Sustainable Supply Chains in the Industry X.0 Era: Overcoming Integration Challenges in the UAE

Abstract

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions.

1. Introduction

Sustainability is now a top global priority. With the global shift to sustainable supply chains (SSCs), adopting sustainability initiatives is no longer an option for the UAE. By incorporating sustainable solutions, companies commit to social responsibility and set up new objectives related to their environmental impact and reach a new level of economic efficiency across the supply chain (SC), which will enable them to succeed in SC agility. In the case of the UAE where SCs have a critical role to play in the oil and gas industry worldwide, sustainable SCs are crucial to support the UAE’s economy development. The implementation of sustainable technologies in SCM has huge potential in increasing company reliability, thus enhancing its reputation, increasing customer satisfaction, and reducing costs. UAE organizations and its politicians might not fully capitalize on SSC benefits if they do not know about integration barriers and how to deal with them. Sustainability initiatives have certainly been filled with integration issues within the UAE, from weather issues to technological preparedness, inadequate skills, regulations, and behavioral resistance. The UAE needs to overcome these challenges to achieve its 2050 sustainability plan objectives and increase its competitive advantage in the global economy. This research analytically examines these barriers to better understand how we can afford a seamless integration of sustainability initiatives in UAE SCs. Filling this gap will provide a strategic plan that organizations in the region can follow to implement sustainability initiatives effectively in their SCs.

In this context, this paper focuses on three main research questions:

• What are the barriers for the adoption of sustainability initiatives in the UAE SC contexts?
• How can these initiatives be effectively aligned with existing supply chain (SC) systems and processes?
• What strategies can be employed to ensure a seamless and successful integration of sustainable solutions in SCs?

In the following section of the paper, a variety of issues that make implementing sustainability initiatives difficult in the UAE are highlighted. The inclusion of Fuzzy set theory aids in handling the inherent uncertainty and subjectivity in expert judgments. Following the shortlisting of the factors, we suggest strategies on how to overcome these problems and then use the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to prioritize these strategies to overcome the barriers and align sustainability initiatives with the existing SC.

The rest of the paper is structured as follows. Section 2 reviews related literature. Section 3 outlines the methodology. Section 4 discusses the application of the suggested method. Section 5 presents the findings and discussion. Section 6 covers implications, while Section 6 concludes the paper and proposes directions for future research.

2. Literature Review

With recent changes in the SC, sustainability has begun to play a major role in various organizational functions. Incorporating sustainable technologies into SCs has become essential for making things more efficient, cutting costs, and meeting global sustainability goals. This is important in the UAE, where the government has promised zero carbon emissions by 2050. Multiple projects are currently running with the aim of achieving this objective.

2.1. The UAE Sustainability Plan and Objectives

The UAE is trying to help the environment by focusing on sustainable development. It follows global guidelines such as the UN’s Sustainable Development Goals and the Paris Agreement. The country aims to become more environmentally friendly by 2050 by moving away from using oil and instead using renewable energy and sustainable practices. This includes reducing carbon emissions and taking steps to combat climate change. The key objectives include

• Energy Transition: By 2050, the UAE aims to reduce its carbon footprint significantly by increasing the contribution of clean energy to 50% of its energy mix, emphasizing solar, nuclear, and other renewable energy technologies. The UAE has pledged to significantly increase its renewable energy output to 14 gigawatts by 2030 (UAE Ministry of Economy, 2024).
• Sustainable Infrastructure: They are also working on sustainable infrastructure, like the Dubai 2040 Urban Master Plan and Abu Dhabi Sustainable Infrastructure Policy, to ensure that the cities are well developed for the environment and the economy (UAE Government Portal, 2024).
• Circular Economy: Within the UAE’s environmental approach, emphasis is placed on a reduction in waste, recycling, and seeking sustainability within an SC. The government is also working on providing industries with an eco-friendly background and reducing any damage they might be causing to the environment (Al Tamimi & Company, 2023).
• Stakeholder Engagement: Public–private partnership and international cooperation form the core of the UAE’s approach to leveraging the power of collective action to achieve its sustainability targets by 2050, as set out by the UN under the Sustainable Development Goals (SDGs) (UAE Ministry of Economy, 2024).

The UAE is also investing heavily, over USD 40 billion, in renewable energy projects both at home and abroad. By 2030, the country expects to generate at least 14 GW of clean energy capacity (UAE Government Portal, 2024).

2.2. Sustainable SC Challenges Within the UAE

Even though they have made these promises, there are still barriers to using sustainable solutions in SCs. Some of the problems include people not being open to new ways of doing things, not having the right skills, and not being transparent about what they are doing (Marshall et al., 2014). This review looks at all the research performed to see the main issues and how to deal with them, especially in the UAE.

Sustainable supply chain management (SSCM) involves managing supply chains to protect the environment, society, and the economy. Ahi and Searcy (2015) describe SSCM as a complex system in which companies must balance making money with doing the right thing for the planet. This balancing act is usually triggered by rules, what customers want, and the need for a good reputation (Dey et al., 2016). Making SCs sustainable can help things run better and cost less. According to Centobelli et al. (2018), companies that use sustainable practices in their SCs often use fewer resources, make less waste, and spend less on getting things from one place to another. However, how much they benefit from this depends on how well they can overcome the obstacles standing in the way of being sustainable inside and outside the company.

The UAE is doing much to become sustainable. However, some challenges are still faced, mainly when businesses use sustainability within SCs. A few of the major issues include

• Sustainability definition

The primary challenge in positing and implementing a sustainability perspective or approach to value creation in organizations lies in appropriately defining sustainability as this affects the mission, vision, and major objectives of the organization (McFarlane & Ogazon, 2011).

