Collaborative Sustainability: Driving India’s Electric Two-Wheeler Market Through Vendor Synergy

1. Introduction

The impressive proliferation of electric vehicles (EVs) in India and especially the electric two-wheelers (E2Ws more suited for India’s dense urban infrastructure), signal the Indian intent towards a greener and more sustainable future in coherence with world trends. As the industry grows, the supplier or the vendor base is bound to also proliferate in order to keep pace and meet the demand. 85–88% of India’s oil needs come from imports (British Petroleum, 2020; Kala, 2025; Pandey, 2022; Sharma, 2025), negatively impacting own resilience and self-sufficiency (Dua et al., 2024). Indian EV market is set to expand to USD 110.7 billion by 2029 from USD 54.41 billion in 2025 with compounded annual growth rate (CAGR) of 19.44% over 2025–2029 (Mordor Intelligence, 2025). The IE2W market CAGR of 29.07% is expected to reach USD 1.3 billion in sales by 2028 (Dhaked & Birla, 2021). EVs will accelerate the shift towards a circular economy (CE), aligning with governmental nudge through measures like FAME (Faster Adoption and Manufacture of Hybrid and Electric Vehicles scheme 2015), and FAME II scheme (NAB, 2019). Government of India (GoI) desires ‘E-Vehicles Only’ on the roads by 2030 (Dhawan et al., 2017; Khurana et al., 2020). With this backdrop, the IE2W industry needs to develop strategic partnerships with their vendors so as to assure timely supplies in the desired quantity and quality.

Environmentally conscious supply chain management (SCM) processes are a significant advantage to the EV industry and hence the IE2W sector by association (Balakrishnan & Suresh, 2018), referred to as the management of the environment in SCM of the auto sector (Liu et al., 2016). The empirical analysis of green SCM practices in Indian automobile industry carried out by Luthra, Garg, et al. (2014), bears relevance to the EV and IE2W industry also, considering that processes of production, distribution, warehousing, franchising, transportation, exports, sales and after sales support, are common. Ghosh et al. (2022) have studied an optimal strategic sourcing model for green SCM, in the Indian automobile industry. Significantly today, the availability of literature and research on the IE2W sector on vendor partnerships and sustainability, is very limited.

Keeping in mind business interests, sustainability endeavours, though overall benevolent, needs to be profitable to the company while protecting the environment and meeting the firm’s social commitments (Panigrahi et al., 2019). Studies by D. Das (2017), Winter & Knemeyer, (2013), Gopal & Thakkar, (2016), Wang & Dai, (2018), Luthra et al., (2016), Sopadang et al., (2017) and S. Kumar & Dutt (2022), have explored economic performance and profitability of the firms in the context of environmental awareness and responsibilities.

This paper seeks to relationship between strategic vendor partnerships (SVP) and sustainable performance in India’s E2W (IE2W) sector, which is spearheading India’s EV revolution. Existing research has focused on technology and policy as the key drivers of electric mobility; however, there is little material on the relationship between SVP and sustainability in IE2W sector. It is evident that sustainability of E2W growth would centre around the ability of IE2W manufacturers to structure and govern the upstream supply chain, and that is where SVP and its importance come into play. SVP aims at incorporating environmental and social considerations in operational decisions, without compromising on cost effectiveness, competitiveness and resilience.

Existing research on sustainable supply chain management (SSCM) brings out how collaborative vendor relationships leading to green procurement can have a positive bearing on environmental and economic performance. In the IE2W industry too, joint investments, shared standards and streamlined logistics can improve resource efficiency and reduce emissions. However, research in the IE2W sector is lacking at scale especially in the field of vendor partnerships. This is glaring because logically, strong SVP models will lead to the long-term sustainability of the companies, making this field a strong contender for more research. Further, the existing studies are largely conceptual or literature reviews and do not bring out empirical work to quantify how SVP can translate into measurable sustainability outcomes.

This study seeks to bridge that gap and examine the relationship between SVP and sustainability in IE2W sector. The study lays a structure where SVP is conceptualized as a multidimensional construct containing: (a) outsourcing; (b) strategic vendor selection and long-term agreements or partnerships (VSLTA); (c) vendor managed inventory (VMI). These three constructs are juxtaposed to relate to how they impact sustainability in operations and sustainable performance, without compromising on the economic factors and competitiveness.

