Prioritising criteria for sustainable battery supplier selection in the EV sector using fuzzy-ordinal priority approach Open Access

Recently, humans have seen escalation in extreme weather conditions, bigger floods and more devastating extreme weather events, which are getting worse (Elliot et al., 2025). This global climate change crisis envisages governments and environmental agencies developing sustainable strategies to mitigate rising carbon emissions (Omri and Boubaker, 2024). The transportation sector alone is accountable for approximately 23% of carbon emissions, which is nearly one-fifth of total carbon emissions across the globe (Solaymani and Botero, 2025). Recently, a report published by Climate watch (2024) claimed that leading global economies such as China, the United States of America, the European Union and India are some of the prime emitters in the transportation sector. To curb this rising carbon emission, several countries have adopted potential solutions in the automotive sector. Solutions include transforming the automative sector from conventional combustion-based vehicles to electric vehicles (EVs) and the adoption of advanced biofuels such as compressed natural gas, ethanol-blended petrol and green hydrogen (De Paula Leite et al., 2025).

Many countries, including India, have been promoting electric-based transport systems on a large scale. This paradigm shift also drives huge demand for the EV battery market, and by 2028, it is expected to be worth $27.70 bn (IBEF, 2024). Therefore, the role of battery suppliers becomes essential for a sustainable battery supply chain (Kempston et al., 2025). Like any other supply chain, the EV supply chain also poses several challenges, such as a supply shortage of upstream minerals in the current dynamic market (Lou et al., 2025). Other challenges include operational challenges, regulatory requirements and adopting technological innovations (Tripathy et al., 2024). The high purchase price of EVs in developing countries, limited charging infrastructure, critical reserves such as lithium, nickel, cobalt, manganese and graphite and advanced technologies shared by battery manufacturers are some of the risks hindering the widespread adoption of EVs (Alshahapy et al., 2025). Past studies evaluating suppliers often used limited criteria and lacked flexible and integrated frameworks capable of handling the uncertainties and complexities inherent in supply chains of emerging markets. In contrast, the recent literature highlights the need for integrating comprehensive criteria and fuzzy logic to address the evolving complexity and uncertainty in sustainable battery supplier selection (Wei et al., 2022). Given the research gap, the presented research applied a multi-criteria decision-making approach under a fuzzy environment to prioritise the criteria and sub-criteria for selecting sustainable battery suppliers for EV manufacturers.

The present study adopted a theoretical background to identify the criteria and sub-criteria from a dual theoretical lens. The study adopted the technological, organisational and environmental (TOE) framework with dynamic capability (DC) theory. The combination was adopted due to the dynamic nature of the EV market, which required a thorough investigation before the selection of the suppliers of an essential component like the battery. Within the TOE framework, technological capability incorporates essential criteria such as battery energy density and safety standards. On the other hand, organisational ability comprises financial resilience and workforce expertise. Finally, the environmental dimension covers compliance with legal frameworks, market dynamics and stakeholder commitments towards ethical sourcing to curb carbon emissions (Tripathy et al., 2024). At the same time, the DC theory stresses that battery suppliers update their operations to handle the uncertain market conditions.

From this dual theoretical underpinning, the research objective and research questions are framed as follows:

To answer the above research questions, the following are the research objectives:

This study systematically identifies and validates a comprehensive set of criteria and sub-criteria necessary for sustainable battery supplier selection in electric vehicle manufacturing, particularly in emerging economies such as India. Using the OPA-F ensures that the criteria are prioritised in a way that is strong, clear and unbiased, so that objective rankings can be made even when experts are not sure or do not have all the information.

The outline of the remaining section of the paper is as follows. In Section 2, there is a review of the literature that presents the theoretical background and the most important criteria for sustainable EV supplier selection. Section 3 explains the research methodology, whereas Section 4 presents the solution procedure. Section 5 discusses the findings and their theoretical and practical implications. Lastly, Section 6 presents a conclusion and the scope for future work.

