrity (C02), real-time condition monitoring (C15), and responsible data exchange (C17). Third, once these foundations are in place, they should develop integrator capabilities, such as routing optimization (C19), emission management (C11), and eco-responsible accountability (C10), which scale and synchronize sustainability gains across operations. Finally, companies can embed ancillary capabilities—such as sustainable freight booking (C20) and green fleet coordination (C13)—to reinforce alignment with ESG metrics and enhance service differentiation. For example, a company operating in temperature-sensitive logistics, such as pharmaceuticals or perishable food, can apply the framework to prioritize the implementation of real-time cargo condition monitoring (C15), responsible data exchange (C17), and cargo data integrity (C02). These pioneer capabilities are foundational for ensuring traceability, safety, and environmental compliance. Once established, they enable the development of integrator capabilities such as eco-responsible accountability (C10) or emission management (C11), further strengthening the company's sustainability performance.

By following this phased and interdependent logic, the ICF-SRT enables companies to translate their strategic sustainability objectives into concrete operational plans. It also helps firms demonstrate alignment with ESG standards that are increasingly demanded by investors, regulators, and consumers, while improving resource efficiency, reducing emissions, and strengthening their resilience.

## 6.3 | Police-Makers Contributions and Implications

The findings underscore the critical need for supporting infrastructure and policies that enable the adoption of advanced technologies in road freight transportation. An improved understanding of capability requirements and their impact on sustainability enables policymakers to devise more targeted regulations and incentives that promote the adoption of green technologies and sustainable practices within the industry.

The ICF-SRT framework provides a practical foundation for designing policy tools. By clarifying the hierarchical roles and influence of each capability, public authorities can target initial investments and subsidies toward pioneering capabilities with high systemic impact, such as digital cargo monitoring, data reliability, and infrastructure for responsible data exchange. These core elements establish the conditions for broader adoption of integrator and auxiliary capabilities across the industry. Additionally, regulatory frameworks, particularly those related to ESG reporting, emissions disclosure, and logistics certification, can be enhanced by incorporating indicators that

demonstrate the maturity and integration of sustainability-related capabilities. For example, policies that mandate emission tracking (e.g., C11), fleet efficiency metrics (C12), or booking transparency (C20) can help align industry practices with national and international decarbonization targets.

Such initiatives can generate significant environmental benefits, improve operational efficiencies, and ensure the transportation sector moves toward broader sustainability objectives. Furthermore, the framework supports the development of multi-stakeholder partnerships, pilot projects, and funding mechanisms that encourage not only the adoption of green technologies but also their strategic implementation in coordination with capability development. In this way, public policy can shift from isolated mandates to systemic enablers of sustainable competitive advantage, key to the NRBV perspective.

## 7 | Conclusion

This study systematically explored the complex interrelationships among tech-enabled capabilities within road freight transportation, emphasizing their essential role in promoting industry-wide sustainability. Using the NRBV theory as a theoretical lens, this study identified key capabilities that support the environmental, social, and economic dimensions of sustainability. It demonstrates how these capabilities interconnect to drive sustainable competitive advantages in road freight transportation. The insights from this study offer a comprehensive understanding of the strategic deployment of technological innovations to achieve a competitive advantage through sustainable practices, with significant implications for researchers, managers, and policymakers.

Despite its contributions, this study acknowledges certain limitations, primarily its reliance on specific methods, such as ISM and Fuzzy MICMAC, which may restrict the analysis of capabilities and their assumed relationships. Future research could expand our study by incorporating a broader range of emerging technologies and by evaluating these capabilities in diverse logistical and operational contexts. Additionally, longitudinal studies could offer deeper insights into the long-term sustainability impacts of these capabilities. Future studies could also employ structural equation modeling (SEM) to validate the proposed model statistically. While ISM effectively develops a preliminary model, SEM can provide path coefficients for the various relationships between the tech-enabled capabilities, thus offering a more robust statistical validation of the framework. Finally, investigating barriers to technology adoption, especially in less developed markets, could also provide valuable directions for future research, allowing for a more nuanced understanding of the global challenges and opportunities in enhancing road freight transportation sustainability.

## Author Contributions

**Vitor Furlanetto Barrionuevo:** conceptualization, methodology design, data gathering, content analysis, original draft preparation, software/database, visualization, writing. **Mario Henrique Callefi:** validation, review and editing, writing, supervision. **Moacir Godinho Filho:** resources, methodology design, data curation, validation, review and editing, supervision. **Matthias Thürer:** validation, review
and editing, supervision. **Gilberto Miller Devós Ganga**: validation, review and editing, supervision. **Marina Ivanova**: validation, review and editing, writing.

## Acknowledgments

The Federal Ministry for Economic Affairs and Climate Action (BMWK) funded the project on which this report is based under the funding code 01MK22001B. Open Access funding enabled and organized by Projekt DEAL.

## References

Acheampong, A. O., J. Dzator, M. Dzator, and R. Salim. 2022. "Unveiling the Effect of Transport Infrastructure and Technological Innovation on Economic Growth, Energy Consumption and CO2 Emissions." *Technological Forecasting and Social Change* 182: 121843. https://doi.org/10.1016/j.techfore.2022.121843.

Agnusdei, G. P., M. G. Gnoni, F. Sgarbossa, and K. Govindann. 2022. "Challenges and Perspectives of the Industry 4.0 Technologies within the Last-Mile and First-Mile Reverse Logistics: A Systematic Literature Review." *Research in Transportation Business & Management* 45: 100896. https://doi.org/10.1016/j.rtbm.2022.100896.

Ahmadi, P. 2019. "Environmental Impacts and Behavioral Drivers of Deep Decarbonization for Transportation Through Electric Vehicles." *Journal of Cleaner Production* 225: 1209–1219. https://doi.org/10.1016/j.jclepro.2019.03.334.

Alp, O., T. Tan, and M. Udenio. 2022. "Transitioning to Sustainable Freight Transportation by Integrating Fleet Replacement and Charging Infrastructure Decisions." *Omega* 109: 102595. https://doi.org/10.1016/j.omega.2022.102595.

Amazon. 2025. *Transportation*. Amazon. https://sustainability.about.amazon.com/climate-solutions/transportation.