The primary objective is to test whether and how these SVP dimensions influence sustainable performance environment (emissions, waste), social (workplace practices, upskilling and vendor development), and performance (profitability, flexibility, quality, innovation, customer delight), consistent with triple bottom line (3BL) perspectives of SSCM.

To navigate the complex dynamics of IE2W supply chains, this research is driven by three core questions. First, to explore the 3BL impact of outsourcing, to determine that when firms hand over specific operations to strategic partners, how does this actively change their environmental footprint, social equity, and economic viability? Second, to study the human element of these supply chains by examining VSLTA, to understand whether careful vetting and a commitment to enduring relationships with vendors can naturally empower more sustainable performance outcomes. Finally, to investigate the operational mechanics of VMI to determine if shifting inventory control directly to vendors can streamline logistics and cut waste enough to significantly boost the overall sustainability of the supply chain.

2. Expected Outcomes of the Study

The study is expected to deliver the following real-world and academic impact:

• Moving beyond basic teamwork by showing that in the supply chain community, deep, structured partnership mechanisms, rather than surface-level collaborations, can be a more genuine engine of sustainability in the E2W space.
• Defining the partnership blueprint by breaking down exactly what makes SVP successful (outsourcing, VSLTA and VMI) and to prove how these practices can benefit society, the environment and profitability (3BL).
• Bringing real-world data to the table, since presently, most research on India’s E2W market relies heavily on policy reviews and high-level theory. This study injects much-needed, on-the-ground primary data to illuminate what is actually happening on the factory floors and in the boardrooms. It would introduce fresh, primary field data to an Indian E2W research landscape that currently lacks it.
• Providing an industry playbook, and give E2W manufacturers actionable guidance on how to build and formalize vendor relationships that drive both green innovation and bottom-line growth in a fast-moving market.

3. Methodology

The study adopts a quantitative cross-section survey design to test hypothesized relationship between SVP constructs and sustainable performance This model is considered suitable for analysing associated patterns across a relatively large number of firms and to estimate effects of these latent constructs (independent variables).

On one hand, the sample involved the OEMs (IE2W industry, only where the sales figures, turnover, revenue details were available in the public domain); and on the other hand, involved the multitude of the vendors, who are drawn from the plethora of manufacturers of battery/battery management systems (BMS), power electronics, chassis, and other critical components and parts for EVs. The selection considers that vendor partnerships and resultant sustainability outcomes arise from the eco-system if OEMs work together with vendor-channels rather than going alone.

The sample was drawn and compiled from industry lists, trade bodies, government EV manufacturer registries and public databases, with the unit of analysis being the ‘firm’ and unit of observation being the ‘individual’ being approached within the firm. From larger firms, multiple responses were endeavoured to be drawn across disciplines and functions, in order to reduce single-informant bias. To support multivariate analysis, the study targeted at least 200 valid responses across OEMs and vendors in keeping with the requirements of structural equation modelling (SEM) and multiple regression with multiple latent constructs and indicators.

A structured questionnaire was developed after vetting of industry experts, with 5 sections (demographic data capture, outsourcing, VSLTA, VMI and sustainable performance indicators) and 35 questions. Whenever possible, the existing literature on the topic was tapped for SSCM and SVP scales and customized for the IE2W setting. The initial pilot test with 50 (EV vendors and IE2W OEMs) was conducted, to help refine the questionnaire, remove superfluous questions and to improve readability. The revised questionnaire was floated to the decided sample size. Participation was voluntary and anonymity was assured. All the ethical guidelines were adhered to: informed consent, voluntary participation, anonymity, and ensuring that there were no organizational identifiers). Reminders through follow-up emails and social media text messages were resorted to, in order to improve response. Data screening to check the robustness of received inputs and descriptive statistics profile was done. Exploratory Factor Analysis (EFA) was performed on SVP and sustainable performance items to verify dimensionality and to refine the measurement model. Confirmatory Factor Analysis (CFA) was conducted to validate the latest construct structure and assess convergent and discriminate validity (Average Variance Extracted - AVE, inter-construct correlations).