This section provides an overview of the literature and the theoretical validation relevant to the criteria and sub-criteria for sustainable battery supplier selection for EVs. A single theoretical framework, either TOE or DC theory, may not be enough to capture the full range of criteria; therefore, the presented study aims to develop a broader evaluation framework for the selection of battery suppliers in the EV sector.

The TOE framework covers how technological, organisational and environmental factors affect supplier evaluation; however, it overlooks the dynamic capabilities that need to adapt, innovate and respond to frequently changing markets and regulations.

Conversely, the DC theory emphasises the firm’s dynamic capabilities and looks at the opportunities that enable firms to deal with uncertain market situations. However, DC theory alone cannot address contextual willingness criteria entirely, which are essential for making decisions regarding the battery supplier selection problem, such as meeting regulations for net zero emissions, firms’ ability to increase production capacity in dynamic demands and assessing firms’ technological readiness with evolving technological advancement. This dual-theory perspective enables the present study to provide a deeper, more actionable way to identify and evaluate the battery supplier’s criteria. It will lead industry practitioners to address structural and adaptive demands under the dynamic and complex business environment.

Tornatzky and Fleischer (1990) proposed the TOE framework to examine the essential technological, organisational and environmental criteria that affect the firm’s business goals. Through the TOE framework, businesses can holistically view various innovation adoptions and efficiently manage their internal resources to meet the stakeholders’ requirements (Addy et al., 2023). The technological dimension enables decision-makers to scrutinise the state of battery technology along with the other criteria for battery suppliers, including energy density, consistency in performance, safety standards and prevailing sustainable manufacturing practices (Sang et al., 2024). The organisational capability dimension considers a firm-specific feature, such as the company size, organisational agility, firm competencies, including human capital and skills, managerial expertise and long-term vision that regulate an organisation’s ability to participate in sustainable practices (Ajgaonkar et al., 2021). Finally, the environmental dimension captures criteria like regulatory oversight, established standards and expectations from customers and stakeholders (Dadhich and Hiran, 2022). Prior studies have widely adopted the TOE framework in diverse research areas, including e-commerce, suppliers’ evaluation, financial performance of organisations, resource planning and industry readiness for the fourth industrial revolution (Li and Che, 2024; Zhong et al., 2025). Benchis et al. (2025) applied the TOE framework to examine the adoption of blockchain in public and private sectors, demonstrating the impact of technological and environmental factors on organisational readiness. Similarly, Kumar and Singh (2024) explored enterprise metaverse adoption through the TOE model and highlighted the crucial role of competitive pressure, organisational fit and technology uncertainty. Table 1 shows the criteria and sub-criteria identified under the TOE framework.

Teece et al. (1997) proposed the dynamic capabilities (DC) theory, which emphasises understanding of how organisations adjust their internal and external processes to reconfigure them under uncertain business environments. The DC theory extends the resource-based view (RBV) framework. RBV has limitations that emphasise firm capabilities under certain circumstances. While the DC theory overcomes this limitation, it focuses on a firm’s capabilities to continuously progress to sustain competitive advantage under volatile technology-driven markets (Ferreira and Ferreira, 2024). The adoption of DC theory for the present research helps investigate the criteria and sub-criteria under a dynamic business environment for selecting the sustainable battery suppliers for EV manufacturers. The DC theory enables the research to explore battery manufacturers’ flexibility and innovation capability in responding to changing market circumstances. These capabilities enable companies to identify and adopt new technological advances and adapt to changing regulations, such as recycling technological innovations and the circular economy (Chari et al., 2022). Also, Vanpoucke et al. (2014) underlined that the DC theory explored the firm’s dynamic capabilities under three components, i.e. sensing, seizing and transforming. Recent studies highlight that several challenges, such as uncertain consumer preferences, globalisation and supply chain distribution, drive organisations to consider suppliers’ dynamic capabilities during the supplier evaluation to achieve sustainability and resilience (Benabdellah et al., 2024). Grounded on the DC theory, the following criteria and sub-criteria concerning battery suppliers’ selection have been identified and are defined in Table 2.

