Analyzing barriers to Green IoT adoption in Vietnamese industrial enterprises using Analytic Hierarchy Process approach

Análise das barreiras à adoção da Internet das Coisas Verde nas empresas industriais do Vietnã utilizando a abordagem do Processo de Análise Hierárquica

Abstract

Abstract  Green Internet of Things (GIoT) plays a crucial role in advancing green digital transformation toward Industry 5.0 by enhancing energy efficiency and reducing carbon emissions. However, its diffusion in developing countries such as Vietnam still faces multiple obstacles. This study investigates barriers to GIoT adoption in Vietnamese industrial enterprises to identify the most critical ones. Drawing on the Analytic Hierarchy Process approach and the Technology-Organization-Environment framework, we identify and classify barriers to GIoT adoption into three groups and 10 specific barriers. The barriers are then analyzed using data collected from 41 domain specialists, senior managers, and executives from Vietnamese industrial enterprises. The results indicate that environmental barriers exert the greatest influence, followed by organizational and technological barriers. In terms of global priority, the most critical barriers are the lack of a supporting ecosystem, insufficient regulatory pressure, and limited data security. At the TOE group level, the most significant barriers include limited data security (technological), the shortage of skilled personnel (organizational), and the lack of a supporting ecosystem (environmental). In addition, the study provides a comparative assessment between the manufacturing and construction sectors. It also provides practical recommendations for policymakers and enterprises to foster GIoT adoption in Vietnamese industrial enterprises.

Keywords:
Green IoT; Adoption barriers; Analytic Hierarchy Process (AHP); Industrial 5.0; Manufacturing; Construction

Resumo

Resumo  A Internet das Coisas Verde (GIoT) desempenha um papel crucial no avanço da transformação digital sustentável rumo à Indústria 5.0, ao melhorar a eficiência energética e reduzir as emissões de carbono. No entanto, a sua difusão em países em desenvolvimento, como o Vietnã, ainda enfrenta múltiplos obstáculos. Este estudo investiga as barreiras à adoção da GIoT em empresas industriais vietnamitas, com o objetivo de identificar as mais críticas. Com base no método Analytic Hierarchy Process (AHP) e no modelo Technology-Organization-Environment (TOE), identificamos e classificamos as barreiras à adoção da GIoT em três grupos e dez fatores específicos. Em seguida, essas barreiras são analisadas e classificadas com base em dados coletados de 41 especialistas da área, gestores seniores e executivos de empresas industriais vietnamitas. Os resultados indicam que as barreiras ambientais exercem a maior influência, seguidas pelas barreiras organizacionais e tecnológicas. Em termos de prioridade global, as barreiras mais críticas são a ausência de um ecossistema de suporte, a insuficiente pressão regulatória e as limitações relacionadas à segurança de dados. No nível dos grupos do modelo TOE, as barreiras mais significativas incluem limitações na segurança de dados (tecnológica), escassez de pessoal qualificado (organizacional) e ausência de um ecossistema de suporte (ambiental). Além disso, o estudo apresenta uma avaliação comparativa entre os setores de manufatura e construção. Também fornece recomendações práticas para formuladores de políticas e empresas, com o objetivo de promover a adoção da GIoT em empresas industriais no Vietnã.

Palavras-chave:
Internet das Coisas Verde; Barreiras à adoção; Processo de Análise Hierárquica; Indústria 5.0; Manufatura; Construção

1 Introduction

Growing pressure to achieve sustainable development and industrial decarbonization has fundamentally reshaped the trajectory of industrial transformation. Beyond the efficiency-oriented paradigm of Industry 4.0, recent discourse has increasingly emphasized Industry 5.0, which promotes a transition towards human-centric, agile, and sustainable industrial management systems (Golovianko et al., 2023). Within this emerging paradigm, industrial enterprises are required not only to enhance productivity but also to reduce energy consumption and greenhouse gas emissions while aligning digital transformation with environmental and societal objectives. In this context, the Green Internet of Things (GIoT) has emerged as a critical enabler of green digital transformation, embedding environmental goals such as energy efficiency, emission reduction, and resource optimization directly into connected systems and data-driven industrial operations (Albreem et al., 2017). By enabling energy-aware sensing, low-power communication, and intelligent monitoring, GIoT supports the implementation of sustainability-oriented principles advocated by Industry 5.0, particularly in energy- and emission-intensive industrial sectors.

Previous studies have reported that GIoT architectures and energy-efficient communication protocols contribute significantly to emission reduction and resource conservation in smart grids, smart buildings, and sustainable industrial systems (Chinipardaz et al., 2024; Schuler & Kotsis, 2024). Despite this recognized potential, GIoT adoption in industrial enterprises has remained limited, especially in developing and emerging economies, due to multiple and interrelated challenges (Liu & Mishra, 2022). The existing evidence in academia remains fragmented and primarily descriptive, offering limited guidance for systematically comparing and prioritizing these barriers at the enterprise level. Notably, empirical studies on the adoption of IoT, in general, and GIoT, in particular, in Vietnam have been scarce and predominantly qualitative, with few sector-specific comparative analyses (Das et al., 2025; Thuy, 2021). Therefore, this study aims to identify, classify, and prioritize barriers to GIoT adoption in Vietnamese industrial enterprises by applying a semi-quantitative approach based on the Analytic Hierarchy Process (AHP). Furthermore, the study conducts a comparative analysis of the manufacturing and construction sectors, which are selected because both are highly energy- and emission-intensive industries but differ substantially in their technological structures and institutional environments, leading to distinct GIoT adoption challenges.

To achieve the research objective, this study proposes and investigates three research questions:

1. What are the main barriers to GIoT adoption in Vietnamese industrial enterprises?

2. What is the relative importance of these barriers in Vietnamese industrial enterprises in general and in two major sectors, namely manufacturing and construction?

3. What solutions should be prioritized to overcome these barriers?

This study employs the Analytic Hierarchy Process (AHP) to evaluate and prioritize barriers to GIoT adoption in Vietnam’s industrial sectors. It contributes to the literature by extending the TOE framework in an emerging economy context and by providing a structured ranking of barriers using AHP. Practically, the findings offer actionable insights for policymakers and firms to prioritize strategies for GIoT implementation.

2 Literature review

GIoT refers to the design, deployment, and operation of IoT-based systems that explicitly consider environmental sustainability objectives, including energy efficiency, emission reduction, and resource optimization (Albreem et al., 2017; Alsharif et al., 2023). While GIoT builds upon the technological foundation of IoT, its adoption barriers are not merely an extension of traditional IoT adoption challenges. Conventional IoT adoption barriers primarily focus on technological and organizational issues such as infrastructure readiness, cost, interoperability, and data security (Cheng et al., 2024). In contrast, GIoT introduces additional constraints related to environmental sustainability, including energy efficiency requirements, carbon emission monitoring, compliance with environmental regulations, and the need for green-oriented ecosystems (Liu & Mishra, 2022; Rahimian et al., 2025). As a result, GIoT adoption barriers are not simply a subset of IoT barriers but represent an extension and reconfiguration under sustainability and regulatory pressures (Liu & Mishra, 2022; Rahimian et al., 2025). This distinction highlights the need to examine GIoT adoption barriers as a distinct research problem rather than to apply existing IoT adoption frameworks directly. In contrast to conventional IoT, which primarily emphasizes connectivity and operational efficiency, GIoT reframes connected technologies as instruments for supporting sustainable development and green transformation across industrial and infrastructural systems. Over the past decade, GIoT has attracted increasing scholarly attention, driven by concerns over the environmental footprint of large-scale IoT deployments and the growing alignment between digital transformation, sustainability agendas, and the emerging paradigm of Industry 5.0, which focuses on the integration of human-centricity, resilience, and sustainability in industrial systems (Golovianko et al., 2023; Sørensen, 2023).