The end object being to ensure a rigorous empirical assessment of how the various facets of SVP impact the sustainable performance in IE2W sector.

4. Components of Strategic Vendor Partnerships

4.1. Outsourcing

Systematic outsourcing can leverage specialization in selected OEMs which will be very expensive to recreate in-house. It will also help in the adoption of cleaner technologies and achieve scale efficiencies in upstream processes, promoting sustainability in the EV supply chain (Ghosh et al., 2022), by transferring certain activities to specialized partners operating with greener technologies, optimized networks, and stronger environmentally managed systems. When effectively done, outsourcing can enable EV companies to reduce own footprint and boost resource efficiency and compliance across the value chain, positively enhancing the performance dynamics (McIvor, 2009; Novak & Stern, 2008).

Specialized logistics and third-party logistics (3PL) partners often use route-optimization software, high-capacity consolidation, and EV fleets, thereby cutting transport emissions (Williment, 2025) and energy waste in EV distribution (Ciravegna et al., 2013). Green logistics firms possess energy-efficient warehousing capabilities, recyclable packaging, and digital documentation, reducing wastes. They also possess ESG and data capabilities enabling carbon tracking, traceability, monitoring of Scope 3 emissions and real-time reporting, rendering the supply chain more efficient (Chernova et al., 2023). In manufacturing, outsourcing non-core components or electronics to such suppliers enable cleaner production, higher yields, reduced energy consumption, emissions and waste, thus leaving EV manufacturers to retain focus on core green technologies like batteries and power electronics. The environmental standards on outsourced firms is however audited periodically to ensure compliances are met (Bevilacqua et al., 2017).

4.2. Vendor Selection and Long-Term Agreements (VSLTA)

Appropriate VSLTA promote sustainability in the EV supply chain by locking-in partners meeting rigorous environmental and social standards (Singh & Pandey, 2024). They facilitate joint investment in greener technologies which reduce costs (Feng et al., 2024). This alignment reduces lifecycle impacts of batteries, components, and logistics, and stabilizes supply for scaling EV adoption.

Selecting vendors using green criteria—such as energy-efficient production, certified environmental management systems, responsible raw material sourcing, and safe working conditions—ensures eco-friendly manufacturing strategies for EV manufacturers (Chen, 2022; Galan & Zuñiga-Vicente, 2023). Long-term agreements then create predictable demand and margins, encouraging suppliers to invest in low-carbon processes, renewable energy, closed-loop material recovery, and safer operations (Ramanathan & Gunasekaran, 2014; Shin et al., 2019; Zhou, 2021). Such contracts contain environmental performance clauses and emission targets, and link payments accordingly depending on compliance. VSLTA in the EV sector is critical to achieving net-zero goals across the full value chain considering that batteries dominate manufacturing and cost structures. VSLTA can lead to effective joint problem solving, knowledge sharing and collaboration in green processes beyond the limitations of the agreement contracts.

4.3. Vendor Managed Inventory (VMI)

Effective VMI promotes sustainability in the EV supply chain by aligning production, inventory, and logistics decisions with real demand, cutting waste and emissions throughout the network (Juned & Farooquie, 2022; van den Bogaert & van Jaarsveld, 2022). In VMI, the supplier monitors usage data and periodically replenishes requirements of the EV firm, for better efficiencies of production and reduced emissions. By optimizing order quantities and delivery frequency, VMI reduces excess inventory, obsolescence, and scrap. This is especially important in the EV industry which deals with high-value EV parts and battery components with limited shelf life. VMI optimises transport use, by minimizing rush shipments and enabling fuller truckloads or multimodal options. Integrated VMI models with visible carbon pricing show optimised cost and emissions without compromising service levels. VMI can also incorporate robust digital tools, IoT sensors, and blockchain to improve traceability, quality, and reduce defects, further shrinking environmental footprint in the supply chain.

5. Data Analysis and Interpretation

Data from the respondents was analysed using the appropriate tools and the following results emerged.