In earlier years, the procurement process was highly labour-intensive and emphasised local sourcing, where the major criteria were trust and long-term partnership (Spina et al., 2013). The traditional supplier selection problem was focused on cost minimisation and less strategic input was considered, which led to inconsistencies and inefficiencies. However, with Industrial Revolution 4.0 and sustainable development, supplier selection problems transformed drastically. Digital technologies and electronic procurement processes started to be considered from order to payment. It expanded the sourcing from regional boundaries to the global market, leading to innovation, quality and sustainability perspectives in the procurement process (Sahoo et al., 2024).

At present, researchers have explored multiple dimensions in suppliers’ selection problems. For instance, Sheykhizadeh et al. (2024) explored the lean, agile and green practices criteria for the pharmaceutical supplier selection using a hybrid fuzzy multi-attribute decision-making approach. Rostami et al. (2023) investigated the factors for a viable supplier selection. And considered agility, resiliency, sustainability, digitalisation aspects and applied the goal programming-based fuzzy best–worst method for supplier selection. Ulutaş et al. (2024) applied a grey hybrid MCDM approach to identify and rank the suppliers for mitigating the risk during supply chain disruptions. The available literature indicates that multiple studies have explored various dimensions of supplier selection using the MCDM approaches that consider a wider range of operational and sustainability-related factors (Moktadir et al., 2025). Researchers have explored several dimensions, including resilience, environmental compliance, regulatory alignment and circular economy capabilities, with respect to suppliers’ selection problems (Çakır and Serdarasan, 2025).

It has been observed in the literature review that prior studies have explored the supplier selection problem in various dimensions. However, a comprehensive framework is still missing in the existing literature to handle current operational requirements and the growing demands for sustainability, innovation and resilience in the supply chain of EVs. Most studies emphasise limited dimensions, including technological criteria or environmental compliance and fail to incorporate organisational readiness or dynamic adaptability into suppliers’ assessment frameworks. Therefore, the present study adopted the OPA-F approach with dual lenses of TOE and DC theory to formulate a forward-looking battery supplier selection model, effectively prioritising criteria and sub-criteria within the context of ambiguity and the dynamic supply chain risks in the EV sector.

Figure 1 illustrates that the model enables suppliers to be ranked not only according to how well they meet operational, regulatory and technological requirements but also based on their ability to identify and capitalise on new opportunities, adapt processes and drive long-term change in response to evolving market and policy conditions.

This method prioritises the criteria and sub-criteria of battery suppliers. A multimethodology has been applied to achieve the objective. Initially, broader criteria and their specific sub-criteria were identified through a literature survey. Thereafter, experts were consulted for validation and finalisation of criteria and sub-criteria. Finally, 18 broad criteria and 39 specific sub-criteria were finalised and considered for further evaluation. Experts rated their opinion regarding criteria and sub-criteria ratings using linguistic variables on a seven-point scale. Collected experts’ judgements were used as parameters for the fuzzy ordinal priority approach. A brief overview of the OPA-F is provided in the subsection below. Moreover, the expert details and linguistic scale are shown in Tables 3 and 4, respectively.

The ordinal priority approach (OPA) is a recently developed multi-attribute decision-making (MADM) method by Ataei et al. (2020). OPA is gaining popularity in group decision-making methods because it removes most of the demerits of some existing MADM methods. OPA removes the following major demerits of existing MADM methods of the same category (like SWARA) as well as of other categories (like BWM, ANP, AHP, VIKOR and TOPSIS):

1. It does not require pairwise comparisons like BWM, ANP and AHP (Ataei et al., 2020).

2. It allows decision-makers to leave any criterion unprioritised and to allocate the same priority to more than one criterion (Ataei et al., 2020; Sarkar et al., 2024).

3. It does not require an average of experts’ opinions for aggregation like SWARA (Ataei et al., 2020).

4. It does not require normalisation like VIKOR and TOPSIS (Ataei et al., 2020).

5. It does not require a predefined scale like the pairwise comparison methods; therefore, it is free from scale limitation (Pamučar et al., 2022b).