Sustainability is a broad and multi-faceted concept that varies in interpretation across different stakeholders—businesses may focus on economic sustainability, while governments might prioritize environmental policies, and NGOs emphasize social equity. This diversity in perspectives creates challenges in implementing sustainability within SCs, as each stakeholder might have different goals and criteria for success. Therefore, it is crucial for all parties involved to first agree on a common definition of sustainability, aligning their expectations and objectives. Only by reaching this consensus can meaningful and unified sustainability practices be effectively integrated into SC operations.

2.

Lack of Competent Workforce

Even though the UAE has ambitious plans, the need for more professionals in managing sustainability restricts the application of more advanced technologies for sustainable development. Most SC managers and logistics executives have limited awareness of green supply chain management and are unfamiliar with green SC practices, creating a skills gap (The UAE Ministry of Education, 2024; UAE Government Portal, 2024).

More talented, qualified people dealing with sustainability issues are needed. Such experts have been hard to come by. Those people are also lacking in many more countries, as emerging markets like the UAE need many people who understand sustainability and SC management (Zhu et al., 2013; Jebbor et al., 2025a, 2025b). Organizations have been recommended to provide training regarding sustainability and SC management.

3.

Resistance to Change

One of the major issues for companies in the UAE in their SCs is that they often want to stay the same (Walker et al., 2008). This is mainly because of how their company works and their beliefs, which are often rooted in organizational culture. Many businesses, including tiny and medium enterprises (SMEs), hesitate to adopt sustainable SC solutions due to cost concerns, cultural resistance, and a perception that green technologies may disrupt established operations (Al Tamimi & Company, 2023).

4.

SC transparency issues

The main challenge in moving towards sustainable SCs is the need for more transparency and data sharing across different SC stages. This makes monitoring the progress of sustainability projects very difficult, such as tracking the carbon footprint of products and services, due to fragmented and poorly integrated logistics networks (UAE Ministry of Economy, 2024). The lack of collaboration between stakeholders across the SC is the major reason for transparency issues.

5.

Lack of customer awareness

Although consumer demand for sustainable products is growing worldwide, many consumers in the UAE still prioritize low costs and convenience, which can discourage companies from fully committing to sustainability (Al Tamimi & Company, 2023).

6.

Regulatory disparities

The need for a single-window regulatory system, combined with incoherent policy implementation across the various Emirates, ensures that businesses find it a mammoth task to adopt sustainability. Inconsistencies in the regulatory framework result in operational inefficiencies and confusion for companies across multiple Emirates (Al Tamimi & Company, 2023; UAE Government Portal, 2024).

7.

Upfront costs

In addition to the previously mentioned challenges, companies may be reluctant to adopt sustainable technologies due to the perceived high upfront costs and uncertainty regarding the return on investment (Marshall et al., 2014).

8.

Poor Governance

One of the challenges is poor governance, including a lack of supportive leadership, which discourages employees from engaging in any initiatives related to sustainability in their SCs.

To address these challenges, researchers suggest a need for organizational culture change. Evangelista et al. (2018) recommend a combination of top-down leadership initiatives and employee engagement strategies to create a pro-sustainability culture within organizations. The study by Khurana et al. (2022) also highlights the importance of aligning sustainability with organizational values and long-term strategic goals to reduce resistance. Gold et al. (2016) indicate that organizations often need more human capital to implement and manage sustainable SC technologies. In places like the UAE, there is a significant need for people who know much about sustainability, but there are not enough of them (Dubey et al., 2021). Luthra and Mangla (2018) suggest that companies invest in training programs focused on sustainability and SC management to bridge this gap. Many experts suggest using new technologies like blockchain and the Internet to address the issue (Saberi et al., 2019; Tseng et al., 2013).

2.3. Sustainability Strategies/Initiatives Within the UAE SCs

The UAE has introduced different steps to support long-term growth in the SC, including new regulations and standards, designing new technologies, and engaging all relevant stakeholders.

• Green Logistics and Smart Technologies: The UAE is making SCs more efficient by using technology like the IoT, blockchain, and artificial intelligence (AI). This helps track where products come from and how they get to stores, and it also helps companies make better choices about their SCs (UAE Government Portal, 2024).
• Capacity Building and Training: The government has initiated educational programs and training on sustainability to address the skills gap. Such programs would target logistics and SC professionals and give them special training on managing sustainable SCs, carbon reporting, and green procurement (Al Tamimi & Company, 2023).
• Public–Private Partnerships (PPPs): The UAE government works closely with the private sector to finance and implement sustainable SC initiatives. For example, collaborations with large logistics companies like DP World have enabled the deployment of sustainable shipping technologies and the construction of green ports (UAE Government Portal, 2024).
• Circular Economy and Waste Reduction: The UAE is integrating circular economy (CE) principles into its SCs to reduce waste and encourage the reuse of materials. The UAE Circular Economy Policy encourages businesses to minimize waste and pollution by rethinking product design, enhancing material efficiency, and creating closed-loop SCs (Al Tamimi & Company, 2023).
• Renewable Energy and Green Infrastructure: The UAE invests much in green infrastructure projects, like solar-powered logistic hubs, that enable SCs to power through clean energy. It reduces carbon emissions, hence increasing the energy efficiency of logistics operations (UAE Ministry of Economy, 2024; UAE Government Portal, 2024).