Existing GIoT research has converged around several major themes. A substantial body of work focuses on the development of frameworks and architectures for implementing GIoT solutions, proposing energy-aware system designs and exploring whether green functions should be embedded as standalone architectures or integrated with complementary infrastructures such as Big Data analytics platforms and Energy Management Systems (EMS) (Albreem et al., 2017; Alsharif et al., 2023). Another major research stream aims to demonstrate the benefits of GIoT, with empirical and technical studies showing that GIoT applications reduce energy consumption, lower operational costs, improve resource efficiency, and mitigate carbon emissions in domains such as manufacturing, smart grids, smart buildings, and sustainable urban systems (Chinipardaz et al., 2024; Pandiyan et al., 2024). In addition, comparative and contextual studies examine differences between conventional IoT and GIoT-enabled systems, as well as variations across sectors and implementation contexts, highlighting the importance of industrial characteristics and regulatory environments in shaping GIoT outcomes (Sørensen, 2023; Ullah et al., 2026).

Alongside these research trends, barriers to GIoT adoption have emerged as a research direction with substantial academic and practical relevance. Prior studies have acknowledged that the diffusion of GIoT faces complex and interrelated challenges at the enterprise level; however, most existing research remains descriptive or exploratory, providing general assessments of constraints without systematically evaluating their relative importance or prioritizing them from the perspective of adopting firms (Rejeb et al., 2024; Thuy, 2021). Methodologically, empirical work on GIoT barriers has been dominated by qualitative analyses and conventional survey-based approaches, while semi-quantitative or multi-criteria decision-making methods have been applied only in a limited number of sector-specific contexts (Rejeb et al., 2024). Furthermore, the existing evidence base was geographically and sectorally uneven, with limited empirical research conducted in developing economies and few comparative analyses across environmentally intensive industrial sectors, particularly manufacturing and construction (Ullah et al., 2026).

These limitations indicate a clear research gap for studies that move beyond general descriptions to systematically evaluate and prioritize barriers to GIoT adoption, while explicitly addressing sectoral differences within emerging economies. Addressing this gap requires structured methodological approaches capable of incorporating expert judgment and capturing the relative importance of complex and interrelated barriers. Therefore, this study aims to systematically assess and prioritize barriers to GIoT adoption in industrial enterprises in a developing economy such as Vietnam, and to provide a comparative analysis of the manufacturing and construction sectors to generate evidence-based insights for both academic research and practical implementation.

3 Methodology

3.1 Analytic Hierarchy Process (AHP)

This study employs the Analytic Hierarchy Process (AHP) approach, developed by Saaty in 1980. AHP is a semi-quantitative method designed to address complex decision-making problems involving multiple criteria by enabling pairwise comparisons and the calculation of relative weights. A key advantage of AHP lies in its ability to integrate both qualitative and quantitative aspects within a single framework, while verifying the consistency of judgments through the consistency ratio (CR) (Saaty, 2008). The method is particularly suitable for this study because barriers to GIoT adoption represent an emerging research topic, and GIoT has not yet been widely implemented across enterprises, making large-scale survey-based quantitative methods difficult to apply and conventional quantitative scales insufficient. Numerous scholars have applied AHP in similar research contexts, such as analyzing technological barriers related to artificial intelligence, blockchain, logistics, and supply chain management, confirming its relevance and applicability to the present research context (Mustapha et al., 2022; Rejeb et al., 2024).

The AHP procedure in this study consists of the following steps:

1. structuring the decision hierarchy based on the TOE framework,

2. conducting pairwise comparisons using Saaty’s scale,

3. calculating criteria weights,

4. checking the consistency of judgments.

The rationale for selecting AHP has been clarified based on prior comparative studies on MCDM methods. Previous research indicates that AHP is particularly suitable for structuring complex decision problems and deriving criteria weights through pairwise comparisons, especially in hierarchical contexts. In contrast, DEMATEL is mainly used to analyze causal relationships among factors, while TOPSIS focuses on ranking alternatives based on their distance from an ideal solution. ISM, on the other hand, is primarily applied to develop structural relationships among variables rather than to prioritize them (Huang & Li, 2012; Koca & Yıldırım, 2021). Therefore, AHP is more appropriate for this study, which aims to prioritize barriers within a hierarchical TOE framework.

3.2 Identification of barriers and development of the AHP-based research framework

To identify the barriers to GIoT adoption, this study conducted a systematic literature review using the Scopus database. A targeted search was performed by combining relevant keywords related to GIoT adoption and industrial contexts, including (“Green IoT” OR “Sustainable IoT”) AND (“adopt”* OR “implement”* OR “diffusion” OR “acceptance”) AND (“barrier” OR “challenge” OR “obstacle”* OR “constraint”* “inhibitor”*) AND (“manufacturing” OR “construction” OR “industrial sector”) AND (“emerging economies” OR “developing countries”). The review primarily focused on studies published between 2020 and 2025 to capture the most recent developments in the field. The initial search yielded 186 articles, which were screened through a systematic process involving duplicate removal, title and abstract screening, and full-text assessment based on predefined inclusion and exclusion criteria. As a result, eleven high-quality peer-reviewed articles published in Q1–Q3 Scopus-indexed journals were selected for detailed analysis. A qualitative content analysis was then conducted to extract and classify barriers according to the Technology–Organization–Environment (TOE) framework. Similar barriers were consolidated and refined, resulting in a final set of ten barriers. Based on this structure, the AHP hierarchy is developed with three levels. The first level represents the overall objective, which is to prioritize barriers to GIoT adoption. The second level consists of the three TOE dimensions: technological, organizational, and environmental barriers. The third level includes the ten specific barriers identified from the literature. Section 4.1 presents the barriers in detail.

3.3 Survey design and data collection

Based on the Analytic Hierarchy Process (AHP) approach and identified barriers to GIoT, the questionnaire consisted of two main sections: (1) general information about the enterprise and the respondent, and (2) identification and pairwise assessment of barriers to GIoT adoption using Saaty’s 1-9 scale. Prior to the official survey, a pilot survey was conducted with five enterprises to test the clarity and relevance of the questionnaire items and the feasibility of applying Saaty’s 1-9 scale for pairwise comparisons (Figure 1). Feedback from the pilot survey was used to refine the wording of several questions and improve the survey instructions.

Figure 1
GIoT adoption barrier scale.