Amazon Freight. 2024a. *Carbon Savings With Intermodal Shipping*. Amazon Freight. https://freight.amazon.co.uk/newsroom/carbon-savings-intermodal-shipping.

Amazon Freight. 2024b. *Sustainability Investments in Transportation: How Amazon Leads The Way*. Amazon Freight. https://freight.amazon.com/newsroom/2024-sustainability-investments.

Amit, R., and P. J. H. Schoemaker. 1993. "Strategic Assets and Organizational Rent." *Strategic Management Journal* 14, no. 1: 33–46. https://doi.org/10.1002/smj.4250140105.

Attri, R., N. Dev, and V. Sharma. 2013. "Interpretive Structural Modelling (ISM) Approach: An Overview." *Research Journal of Management Sciences* 2, no. 2: 3–8.

Awan, U., I. Gölgeci, D. Makhmadshoev, and N. Mishra. 2022. "Industry 4.0 and Circular Economy in an Era of Global Value Chains: What Have We Learned and What Is Still to Be Explored?" *Journal of Cleaner Production* 371: 133621. https://doi.org/10.1016/j.jclepro.2022.133621.

Baffour Gyau, E., Y. Li, and D. Adu. 2024. "Investigating the Impact of ICT on Transport-Based CO2 Emissions: Empirical Evidence from a Quantile Cointegration Regression Analysis." *Environmental Science and Pollution Research International* 31, no. 3: 4606–4629. https://doi.org/10.1007/s11356-023-31395-7.

Bagloee, S. A., M. Tavana, M. Asadi, and T. Oliver. 2016. "Autonomous Vehicles: Challenges, Opportunities, and Future Implications for Transportation Policies." *Journal of Modern Transportation* 24, no. 4: 284–303. https://doi.org/10.1007/s40534-016-0117-3.

Baygin, M., O. Yaman, N. Baygin, and M. Karakose. 2022. "A Blockchain-Based Approach to Smart Cargo Transportation Using UHF RFID." *Expert Systems with Applications* 188, no. February 2022: 116030. https://doi.org/10.1016/j.eswa.2021.116030.

Bhosale, V. A., and R. Kant. 2016. "An Integrated ISM Fuzzy MICMAC Approach for Modelling the Supply Chain Knowledge Flow Enablers." *International Journal of Production Research* 54, no. 24: 7374–7399. https://doi.org/10.1080/00207543.2016.1189102.

Bianco, D., M. Godinho Filho, L. Osiro, G. M. D. Ganga, and G. L. Tortorella. 2023. "The Driving and Dependence Power Between Lean Leadership Competencies: An Integrated ISM/Fuzzy MICMAC Approach." *Production Planning and Control* 34, no. 11: 1037–1061. https://doi.org/10.1080/09537287.2021.1969047.

Biocair. 2025. *Temperature-Controlled Packaging for Pharma and Life Sciences*. Biocair. https://www.biocair.com/expertise/packaging-technology.

Boonpanya, T., and T. Masui. 2021. "Assessing the Economic and Environmental Impact of Freight Transport Sectors in Thailand Using Computable General Equilibrium Model." *Journal of Cleaner Production* 280: 124271. https://doi.org/10.1016/j.jclepro.2020.124271.

Bryman, A. 2006. "Integrating Quantitative and Qualitative Research: How Is It Done?" *Qualitative Research* 6, no. 1: 97–113. https://doi.org/10.1177/1468794106058877.

BSR. 2016. *Looking Under the Hood: ORION Technology Adoption at UPS*. BSR. https://www.bsr.org/en/case-studies/center-for-technology-and-sustainability-orion-technology-ups.

Bumblauskas, D., A. Mann, B. Dugan, and J. Rittmer. 2020. "A Blockchain Use Case in Food Distribution: Do You Know Where Your Food Has Been?" *International Journal of Information Management* 52, no. June 2020: 102008. https://doi.org/10.1016/j.ijinfomgt.2019.09.004.

Callefi, M. H., G. M. D. Ganga, M. Godinho Filho, M. M. Queiroz, V. Reis, and J. G. M. dos Reis. 2022. "Technology-Enabled Capabilities in Road Freight Transportation Systems: A Multi-Method Study." *Expert Systems with Applications* 203: 117497. https://doi.org/10.1016/j.eswa.2022.117497.

Callefi, M. H., G. M. D. Ganga, M. Godinho Filho, E. Ribeiro da Silva, L. Osiro, and V. Reis. 2024a. "A Roadmap for Prioritising Technology-Enabled Capabilities in Road Freight Transportation Management." *International Journal of Logistics Research and Applications* 1–26. https://doi.org/10.1080/13675567.2024.2389069.

Callefi, M. H., G. M. D. Ganga, M. Godinho Filho, E. Ribeiro da Silva, L. Osiro, and V. Reis. 2024b. "Illuminating the Road Ahead: Unlocking the Potential of ICTs for Enhanced Data Visibility in Road Transportation." *Industrial Management and Data Systems* 124, no. 2: 786–819. https://doi.org/10.1108/IMDS-02-2023-0115.

Callefi, M. H., P. Grzona, A. Bueno, D. S. Santos, and M. Godinho Filho. 2024. *Unlocking the Potential of Blockchain in Road Freight Transportation Operations*, 77–88. Cham: Springer. https://doi.org/10.1007/978-3-031-71633-1_6.

Callefi, M. H., S. Y. Shiramizu, L. A. de Carvalho, M. Thürer, S. L. Cotrim, and L. D. Del Rosso Calache. 2025. "Exploring Barriers to ICT Adoption in Road Freight Operations." *Procedia Computer Science* 253: 1702–1711. https://doi.org/10.1016/j.procs.2025.01.232.

Çatay, B., and İ. Sadati. 2023. "An Improved Matheuristic for Solving the Electric Vehicle Routing Problem with Time Windows and Synchronized Mobile Charging/Battery Swapping." *Computers and Operations Research* 159: 106310. https://doi.org/10.1016/j.cor.2023.106310.

Centobelli, P., R. Cerchione, E. Esposito, and Shashi. 2020. "Evaluating Environmental Sustainability Strategies in Freight Transport and Logistics Industry." *Business Strategy and the Environment* 29, no. 3: 1563–1574. https://doi.org/10.1002/bse.2453.