The UAE is ready to lead the region in creating more resilient, efficient, and sustainable SCs. Long-term strategies for environmental and economic growth place the UAE in a vantage position to take up the role of a global hub for green logistics and sustainable trade. Significant hurdles lie along the pathway to sustainable SCs in the UAE, each with technological integration, cultural resistance, and a shortage of skills. Most of the barriers have yet to be overcome in the UAE; it needs government-led initiatives along with innovations in technology and public–private partnership approaches. It would therefore be necessary for education to be constantly improved, eco-friendly behaviors to be engendered, and teams to be further strengthened to ensure SCs make long-term contributions towards attaining the country’s environment-related goals.

2.4. AI-Driven Decision Support Systems for Social Entrepreneurship

Artificial intelligence (AI) is radically changing sustainable SC management by facilitating data-driven decision-making approaches and innovative social entrepreneurship. Within the context of the UAE, the utilization of AI technologies such as machine learning, blockchain, Natural Language Processing (NLP), and IoT becomes more frequent to break the barrier in sustainability and develop new channels in terms of social impact. Such integration corresponds to the three main topics of theoretical approaches that justify the potential of AI as the transformative phenomenon:

• Institutional Theory Perspective

The Institutional Theory argues that the institutions and practices of organizations are greatly affected by their institutional environments which are made up of regulatory, normative, and cultural–cognitive pillars (Bin Idrees et al., 2025). Legitimacy and compliance with such external pressures are desired by organizations instead of pure operational efficiency. In sustainable chains of supply, it is reflected in the form of adherence to environmental laws, adherence to industry norms, and reacting according to the demands of society to maintain ethical behavior.

Regulatory gaps (RG barrier) can be mitigated through the application of AI that allows setting up uniform, easily understood systems without jurisdiction fragments. Blockchain-based systems develop unmodifiable databases of carbon footprints and the method of ethical sourcing, which will enable compliance on a cross-Emirates level and help meet international standards (Li, 2025). As another instance, the UAE Zero Carbon blockchain initiative provides social entrepreneurs with renewable sustainability credits, improving access to markets and decreasing the expense of compliance by approximately one-third (UAE Ministry of Economy, 2024).

2.

Stakeholder Theory Application

Stakeholder Theory contends that managers need to coordinate and reconcile the interests of every stakeholder that has an interest in the company (Freeman & McVea, 2005) beyond the uni-dimensional emphasis of profit to shareholders. Such a broad spectrum of people includes employees, customers, suppliers, local communities, government bodies, and NGOs. Good stakeholder management is essential in the case of social enterprises, whose main objective is always to generate value to various societal groups.

Advanced analysis has been proposed to overcome the barriers to more sustainable definitions (SDs) because of a divergence between the expectations of stakeholders. NLP algorithms are used to evaluate communications of government organizations, NGOs, and communities to pinpoint common priorities, whereas identical collaborative AI platforms made it possible to reach an agreement (Moon, 2023; Jebbor et al., 2024). Such an approach is validated by the UAE SustainHub system, which translates sustainability goals into objective measures that different stakeholders can act on and achieve a 45 percent rise in cross-sector alignment (Issac, 2024).

3.

Dynamic Capabilities Framework

The Dynamic Capabilities Framework is a strategic management theory which attributes how companies may achieve competitive advantage in fast-changing environments by creating and modifying their own internal assets (Savastano et al., 2022). It is defined as the capability of a firm to integrate, construct, and reorganize both internal and external competences in a manner that will respond to the evolving environments at a high rate. These competencies are divided into detecting opportunities and threats, capturing opportunities and changing the organization in order to remain competitive.

Social entrepreneurs, with the help of AI, build flexible capabilities of nimbleness in the system of the innovation. Predictive analytics streamline resource inputs in CE frameworks (CE strategy) where machine learning predicts unforeseeable problems in SCs that make it possible to respond in advance. These capabilities help UAE startups such as “EcoChain” to connect the producers of industrial waste and the micro-enterprises of recycling to generate 120+ green jobs and divert 15,000 tons of waste on a yearly basis (UAE Government Portal, 2024).

Emerging Challenges:

There are challenges to the implementation of AI despite its potential:

• Data Sovereignty: The cloud-based AI of the UAE does not match localization laws
• Skill Gaps: A total of 68 percent of social entrepreneurs are not AI literate (Dubai Department of Economy & Tourism, 2024).

Algorithmic Bias: The risk of continuing exclusion in hiring/supplier selection.

These challenges underscore the need for ethical frameworks that balance innovation with equity—a frontier where social entrepreneurs play a critical role in shaping AI’s societal impact.

3. Methodology

Sustainable SCs are designed to reduce the environmental footprint while ensuring economic efficiency and social responsibility (

Table 1. Key AI applications in the UAE’s sustainable SCs.

AI Technology	Social Entrepreneurship Impact	UAE Case Example
Blockchain	Enables ethical certification (halal, fair-trade) for social enterprises	“GreenCert” platform verifies sustainable palm oil sourcing for 50+ SMEs
IoT Sensors	Real-time monitoring of water/energy use in agriculture	Sharjah-based “AquaAI” reduces farm water waste by 40%
Predictive Analytics	Optimizes renewable energy distribution in logistics	“SolarGrid” startup powers 200+ desert warehouses via AI-driven allocation
Computer Vision	Automates waste sorting for recycling cooperatives	Dubai “RecycleAI” startup creates jobs for disabled workers in sorting facilities

). In the global context, these SCs are increasingly becoming a priority as businesses seek to align with the UN’s Sustainable Development Goals. The methodology in this paper adopts a hybrid approach consisting of Fuzzy Delphi and Fuzzy TOPSIS. The barriers are collected from the literature and shortlisted based on the Fuzzy Delphi approach. Questionnaires are prepared and circulated among the experts to collect and analyze their opinions. Following the shortlisting of the factors based on expert input and Fuzzy Delphi, the strategies are identified to overcome these barriers. The Fuzzy TOPSIS method is used to prioritize these strategies, overcome the barriers, and align sustainability with the existing SC. The prioritization of these strategies through Fuzzy TOPSIS provides a roadmap for organizations seeking to integrate sustainability practices into their SCM. The methodology follows the steps given in Figure 1.