The official questionnaire was distributed via email to 120 industrial enterprises, comprising 60 manufacturing firms operating in various sectors, including mechanical engineering, electronics, textiles, food and beverages, and consumer goods, as well as 60 construction firms that specialize in civil construction, hydraulic construction, and industrial plant construction. Subsequently, we conducted follow-up phone calls to arrange personal interviews with willing participants. In total, 41 valid interviews were conducted, including 21 manufacturing enterprises and 20 construction enterprises. Each interview lasted approximately 60 minutes.

Table 1 shows the profiles of the interviewees, who are senior managers and executives with extensive knowledge of IoT technology and green practices within their organizations. In terms of industry experience, 15 experts had between 5 and 10 years of experience, 18 experts had between 10 and 15 years, and 8 experts had more than 15 years of professional experience. Regarding professional positions, the sample included 14 senior managers, 14 technical specialists, and 13 project engineers, ensuring a balanced representation across strategic and operational levels. Experts were selected based on predefined criteria, including at least five years of industry experience (all participants), direct involvement in digital transformation or IoT-related initiatives, and familiarity with sustainability or environmental issues. These characteristics confirm that the selected experts possess sufficient knowledge and practical experience to provide reliable judgments.

Table 1
Profile of expert respondents.

Respondent Profile	Category	Count (N=41)	Percentage (%)
Industry Experience	5–10 years	15	36.60
	10–15 years	18	43.90
	>15 years	8	19.50
Professional Title	Senior Managers	14	34.10
	Technical Experts	14	34.10
	Project Engineers	13	31.70

3.4 Analysis of empirical data

Survey data were processed in strict accordance with the standard AHP procedure. Pairwise comparison judgments provided by respondents were aggregated using the geometric mean to construct consolidated comparison matrices at each level of the AHP hierarchy. The analysis was conducted hierarchically, first comparing the main groups of barriers (technological, organizational, and environmental), and subsequently comparing individual barriers within each group. For each comparison matrix, the relative weights of barriers were derived using the principal eigenvector corresponding to the maximum eigenvalue (${\lambda }_{\text{max}}$). Local weights obtained at the lower level were then synthesized with the weights of higher-level barrier groups to calculate global priority weights. Specifically, the global weight of each barrier was calculated by multiplying its local weight within the group by the corresponding weight of the barrier group.

$$\text{Global weight}(\text{W}2)=\text{Barrier group weight}(TOE)\times \text{Local weight}(\text{W}1)$$ (1)

All AHP computations, including aggregation of individual judgments, eigenvector estimation, and consistency verification, were performed in Microsoft Excel using customized spreadsheet formulas.

A key requirement of the AHP method is the consistency of pairwise evaluations. Accordingly, the consistency ratio (CR) was calculated based on the consistency index (CI) and the random index (RI) to assess the reliability of the judgment matrices, as follows:

$$CR=\frac{CI}{RI}$$ (2)

$$CI=\frac{{\lambda }_{\max }-\text{n}}{\text{n}-1}$$ (3)

Where: ${\text{λ}}_{\text{max}}$ is the maximum eigenvalue of the matrix; n is the number of criteria in the matrix.

A matrix is considered consistent when CR < 0,1. If the CR exceeds this threshold, the data should be re-examined or adjusted.

4 Findings

4.1 Synthesis of GIoT adoption barriers

Table 2 summarises this process by mapping each of the ten barriers to the eleven high-ranking research papers. A tick mark in the table indicates that the corresponding article explicitly discusses that barrier. Based on these ten identified barriers, Figure 2 illustrates a mind map that systematically classifies them into the three dimensions of the TOE framework: technology, organization, and external environment. This visual mapping provides a structured overview of the conceptual framework and serves as the foundation for constructing the subsequent AHP hierarchical model.

Table 2
Summary of barriers to GIoT adoption.

Barriers	(Alsharif et al., 2023)	(Liu & Mishra, 2022)	(Rejeb et al., 2024)	(He & Turner, 2021)	(Altameem, 2022)	(Shahriar et al., 2024)	(Vinodh & Shimray, 2022)	(Thuy, 2021)	(Ali & Mahmood, 2024)	(Jalil et al., 2025)	(Ullah et al., 2026)
Lack of technical infrastructure and integration	✓	✓	✓	✓	✓	✓	✓		✓	✓	✓
Lack of established connection standards	✓	✓	✓		✓		✓		✓		✓
Limited data security	✓	✓	✓	✓		✓			✓	✓
High investment costs			✓	✓	✓	✓			✓	✓
Lack of support from leadership					✓	✓			✓	✓
Shortage of skilled personnel			✓	✓	✓	✓			✓	✓
Green strategies not aligned with IoT	✓	✓						✓		✓	✓
Lack of supportive GIoT policies		✓	✓					✓	✓	✓	✓
Insufficient regulatory pressure		✓	✓						✓	✓	✓
Lack of a supporting ecosystem		✓	✓	✓			✓		✓	✓	✓

Figure 2
Key barriers to Green IoT adoption in Vietnam.

4.1.1 Group 1: Technological barriers

• Lack of technical infrastructure:Lack of technical infrastructure Lack of technical infrastructure is a major technological barrier to GIoT adoption. It refers to the insufficiency of essential digital resources, including reliable communication networks, energy-efficient sensing devices, data storage and processing capabilities, and the ability to integrate GIoT solutions with legacy systems. Since GIoT builds upon conventional IoT while requiring higher standards of energy efficiency and environmental performance, robust connectivity and seamless system integration are critical prerequisites. Studies in developing and emerging economies consistently show that manufacturing and construction firms often lack the digital infrastructure needed to support IoT-based solutions, limiting the implementation and scalability of GIoT initiatives (Ali & Mahmood, 2024; Altameem, 2022; Jalil et al., 2025; Shahriar et al., 2024; Ullah et al., 2026; Vinodh & Shimray, 2022). Consequently, infrastructural deficiencies remain a key obstacle to achieving the energy-efficiency and emission-reduction benefits of GIoT (Alsharif et al., 2023; Liu & Mishra, 2022).

• Lack of established connection standards:Lack of established connection standards refers to the fragmentation and incompatibility of communication standards that prevent heterogeneous devices, platforms, and applications from communicating and interoperating effectively. Foundational IoT research has identified fragmented communication standards as a central technical obstacle to interoperability and large-scale system integration (Atzori et al., 2010). Building on this foundation, previous studies indicate that the absence of stable and widely adopted standards was even more problematic in the context of GIoT, as GIoT systems require interoperability while simultaneously addressing energy-efficient communication and environmental optimization objectives (Alsharif et al., 2023; Liu & Mishra, 2022). Empirical studies conducted in logistics and developing-country contexts report that incompatible standards hindered cross-organizational system integration, thereby constraining the adoption of IoT-and GIoT-based solutions (Altameem, 2022; Rejeb et al., 2024). Similar interoperability challenges are documented in construction and fabrication environments (Vinodh & Shimray, 2022) and in Industry 4.0-oriented manufacturing, where fragmented standards limit system scalability and integration (Ali & Mahmood, 2024; Ullah et al., 2026).