5.1. Measurement Model

Measurement model exhibits good internal consistency, convergent and lack of multicollinearity among all measures. The alpha value of Cronbach recorded between 0.829 and 0.891 is higher than the recommended value of 0.70, meaning that the items used in measuring the internal consistency of the measurement are highly consistent. Similarly, the constructs are also robust and reliable, which is again, confirmed by the composite reliability values of 0.835 to 0.892 (Hair et al., 2019).The convergent validity is quite high since all the constructs have AVE value above 0.50 (range of AVE 0.593 to 0.696) which means that a large percentage of variance in the indicators is captured by the different latent constructs instead of error in measurement (Fornell & Larcker, 1981). Moreover, the standardized outer loadings of all the indicators are above the acceptable level of 0.70, which validates the strong reliability of the indicators. In addition, the entire values of the collinearity variance inflation factor (VIF) are between 1.543 and 2.48, which is significantly below the critical value of 3.3. This proves the non-existence of the multicollinearity problems and indicates that the common method bias is not likely to influence the findings. A combination of these results suggests that the measurement model of the study meets all of the suggested requirements and is suitable and valid to further structural equation modelling (SEM) and testing.

Table 1. Assessment of Internal Consistency, Convergent Validity and Full Collinearity.

Constructs	Item	Loadings	FC	CA	CR	AVE
OS	OS_1	0.843	2.201	0.869	0.869	0.656
	OS_2	0.817	2.034
	OS_3	0.796	1.834
	OS_4	0.818	1.981
	OS_5	0.774	1.719
VMI	VMI_1	0.785	1.715	0.85	0.853	0.626
	VMI_2	0.794	1.724
	VMI_3	0.735	1.514
	VMI_4	0.813	1.868
	VMI_5	0.759	1.732
VSLTA	VSLTA_1	0.74	1.554	0.837	0.842	0.605
	VSLTA_2	0.848	2.283
	VSLTA_3	0.81	2.126
	VSLTA_4	0.818	1.985
	VSLTA_5	0.734	1.571
SVP	SVP_1	0.829	2.108	0.875	0.878	0.668
	SVP_2	0.86	2.404
	SVP_3	0.812	1.939
	SVP_4	0.822	2.089
	SVP_5	0.76	1.731
SP	SP_1	0.792	1.787	0.852	0.856	0.628
	SP_2	0.824	1.924
	SP_3	0.783	1.838
	SP_4	0.774	1.729
	SP_5	0.787	1.742

Legend: CA= Cronbach Alpha, FC: Full Collinearity, CR: Composite Reliability, AVE: Average Variance Extracted, OS: Outsourcing, SVP: Strategic Vendor Partnership, VSLTA: Vendor Selection and Long Term Agreements, SP: Sustainable Performance.

Table 1. Assessment of Internal Consistency, Convergent Validity and Full Collinearity.

Constructs	Item	Loadings	FC	CA	CR	AVE
OS	OS_1	0.843	2.201	0.869	0.869	0.656
	OS_2	0.817	2.034
	OS_3	0.796	1.834
	OS_4	0.818	1.981
	OS_5	0.774	1.719
VMI	VMI_1	0.785	1.715	0.85	0.853	0.626
	VMI_2	0.794	1.724
	VMI_3	0.735	1.514
	VMI_4	0.813	1.868
	VMI_5	0.759	1.732
VSLTA	VSLTA_1	0.74	1.554	0.837	0.842	0.605
	VSLTA_2	0.848	2.283
	VSLTA_3	0.81	2.126
	VSLTA_4	0.818	1.985
	VSLTA_5	0.734	1.571
SVP	SVP_1	0.829	2.108	0.875	0.878	0.668
	SVP_2	0.86	2.404
	SVP_3	0.812	1.939
	SVP_4	0.822	2.089
	SVP_5	0.76	1.731
SP	SP_1	0.792	1.787	0.852	0.856	0.628
	SP_2	0.824	1.924
	SP_3	0.783	1.838
	SP_4	0.774	1.729
	SP_5	0.787	1.742

Legend: CA= Cronbach Alpha, FC: Full Collinearity, CR: Composite Reliability, AVE: Average Variance Extracted, OS: Outsourcing, SVP: Strategic Vendor Partnership, VSLTA: Vendor Selection and Long Term Agreements, SP: Sustainable Performance.