3.1. Factor Identification

The identification of barriers to integrating sustainability practices in SC was carried out by keyword searching on databases such as Google Scholar, IEEE Xplore, Scopus, and industry reports. It is a systematic approach used in the literature. A set of relevant keywords for the present study, including “sustainability practices”, “Sustainable SC”, “SDG”, “barriers”, “challenges”, “ESG”, “organizational resistance”, and “UAE”, were used along with the Boolean operators “AND,” “OR,” and “NOT” to identify the research papers. Once papers were identified, inclusion and exclusion criteria were employed to filter out irrelevant studies and focus on high-quality, peer-reviewed articles that specifically address the integration of sustainability initiatives in SCs. Subsequently, the identified literature was analyzed to extract common themes and recurring issues that signify the barriers to sustainability integration given in

Table 2. Barriers to achieving sustainable SCs in the UAE.

S.No	Notation	Barriers	Description	References
1	SD	Sustainability definition	Sustainability is interpreted differently by various stakeholders, so reaching a common definition is the first challenge to overcome in order to effectively implement it in the SC	McFarlane and Ogazon (2011)
2	OC	Organizational culture	Companies resist organizational change	Walker et al. (2008)
3	HUC	High upfront costs	Sustainability practices are costly and there is an uncertainty regarding the return on investment (ROI)	Marshall et al. (2014)
4	LSL	Lack of supportive leadership	Lack of support from top management in implementing sustainability initiatives	Zhu et al. (2013)
5	LSE	Lack of sustainability experts	Talented, qualified people dealing with sustainability issues are not available	Zhu et al. (2013) UAE Government Portal (2024)
6	RC	Resistance to change	Most stakeholders are reluctant to adapt to sustainability practices due to perceived risks, costs, and changes in existing processes	Al Tamimi & Company (2023)
7	LoT	Lack of transparency	Lack of transparency and data sharing across different SC stages	UAE Ministry of Economy (2024)
8	LCA	Lack of customer awareness	Consumers still prioritize low costs and convenience over sustainable and green products	Al Tamimi & Company (2023)
9	RG	Regulatory gaps	Inconsistencies in the regulatory framework result in operational inefficiencies and confusion for companies	Al Tamimi & Company (2023)
10	DA	Data availability	Many sustainability initiatives are constrained by the availability of relevant data such as the tracking of carbon emissions across the SC	Hák et al. (2012)

. Similarly, the strategies were identified, and they are shown in

Table 3. Strategies for implementing sustainability practices.

S.No	Notation	Strategies	Description	Impact on SC	References
1	TR	Training	Invest in training programs related to sustainability	A workforce educated on sustainability efforts and goals	Zhu et al. (2013); Dubey et al. (2021); Luthra and Mangla (2018)
2	EE	Employee engagement	Engage employees in changing the organizational culture	Changed employee behavior	Evangelista et al. (2018)
3	AMV	Alignment with mission and vision	Align sustainability with organization values and long-term objectives	Purpose-driven SC	Khurana et al. (2022)
4	UNT	Use of new technologies	Use new technologies like blockchain and the IoT	Enhanced transparency, traceability, and secured transactions and improved monitoring of goods throughout the SC	Saberi et al. (2019); Tseng et al. (2013)
5	PC	Partnerships and external collaborations	Collaborations with upstream and downstream value chain partners and external parties such as academic, public, and non-governmental organizations	Enhanced SC efficiency and resilience	UAE Government Portal (2024); Danciu (2013)
6	CE	Circular economy	Rethinking product design, creating closed-loop SCs	Enhanced material efficiency	Al Tamimi & Company (2023)
7	GE	Green infrastructure	Invest in solar-powered logistic hubs that enable SCs to power through clean energy	Reduced carbon emissions, hence increasing the energy efficiency of logistics operations	UAE Ministry of Economy (2024); UAE Government Portal (2024)
8	Inn	Sustainable innovation	Innovations were made in products, processes, and business models	Reduced operational costs, production costs, and capital costs	Danciu (2013)
9	CTM	Commitment by top management	A strong commitment from top management that moves beyond compliance and efficiency to avoid risks and minimize costs	Enhanced corporate reputation and long-term cost savings	Raderbauer (2011)
10	SRM	Sustainability risk management	Good risk management is needed to evaluate and fight against sustainability risks that may occur	Increased resilience in SC	Danciu (2013)
11	Eco-E	Eco-efficiency	Adopt a new way of thinking about meeting customer needs	Increased resource productivity	Danciu (2013)
12	GS	Governance structure	Set up a governance structure	Enhanced company value, thus achieving sustainable competitive advantage	Li (2025)

.

3.2. Questionnaire and Panel Selection

For this research, we selected a sample of respondents who work within the SC industry and were situated in the UAE region who have substantial working experience with the implementation of sustainability solutions within their organizations (Appendix A). Previous Fuzzy Delphi applications involved 10–50 experts (Cheng & Lin, 2002). We approached stakeholders via email/LinkedIn, and a total of 102 SC experts generously consented to participate in our research. To ensure transparency and representativeness, recruitment followed a stratified approach across key sectors, and the demographic profiles are shown in

Table 4. Experts’ demographic profiles.