• Limited data security:Limited data security refers to concerns related to cyber-attacks, data breaches, and privacy risks arising from the collection, transmission, and sharing of data in connected systems. Foundational IoT research has identified security and privacy as core technical challenges that constrain trust and large-scale adoption (Atzori et al., 2010). Building on this foundation, previous studies indicate that data security issues are more pronounced in GIoT environments, as the proliferation of interconnected and resource-constrained devices expands the attack surface and intensifies challenges in protecting operational and personal data (Alsharif et al., 2023; Liu & Mishra, 2022). Empirical studies report that organizations in logistics and forestry contexts are reluctant to share data through IoT-based platforms due to perceived security vulnerabilities and cyber risks (He & Turner, 2021; Rejeb et al., 2024). Similar concerns are documented in manufacturing sectors, where firms feared operational disruptions and the leakage of sensitive or proprietary information, thereby constraining the adoption of IoT-and GIoT-enabled solutions (Ali & Mahmood, 2024; Jalil et al., 2025; Shahriar et al., 2024).

4.1.2 Group 2: Organizational barriers

• High investment costs:High investment costs refer to the substantial financial resources required for acquiring connected devices, upgrading communication infrastructure, integrating digital systems, and training employees, together with uncertainty regarding the financial returns of such investments. Previous studies focusing on manufacturing and industrial contexts report that financial constraints and budget limitations significantly delay the adoption of IoT and digital technologies, particularly in developing and emerging economies (Ali & Mahmood, 2024; Altameem, 2022). Empirical evidence further indicates that investment costs are among the most critical barriers for manufacturing firms, as identified through multi-criteria decision-making approaches (Shahriar et al., 2024). Consistently, studies examining small and medium-sized manufacturing enterprises highlight high perceived costs and unclear return on investment as key obstacles constraining the adoption of GIoT solutions (Jalil et al., 2025).

• Lack of support from leadership:Lack of support from leadership refers to weak commitment and limited strategic engagement from top management toward GIoT initiatives, resulting in insufficient prioritization, resource allocation, and organizational alignment. Previous studies report that, in the absence of strong leadership support, IoT-related projects lack strategic direction and adequate budgets, thereby constraining their implementation and scalability (Altameem, 2022). Empirical evidence from manufacturing and construction contexts further indicates that top-management reluctance significantly slows the adoption of Industry 4.0-oriented technologies, including IoT-based systems (Shahriar et al., 2024; Vinodh & Shimray, 2022). Consistently, studies examining GIoT and digital transformation in industrial settings show that leadership plays a critical role in articulating vision, mobilizing resources, and overcoming internal resistance; without such support, GIoT initiatives tend to remain fragmented or limited to small-scale pilot projects (Ali & Mahmood, 2024; Jalil et al., 2025).

• Shortage of skilled personnel:Shortage of skilled personnel refers to the lack of employees with the technical, analytical, and cybersecurity competencies required to design, implement, and operate GIoT systems within industrial settings. Previous studies focusing on developing and emerging economies report that limited human resources and insufficient technical expertise significantly constrain the implementation of IoT-related projects in manufacturing organizations (Altameem, 2022). Empirical evidence further indicates that manufacturing firms experience difficulties in recruiting or training personnel who possess both domain-specific operational knowledge and IoT-related technical skills, thereby slowing digital and Industry 4.0-oriented transformation initiatives (Shahriar et al., 2024). Consistently, studies examining GIoT and digital transformation in industrial contexts show that limited awareness of GIoT concepts among managers and employees reduces organizational readiness and weakens firms’ capacity to implement and scale GIoT solutions (Ali & Mahmood, 2024; Jalil et al., 2025).

• Green strategies not aligned with IoT:Green strategies not aligned with IoT refer to the misalignment between a firm’s environmental or corporate social responsibility (CSR) strategy and its Internet of Things-related initiatives, whereby digital investments are pursued without being explicitly linked to sustainability objectives. Previous studies indicate that effective GIoT implementation requires the integration of environmental goals into the design and deployment of IoT systems; otherwise, technological initiatives tend to improve operational efficiency without delivering corresponding environmental benefits (Alsharif et al., 2023; Liu & Mishra, 2022). Empirical evidence from Vietnam shows that green growth policies and digital transformation agendas were often promoted in parallel but remain weakly integrated at the enterprise level, thereby limiting the environmental impact of digital technologies (Thuy, 2021). Consistently, studies examining GIoT adoption in industrial and manufacturing contexts demonstrate that strategic alignment between green objectives and digital technologies was critical for enhancing environmental performance and realizing the sustainability potential of GIoT solutions (Ali & Mahmood, 2024; Jalil et al., 2025; Ullah et al., 2026).

4.1.3 Group 3: External-environment barriers

• Lack of supportive GIoT policies:Lack of supportive GIoT policies refers to fragmented, evolving, or ambiguous regulatory frameworks related to digital transformation, data protection, and environmental performance, which create uncertainty for firms considering GIoT adoption. Previous studies indicate that policy frameworks governing GIoT and related digital-environmental initiatives remain underdeveloped or insufficiently coordinated in many countries, thereby weakening institutional support for firm-level implementation (Liu & Mishra, 2022). Empirical evidence from Vietnam shows that enterprises face difficulties navigating frequently changing and loosely integrated regulations associated with green growth and digital transformation agendas, which constrain long-term planning and investment in GIoT solutions (Thuy, 2021). Consistently, studies examining GIoT adoption in industrial and manufacturing contexts reported that the absence of clear, stable, and supportive policy incentives discourages firms from committing financial and organizational resources to GIoT initiatives (Ali & Mahmood, 2024; Jalil et al., 2025; Ullah et al., 2026).

• Insufficient regulatory pressure:Insufficient regulatory pressure refers to the limited strength of regulatory enforcement and institutional demands that fail to create compelling external incentives for firms to adopt GIoT solutions. Previous studies indicate that, in the absence of clear and enforceable regulations, firms face little pressure to internalize environmental objectives within their digital investment decisions, thereby delaying GIoT adoption (Liu & Mishra, 2022). Empirical research further shows that weak regulatory and market pressures reduce the perceived urgency of green digital transformation among enterprises, particularly small and medium-sized firms in developing and emerging economies (Ali & Mahmood, 2024; Jalil et al., 2025; Ullah et al., 2026). As a result, GIoT initiatives are often deprioritized or postponed, despite their potential environmental and efficiency benefits.

• Lack of a supporting ecosystem:Lack of a supporting ecosystem refers to the absence of a mature and well-developed environment for GIoT, including limited availability of solution providers, specialized service vendors, training institutions, industry platforms, and standard-setting bodies. Previous studies indicate that weak ecosystem conditions constrain firms’ access to appropriate technologies, technical expertise, and implementation support, thereby slowing the adoption of GIoT solutions (Liu & Mishra, 2022; Rejeb et al., 2024). Empirical evidence from construction and industrial contexts further shows that the fragmented and project-based nature of these industries limits collaboration among stakeholders and hinders the development of integrated digital-green ecosystems (Vinodh & Shimray, 2022). Consistently, studies focusing on GIoT adoption in emerging and developing economies report that, in the absence of supportive partners and institutional networks, particularly for small and medium-sized enterprises, firms face significant difficulties in experimenting with, implementing, and scaling up GIoT initiatives (Ali & Mahmood, 2024; Jalil et al., 2025; Ullah et al., 2026).