Chen, T., H. Sun, K. F. Tai, and C. K. Heng. 2022. "Analysis of the Barriers to Implementing Building Integrated Photovoltaics in Singapore Using an Interpretive Structural Modelling Approach." *Journal of Cleaner Production* 365: 132652. https://doi.org/10.1016/j.jclepro.2022.132652.
Clayton, M. J. 1997. "Delphi: A Technique to Harness Expert Opinion for Critical Decision-Making Tasks in Education." *Educational Psychology* 17, no. 4: 373-386. https://doi.org/10.1080/0144341970170401.

Coughlin, J. F., B. Reimer, and B. Mehler. 2011. "Monitoring, Managing, and Motivating Driver Safety and Well-Being." *IEEE Pervasive Computing* 10, no. 3: 14-21. https://doi.org/10.1109/MPRV.2011.54.

Crainic, T. G., G. Perboli, and M. Rosano. 2018. "Simulation of Intermodal Freight Transportation Systems: A Taxonomy." *European Journal of Operational Research* 270, no. 2: 401-418. https://doi.org/10.1016/j.ejor.2017.11.061.

Dalkey, N., and O. Helmer. 1963. "An Experimental Application of the DELPHI Method to the Use of Experts." *Management Science* 9, no. 3: 458-467. https://doi.org/10.1287/mnsc.9.3.458.

de Mello Bandeira, R. A., G. V. Goes, D. N. Schmitz Gonçalves, M. A. de D'Agosto, and C. M. de Oliveira. 2019. "Electric Vehicles in the Last Mile of Urban Freight Transportation: A Sustainability Assessment of Postal Deliveries in Rio de Janeiro-Brazil." *Transportation Research Part D: Transport and Environment* 67: 491-502. https://doi.org/10.1016/j.trd.2018.12.017.

Dwivedi, A., S. Pratap, and F. Zhou. 2023. "Antecedents of Freight Transportation for Sustainable Supply Chain in the Post-COVID Era: An Emerging Market Study." *International Journal of Emerging Markets* 18, no. 6: 1453-1471. https://doi.org/10.1108/IJOEM-01-2022-0065.

Einride. 2023. *How GE Appliances is making the switch – and driving transformation*. Einride. https://www.einride.tech/insights/how-ge-appliances-is-making-the-switch-and-driving-transformation.

Fanti, M. P., G. Iacobellis, M. Nolich, A. Rusich, and W. Ukovich. 2017. "A Decision Support System for Cooperative Logistics." *IEEE Transactions on Automation Science and Engineering* 14, no. 2: 732-744. https://doi.org/10.1109/TASE.2017.2649103.

Farooque, M., A. Zhang, and Y. Liu. 2019. "Barriers to Circular Food Supply Chains in China." *Supply Chain Management* 24, no. 5: 677-696. https://doi.org/10.1108/SCM-10-2018-0345.

Fleet Owner. 2019. *Study: New Research Finds TuSimple Trucks Are At Least 10% More Fuel Efficient Than Traditional Trucks*. Fleet Owner. https://www.fleetowner.com/emissions-efficiency/article/21118892/study-new-research-finds-tusimple-trucks-are-at-least-10-more-fuel-efficient-than-traditional-trucks.

Flexport. 2025. *Climate Solutions Tailored to Your Decarbonization Goals*. Flexport. https://www.flexport.com/products/climate/.

Flodén, J., F. Bärthel, and E. Sorkina. 2017. "Transport Buyers Choice of Transport Service—A Literature Review of Empirical Results." *Research in Transportation Business and Management* 23: 35-45. https://doi.org/10.1016/j.rtbm.2017.02.001.

Francis, J. J., M. Johnston, C. Robertson, et al. 2010. "What is an Adequate Sample Size? Operationalising Data Saturation for Theory-Based Interview Studies." *Psychology and Health* 25, no. 10: 1229-1245. https://doi.org/10.1080/08870440903194015.

FreightWaves. 2025. *Tive: Revolutionizing Fraud Prevention Through Real-Time Visibility*. FreightWaves. https://www.freightwaves.com/news/tive-revolutionizing-fraud-prevention-through-real-time-visibility.

Fu, L., Z. Sun, L. Zha, et al. 2020. "Environmental Awareness and Pro-environmental Behavior Within China's Road Freight Transportation Industry: Moderating Role of Perceived Policy Effectiveness." *Journal of Cleaner Production* 252: 119796. https://doi.org/10.1016/j.jclepro.2019.119796.

Fulzele, V., and R. Shankar. 2022. "Improving Freight Transportation Performance Through Sustainability Best Practices." *Transportation Research Part A: Policy and Practice* 165: 285-299. https://doi.org/10.1016/j.tra.2022.09.009.

Gastón Cedillo-Campos, M., J. Eduardo Flores-Franco, and D. Covarrubias. 2024. "A Physical Internet-Based Analytic Model for

Reducing the Risk of Cargo Theft in Road Transportation." *Computers and Industrial Engineering* 190: 110016. https://doi.org/10.1016/j.cie.2024.110016.

Ghisolfi, V., L. Antal Tavasszy, H. de Almeida, G. Rodriguez Correia, G. de Lorena Diniz Chaves, and G. Mattos Ribeiro. 2024. "Dynamics of Freight Transport Decarbonization: A Simulation Study for Brazil." *Transportation Research Part D: Transport and Environment* 127: 104020. https://doi.org/10.1016/j.trd.2023.104020.

Giganti, P., M. Borrello, P. M. Falcone, and L. Cembalo. 2024. "The Impact of Blockchain Technology on Enhancing Sustainability in the Agri-Food Sector: A Scoping Review." *Journal of Cleaner Production* 456: 142379. https://doi.org/10.1016/j.jclepro.2024.142379.

Godinho Filho, M., S. V. Q. de Almeida, M. Lage Junior, L. Osiro, B. Lima, and M. H. Callefi. 2025. "A Path to Follow to Overcome Foundational Barriers to the Adoption of Artificial Intelligence within the Manufacturing Industry: A Conceptual Framework." *Enterprise Information Systems* 19, no. 1-2: 30-63. https://doi.org/10.1080/175175757202548685.

Harris, I., Y. Wang, and H. Wang. 2015. "ICT in Multimodal Transport and Technological Trends: Unleashing Potential for the Future." *International Journal of Production Economics* 159: 88-103. https://doi.org/10.1016/j.ijpe.2014.09.005.