Due to inherent limitations in human judgements, group decision-making is often constrained by uncertainty, vagueness, imprecision and incompleteness of information. The OPA-F incorporates fuzzy set theory to address such vagueness and uncertainties (Pamučar and Deliktaş, 2025). Further, it assures precise and reliable decisions even in the case of an incomplete dataset (Sadeghi et al., 2023). Furthermore, it leverages the strength of fuzzy MADM and linear programming to integrate subjective expert judgement and mathematical optimisation (Mahmoudi et al., 2022).

Mahmoudi et al. (2022) have proposed the OPA-F. This approach uses fuzzy data instead of ordinal data as input parameters. Resultantly, the coefficients of the objective function and constraints become fuzzy coefficients. Therefore, OPA-F belongs to the class of fully fuzzy linear programming problems. Before explaining the research steps, the notation of sets, indices, parameters and variables are presented in Table 5.

The research workflow phases are presented in Figure 2, while detailed research steps of OPA-F are outlined below.

• STEP 1: Identification and finalisation of criteria/sub-criteria: This step belongs to the criteria determination phase. Under this step, 18 criteria and 39 sub-criteria were identified through the literature survey. The identified criteria and sub-criteria were validated and finalised by the experts.

• STEP 2: Selection of experts: This step is a part of the data collection phase. Under this step, 15 experts were selected based on years of experience and expertise. The experts were either affiliated with leading Indian battery manufacturing companies, top Indian electric vehicle companies or were academicians actively researching the battery supply chain. As such, all experts in the panel were from diverse backgrounds, so group dynamics were preserved. Also, all experts were approached through professional channels, including email and LinkedIn. However, only ten responded to the survey. Based on a review of panel sizes in earlier studies that employed the OPA method – for instance, Du et al. (2023) used a panel size of ten; Pamucar et al. (2022a) used 4 experts; Mahmoudi and Javed (2022) used five; Sadeghi et al. (2023), Pamucar et al. (2022b) and Pamucar et al. (2023) each used six experts and Mahmoudi and Javed (2022) used seven – this study adopted a panel size of ten.

The domain of expertise and experience of respondent experts are provided in Table 3.

• STEP 3: Collection of experts’ opinion: This step is also part of the data collection phase, during which experts were asked to rate criteria and sub-criteria using a seven-point linguistic scale presented in Table 4. The seven-point linguistic variable balances greater expressing capability and ease of use, as stated by Büyüközkan et al. (2024). Experts were free to allocate the same priority to multiple criteria/sub-criteria or leave any criteria/sub-criteria unprioritised. Table 6 presents experts’ ratings for the priority of criteria using linguistic variables.

• STEP 4: Linear programming problems formulation: This step is included within the problem formulation phase. To determine the weight of criteria and sub-criteria, two linear programming problems, namely Subproblem-1 (Equations (1)–(10)) and Subproblem-2 (Equations (11)–(20)), are formulated in the general algebraic modelling system (GAMS). Both subproblems are fully fuzzy linear programming problems. Data collected in step 3 was used as parameters for the formulation.

The two formulated subproblems are as follows:

Subproblem-1: LPP formulation considering 18 criteria and ten experts

Subject to,

Subproblem-2: LPP formulation considering 39 sub-criteria and ten experts

Subject to,

• STEP 5: Determine fuzzy weights of criteria and sub-criteria: This step is a part of the solution phase. In this step, the subproblems were solved using the CPLEX solver, and fuzzy weights of criteria and sub-criteria were obtained. The lower limit, most likely value, and upper limit of fuzzy weight of criteria are obtained using Equations (6) and (8), likewise, Equations (16) and (18) were used to compute limits of fuzzy weight of sub-criteria.

• STEP 6: Convert fuzzy to crisp weight: This step is a part of the solution phase. The obtained fuzzy weights under STEP 5 are converted to crisp weights for comparison. Crisp weights of criteria and sub-criteria are obtained after defuzzification using Equations (9) and (19), respectively.