Table 2. Discriminant Analysis –HTMT.

	1	2	3	4	5
Outsourcing
Strategic Vendor Partnership (SVP)	0.639
Sustainable Performance	0.49	0.74
Vendor Managed Inventory (VMI)	0.46	0.649	0.557
Vendor Selection and Long-Term Agreements (VSLTA)	0.33	0.56	0.346	0.424

Table 2. Discriminant Analysis –HTMT.

	1	2	3	4	5
Outsourcing
Strategic Vendor Partnership (SVP)	0.639
Sustainable Performance	0.49	0.74
Vendor Managed Inventory (VMI)	0.46	0.649	0.557
Vendor Selection and Long-Term Agreements (VSLTA)	0.33	0.56	0.346	0.424

Heterotrait Monotrait (HTMT) ratio was used in determining the discriminant validity between the constructs as suggested by Henseler et al., (2015). Outsourcing and SVP have a moderate relationship that is theoretically valid, with a value of 0.639 but the value is significantly lower than the conservative value of 0.85, a fact that proves construct distinctiveness. The SVP and Sustainable Performance relationship has an HTMT value of 0.740 indicating a relatively strong yet an acceptable relationship indicating that strategic partnerships produce results of sustainability without overlapping between the two concepts. On the same note, VMI records HTMT values of 0.460 with Outsourcing, 0.649 with SVP, and 0.557 with Sustainable Performance, which reveal similar but operationally distinct operational practices. Moreover, VSLTA is relatively lower than the other constructs with the highest HTMT values appearing between 0.330 and 0.560, which supports the uniqueness of the given governance-related construct. In general, the findings indicate that the sufficient level of discriminant validity was achieved, which confirms that all the constructs are used to measure different theoretical dimensions, and the measurement model is suitable to continue the structural model analysis and test the hypothesis (Henseler et al., 2015).

5.2. Evaluation of Structural Model

The findings of the structural model analyze are highly empirical, in proving all the postulated hypotheses, which indicates the importance of supply chain practices in the development of strategic partnership and sustainable performance. These findings show that Outsourcing has a significant and significant positive impact on SVP (β = 0.356, p < .001), which means that companies which outsource are more likely to establish more positive working relationships with their vendors. This correlation has a moderate effect size (f² = 0.212) and has a high degree of explaining a significant share of the SVP variance (R² = 0.509; Q² = 0.490), which indicates its practical importance. On the same note, VMI has a powerful and important influence on SVP (β = 0.644, p =.001), coming out as the strongest predictor of the antecedents with a significant effect size (f²= 0.164). This is an indication that tight inventory coordination and information sharing systems have a tremendous impact on enhancing long term cooperation between vendors. Besides that, there is also a positive and significant effect of VSLTA on SVP (β = 0.323, p <.001; f²= 0.121) and systematic vendor evaluation and contractual commitment are important governance factors in the development of strategic relationships.

Moreover, the findings prove that SVP is a strong positive influence on Sustainable Performance (β = 0.266, p =.001). This correlation accounts for a significant amount of variation in Sustainable Performance (R² = 0.415) and has a high predictive value (Q² = 0.677) and a large effect size (f² = 0.709). These results indicate that, through the establishment of trustful, long-term, and collaborative partnership with vendors, the attainment of economic, environmental, and social sustainability results is of high importance. On the whole, the structural model is based on the sequential and integrated process where operational and governance-focused supply chain practices (outsourcing, inventory coordination, and vendor selection) jointly optimize SVPs, which subsequently leads to sustainable performance. In that way, the findings can be considered as a strong empirical support of the offered conceptual framework and a significant role of relational supply chain approaches in the achievement of long-term sustainability goals.

Table 3. Structural Model Evaluation.

Hypothesis	Hypothesized Relationship	Path Coefficient	SD	P values	Decision	R2	F2	Q2
H1	OS→SVP	0.356	0.051	0	Accepted	0.509	0.212	0.49
H2	VMI→SVP	0.644	0.039	0	Accepted		0.164
H3	VSLTA→SVP	0.323	0.054	0	Accepted		0.121
H4	SVP→SVP	0.266	0.049	0	Accepted	0.415	0.709	0.677

Table 3. Structural Model Evaluation.