Hart, S. L. 1995. "A Natural-Resource-Based View of the Firm." *Academy of Management Review* 20, no. 4: 986. https://doi.org/10.2307/258963.

Hsu, C., K. Choon Tan, S. Hanim Mohamad Zailani, and V. Jayaraman. 2013. "Supply Chain Drivers That Foster the Development of Green Initiatives in an Emerging Economy." *International Journal of Operations & Production Management* 33, no. 6: 656-688. https://doi.org/10.1108/ijopm-10-2011-0401.

Hsu, Y. L., C. H. Lee, and V. B. Kreng. 2010. "The Application of Fuzzy Delphi Method and Fuzzy AHP in Lubricant Regenerative Technology Selection." *Expert Systems with Applications* 37, no. 1: 419-425. https://doi.org/10.1016/j.eswa.2009.05.068.

Hu, W., J. Dong, K. Yang, and Z. Chen. 2025. "Understanding Key Factors Affecting Underground Logistics Systems Adoption and Front-End Planning: A Cross-Sectoral Investigation and Insights into Emerging Mobility Solutions." *Transport Policy* 164: 160-177. https://doi.org/10.1016/j.tranpol.2025.02.002.

Huang, C., R. Salehi, A. G. Stefanopoulou, and T. Ersal. 2023. "Hardware-in-the-Loop Exploration of Energy Versus Emissions Trade-Off in Eco-Following Scenarios for Connected Automated Vehicles." *International Journal of Engine Research* 24, no. 4: 1643-1654. https://doi.org/10.1177/14680874221098212.

Ishikawa, A., M. Amagasa, T. Shiga, G. Tomizawa, R. Tatsuta, and H. Mieno. 1993. "The Max-Min Delphi Method and Fuzzy Delphi Method via Fuzzy Integration." *Fuzzy Sets and Systems* 55, no. 3: 241-253. https://doi.org/10.1016/0165-0114(93)90251-C.

Jedermann, R., C. Behrens, D. Westphal, and W. Lang. 2006. "Applying Autonomous Sensor Systems in Logistics-Combining Sensor Networks, RFIDs and Software Agents." *Sensors and Actuators, A: Physical* 132 1 SPEC. ISS.: 370-375. https://doi.org/10.1016/j.sna.2006.02.008.

Kandasamy, W. B. V., F. Smarandache, and K. Ilanthenral 2007. Elementary Fuzzy Matrix Theory and Fuzzy Models for Social Scientists. http://arxiv.org/abs/math/0702144.

Kashav, V., and C. P. Garg. 2025. "From Innovation to Adoption: A Framework-Based Evaluation of Sustainable Adoption Strategies for eVTOL Vehicles in Shared Passenger and Freight Transportation System." *Journal of Air Transport Management* 124: 102761. https://doi.org/10.1016/j.jairtraman.2025.102761.

Kelle, P., J. Song, M. Jin, H. Schneider, and C. Claypool. 2019. "Evaluation of Operational and Environmental Sustainability Tradeoffs in Multimodal Freight Transportation Planning." *International Journal*
of Production Economics 209: 411-420. https://doi.org/10.1016/j.ijpe. 2018.08.011.

Kermanshah, A., H. Baroud, and M. Abkowitz. 2020. "Cyber-Physical Technologies in Freight Operations and Sustainability: A Case Study of Smart GPS Technology in Trucking." *Sustainable Cities and Society* 55, no. October 2019: 102017. https://doi.org/10.1016/j.scs.2020.102017.

Khanra, S., P. Kaur, R. P. Joseph, A. Malik, and A. Dhir. 2021. "A Resource-Based View of Green Innovation as a Strategic Firm Resource: Present Status and Future Directions." *Business Strategy and the Environment* 31, no. 4: 1395-1413. Portico. https://doi.org/10.1002/ bse.2961.

Kshetri, N. 2018. "1 Blockchain's Roles in Meeting Key Supply Chain Management Objectives." *International Journal of Information Management* 39, no. December 2017: 80-89. https://doi.org/10.1016/j. ijinfomgt.2017.12.005.

Kumar, A., and R. Anbanandam. 2020. "Environmentally Responsible Freight Transport Service Providers' Assessment Under Data-Driven Information Uncertainty." *Journal of Enterprise Information Management* 34, no. 1: 506-542. https://doi.org/10.1108/ JEIM-12-2019-0403.

Kumar, A., M. Calzavara, N. R. Velaga, A. Choudhary, and R. Shankar. 2019. "Modelling and Analysis of Sustainable Freight Transportation." *International Journal of Production Research* 57, no. 19: 6086-6089. https://doi.org/10.1080/00207543.2019.1642689.

Kumar, A., M. A. Kaviani, A. Hafezalkotob, and E. K. Zavadskas. 2017. "Evaluating Innovation Capabilities of Real Estate Firms: A Combined Fuzzy Delphi and DEMATEL Approach." *International Journal of Strategic Property Management* 21, no. 4: 401-416. https://doi.org/10. 3846/1648715X.2017.1409291.

Li, X., C. Zhang, and H. Zhu. 2023. "Effect of Information and Communication Technology on CO2 Emissions: An Analysis Based on Country Heterogeneity Perspective." *Technological Forecasting and Social Change* 192: 122599. https://doi.org/10.1016/j.techfore.2023. 122599.

Melnyk, S. A., M. Thürer, C. Blome, T. Schoenherr, and S. Gold. 2024. "(Re)-Discovering Simulation as a Critical Element of OM/SCM Research: Call for Research." *International Journal of Operations & Production Management* 44, no. 7: 1376-1389. https://doi.org/10.1108/ IJOPM-08-2023-0665.

Meyer, T., H. A. von der Gracht, and E. Hartmann. 2022. "Technology Foresight for Sustainable Road Freight Transportation: Insights from a Global Real-Time Delphi Study." *Futures and Foresight Science* 4, no. 1: 1-16. https://doi.org/10.1002/ff02.101.

Miklautsch, P., and M. Woschank. 2022. "A Framework of Measures to Mitigate Greenhouse Gas Emissions in Freight Transport: Systematic Literature Review from a Manufacturer's Perspective." *Journal of Cleaner Production* 366: 132883. https://doi.org/10.1016/j.jclepro.2022. 132883.