• STEP 7: Determination of global weight using multiplicative aggregation and ranking:

This phase is associated with the solution integration phase. Both subproblems’ results are combined, and the global weights of sub-criteria are obtained using multiplicative aggregation. Equation (21) is used to obtain the global weight of sub-criteria.

After obtaining global weights, all global weights are sorted and ranked. The global rank of sub-criteria is presented in Table 7.

• STEP 8: Final decision and validation: The obtained rank is validated by experts.

The results presented are based on the selection of EV suppliers from both TOE and DC theory perspectives. Increasing demand for electric vehicles urges organisations to adopt sustainable and resilient battery supply chains. Electric vehicles offer eco-friendly transportation, mitigating environmental issues and helping achieve the circular economy goals. Batteries rank at the top among the highly critical components, so they need careful investigation. Therefore, the presented research identified supplier selection parameters for battery selection using TOE and dynamic capabilities theory. Manufacturers must understand the importance of responsible sourcing to install environmentally sustainable and reliable batteries. The research identified 18 criteria under technological, organisational, environmental, sensing, seizing and transformational capabilities. Further, these dimensions contain 39 sub-dimensions and are prioritised using the OPA-F technique. This technique is quite useful when decision-making is complex because of the many contradictory criteria. Moreover, OPA-F helps to overcome human bias issues and offers reliable results. Based on the analysis, “battery policy adherence” ranks first, which lies under the regulatory pressure dimension of the environmental dimension. A regulatory framework based on governing battery life cycle facilitates a cradle-to-cradle perspective and reinforces decarbonisation. Since electric vehicles are booming, customers are still quite ambivalent about purchasing them because of the safety features. Therefore, it is a must for the government to formulate strict rules and regulations. Li et al. (2025) examined similar results and discussed the role of safety management strategy in enhancing the battery life cycle. Another study by Jia et al. (2025) emphasised enhancing battery safety. Also, the government must formulate a minimum threshold for recyclable materials while manufacturing EVs to promote a circular economy, securing the supply of critical materials.

“Environment compliance” ranked second, since manufacturing batteries involves energy-intensive processes like the extraction of minerals; therefore, organisations must research sustainable manufacturing methods to ensure green practices and environmental compliance. Compliance for the ban on the usage of hazardous materials and carbon footprint disclosure, along with battery tagging with a QR code, must be obligatory. Çakır and Serdarasan (2025) applied a multi-objective methodology to examine and design circular practices for electric vehicle batteries, examining sustainability dimensions including reducing cost, resource optimisation, emission control and social equity. “Safety certification” ranked third, ensuring the selected supplier meets the required standards and compliances. Moreover, it helps to gain customer satisfaction and reduces legal and regulatory challenges. Apart from meeting compliances, safety certification affirms trustworthiness, reliability and battery supplier credibility. Customers are concerned about purchasing EVs due to safety issues, fire hazards and battery reliability. Supplier safety certifications can help take customers into confidence and enhance their intentions to purchase EVs. Moreover, suppliers having safety certifications can have a competitive advantage besides mitigating risks.

The research identified a fourth significant criterion as “technology scouting,” as technology is not stagnant and keeps changing frequently, which demands that organisations become agile and flexible to accommodate technological innovations as early as possible. Exploring and investigating upcoming technologies is essential to enhance safety and reduce battery costs so that suppliers can adopt technological innovations and get competitive advantages. Benitez et al. (2022) mentioned similar aspects in the context of Industry 4.0 and highlighted the role of horizontal and vertical technology collaboration. Van den Adel et al. (2023) also analysed the role of information scouting to overcome supply chain disruptions, making the supply chain more resilient and flexible. “End-of-life management” ranked fifth, with the growth of automobiles and focus on circular economy perspectives, so it is essential to develop sustainable ways of battery disposal. As per EY reports, the energy storage capacity of EV batteries reduces below 80% over 10–20 years. After this, depending on the battery, it must be recycled, refurbished or discarded. Managers must work with suppliers to enhance battery scrap to end-of-first-life batteries to reutilise it to the maximum possible level.