Sector	% Representation	Avg. Experience (Years)	Recruitment Channel
Oil and Gas	45%	18.2	Industry Associations (60%)
Logistics and Transport	30%	12.7	Official Portals (5%)
Government Agencies	15%	15.4	Official Portals (5%)
Academia and Startups	10%	20.1	Academic Networks (5%)

Total participants: N = 102.

. We conducted online sessions to help the experts understand our research topic and answer their queries. Our questionnaire was thoughtfully formulated to comprehensively depict the barriers and strategies we aimed to explore.

Data Quality Assurance Protocol:

• Iterative Delphi Process:

  ○

  Conducted 3 rounds of anonymous questionnaires.

  ○

  Stopped when consensus stability reached (d ≤ 0.2 threshold).

• Bias Mitigation Measures:

  ○

  Sectoral weighting: Adjusted input weights based on UAE’s economic contribution of each sector.

  ○

  Blind analysis: Separated demographic data from response analysis.

  ○

  Negative case analysis: Actively sought dissenting opinions.

• Validation Mechanisms:

  ○

  Pilot testing with 5 SC directors to refine linguistic scales.

  ○

  Intra-class correlation (ICC = 0.87) confirmed inter-rater reliability.

  ○

  Post hoc interviews with 15% of participants to verify interpretations.

• Expertise Validation:

Participants’ sustainability proficiency was categorized as follows:

• Advanced/Expert: 68% (primarily oil/gas and government sectors);
• Intermediate: 25% (logistics and academia);
• Basic: 7% (SME representatives).

The content validity of the questionnaire consisted in a pre-test of five senior SC directors, according to IOC standards, so as to ensure that every item was more or less being related to the study purpose prior to broad distribution.

This stratified method guaranteed proportionate representation and consideration of the UAE economy in which oil/gas predominates sustainability investments. The last panel included experts who could analyze the problem of technical implementation (e.g., the integration of AI) and the barriers to organization-wide changes.

3.3. Fuzzy Delphi Implementation

To assess the barriers of integrating sustainability initiatives in the SC, the identified barriers were assessed on a linguistic scale, which included categories such as “Extremely Important (EI)” with corresponding Fuzzy values of (0.75, 1.0, 1.0), “Important (I)” with Fuzzy values of (0.5, 0.75, 1.0), “Moderately Important (MI)” with Fuzzy values of (0.25, 0.5, 0.75), “Least Important (LI)” with Fuzzy values of (0, 0.25, 0.5), and “Not Important (NI)” with Fuzzy values of (0, 0, 0.25). The authors have further shortlisted these particular barriers identified using the Fuzzy Delphi method. The Fuzzy elements are given as $\tilde{x}=\left(l_{ij}{,m}_{ij},u_{ij}\right)$, with i = 1, 2, …, n and j = 1, 2, …, m. The ${\tilde{w}}_j$ of the elements are $\tilde{w}=\left(l_j{,m}_j,u_j\right)$, for which $m_j={\left({\sum }_i^m{m}_{ij}\right)}^{1/n}$, $l_j=min\left\{l_{ij}\right\}$, and $u_j=max\left\{u_{ij}\right\}$. The crisp values are obtained using the following equation:

$$C_j={\displaystyle \frac{L_j{+4M}_j+U_j}{6}}$$

where $C_j$ is the crisp value, $L_j$ is the average of lower values of $l_{ij}$, $M_j$ is the mean of the average of the values of $m_{ij}$, and $U_j$ is the average of upper values of $u_{ij}$.

The FDT is a consensus and an aggregate value calculated based on Fuzzy values, and D is the degree of inconsistency among the expert judgments. FDT values of 0.75 and below and D values of 0.2 and below are used as accepted criteria in further analysis.

The final step of the method is the selection of the criteria to be included in future studies. The consistency of the judgements (d) is obtained using

$$\left(M,m\right)=\sqrt{{\displaystyle \frac{1}{3}}\left[{\left(L-l_{ij}\right)}^2+{\left(M-m_{ij}\right)}^2+{\left(U-u_{ij}\right)}^2\right]}$$

where $x=\left(l_{ij}{,m}_{ij},u_{ij}\right)$ represents diffused values and M = (L, M, U) is the average of the diffused values. According to Cheng and Lin (2002), the judgment is considered consistent when the value of d is lower or even equal to 0.2. Also, the consensus has to be higher than 0.75 according to Murry and Hammons (1995). The procedure used to select a criterion is based on the following constraints:

• If cj ≥ 0.75 and d ≤ a 0.2, the criteria j is accepted.
• If cj ≤ 0.75 and d ≥ a 0.2, the criteria j is rejected.

Only those barriers that achieve a consensus higher than or equal to 0.75 and a level of inconsistency lower than or equal to 0.2 will be selected; otherwise, they will be rejected. The preferences of all the experts are collected and assessed. The barriers selected in this step are used for prioritizing the strategies in the next step.

3.4. Fuzzy TOPSIS

In this step, Fuzzy TOPSIS methodology is used to prioritize the strategies utilized to evaluate the barriers to implementing sustainability initiatives in the SC. The choice of TOPSIS methodology is influenced by the notion that it evaluates alternatives concerning each criterion, which is a major advantage. Moreover, it is easier to obtain responses as there are no consistency issues in this method as opposed to AHP or BWM. The views of the experts on the challenges and strategies for cloud computing are vague and subjective. It is important to integrate Fuzzy set theory in decision-making as it can overcome the ambiguity in the opinions.