Based on this hierarchical structure, the AHP method is applied to determine the relative importance of the identified barriers (Figure 2).

4.2 AHP analysis results

4.2.1 Results for the overall sample

The AHP analysis for the overall sample is presented in Table 3 according to Equations (1), (2), and (3) with Barrier group weight (T) = 0.251, Barrier group weight (O) = 0.315, Barrier group weight (E) = 0.434, which illustrates the differences in the level of barriers across the TOE groups and specific barriers.

Table 3
Results of barrier analysis for the overall sample.

Barriers	Barrier group	Local Weight (W1)	Global Weight (W2)	CR index	Ranking in each group	Ranking in all barriers
Limited data security	Technology	0.459	0.115	0.012	1	3
Lack of established connection standards	Technology	0.287	0.072		2	8
Lack of technical infrastructure	Technology	0.254	0.064		3	9
Shortage of skilled personnel	Organization	0.293	0.092	0.012	1	5
Green strategies not aligned with IoT	Organization	0.290	0.091		2	6
Lack of support from leadership	Organization	0.238	0.075		3	7
High investment costs	Organization	0.179	0.056		4	10
Lack of supporting ecosystem	Environment	0.428	0.186	0.03	1	1
Insufficient regulatory pressure	Environment	0.330	0.143		2	2
Lack of supportive GIoT policies	Environment	0.242	0.105		3	4

The aggregated AHP results for the entire survey sample indicate that environmental barriers carry the highest weight, underscoring the dominant role of institutional context and the supporting ecosystem in the deployment of GIoT in Vietnam. Within this group, “lack of a supporting ecosystem” is identified as the most critical barrier (W1 = 0.428, CR = 0.03), followed by “Insufficient regulatory pressure” (W1 = 0.330) and “lack of supportive GIoT policies” (W1 = 0.242). These findings suggest that enterprises rely heavily on external support and highlight the absence of strong legal enforcement and consistent policy guidance from the government.

In the organizational group, the barriers exhibit relatively even influence, with “shortage of skilled personnel” standing out as the most significant (W1 = 0.293), followed by “green strategies not aligned with IoT” (W1 = 0.290). This reflects the reality that many firms lack specialized teams to operate and leverage IoT systems, and that green development strategies are not yet systematically integrated into digitalization and IoT initiatives.

The technology group is assigned a lower overall weight compared with the other two groups, but still presents notable challenges. The most significant barrier in this category is “limited data security” (W1 = 0.459, CR = 0.012), which also emerges as the most critical technological barrier. Concerns about information security thus remain a major obstacle to IoT implementation. Other barriers, such as “lack of established connection standards” (W1 = 0.287) and “lack of technical infrastructure” (W1= 0.254), also constrain the scalability and integration of IoT systems.

Overall, Table 3 shows that Vietnamese enterprises are most strongly affected by external environmental barriers, while organizational and technological barriers reflect internal limitations. Importantly, all pairwise comparison matrices yield a consistency ratio (CR) below 0,1, confirming the high reliability and objectivity of the analysis. The ranking across all barriers highlights “lack of supporting ecosystem” (W2 =0.186), “Insufficient regulatory pressure” (W2 =0.143), and “Limited data security” (W2 = 0.115) as the top three barriers that policymakers and managers should prioritize for mitigation.

4.2.2 Analysis of Green IoT adoption barriers for the manufacturing sector

The AHP analysis for manufacturing enterprises, presented in Table 4 according to Equation (1), (2) and (3) with Barrier group weight (T) = 0.179, Barrier group weight (O) = 0.297, Barrier group weight (E) = 0.524, shows that environmental barriers continue to play a prominent role, while technological and organizational barriers also exert a considerable influence.

Table 4
Analysis of barriers in the manufacturing sector.

Barriers	Barrier group	Local Weight (W1)	Global Weight (W2)	CR index	Ranking in each group	Ranking in all barriers
Limited data security	Technology	0.593	0.106	0.075	1	4
Lack of established connection standards	Technology	0.242	0.043		2	8
Lack of technical infrastructure	Technology	0.163	0.029		3	10
Green strategies not aligned with IoT	Organization	0.370	0.110	0.073	1	3
Shortage of skilled personnel	Organization	0.311	0.093		2	5
Lack of support from leadership	Organization	0.195	0,058		3	7
High investment costs	Organization	0.122	0.036		4	9
Lack of supporting ecosystem	Environment	0.514	0.270	0.089	1	1
Insufficient regulatory pressure	Environment	0.311	0.163		2	2
Lack of supportive GIoT policies	Environment	0.173	0.091		3	6

In the technological group,“Limited data security” emerges as the most critical barrier (W1 = 0.593, CR = 0.075), followed by “lack of established connection standards” (W1 = 0.242) and “lack of technical infrastructure” (W1 = 0.163). These findings highlight that concerns over information security and the absence of robust technical standards remain major obstacles to IoT adoption and scalability.

In the environmental group, “lack of a supporting ecosystem” is identified as the most influential barrier (W1 = 0.514, CR = 0.089), followed by “Insufficient regulatory pressure” (W1 = 0.311) and “lack of supportive GIoT policies” (W1 = 0.173). This indicates that enterprises still depend heavily on external support while lacking strong institutional enforcement and consistent policy guidance from the government.

The organizational barriers show relatively balanced influence, with “Green strategies not aligned with IoT” standing out as the most significant (W1 = 0.370, CR = 0.073), followed by “Shortage of skilled personnel” (W1 = 0.311). This reflects the reality that many firms lack both systematic integration of green development strategies into IoT initiatives and the skilled workforce required for implementation. Other barriers, such as “lack of support from leadership” (W1 = 0.195) and “high investment costs” (W1 = 0.122), also contribute to organizational constraints but exert less impact.

Overall, Table 4 demonstrates that Vietnamese enterprises are most strongly affected by enviromental barriers, followed by organizational and technological challenges. Importantly, all pairwise comparison matrices yield a consistency ratio (CR) below 0,1, confirming the high reliability of the analysis. The ranking across all barriers highlights “lack of supporting ecosystem ” (W2 =0.270), “Insufficient regulatory pressure” (W2 = 0.163), and “Green strategies not aligned with IoT” (W2 = 0.110) as the top three barriers that policymakers and managers should prioritize in addressing.

4.2.3 Analysis of Green IoT adoption barriers for the construction sector

The AHP analysis for construction enterprises, presented in Table 5 below according to Equations (1), (2), and (3) with Barrier group weight (T) = 0.459, Barrier group weight (O) = 0.305, Barrier group weight (E) = 0.236, indicates that technological barriers play a dominant role, while organizational and environmental barriers continue to exert a considerable influence.

Table 5
Analysis of barriers in the construction sector.