Mlepo, A. T. 2022. "Attacks on Road-Freight Transporters: A Threat to Trade Participation for Landlocked Countries in Southern Africa." *Journal of Transportation Security* 15, no. 1-2: 23-40. https://doi.org/10. 1007/s12198-021-00242-6.

Mohanty, M. 2018. "Assessing Sustainable Supply Chain Enablers Using Total Interpretive Structural Modeling Approach and Fuzzy-MICMAC Analysis." *Management of Environmental Quality: An International Journal* 29, no. 2: 216-239. https://doi.org/10.1108/MEQ-03-2017-0027.

Müller, C. 2024. "Transition to Battery-Electric and Fuel Cell Heavy-Duty Trucks: A Multi-Level, Multi-Dimensional Approach." *Transportation Research Part D: Transport and Environment* 127: 104052. https://doi.org/10.1016/j.trd.2024.104052.

Murray, T. J., L. L. Pipino, and J. P. Van Gigch. 1985. "A Pilot Study of Fuzzy Set Modification of Delphi." *Human Systems Management* 5, no. 1: 76-80. https://doi.org/10.3233/HSM-1985-5111.

Nazarian-Jashnabadi, J., S. Rahnamay Bonab, G. Haseli, H. Tomaskova, and M. Hajiaghaei-Keshteli. 2023. "A Dynamic Expert System to Increase Patient Satisfaction with an Integrated Approach of System Dynamics, ISM, and ANP Methods." *Expert Systems with Applications* 234: 121010. https://doi.org/10.1016/j.eswa.2023.121010.

Nenavani, J., A. Prasuna, S. N. V. Siva Kumar, and A. Kasturi. 2024. "ESG Measures and Financial Performance of logistics Companies." *Letters in Spatial and Resource Sciences* 17, no. 1: 5. https://doi.org/10. 1007/s12076-023-00358-4.

Núñez-Merino, M., J. M. Maqueira-Marín, J. Moyano-Fuentes, and P. J. Martínez-Jurado. 2020. "Information and Digital Technologies of Industry 4.0 and Lean Supply Chain Management: A Systematic Literature Review." *International Journal of Production Research* 58, no. 16: 5034-5061. https://doi.org/10.1080/00207543.2020.1743896.

Osorio-Tejada, J. L., E. Llera-Sastresa, and S. Scarpellini. 2024. "Environmental Assessment of Road Freight Transport Services Beyond the Tank-to-Wheels Analysis Based on LCA." *Environment, Development and Sustainability* 26, no. 1: 421-451. https://doi.org/10. 1007/s10668-022-02715-7.

Pålsson, H., and G. Kovács. 2014. "Reducing Transportation Emissions: A Reaction to Stakeholder Pressure or a Strategy to Increase Competitive Advantage." *International Journal of Physical Distribution and Logistics Management* 44, no. 4: 283-304. https://doi.org/10.1108/ IJPDLM-09-2012-0293.

Parviziomran, E., V. Elliot, and R. Bergqvist. 2024. "Financing Dynamics in Sustainable Heavy-Duty Road Transport: An Agent-Based Modeling Approach." *Transportation Research Part D: Transport and Environment* 132: 104258. https://doi.org/10.1016/j.trd. 2024.104258.

Patel, M. N., A. A. Pujara, R. Kant, and R. K. Malviya. 2021. "Assessment of Circular Economy Enablers: Hybrid ISM and Fuzzy MICMAC Approach." *Journal of Cleaner Production* 317: 128387. https://doi.org/ 10.1016/j.jclepro.2021.128387.

Pathak, D. K., L. S. Thakur, and S. Rahman. 2019. "Performance Evaluation Framework for Sustainable Freight Transportation Systems." *International Journal of Production Research* 57, no. 19: 6202-6222. https://doi.org/10.1080/00207543.2019.1602741.

Pecorari, P. M., and C. R. C. Lima. 2021. "Correlation of Customer Experience with the Acceptance of Product-Service Systems and Circular Economy." *Journal of Cleaner Production* 281: 125275. https:// doi.org/10.1016/j.jclepro.2020.125275.

Pfoser, S. 2022. "Developing User-Centered Measures to Increase the Share of Multimodal Freight Transport." *Research in Transportation Business & Management* 43: 100729. https://doi.org/10.1016/j.rtbm. 2021.100729.

Raj, T., R. Shankar, and M. Suhaib. 2008. "An ISM Approach for Modelling the Enablers of Flexible Manufacturing System: The Case for India." *International Journal of Production Research* 46, no. 24: 6883-6912. https://doi.org/10.1080/00207540701429926.

Rathore, B., R. Gupta, B. Biswas, A. Srivastava, and S. Gupta. 2022. "Identification and Analysis of Adoption Barriers of Disruptive Technologies in the Logistics Industry." *International Journal of Logistics Management* 33, no. 5: 136-169. https://doi.org/10.1108/ IJLM-07-2021-0352.

Risso, L. A., G. M. D. Ganga, M. Godinho Filho, I. A. de Santa-Eulalia, T. Chikhi, and E. Mosconi. 2023. "Present and Future Perspectives of Blockchain in Supply Chain Management: A Review of Reviews and Research Agenda." *Computers and Industrial Engineering* 179: 109195. https://doi.org/10.1016/j.cie.2023.109195.

Ruiz-Benitez, R., C. López, and J. C. Real. 2017. "Environmental Benefits of Lean, Green and Resilient Supply Chain Management: The Case of the Aerospace Sector." *Journal of Cleaner Production* 167: 850-862. https://doi.org/10.1016/j.jclepro.2017.07.201.
Schröder, M., and P. Cabral. 2019. "Eco-Friendly 3D-Routing: A GIS Based 3D-Routing-Model to Estimate and Reduce CO2-Emissions of Distribution Transports." *Computers, Environment and Urban Systems* 73: 40–55. https://doi.org/10.1016/j.compenvurbsys.2018.08.002.

Shah, K. J., S. Y. Pan, I. Lee, et al. 2021. "Green Transportation for Sustainability: Review of Current Barriers, Strategies, and Innovative Technologies." *Journal of Cleaner Production* 326: 129392. https://doi.org/10.1016/j.jclepro.2021.129392.