Hypothesis	Hypothesized Relationship	Path Coefficient	SD	P values	Decision	R2	F2	Q2
H1	OS→SVP	0.356	0.051	0	Accepted	0.509	0.212	0.49
H2	VMI→SVP	0.644	0.039	0	Accepted		0.164
H3	VSLTA→SVP	0.323	0.054	0	Accepted		0.121
H4	SVP→SVP	0.266	0.049	0	Accepted	0.415	0.709	0.677

Figure 1. Structural Model.

Figure 1. Structural Model.

Table 4. PLS Predict.

	Q²predict	RMSE	MAE
Strategic Vendor Partnership	0.49	0.718	0.553
Sustainable Performance	0.272	0.858	0.677

Table 4. PLS Predict.

	Q²predict	RMSE	MAE
Strategic Vendor Partnership	0.49	0.718	0.553
Sustainable Performance	0.272	0.858	0.677

The PLS-Predict test was used to determine the out of sample predictive relevance of the proposed structural model and the results show high predictive powers in the endogenous constructs. SVP (Q² = 0.490) and Sustainable Performance (Q² = 0.272) have positive Q² predict values, which indicate that the model has a better predictive accuracy than a native benchmark, which proves satisfactory predictive relevance (Shmueli et al., 2019). Moreover, the error measures in the forms of RMSE (0.718 with SVP and 0.858 with Sustainable Performance) and mean absolute error (MAE) of 0.553 and 0.677 respectively are relatively low and therefore there is not a significant deviation of the predicted and observed values and therefore good prediction accuracy. The SVP has a relatively smaller Root-mean-squared error (RMSE) and MAE, indicating that this construct predicts better. The results presented by the PLS-Predict, in general, allow concluding that the PLS-SEM model is characterized by high predictive validity and practical application and can be applied to the real-life situation of supply chain decision-making to predict strategic vendor relationships and sustainable performance results.

Table 5. Model Fit.

	Saturated model	Estimated model
SRMR	0.052	0.056
d_ULS	0.868	1.037
d_G	0.276	0.283
Chi-square	497.257	506.781
NFI	0.875	0.873

Table 5. Model Fit.

	Saturated model	Estimated model
SRMR	0.052	0.056
d_ULS	0.868	1.037
d_G	0.276	0.283
Chi-square	497.257	506.781
NFI	0.875	0.873

The model fit indices show that the overall measure of the structural model fits well. The values of the SRMR of both the saturated and estimated model are much lower than the standard 0.08 value of good model fit, which validates good model fit. The NFI values indicate that there is a reasonable fitment of the complex model of PLS-SEM. Moreover, the close proximity of d_ULS, d_G, and chi square values between the two models means that there is no misspecification of the model and is being appropriately estimated.

6. Discussion

The results of the hypothesis testing present valuable information about the supply chain practices that can affect strategic vendor relationships and sustainable performance in the IE2W industry. The findings are very strong to prove the conceptual framework proposed and show that there is a sequence of the relationship process in which operational and governance oriented supply chain practices generate sustainability outcomes via enhanced vendor alliances. These results are in line with relational exchange theory, transaction cost economics, and sustainable supply chain management literature, and further extrapolate these theories to the fast growing electric mobility industry in India.

6.1. Outsourcing and Strategic Vendor Partnership

The findings can be summarized in that offshoring has a big contribution in enhancing SVP. The companies that outsource their most important elements and functions are more likely to depend on long-term interaction and cooperation with the vendors, their coordination, and trust-building strategies. Outsourcing in the IE2W sector helps companies simplify their operations and concentrate on their primary products, namely design, innovation and expansion to new markets whereas vendors help in development of specialized services. This result is consistent with the relational exchange theory that indicates that recurrent interaction and dependency established by outsourcing develop closer strategic-relationships than arms-length transactions.