The integrated theoretical foundation of TOE and dynamic capability theory for supplier selection adds to the existing scholarly literature. TOE includes external institutional pressures, technological and organisational perspectives and DC theory focuses on internal adaptation to change in market conditions. By integrating theoretical perspectives, organisations urge to develop dynamic capabilities in response to change in institutional pressures. Using this, the study provides a strong theoretical background to analyse various pivotal criteria for EVs supplier selection and examines the capability to respond to sustainability and regulatory perspectives. Also, the study conceptualised various criteria related to technological acumen, organisational readiness and environmental aspects. Also, the dynamic criteria assess the readiness of the organisation to respond and adapt to the changing customer demand patterns and attain sustainable competitive advantage. Besides this, applying a fuzzy ordinal approach helps to overcome vagueness that arises due to bias in human judgements, which results in effective decision-making. The presented research provides a strategic decision-making framework for examining suppliers and selecting one that meets sustainability and technological compliances. Moreover, the results will help policymakers draft policies and regulatory frameworks and enhance supplier standards.

Based on the proposed research study, the following practical implications are drawn: EV managers can design and develop interactive dashboards that include both TOE and dynamic capability-based criteria to better analyse and assess supplier performance, compare different suppliers, monitor risk in real-time, examine what-if scenarios and forecasting. Moreover, dashboards help with continuous improvement by regulating performance, proactive upgradation and organisational readiness for external audits. Policymakers can initiate incentive and subsidies policies to inculcate circular economy spirits among suppliers. This will help meet compliance requirements and trigger technological innovations. Original equipment manufacturer (OEMs) can collaborate with suppliers and co-invest in research and development, skilling employees and digital business transformation for enhanced traceability, and adaptability. Managers must develop risk assessment frameworks for real-time scenarios to formulate a strategic proactive approach to overcome uncertain risks, including geopolitical tensions and regulatory penalties. External supply chain resilience audits can be made at the supplier’s end to assess their readiness towards reconfiguring processes and recovery plans for handling disruptions. Moreover, managers must demand a battery passport to help trace sourcing, manufacturing, compliance and recycling information. Managers must prioritise suppliers with a strategic roadmap to integrate circular economy perspectives concerning ongoing regulatory compliances and long-term goals.

EVs are getting enormous traction to overcome global environmental issues. The research presented explored and evaluated the supplier selection criteria for EVs. Dynamic capability and TOE theory were used to list criteria, which were further validated using expert opinion. The study identified 18 key criteria, further bifurcated into 39 sub-criteria. EVs sales are expected to increase exponentially in the coming few years; therefore, the original equipment manufacturers must take utmost care while selecting the right supplier, which can result in effectively managing supply chain risks. The current research focuses on sustainability parameters, in addition to focusing only on fundamental metrics. Using a fuzzy-ordinal priority approach helps assess sustainable supplier selection criteria for battery supplier selection for EVs. The result discussed that selecting a supplier is not merely dependent on price and compliance alone, it includes a forward-looking approach that enhances innovations, adaptability and alignment with environmental rules and regulations. Also, the results highlighted that sustainable supplier selection largely depends on coordination among organisation and policymakers. Institutional pressure can trigger the suppliers to follow norms for adopting sustainable practices for EVs circular economy initiatives.

Since the presented research was conducted in India, the results may vary; therefore, a similar study can be conducted in different parts of the globe. In developing countries, usually the supply chain operates on a traditional approach; therefore, digital technologies can be integrated to get real-time data, which can help managers in tracing the information and dynamic decision-making. Also, longitudinal studies can be conducted for future research to validate the metrics identified and draw insights about mitigation strategies to overcome challenges. Most of the presented studies are country-based; therefore, a comparative analysis can be done to analyse institutional variations, explore best practices, benchmark supplier competences and harmonise supplier standards.