Shardeo, V., and B. D. Sarkar. 2024. "Adoption of Hydrogen-Fueled Freight Transportation: A Strategy Toward Sustainability." *Business Strategy and the Environment* 33, no. 2: 223–240. https://doi.org/10.1002/bse.3482.

Sharma, V., R. D. Raut, U. H. Govindarajan, and B. E. Narkhede. 2022. "Advancements in Urban Logistics Toward Smart, Sustainable Reforms in Developing Enabling Technologies and Markets." *Kybernetes* 51, no. 3: 1038–1061. https://doi.org/10.1108/K-01-2021-0026.

Sherif, S. U., P. Asokan, P. Sasikumar, K. Mathiyazhagan, and J. Jerald. 2022. "An Integrated Decision Making Approach for the Selection of Battery Recycling Plant Location under Sustainable Environment." *Journal of Cleaner Production* 330: 129784. https://doi.org/10.1016/j.jclepro.2021.129784.

Shi, W., Y. Du, C. H. Chang, S. Nguyen, and J. Wu. 2021. "Spatial Heterogeneity and Economic Driving Factors of SO2 Emissions in China: Evidence from an Eigenvector Based Spatial Filtering Approach." *Ecological Indicators* 129: 108001. https://doi.org/10.1016/j.ecolind.2021.108001.

Singh, P., and G. Agrawal. 2022. "Modelling the Barriers of Weather Index Insurance Service Adoption Integrating Expert Mining and ISM Fuzzy-MICMAC." *Benchmarking* 29, no. 8: 2527–2554. https://doi.org/10.1108/BIJ-04-2021-0183.

Singh, P. K., and P. Sarkar. 2020. "A Framework Based on Fuzzy Delphi and Dematel for Sustainable Product Development: A Case of Indian Automotive Industry." *Journal of Cleaner Production* 246: 118991. https://doi.org/10.1016/j.jclepro.2019.118991.

Singh, R. K., and A. Gupta. 2020. "Framework for Sustainable Maintenance System: ISM-Fuzzy MICMAC and TOPSIS Approach." *Annals of Operations Research* 290, no. 1–2: 643–676. https://doi.org/10.1007/s10479-019-03162-w.

Singh, S., A. Barve, and S. Shanker. 2021. "An ISM-gDEMATEL Framework for Assessing Barriers to Green Freight Transportation: A Case of Indian Logistics System." *International Journal of Sustainable Engineering* 14, no. 6: 1871–1892. https://doi.org/10.1080/19397038.2021.1982063.

Steer, A., and W. Wade-Gery. 1993. "Sustainable Development: Theory and Practice for a Sustainable Future." *Sustainable Development* 1, no. 3: 23–35. https://doi.org/10.1002/sd.3460010306.

Taefi, T. T., J. Kreutzfeldt, T. Held, and A. Fink. 2016. "Supporting the Adoption of Electric Vehicles in Urban Road Freight Transport—A Multi-Criteria Analysis of Policy Measures in Germany." *Transportation Research Part A, Policy and Practice* 91: 61–79. https://doi.org/10.1016/j.tra.2016.06.003.

Tian, Z., R. Y. Zhong, A. Vatankhah Barenji, Y. T. Wang, Z. Li, and Y. Rong. 2021. "A Blockchain-Based Evaluation Approach for Customer Delivery Satisfaction in Sustainable Urban Logistics." *International Journal of Production Research* 59, no. 7: 2229–2249. https://doi.org/10.1080/00207543.2020.1809733.

Ueckerdt, F., C. Bauer, A. Dirnaichner, J. Everall, R. Sacchi, and G. Luderer. 2021. "Potential and Risks of Hydrogen-Based E-Fuels in Climate Change Mitigation." *Nature Climate Change* 11, no. 5: 384–393. https://doi.org/10.1038/s41558-021-01032-7.

Virmani, N., R. K. Singh, V. Agarwal, and E. Aktas. 2024. "Artificial Intelligence Applications for Responsive Healthcare Supply Chains: A Decision-Making Framework." *IEEE Transactions on Engineering Management* 71: 8591–8605. https://doi.org/10.1109/TEM.2024.3370377.

Wang, Y., V. S. Rodrigues, and L. Evans. 2015. "The Use of ICT in Road Freight Transport for CO2 Reduction—An Exploratory Study of UK's Grocery Retail Industry." *International Journal of Logistics Management* 26, no. 1: 2–29. https://doi.org/10.1108/IJLM-02-2013-0021.

Warfield, J. N. 1974. "Developing Interconnection Matrices in Structural Modeling." *IEEE Transactions on Systems, Man, and Cybernetics SMC-4*, no. 1: 81–87. https://doi.org/10.1109/TSMC.1974.5408524.

Watson, R. H. 1978. "Interpretive Structural Modeling—A Useful Tool for Technology Assessment?" *Technological Forecasting and Social Change* 11, no. 2: 165–185. https://doi.org/10.1016/0040-1625(78)90028-8.

Wu, C., L. Xiao, Z. Hu, and Y. Zhou. 2022. "Modeling the Low-Carbon Behaviors' Development Paths of Freight Enterprises Based on A Survey in Zhejiang, China." *Sustainable Cities and Society* 82: 103894. https://doi.org/10.1016/j.scs.2022.103894.