6.2. Vendor Managed Inventory and Strategic Vendor Partnership

The high impact of VMI on SVP underlines the significance of communication, coordination as well joint accountability in the relationships of the supply chain. With vendors actively taking part in controlling the inventory level, making decisions related to replenishment, and making demand predictions, the relationship is automatically shifted to collaborative. The adaptability of VMI in the environment of E2W with the overall importance of demand uncertainty, availability of components, and control of costs will increase the level of openness of operations and interdependence between partners, which will strengthen the strength of the partnership. This is consistent with supply chain integration theory, where collaborative planning and execution are the key sources of relational performance.

6.3. Selection of Vendors and Long-Term Agreements

The beneficial effect of VSLTA on SVP highlights the role of governance presented by the systematic assessment and commitment by contract. The decision of the vendors by their capability, reliability, and strategic alignment and the strengthening of the ties with the vendors by long-term contracts minimizes opportunism and uncertainty. In industries where technological compatibility and sustainability compliance is a must such as the IE2W industry, long-term agreements motivate vendors to invest in innovation, quality improvement, and environmental practices and thus more heavily orient the partnerships.

6.4. Sustainable Performance and Strategic Vendor Partnership

The findings also show that strategic alliances with suppliers can greatly contribute to sustainable performance, including economic effectiveness, environmental sustainability, and long-term sustainability in operations. Effective collaborations help in solving issues collaboratively, innovation based on sustainability, and harmonization of environmental and social objectives throughout the supply chain. In the IE2W sector specifically, there is a strong necessity to cooperate with suppliers to reach sustainability goals associated with battery access, emission decrease, and responsible production. This observation is in line with sustainable supply chain theory that argues that companies can attain the best results on sustainability by engaging in inter-organizational relationships as opposed to acting independently at a firm level.

7. Theoretical Implications

This paper is an addition to the literature on supply chain management and sustainability in that it expands the relational exchange theory, transaction cost economics, and sustainable supply chain theory to the IE2W industry. Contrary to conventional research that also investigates the results of sustainability as a direct operational practice, the study reveals that SVP serves as one of the crucial mediating processes of linking outsourcing, VMI, and VSLTA to sustainable performance. Through the empirical proving of the basic importance of vendor partnerships, the study replaces the theoretical emphasis on the separate firm-based practices with the relationships-based supply chain governance.

Moreover, the results contribute to the sustainability supply chain theory by showing that sustainability performance is not the outcome of technology use or compliance with regulations, but rather entrenched in the inter-firm interdependence. The close relationship between SVP and sustainable performance affirms the opinion that trust over the long term, coordination, and shared responsibility are critical precursors of environmental, economic, and social sustainability. Through this, the research will contribute to the current theory because it will combine operational integration and relational governance as complementary forces of sustainability in the new green industries.

8. Conclusions

This research is an empirical analysis of how outsourcing, vendor management practices, and SVP can facilitate sustainable supply chain performance in the IE2W industry. Results have revealed that outsourcing, vendor-controlled inventory, and the selection of vendors through long-term contracts, play a significant role in strengthening strategic vendor alliances that consequently promote sustainability. The results emphasize that sustainability outcomes are attained only through a coordinated and trust-based relationship with vendors.

The paper also confirms that SVP serves as a key point of contact in the relationship between operational practice and sustainability performance. The companies that invest in working relationships with vendors are more able to achieve greater coordination, less uncertainty and achieve greater innovation as well as ensure sustainability goals are aligned throughout the supply chain. Considering the dynamics of the IE2W industry, which operates on economies of scale, with rapidly evolving technology and ambiguity therein, robust vendor relationships are inescapable for sustainability and resilience.

To summarise, the results reveal that sustainability in the IE2W industry is not only a technical or policy-oriented process, but also defines the manner in which companies regulate and cooperate with their suppliers. Although the efficiency of the operations is achieved through outsourcing and inventory coordination, the quality of vendor relations gives the sustainability edge. This paper thus highlights the importance of IE2W OEMs going beyond transactional management of suppliers and implementing strategic and long-term partnership-based supply chain models.

The study will add value to both theoretical knowledge and practical experience by elucidating the behavioral and relationship channels in the supply chain practices that contribute to sustainability. It has useful implications for manufacturers, vendors and policymakers interested in promoting sustainability in the supply chain of the IE2W sector.