Barriers	Barrier group	Local Weight (W1)	Global Weight (W2)	CR index	Ranking in each group	Ranking in all barriers
Lack of technical infrastructure	Technology	0.442	0.203	0.05	1	1
Lack of established connection standards	Technology	0.339	0.156		2	2
Limited data security	Technology	0.218	0.100		3	3
Lack of support from leadership	Organization	0.317	0.097	≈ 0.1	1	4
High investment costs	Organization	0.290	0.088		2	6
Shortage of skilled personnel	Organization	0.217	0.066		3	9
Green strategies not aligned with IoT	Organization	0.174	0.053		4	10
Lack of supportive GIoT policies	Environment	0.376	0.089	0.0003	1	5
Insufficient regulatory pressure	Environment	0.323	0.076		2	7
Lack of supporting ecosystem	Environment	0.300	0.071		3	8

• In the technological group, the barrier of “lack of technical infrastructure” is considered the most critical (W1 = 0.442, CR = 0.05), followed by “lack of established connection standards” (W1 = 0.339) and “Limited data security” (W1 = 0.218). This reflects the characteristics of the construction industry, which relies heavily on equipment, systems, and technical standards, making technological issues the greatest challenge in implementing GIoT.

Within the organizational group, the most notable barriers are “lack of support from leadership” (W1 = 0.317, CR ≈ 0.1) and “high investment costs” (W1 = 0.290), indicating that many construction enterprises lack both the determination and the resources to invest in GIoT technologies. Other barriers, such as a shortage of skilled personnel and the absence of green strategies integrated with IoT, remain significant internal challenges.

For environmental barriers, “lack of supportive GIoT policies” ranks highest (W1 = 0.376, CR = 0.0003), followed by “Insufficient regulatory pressure” (W1 = 0.323) and “lack of a supporting ecosystem” (W1 = 0.300). This shows that construction enterprises, similar to those in manufacturing, are strongly influenced by policy frameworks and institutional contexts, although the priority rankings differ slightly.

Across all barriers, “lack of technical infrastructure” (W2 = 0.203) emerges as the most critical barrier in the construction sector, underscoring the industry’s strong dependence on physical systems and connectivity for IoT deployment. The second most important barrier is “lack of established connection standards” (W2 = 0.156), highlighting the need for standardized protocols to ensure interoperability and seamless system integration. The third-ranked factor, “Limited data security” (W2 = 0.100), further reflects concerns regarding data protection and the reliability of IoT systems in construction environments.

Figure 3 provides a comparative overview of the global weights of Green IoT adoption barriers across the overall sample, manufacturing, and construction sectors, highlighting notable differences in barrier priorities between the two industries. Overall, the analysis confirms that for the construction sector, technological barriers are the most dominant, while organizational and environmental barriers continue to exert considerable influence. All pairwise comparison matrices have a consistency ratio (CR) below 0.1, ensuring the consistency and reliability of the results.

Figure 3
Comparing the industry-specific weighting of barriers to GIoT adoption.

4.3 Solutions to overcome Green IoT adoption barriers

Based on the AHP results presented above, this study proposes solutions to overcome GIoT adoption barriers at three levels: industrial enterprises in general, manufacturing enterprises, and construction enterprises.

For industrial enterprises in general, the most critical barriers are limited data security, lack of a supporting ecosystem, and insufficient regulatory pressure. Strengthening data security should therefore be an immediate priority. One practical approach is to develop a national Industrial IoT cybersecurity framework aligned with the ISA/IEC 62443 series, which has been widely adopted in countries such as Germany and the United States to secure industrial automation and control systems in IIoT environments (Cindrić et al., 2025).

Such frameworks guide network segmentation, secure remote access, and incident response procedures for connected systems. The lack of a supporting ecosystem can be addressed through coordinated public-private initiatives. For example, Korea’s smart factory programs for SMEs combine financial incentives, technical support, and shared digital infrastructure to accelerate the adoption of advanced digital technologies across the manufacturing base (Chung et al., 2022; Kwon, 2022). Adapting similar approaches could aid the formation of a national GIoT ecosystem in Vietnam. Insufficient regulatory pressure suggests that voluntary measures alone are unlikely to scale GIoT adoption; embedding GIoT criteria into industrial standards, energy efficiency regulations, and public procurement policies, following the example of Singapore’s Green Mark framework, which explicitly links smart technologies to sustainability performance, would create stronger institutional demand for secure and energy-efficient IoT solutions (Building and Construction Authority, 2021).

For manufacturing enterprises, the dominant barriers are (i) lack of a supporting ecosystem, (ii) insufficient regulatory pressure, and (iii) misalignment between green strategies and IoT initiatives. At the institutional level, stronger regulatory frameworks and clearer policy guidelines are required to accelerate GIoT adoption. Governments should establish consistent standards, enforce environmental regulations, and provide incentives such as tax benefits or subsidies to encourage firms to invest in green IoT technologies. In addition, evidence from Korean manufacturing SMEs shows that government-supported smart factory initiatives and cloud-based platforms significantly enhance the adoption of digital technologies and improve innovation performance (Chung et al., 2022). To address the misalignment between green objectives and IoT, firms should integrate decarbonization and ESG targets into their digital transformation roadmaps. Research in Industry 5.0 contexts has emphasized that digital technologies, including IoT, AI, and cyber-physical systems, are increasingly deployed to support energy efficiency and emissions reduction, suggesting that positioning GIoT projects within broader sustainability goals can mobilize top management support and access to green finance (Ghobakhloo et al., 2024; Yanytska, 2025).

For construction enterprises, the most critical barriers are (i) lack of technical infrastructure, (ii) lack of established connection standards, and (iii) limited data security. To overcome infrastructure limitations, construction firms should prioritize the integration of IoT with Building Information Modeling (BIM) to enable real-time monitoring of energy use, resource consumption, and project operations; evidence from Singapore and Australia shows that BIM-IoT integration can improve operational efficiency and sustainability performance in large-scale construction projects (Chen et al., 2023; Liu et al., 2025). Regarding data security, firms should adopt robust cybersecurity frameworks to protect IoT-enabled construction systems. In particular, IEC 62443-based architectures, incorporating segmented OT/IT networks and secure communication protocols, can effectively reduce vulnerabilities, prevent unauthorized access, and ensure data integrity across interconnected systems. In developing regions, the adoption of ISA/IEC 62443 has been increasingly promoted at both organizational and national levels. For example, countries in Asia and Southeast Asia, including India, are leveraging this framework to secure industrial IoT and smart infrastructure systems, while Malaysia has formally incorporated IEC 62443 into its national cybersecurity standards for operational technology (Dataintelo, 2025). Finally, the absence of established connectivity standards can be mitigated by promoting open and interoperable industrial communication standards such as OPC UA to ensure secure data exchange among heterogeneous devices; countries like the Netherlands and Japan have actively promoted such standards within industrial consortia, reducing vendor lock-in and addressing interoperability challenges in construction environments (Carvajal-Flores et al., 2024).

5 Conclusion

This study investigated barriers to GIoT adoption in Vietnamese industrial enterprises using the AHP method and the TOE framework. Based on a systematic literature review, ten barriers were identified and classified into technological, organizational, and environmental dimensions. Drawing on expert evaluations from manufacturing and construction enterprises, the study prioritized these barriers and identified significant sectoral differences.