Step 1: Defining the decision matrix

The opinions of the experts are integrated to assess the priority of the barrier set over the strategies. Linguistic variables were used to capture the opinions of the decision-makers using the ratings given in

Table 5. Linguistic ratings for strategy ranking.

Linguistic Ratings	Strategy (for Pairwise Comparison)
Not Important (NI)	(1,1,3)
Least Important (LI)	(1,3,5)
Moderately Important (MI)	(3,5,7)
Important (I)	(5,7,9)
Extremely Important (EI)	(7,9,9)

.

Step 2: Calculating the consolidated Fuzzy weights of barriers

The final weights of the challenges are adopted from the Fuzzy Delphi calculations based on the expert opinions collected in the survey.

Step 3: Obtaining Fuzzy scores of the pairwise comparisons

The impact of each barrier on each strategy is different. Hence, the inputs from the decision-makers aid in identifying this comparison.

Step 4: Determining Fuzzy positive and Fuzzy negative ideal solutions

The Fuzzy Positive Ideal Solution (FPIS) and Fuzzy negative ideal solution (FNIS) show the ideal solutions.

-

Fuzzy Positive Ideal Solution (FPIS):

$${\mathrm{A}}^{+}=\{\stackrel{~}{{{\mathrm{v}}_1}^{+}},\stackrel{~}{{{\mathrm{v}}_2}^{+}},\dots ,\stackrel{~}{{{\mathrm{v}}_{\mathrm{n}}}^{+}}\}$$

where for each criterion j:

$\stackrel{~}{{{\mathrm{v}}_{\mathrm{j}}}^{+}}$ = (max(v_1j^l, v_2j^l, …, v_mj^l),

max(v_1j^m, v_2j^m, …, v_mj^m),

max(v_1j^u, v_2j^u, …, v_mj^u)

-

Fuzzy Negative Ideal Solution (FNIS):

$${\mathrm{A}}^{-}=\{\stackrel{~}{{{\mathrm{v}}_1}^{-}},\stackrel{~}{{{\mathrm{v}}_2}^{-}},\dots ,\stackrel{~}{{{\mathrm{v}}_{\mathrm{n}}}^{-}}\}$$

where for each criterion j:

$\stackrel{~}{{{\mathrm{v}}_{\mathrm{j}}}^{-}}$ = (min(v_1j^l, v_2j^l, …, v_mj^l),

min(v_1j^m, v_2j^m, …, v_mj^m),

min(v_1j^u, v_2j^u, …, v_mj^u)

Step 5: Evaluating the separation distances

The separation distance of the strategies is calculated using the FPIS and FNIS.

Distance to FPIS for strategy i:

$${{\mathrm{d}}_{\mathrm{i}}}^{+}=\surd \left[{\mathsf{\Sigma }}_{\mathrm{j}}{\left(\mathrm{d}\right(\widetilde{{\mathrm{v}}_{\mathrm{i}\mathrm{j}}},\stackrel{~}{{{\mathrm{v}}_{\mathrm{j}}}^{+}}\left)\right)}^2\right]$$

Distance to FNIS for strategy i:

$${{\mathrm{d}}_{\mathrm{i}}}^{-}=\surd \left[{\mathsf{\Sigma }}_{\mathrm{j}}{\left(\mathrm{d}\right(\widetilde{{\mathrm{v}}_{\mathrm{i}\mathrm{j}}},\stackrel{~}{{{\mathrm{v}}_{\mathrm{j}}}^{-}}\left)\right)}^2\right]$$

Vertex distance between two Fuzzy numbers:

d(ã, Ѣ) = √[⅓((a^l − b^l)² + (a^m − b^m)² + (a^u − b^u)²)]

Step 6: Determining the closeness coefficient and ranking the strategies

The closeness coefficients of the strategies are calculated, and subsequently, the rankings of the strategies are identified.

Closeness coefficient for strategy i:

CC_i = d_i⁻/(d_i⁺ + d_i⁻)

Nomenclature Explanation:

Symbol	Meaning	Example
A+	Fuzzy Positive Ideal Solution	Optimal benchmark
A−	Fuzzy Negative Ideal Solution	Worst-case benchmark
ὐ	Triangular Fuzzy number	(0.3, 0.5, 0.7)
l, m, u	Lower/middle/upper values of Fuzzy number	vijl, vijm, viju
di+	Distance to ideal solution	Smaller value = better
di−	Distance to negative solution	Larger value = better
CCi	Closeness coefficient (0–1)	Rank descending
ã = (al, am, au)	Generic triangular Fuzzy number

4. Application of Proposed Method

In this section, we use the Fuzzy Delphi method, where we try to determine the exact barriers to achieving a sustainable SC. The experts were asked to fill out a questionnaire that helped us understand the prominent barriers. Since it is easier for the experts to provide linguistic opinions, it becomes essential to invoke Fuzzy logic into this problem framework. The first step, according to the professionals in the field, is to rank the barriers that may directly influence the adoption of an SSC. These barriers have also been quantified based on our findings from the industrial experts. The acceptance table and weights of the shortlisted barriers are given in

Table 6. List of barriers based on expert judgments and thresholds.

S. No.	Notation	Barriers	FDT	D	Outcome
1	SD	Sustainability definition	0.75	0.099	Accept
2	OC	Organizational culture	0.79	0.197	Accept
3	HUC	High upfront costs	0.17	0.389	Reject
4	LSL	lack of supportive leadership	0.82	0.200	Accept
5	LSE	Lack of sustainability experts	0.82	0.199	Accept
6	RC	Resistance to change	0.75	0.099	Accept
7	LoT	Lack of transparency	0.76	0.113	Accept
8	LCA	Lack of consumer awareness	0.69	0.143	Reject
9	RG	Regulatory disparities	0.63	0.119	Reject
10	DA	Data availability	0.17	0.389	Reject

.