Zhao, P., Z. Li, Z. Xiao, S. Jiang, Z. He, and M. Zhang. 2023. "Spatiotemporal Characteristics and Driving Factors of CO2 Emissions From Road Freight Transportation." *Transportation Research Part D: Transport and Environment* 125: 103983. https://doi.org/10.1016/j.trd.2023.103983.
# Appendix A

<table><caption>TABLE A1 | Structural self-interaction matrix (SSIM).</caption><thead><tr><th>#</th><th>C22</th><th>C20</th><th>C19</th><th>C17</th><th>C15</th><th>C13</th><th>C12</th><th>C11</th><th>C10</th><th>C09</th><th>C06</th><th>C05</th><th>C04</th><th>C03</th><th>C02</th></tr></thead><tbody><tr><td>C01</td><td>O</td><td>O</td><td>A</td><td>A</td><td>O</td><td>V</td><td>V</td><td>O</td><td>O</td><td>A</td><td>O</td><td>V</td><td>A</td><td>V</td><td>O</td></tr><tr><td>C02</td><td>O</td><td>V</td><td>V</td><td>A</td><td>A</td><td>V</td><td>O</td><td>O</td><td>V</td><td>A</td><td>O</td><td>V</td><td>V</td><td>V</td><td></td></tr><tr><td>C03</td><td>O</td><td>O</td><td>A</td><td>A</td><td>A</td><td>V</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>V</td><td>A</td><td></td><td></td></tr><tr><td>C04</td><td>O</td><td>V</td><td>V</td><td>A</td><td>V</td><td>V</td><td>V</td><td>V</td><td>O</td><td>V</td><td>V</td><td>V</td><td></td><td></td><td></td></tr><tr><td>C05</td><td>A</td><td>V</td><td>A</td><td>A</td><td>A</td><td>A</td><td>A</td><td>A</td><td>O</td><td>A</td><td>A</td><td></td><td></td><td></td><td></td></tr><tr><td>C06</td><td>O</td><td>O</td><td>O</td><td>O</td><td>O</td><td>V</td><td>O</td><td>O</td><td>A</td><td>O</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C09</td><td>O</td><td>V</td><td>V</td><td>A</td><td>X</td><td>V</td><td>V</td><td>V</td><td>O</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C10</td><td>O</td><td>O</td><td>O</td><td>A</td><td>O</td><td>V</td><td>O</td><td>O</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C11</td><td>V</td><td>O</td><td>O</td><td>O</td><td>O</td><td>V</td><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C12</td><td>V</td><td>O</td><td>A</td><td>A</td><td>A</td><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C13</td><td>V</td><td>X</td><td>A</td><td>A</td><td>A</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C15</td><td>O</td><td>V</td><td>V</td><td>A</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C17</td><td>O</td><td>V</td><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C19</td><td>O</td><td>V</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C20</td><td>O</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><caption>TABLE A2 | Initial reachability matrix (IRM).</caption><thead><tr><th>#</th><th>C01</th><th>C02</th><th>C03</th><th>C04</th><th>C05</th><th>C06</th><th>C09</th><th>C10</th><th>C11</th><th>C12</th><th>C13</th><th>C15</th><th>C17</th><th>C19</th><th>C20</th><th>C22</th></tr></thead><tbody><tr><td>C01</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C02</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C03</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C04</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C09</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C15</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C17</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C19</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>C22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>
<table><caption>TABLE A3 | Final reachability matrix (FRM).</caption><thead><tr><th>#</th><th>C01</th><th>C02</th><th>C03</th><th>C04</th><th>C05</th><th>C06</th><th>C09</th><th>C10</th><th>C11</th><th>C12</th><th>C13</th><th>C15</th><th>C17</th><th>C19</th><th>C20</th><th>C22</th></tr></thead><tbody><tr><td>C01</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>*1</td></tr><tr><td>C02</td><td>*1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>*1</td><td>*1</td><td>1</td><td>*1</td><td>*1</td><td>1</td><td>*1</td><td>0</td><td>1</td><td>1</td><td>*1</td></tr><tr><td>C03</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>*1</td></tr><tr><td>C04</td><td>1</td><td>*1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>*1</td></tr><tr><td>C05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>C06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>*1</td></tr><tr><td>C09</td><td>1</td><td>1</td><td>*1</td><td>*1</td><td>1</td><td>0</td><td>1</td><td>*1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>*1</td></tr><tr><td>C10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>*1</td></tr><tr><td>C11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>1</td></tr><tr><td>C12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>1</td></tr><tr><td>C13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>C15</td><td>*1</td><td>1</td><td>1</td><td>*1</td><td>1</td><td>0</td><td>1</td><td>*1</td><td>*1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>*1</td></tr><tr><td>C17</td><td>1</td><td>1</td><td>1</td><td>1</td><td>*1</td><td>1</td><td>1</td><td>*1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>*1</td><td>*1</td></tr><tr><td>C19</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>*1</td></tr><tr><td>C20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>*1</td></tr><tr><td>C22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>*1</td><td>1</td></tr></tbody></table>

<table><caption>TABLE A4 | ISM level partitions.</caption><thead><tr><th>#</th><th>Reachability set</th><th>Antecedent set</th><th>Intersection set</th><th>Level</th></tr></thead><tbody><tr><td>C01</td><td>C01,C03,C05,C12,C13,C20,C22</td><td>C01,C02,C04,C09,C15,C17,C19</td><td>C01</td><td>IV</td></tr><tr><td>C02</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C19,C20,C22</td><td>C02,C04,C09,C15,C17</td><td>C02,C04,C09,C15</td><td>II</td></tr><tr><td>C03</td><td>C03,C05,C13,C20,C22</td><td>C01,C02,C03,C04,C09,C15,C17</td><td>C03</td><td>V</td></tr><tr><td>C04</td><td>C01,C02,C03,C04,C05,C06,C09,C11,C12,C13,C15,C19,C20,C22</td><td>C02,C04,C09,C15,C17</td><td>C02,C04,C09,C15</td><td>II</td></tr><tr><td>C05</td><td>C05,C13,C20</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C17,C19,C20,C22</td><td>C05,C13,C20</td><td>VIII</td></tr><tr><td>C06</td><td>C05,C06,C13,C20,C22</td><td>C02,C04,C06,C10,C17</td><td>C6</td><td>V</td></tr><tr><td>C09</td><td>C01,C02,C03,C04,C05,C09,C10,C11,C12,C13,C15,C19,C20,C22</td><td>C02,C04,C09,C15,C17</td><td>C02,C04,C09,C15</td><td>II</td></tr><tr><td>C10</td><td>C05,C06,C10,C13,C20,C22</td><td>C02,C09,C10,C15,C17</td><td>C10</td><td>IV</td></tr><tr><td>C11</td><td>C05,C11,C12,C13,C20,C22</td><td>C02,C04,C09,C11,C15,C17</td><td>C11</td><td>IV</td></tr><tr><td>C12</td><td>C05,C12,C13,C20,C22</td><td>C01,C02,C04,C09,C11,C12,C15,C17,C19</td><td>C12</td><td>V</td></tr><tr><td>C13</td><td>C05,C13,C20,C22</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C17,C19,C20</td><td>C05,C13,C20</td><td>VI</td></tr><tr><td>C15</td><td>C01,C02,C03,C04,C05,C09,C10,C11,C12,C13,C15,C19,C20,C22</td><td>C02,C04,C09,C15,C17</td><td>C02,C04,C09,C15</td><td>II</td></tr><tr><td>C17</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C17,C19,C20,C22</td><td>C17</td><td>C17</td><td>I</td></tr><tr><td>C19</td><td>C01,C03,C05,C12,C13,C19,C20,C22</td><td>C02,C04,C09,C15,C17,C19</td><td>C19</td><td>III</td></tr><tr><td>C20</td><td>C05,C13,C20,C22</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C17,C19,C20,C22</td><td>C05,C13,C20,C22</td><td>VIII</td></tr><tr><td>C22</td><td>C05,C20,C22</td><td>C01,C02,C03,C04,C05,C06,C09,C10,C11,C12,C13,C15,C17,C19,C20,C22</td><td>C20,C22</td><td>VII</td></tr></tbody></table>
<table><caption>TABLE A5 | Binary direct reachability matrix (BDRM).</caption><thead><tr><th>#</th><th>C01</th><th>C02</th><th>C03</th><th>C04</th><th>C05</th><th>C06</th><th>C09</th><th>C10</th><th>C11</th><th>C12</th><th>C13</th><th>C15</th><th>C17</th><th>C19</th><th>C20</th><th>C22</th></tr></thead><tbody><tr><td>C01</td><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C02</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C03</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C04</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>C06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C09</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>C15</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C17</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td></tr><tr><td>C19</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>C20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