Our findings show that environmental barriers exert the greatest influence on GIoT adoption overall, followed by organizational and technological barriers. Among the identified barriers, the lack of a supportive ecosystem, insufficient regulatory pressure, and limited data security emerged as the most critical. Furthermore, the results indicate that manufacturing enterprises are primarily constrained by ecosystem and regulatory issues, whereas construction enterprises are more affected by technological challenges, particularly infrastructure readiness and interoperability standards.

5.1 Contributions

This study makes several contributions to the emerging GIoT literature. First, it extends existing research on GIoT adoption by developing a comprehensive TOE-based framework that consolidates ten key barriers identified from recent high-quality studies. In doing so, the study contributes to the theoretical understanding of how technological, organizational, and environmental factors jointly influence GIoT adoption in developing economies.

Second, unlike most previous studies that provide descriptive discussions of GIoT challenges, this research applies the AHP method to prioritize barriers and quantify their relative importance systematically. This research offers a more rigorous understanding of which barriers deserve greater managerial and policy attention.

Third, the study provides one of the first empirical assessments of GIoT adoption barriers in Vietnam and contributes evidence from an emerging economy context, where institutional and ecosystem-related factors play a particularly important role. The findings, therefore, enrich the growing body of research on sustainable digital transformation in developing countries.

Finally, by comparing manufacturing and construction enterprises, the study demonstrates that GIoT adoption barriers are sector-specific rather than uniform across industries. This comparative perspective provides a more nuanced understanding of GIoT implementation and offers practical guidance for designing targeted policies and industry-specific adoption strategies.

5.2 Limitations and future research

Despite its contributions, this study has several limitations. First, the analysis focuses on manufacturing and construction enterprises in Vietnam, which may limit the generalizability of the findings to other sectors and countries. Second, although AHP provides a structured approach to prioritization and consistency is verified through CR values, the results remain dependent on respondents’ subjective judgments. Third, the study examines barriers at a single point in time and does not capture their evolution as technologies and institutional environments change.

Future research could expand to a wider range of industrial sectors to enhance generalizability and combine the AHP approach with the Delphi method or other quantitative techniques to minimize subjectivity. In addition, Longitudinal studies would also be valuable for understanding how GIoT adoption barriers evolve throughout different stages of digital and green transformation.

Acknowledgements

The authors would like to thank the participating industrial enterprises in Vietnam and all respondents for their valuable time and cooperation during the data collection process. We are also grateful to colleagues and domain experts for their insightful comments and constructive feedback, which contributed to improving the quality of this study.

Statement on Data Availability

All the analyzed data are included in the manuscript. The raw data will be available on reasonable request to the corresponding author.

• Financial support:
  None.
• How to cite:
  Bui, H. Q., Tran, T. H., & Vu, T. D. (2026). Analyzing barriers to Green IoT adoption in Vietnamese industrial enterprises using Analytic Hierarchy Process approach. Gestão & Produção, 33, e16325. https://doi.org/10.1590/1806-9649-2026v33e16325