The accepted/shortlisted barriers are sustainability definition (SD), organizational culture (OC), lack of supportive leadership (LSL), resistance to change (RC), lack of transparency (LoT) and lack of sustainability experts (LSE). Furthermore, based on our methodology, the strategies for overcoming the shortlisted barriers were identified. The strategies were discussed with SC experts, and based on the consensus reached, we shortlisted the strategies. In this process, we also considered the opinions of decision-makers regarding the prioritization of these strategies by conducting interviews with 10 industry experts. The shortlisted barriers of the FDM approach were used to prioritize the strategies. The impact of each barrier on each strategy is different. The decision matrix aids in identifying this comparison concerning each decision-maker.

Table 7. Strategy ratings of Expert 1.

	Barriers
Strategies	SD	OC	LSL	RC	LoT	LSE
TR	Extremely Important	Extremely Important	Extremely Important	Important	Least Important	Extremely Important
EE	Extremely Important	Extremely Important	Important	Important	Important	Least Important
AMV	Extremely Important	Not Important	Extremely Important	Extremely Important	Least Important	Least Important
UNT	Least Important	Important	Important	Important	Extremely Important	Extremely Important
PC	Least Important	Important	Extremely Important	Extremely Important	Extremely Important	Not Important
CE	Least Important	Important	Least Important	Not Important	Extremely Important	Not Important
GE	Least Important	Important	Least Important	Important	Important	Moderately Important
Inn	Moderately Important	Important	Least Important	Not Important	Important	Moderately Important
CTM	Important	Important	Extremely Important	Important	Important	Moderately Important
SRM	Important	Not Important	Extremely Important	Important	Important	Moderately Important
Eco-E	Important	Important	Extremely Important	Not Important	Important	Important
GS	Extremely Important	Extremely Important	Extremely Important	Not Important	Important	Moderately Important

provides the linguistic ratings of Expert 1 in a similar manner the data was collected from all experts.

Table 8. Distance measures and closeness coefficients of strategies.

Notation	Strategies	d+	d−	cci	Rank
TR	Training	3.8607	0.5542	0.1255	1
EE	Employee engagement	3.8089	0.6715	0.1499	2
AMV	Alignment with mission and vision	3.4682	1.4509	0.2950	5
UNT	Use of new technologies	3.5395	1.6355	0.3160	7
PC	Partnerships and external collaborations	3.4296	1.4993	0.3042	6
CE	Circular economy	3.0356	2.2040	0.4206	12
GE	Green infrastructure	3.2152	2.0999	0.3951	11
Inn	Sustainable innovation	3.3333	1.7928	0.3497	9
CTM	Commitment by top management	3.7736	0.7532	0.1664	3
SRM	Sustainability risk management	3.4038	1.7604	0.3409	8
Eco-E	Eco-efficiency	3.3215	1.8116	0.3529	10
GS	Governance structure	3.5042	1.3668	0.2806	4

provides the distance measures and closeness coefficients of the strategies.

The weights of the barriers computed from FDM are used in the calculation of the Fuzzy TOPSIS. The values of all the decision-makers are combined based on the steps detailed above to generate the distance measure and closeness coefficients of the strategies.

The separation distance of the strategies is calculated using the FPIS and FNIS. This helps us understand the barriers and prioritize them with respect to which strategy will correctly apply to them. The strategies discussed in this research were thoroughly taken from the industry’s point of view. The ranks are given based on the closeness coefficient in decreasing order (Varchandi et al., 2024).

5. Findings and Discussion

The Fuzzy Delphi method identified critical challenges in enhancing SC sustainability. It revealed that clean language can assist us in wrapping our heads around complex problems. Some major roadblocks include people having different ideas about what sustainability means, not having strong leadership support, resistance to making changes, not being open about what is happening, and not having enough sustainability experts. Focusing on improving employee participation and delegating senior management is an important strategy to ensure that the company adopts sustainable SC practices. Industry and consulting experts confirm that these realistic and achievable strategies will have a significant impact. Organizations should provide comprehensive training programs to strengthen organizational leadership.

5.1. Interpretation of Strategy Prioritization

The analysis of the prioritization of the strategy demonstrates deep understanding regarding how the obstacles of sustainability can be overcome in the UAE SCs. Training (TR) was the strategy with the highest rating (cci = 0.1255), in large part based on the fact that AI-driven sustainability tools have skill gaps in high demand, which directly alleviates the lack of sustainability experts (LSE) barrier. This finding was dynamically supported by an oil and gas director (18 years of experience) who pointed out the following: “Our engineers are strong in traditional chain management, and need skills on critical AI implementation, carbon accounting. Niche training is not optional- it is the base of reliable sustainability transformation.” This need was especially strong among SMEs, where 78% of professionals reported TR to be very important to conduct any internal upskilling because of resource scarcity.

Ranked immediately after was employee engagement (EE) (Rank 2, cci = 0.1499), which addresses the issue of deep-rooted cultural resistance (organizational culture barrier) with bottom-up change. Curiously, the EE received increased priority among the experts in the government sector by 35 percent compared to the answers provided by experts in the private sector, which revealed the focus of the UAE at the public-sector level on the transformation of cultures. One logistics manager (12 years’ experience) gave this explanation of this priority: You can structure the outline of perfect governance structures; however, without actual buy-in of the staff, they are merely on paper. Upon introducing gamified sustainability dashboards, within six months, we saw that energy consumption in employee-led projects was reduced by 23%.