<table><caption>TABLE A6 | Fuzzy direct reachability matrix (FDRM).</caption><thead><tr><th>#</th><th>C01</th><th>C02</th><th>C03</th><th>C04</th><th>C05</th><th>C06</th><th>C09</th><th>C10</th><th>C11</th><th>C12</th><th>C13</th><th>C15</th><th>C17</th><th>C19</th><th>C20</th><th>C22</th></tr></thead><tbody><tr><td>C01</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C02</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>0.9</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0.5</td><td>0.5</td><td>0</td></tr><tr><td>C03</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C04</td><td>0.7</td><td>0</td><td>0.7</td><td>0</td><td>1</td><td>0.5</td><td>0.5</td><td>0</td><td>0.5</td><td>0.9</td><td>0.9</td><td>0.7</td><td>0</td><td>0.9</td><td>0.5</td><td>0</td></tr><tr><td>C05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td></tr><tr><td>C06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C09</td><td>0.7</td><td>0.7</td><td>0</td><td>0</td><td>0.9</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0.7</td><td>0</td><td>0.7</td><td>0.7</td><td>0</td></tr><tr><td>C10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr><tr><td>C12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr><tr><td>C13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td></tr><tr><td>C15</td><td>0</td><td>0.7</td><td>0.9</td><td>0</td><td>0.9</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0.7</td><td>0.9</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>0</td></tr><tr><td>C17</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0.7</td><td>0.9</td><td>0</td><td>0.7</td><td>0.7</td><td>0</td><td>0.9</td><td>1</td><td>0.9</td><td>0</td><td>0.9</td><td>1</td><td>0</td></tr><tr><td>C19</td><td>0.5</td><td>0</td><td>0.5</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td></tr><tr><td>C20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>C22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
<table><caption>TABLE A7 | Fuzzy MICMAC stabilized matrix (FMSM).</caption><thead><tr><th>#</th><th>C01</th><th>C02</th><th>C03</th><th>C04</th><th>C05</th><th>C06</th><th>C09</th><th>C10</th><th>C11</th><th>C12</th><th>C13</th><th>C15</th><th>C17</th><th>C19</th><th>C20</th><th>C22</th><th>Driving power</th></tr></thead><tbody><tr><td>C01</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>2.8</td></tr><tr><td>C02</td><td>0.7</td><td>0</td><td>0.7</td><td>0</td><td>1</td><td>0.7</td><td>0.5</td><td>0</td><td>0.5</td><td>0.9</td><td>0.9</td><td>0.7</td><td>0</td><td>0.9</td><td>0.7</td><td>0.5</td><td>8.7</td></tr><tr><td>C03</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>1.9</td></tr><tr><td>C04</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0</td><td>0.9</td><td>0</td><td>0.5</td><td>0</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0.7</td><td>0</td><td>0.7</td><td>0.7</td><td>0.7</td><td>8.8</td></tr><tr><td>C05</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td></tr><tr><td>C06</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>1.9</td></tr><tr><td>C09</td><td>0.5</td><td>0.7</td><td>0.9</td><td>0.5</td><td>0.9</td><td>0</td><td>0.5</td><td>0.5</td><td>0</td><td>0.7</td><td>0.9</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>0.7</td><td>8.2</td></tr><tr><td>C10</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>2.8</td></tr><tr><td>C11</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>2.6</td></tr><tr><td>C12</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>1.9</td></tr><tr><td>C13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>1.7</td></tr><tr><td>C15</td><td>0.7</td><td>0.7</td><td>0.7</td><td>0.5</td><td>0.9</td><td>0</td><td>0</td><td>0.5</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0.7</td><td>0</td><td>0.7</td><td>0.7</td><td>0.7</td><td>9.1</td></tr><tr><td>C17</td><td>0.7</td><td>0.7</td><td>0.9</td><td>0.5</td><td>1</td><td>0.7</td><td>0.5</td><td>0.5</td><td>0.7</td><td>0.9</td><td>0.9</td><td>0.7</td><td>0</td><td>0.9</td><td>0.7</td><td>0.7</td><td>11</td></tr><tr><td>C19</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.7</td><td>4</td></tr><tr><td>C20</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.7</td><td>0.5</td><td>1.9</td></tr><tr><td>C22</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.5</td><td>0</td><td>0.5</td></tr><tr><td>Dependece power</td><td>3.3</td><td>2.8</td><td>4.6</td><td>1.5</td><td>11</td><td>1.4</td><td>2</td><td>1.5</td><td>2.6</td><td>4.6</td><td>8.5</td><td>2.8</td><td>0</td><td>3.9</td><td>10.1</td><td>7.9</td><td></td></tr></tbody></table>