References

• Albreem, M. A., El-Saleh, A. A., Isa, M., Salah, W., Jusoh, M., Azizan, M. M., & Ali, A. (2017, November). Green internet of things (IoT): An overview. In2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)(pp. 1-6). New York: IEEE. https://doi.org/10.1109/ICSIMA.2017.8312021
  » https://doi.org/10.1109/ICSIMA.2017.8312021
• Ali, A., & Mahmood, A. (2024). Developing a causal framework of internet of things adoption barriers for agile manufacturing in post COVID-19. International Journal of Engineering Business Management, 16, 18479790231223623. https://doi.org/10.1177/18479790231223623
  » https://doi.org/10.1177/18479790231223623
• Alsharif, M. H., Jahid, A., Kelechi, A. H., Kannadasan, R., Alsharif, M. H., Jahid, A., Kelechi, A. H., & Kannadasan, R. (2023). Green IoT: a review and future research directions. Symmetry, 15(3), 757. https://doi.org/10.3390/sym15030757
  » https://doi.org/10.3390/sym15030757
• Altameem, A. (2022). The challenges of internet of things adoption in developing countries: an overview based on the technical context. In D. C. Wyld, & D. Nagamalai (Eds.), Computer science & technology trends (pp. 111-118). Tamil Nadu, India: AIRCC Publishing Corporation. https://doi.org/10.5121/csit.2022.121910
  » https://doi.org/10.5121/csit.2022.121910
• Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: a survey. Computer Networks, 54(15), 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010
  » https://doi.org/10.1016/j.comnet.2010.05.010
• Building and Construction Authority. (2021). Green Mark 2021 Retrieved in 2025, December 31, from https://www1.bca.gov.sg/buildsg/sustainability/green-mark-certification-scheme/green-mark-2021
  » https://www1.bca.gov.sg/buildsg/sustainability/green-mark-certification-scheme/green-mark-2021
• Carvajal-Flores, D. F., Abril-Jiménez, P., Buhid, E., Fico, G., & Cabrera Umpiérrez, M. F. (2024). Enhancing industrial digitalisation through an adaptable component for bridging semantic interoperability gaps. Applied Sciences, 14(6), 2309. https://doi.org/10.3390/app14062309
  » https://doi.org/10.3390/app14062309
• Chen, Y., Wang, X., Liu, Z., Cui, J., Osmani, M., Demian, P., Chen, Y., Wang, X., Liu, Z., Cui, J., Osmani, M., & Demian, P. (2023). Exploring Building Information Modeling (BIM) and Internet of Things (IoT) integration for sustainable building. Buildings, 13(2), 288. https://doi.org/10.3390/buildings13020288
  » https://doi.org/10.3390/buildings13020288
• Cheng, M., Liu, G., Xu, Y., & Chi, M. (2024). What hinders Internet of Things (IoT) adoption in the Chinese construction industry: A mixed-method. Journal of Civil Engineering and Management, 30(1), 1-18. https://doi.org/10.3846/jcem.2024.19961
  » https://doi.org/10.3846/jcem.2024.19961
• Chinipardaz, M., Khoramfar, A., & Amraee, S. (2024). Green internet of things and solar energy. Environmental Science and Pollution Research International, 31(12), 18296-18312. https://doi.org/10.1007/s11356-023-31141-z PMid:38063961.
  » https://doi.org/10.1007/s11356-023-31141-z
• Chung, J.-E., Oh, S.-G., & Moon, H.-C. (2022). What drives SMEs to adopt smart technologies in Korea? Focusing on technological factors. Technology in Society, 71, 102109. https://doi.org/10.1016/j.techsoc.2022.102109
  » https://doi.org/10.1016/j.techsoc.2022.102109
• Cindrić, I., Jurčević, M., Hadjina, T., Cindrić, I., Jurčević, M., & Hadjina, T. (2025). Mapping of industrial IoT to IEC 62443 standards. Sensors, 25(3), 728. https://doi.org/10.3390/s25030728 PMid:39943366.
  » https://doi.org/10.3390/s25030728
• Das, R., Adam, I. Y., & Oztop, H. F. (2025). Green IoT for energy efficiency: enabling technologies, challenges, and future research directions. Thermal Science and Engineering Progress, 62, 103592. https://doi.org/10.1016/j.tsep.2025.103592
  » https://doi.org/10.1016/j.tsep.2025.103592
• Dataintelo. (2025). ISA/IEC 62443 Security For Warehouse OT Market Research Report 2033 Retrieved in 2025, December 31, from https://dataintelo.com/report/isaiec-3-security-for-warehouse-ot-market
  » https://dataintelo.com/report/isaiec-3-security-for-warehouse-ot-market
• Ghobakhloo, M., Mahdiraji, H. A., Iranmanesh, M., & Jafari-Sadeghi, V. (2024). From industry 4.0 digital manufacturing to industry 5.0 digital society: a roadmap toward human-centric, sustainable, and resilient production. Information Systems Frontiers https://doi.org/10.1007/s10796-024-10476-z
  » https://doi.org/10.1007/s10796-024-10476-z
• Golovianko, M., Terziyan, V., Branytskyi, V., & Malyk, D. (2023). Industry 4.0 vs. Industry 5.0: Co-existence, transition, or a hybrid. Procedia Computer Science, 217, 102-113. https://doi.org/10.1016/j.procs.2022.12.206
  » https://doi.org/10.1016/j.procs.2022.12.206
• He, Z., & Turner, P. (2021). A systematic review on technologies and industry 4.0 in the forest supply chain: a framework identifying challenges and opportunities. Logistics, 5(4), 88. https://doi.org/10.3390/logistics5040088
  » https://doi.org/10.3390/logistics5040088
• Huang, Y.-S., & Li, W.-H. (2012). A study on aggregation of TOPSIS ideal solutions for group decision-making. Group Decision and Negotiation, 21(4), 461-473. https://doi.org/10.1007/s10726-010-9218-2
  » https://doi.org/10.1007/s10726-010-9218-2
• Jalil, M., Marikan, D., Jais, M., Ayupp, K., & Shukri, M. (2025). A Green Internet of Things (GIoT) adoption framework to enhance environmental performance of SMEs through the digital social networking. Business Strategy & Development, 8(3), e70179. https://doi.org/10.1002/bsd2.70179
  » https://doi.org/10.1002/bsd2.70179
• Koca, G., & Yıldırım, S. (2021). Bibliometric analysis of DEMATEL method. Decision Making: Applications in Management and Engineering, 4(1), 85-103. https://doi.org/10.31181/dmame2104085g
  » https://doi.org/10.31181/dmame2104085g
• Kwon, I. (2022). Korea’s institutional framework on sme digitalization. In R. Capello, R. Kitchin, J. Knieling, & N. Lowe (Eds.), Regions and cities Abingdon: Routledge.
• Liu, L., & Mishra, A. (2022). Enabling technologies challenges of green Internet of Things (IoT) towards sustainable development in the era of Industry 4.0. Technological and Economic Development of Economy, 30(2), 1-32. https://doi.org/10.3846/tede.2022.16520
  » https://doi.org/10.3846/tede.2022.16520
• Liu, X., Antwi-Afari, M. F., Li, J., Zhang, Y., & Manu, P. (2025). BIM, IoT, and GIS integration in construction resource monitoring. Automation in Construction, 174, 106149. https://doi.org/10.1016/j.autcon.2025.106149
  » https://doi.org/10.1016/j.autcon.2025.106149
• Mustapha, H., Kassim, R., & Rahmat, A. (2022). Internet of things adoption in manufacturing: an exploratory of organizational antecedents. In A. Ismail, M. A. Mohd Daril, & A. Öchsner (Eds.), Advanced transdisciplinary engineering and technology (pp. 339-351). Cham: Springer. https://doi.org/10.1007/978-3-031-01488-8_28
  » https://doi.org/10.1007/978-3-031-01488-8_28
• Pandiyan, P., Saravanan, S., Kannadasan, R., Krishnaveni, S., Alsharif, M. H., & Kim, M.-K. (2024). A comprehensive review of advancements in green IoT for smart grids: paving the path to sustainability. Energy Reports, 11, 5504-5531. https://doi.org/10.1016/j.egyr.2024.05.021
  » https://doi.org/10.1016/j.egyr.2024.05.021
• Rahimian, A., Ahmed, R., Fazeli, A., Aghdam, E., Singh, A. K., Naji, M., Mohandes, S. R., Antwi-Afari, M., & Zayed, T. (2025). Examining the Barriers to the Adoption of IoT-based technologies in green buildings. Journal of Construction Engineering and Management, 151(3), 04024211. https://doi.org/10.1061/JCEMD4.COENG-14231
  » https://doi.org/10.1061/JCEMD4.COENG-14231
• Rejeb, A., Rejeb, K., & Zrelli, I. (2024). Analyzing Barriers to Internet of Things (IoT) adoption in humanitarian logistics: an ISM–DEMATEL approach. Logistics, 8(2), 38. https://doi.org/10.3390/logistics8020038
  » https://doi.org/10.3390/logistics8020038
• Saaty, T. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. https://doi.org/10.1504/IJSSCI.2008.017590
  » https://doi.org/10.1504/IJSSCI.2008.017590
• Schuler, A., & Kotsis, G. (2024). A systematic review on techniques and approaches to estimate mobile software energy consumption. Sustainable Computing : Informatics and Systems, 41, 100919. https://doi.org/10.1016/j.suscom.2023.100919
  » https://doi.org/10.1016/j.suscom.2023.100919
• Shahriar, H., Islam, M. S., Jahin, M. A., Ridoy, I. A., Prottoy, R. R., Abid, A., & Mridha, M. F. (2024). Exploring internet of things adoption challenges in manufacturing firms: a delphi fuzzy analytical hierarchy process approach. PLoS One, 19(11), e0311643. https://doi.org/10.1371/journal.pone.0311643 PMid:39495813.
  » https://doi.org/10.1371/journal.pone.0311643
• Sørensen, H. (2023). Sustainable AI-IoT systems. IACSIT International Journal of Engineering and Technology, 15(9).
• Thuy, N. (2021). The adoption of the internet of things in Vietnam. International Journal of Innovation, Creativity and Change, 12(4), 22-35.
• Ullah, F., Olatunji, O., & Qayyum, S. (2026). A scoping review of green Internet of Things in construction and smart cities: current applications, adoption strategies and future directions. Smart and Sustainable Built Environment, 15(1), 294-318. https://doi.org/10.1108/SASBE-11-2023-0349
  » https://doi.org/10.1108/SASBE-11-2023-0349
• Vinodh, S., & Shimray, S. (2022). Analysis of barriers for implementation of integrated Lean Six Sigma and Industry 4.0 using interpretive ranking process. The TQM Journal, 35(7), 1761-1776. https://doi.org/10.1108/TQM-04-2022-0121
  » https://doi.org/10.1108/TQM-04-2022-0121
• Yanytska, L. (2025). The rise of human-centric manufacturing in the industry 5.0 era. International Journal of Advanced Manufacturing Technology, 139(9-10), 5067-5077. https://doi.org/10.1007/s00170-025-16192-5
  » https://doi.org/10.1007/s00170-025-16192-5

• Editor-in-Chief
  Pedro Munari

Publication Dates

• Publication in this collection
  21 Aug 2026
• Date of issue
  2026

History

• Received
  31 Dec 2025
• Reviewed
  01 Apr 2026
• Accepted
  15 July 2026