It is important to note that governance structure (GS) was fourth in the ranking notwithstanding its prominent theoretical status. The scholars were unanimous in the stance that governance mechanisms are not efficient without the foundation of EE and Commitment Top Management (CTM). As one consultant observed: governance papers grow dusty without a sense of employee ownership and employee responsibility in leadership—it is the steam that drives the policy machinery. This brings out an essential paradigm shift: structural change requires organizational culture and development of human capital.

5.2. Cross-Sector Implications

Through the analysis, the strong divergences of some of the industry in their strategy priorities have come out, and this is a true sign of industry differences in terms of organizational structure and the limitations of resources at hand. The oil and gas industry topped the list of AI technologies (UNT) with priority 1 level implementation since operations in this sector are capital-intensive, and companies are subject to compliance regulations. One operations director (speaking on the back of a blockchain-enabled emissions tracking investment that cost them USD 4.2M) goes on to explain: *”The point is, we have invested in blockchain-enabled emissions tracking because the regulators want auditable carbon data and you measure ROI in circumvented fines rather than direct cost savings.”* The strategy of focusing on technology is consistent with centralized decision-making circles of the sector and its ability to make capital investments spread over the long term.

On the other hand, SMEs would put training (TR) at the top of their list of priorities (Priority 1) due to the scarcity of resources and the fact that they need to rely on external assistance. A manufacturing SME owner commented: “we cannot buy million-dollar AI systems but government-assisted workshops on solar powered logistics? That is where we are starting to go into sustainability.” This human capital orientation was further reflected when the SME ranked employee engagement (EE) at Position 2, which is far better than the position of the oil/gas industry, indicating that cultural agility instead of technological investment is the key to improving resource-limited settings.

The government sector has shown distinct areas of focus with partnerships (PC) being favored way above other sectors, representing 35 percent. A Dubai sustainability official explained how it works: “Our job is not direct implementation but ecosystem building in a sense that we bridge startups to corporates, universities to logistics hubs and so on.” This middle course indicates the UAE strategy of the public sector which implies the ability to facilitate sustainability via the collaborative frameworks instead of top-down decrees.

These gaps reveal an imperative idea: the path of sustainability in the context of SMEs is contingent solely upon the availability of human development (training and engagement), whereas the role of government acting as the lynchpin to all thematic spheres of work forms within the concept of connectivity needs particular attention in terms of facilitating partnership mechanisms.

6. Conclusions, Limitations, and Future Scope

The research shows that human factors within an organization are the key obstacles to achieving sustainable integration of the SC in UAE Industry X.0 rather than technical or regulatory issues. Proper analysis of 102 professionals in various fields helped us come to the conclusion that the lack of leadership commitment and the deficiency of sustainability expertise overcome other factors such as the cost or regulating issues. The most anticipated factors, such as training and employee engagement, appeared as the key strategies, whereas AI-driven solutions (surprisingly) were ranked close to the bottom (seventh) due to implementation limitations showing that people-driven development is the key to realizing technological potential. In sectoral terms, the use of AI to monitor emissions within the framework of compliance goals is a priority among oil/gas companies, whereas SMEs are concerned about the availability of training programs, and the importance of implementation-specific paths is the subject of increasing attention. Such results will reshape organizational culture as the very key to technological sustainability-oriented solutions.

A number of limitations should be taken note of. Methodologically, operationalizing uncertainty, Fuzzy logic is used as a successful solution; however, triangular number implementation can be over-simplified, especially in socio-technical transitioning, specifically in the cultural resistance machinery. The dominance of the oil/gas industry (45%) in our panel of experts is a possible bias, producing results that lead toward capital-intensive alternatives, and even though our statistical weighting scheme clarified this problem, it remains a potential bias. Also, the UAE-specific prerequisites do not allow easy applied generalizability to the surrounding Gulf countries with different governance patterns, i.e., a centralized monarchic government of Saudi Arabia. Time-wise, our snapshot evaluation will not enable us to track changes in the relationships between barriers and strategies in view of the 2050 sustainability goal that the UAE is expected to meet. Lastly, experts in the importance of sustainability innovation regarding social enterprises were unrepresented (5 percent of the sample), leaving the group with a blind spot about solutions originating with locals.

There are four directions that future research should follow. First, longitudinal surveys ought to follow the dynamics of 30–50 UAE social enterprises for 5 years so as to quantify the ROI on AI-enabled sustainability projects and catalog human–technology capability co-evolution. Second, to compare, UAE federation-based governance could be designated in opposition to Saudi Arabia, who plans to reach a goal by 2030, and Qatar, who uses resource-based governance, so that institutional theory would be applied to finally achieve an explanation for the functionality of the policy. Third, exploratory research is proposed to take advantage of Fuzzy MCDM, and ethnographic research on resisting changes in an organization and applying AI for sentiment analysis of engagement obstacles is recommended. Lastly, there is an immediate necessity to co-create AI ethics principles to resolve data sovereignty clashes with UAE localization regulations and algorithm discrimination in supplier selection, and explainable AI is necessary to establish employee trust and must be designed with the consideration of policymakers, social entrepreneurs, and the impacted population.

The urgency running through these pathways is universal: there is a necessity to maintain a common equilibrium between potential technological advances and cultural awareness that will not only secure Industry X.0 sustainability transitions in a human vacuum but also exploit the potentially transformative benefits of AI. The Emirates experience has shown that sustainable SCs are not developed just with technological adequacy but are also further advanced by institutions which value people as the foundation of innovation.