Situating Stakeholders in India’s Advanced Air Mobility (AAM) Vision

Urban mobility in India is undergoing a transformative phase, driven by rapid urbanization, population growth, and evolving economic dynamics. There is a constant decline in the share of public transport due to a failure in providing quality public transport (Dr. E. Sreedharan, 2011). Advanced Air Mobility (AAM) represents a transformative change for India’s urban mobility paradigm.

Between 2001–2011, the urban population grew from 286 to 377 million, cities expanded substantially, and personal motorisation skyrocketed, causing congestion, pollution and infrastructure strain (Deepak, 2014). This is attributed to the fact that over 70% of India’s GDP is generated in urban areas, attracting rural migrants to cities for employment and education (Shaban et al., 2020). Although India has the world’s largest road network (Govt. of India, 2022) with density comparable to Hong Kong and higher than China and Brazil (Sahoo, 2011). Yet, cities like Bengaluru, Delhi and Mumbai suffer from lowest intra-city speeds due to heavy congestion (World Bank, 2011). The efficiency of a city and its ability to sustain economic growth, heavily depends on its transportation system that allows people and goods to move quickly and affordably (Nayka & Sridhar, 2018). Given the challenges, it is worth considering aerial space to reduce ground congestion and limit the need for expanded road infrastructure. Furthermore, India’s progression in Advanced Air Mobility (AAM) is being justified and accelerated by several high-impact innovations.

The Rationale for Stakeholder Analysis in AAM

India’s institutional structure for AAM is fragmented and rapidly evolving, involving regulatory, technological, infrastructural, and societal actors with overlapping mandates. Analysing these stakeholders is vital for assessing the realism of policy ambitions and identifying institutional interdependencies that shape AAM viability. Evaluating potential collaborators and institutional relationships is also key to optimizing resource allocation and assessing overall project viability. The broader Advanced Air Mobility (AAM) ecosystem encompasses not only vertical flight technology but also supporting systems such as Unmanned Traffic Management (UTM), vertiports, energy supply chains and community integration. Within this contextual frame, the AAM sector’s emergence is tied to five intertwined drivers:

• Regulatory and Governance: The focus is on certification of air vehicles, design of air corridors, licensing of vertiports and harmonisation with air traffic management. It defines how the regulatory ecosystem either enables or constrains stakeholder influence and partnerships (Lansing Wei et al., 2020).
• Technology: This includes aircraft technologies (eVTOLs, drones, hybrid VTOLs), UTM systems, automation or AI integration, communication and navigation systems and energy solutions (batteries & hydrogen). Stakeholders in this scope range from aerospace manufacturers to software providers and the feasibility depends on how partnerships align across firms and global R&D ecosystems (Roland Berger, 2018).
• Infrastructure: Vertiports, drone corridors, charging & refuelling stations, multimodal hubs and integration with urban planning are core to this scope. Stakeholders include airport authorities, municipal planning departments and private infrastructure developers.
• Market and Economics: The scope involves logistics operators, e-commerce platforms, ride-sharing firms, investors and insurance providers. It defines demand potential, pricing strategies, funding structures and the viability of partnerships.
• Socio-Political: The scope here covers community groups and media, focusing on perceptions of safety, noise, environmental impact and equity in access (Straubinger et al., 2020).

By defining the context around India’s policy push for next-generation aerial mobility, will help capture both the regulatory realities and the partnership ecosystem. This approach will further help in identifying the interconnectedness and interdependencies of concepts among different stakeholders. Mitchell et al. (1997), stakeholder salience model posits that an actor’s (stakeholder) importance depends on the possession of power, legitimacy, and urgency. However, salience alone is insufficient in a multi-actor domain characterised by overlapping jurisdictions. Provan & Kenis (2007), extended this understanding through the lens of network governance, emphasising coordination structures, trust, and accountability across inter-organisational networks. Network analysis thereby will reveal both central nodes and peripheral but crucial connectors that enable the diffusion of knowledge and resources within India’s AAM ecosystem. Furthermore, Pressman & Wildavsky (1984), implementation theory underscores the sequential dependencies and institutional bottlenecks that separate policy intention from execution. By tracing how policy decisions cascade through multiple administrative layers, implementation lens provides a dynamic understanding of why well-designed AAM policies may falter during execution. Combining these three lenses produces a multidimensional interpretation of India’s AAM transition. It will ultimately determine how India’s AAM ambitions can move from pilot projects to scalable and commercially viable systems.

Rubric Scoring for Institutional Mapping in AAM

Each lens captures a distinct dimension of stakeholder influence, and their integration enabling a multidimensional understanding of how regulatory, infrastructural, and operational entities collectively shape the trajectory of AAM realisation. To provide a systematic and quantifiable approach in mapping the institutional landscape, a comprehensive rubric scoring methodology is essential. This rubric serves as a foundational tool for evaluating and comparing the roles, influence, and interactions of various stakeholders within the AAM ecosystem. By applying a structured scoring criterion, the assessment process becomes transparent and replicable, enabling decision-makers to identify gaps and opportunities for collaboration.

Salience lens

Drawing from Mitchell et al. (1997), the salience framework is designed to identify who and what really counts in policy formation and decision-making. In the Indian AAM context, stakeholders vary widely in formal authority, institutional legitimacy, and temporal urgency.

• Power reflects the regulatory or financial leverage an entity possesses such as policy framework and certifications.
• Legitimacy denotes the social or institutional recognition to act within the AAM domain which is relevant for statutory bodies and newly emerging actors.
• Urgency captures temporal demand and responsiveness necessary for developers and technology providers striving to accelerate commercialisation.

In the rubric, assigning weighted scores (1-5) to these attributes quantifies stakeholder prominence, revealing hierarchical asymmetries between government, industry, and research institutions. This measurement forms the first layer of analysis to determine who holds decisive power in shaping AAM policies and implementation timelines.

Network lens

While salience isolates the importance of individual actors, the network lens (Provan & Kenis, 2007) captures how influence diffuses through interconnected governance structures.

• Connectivity measures the density of formal and informal linkages among stakeholders.
• Centrality identifies actors that function as nodal hubs in decision-making and information flow.
• Cohesion assesses the degree of policy alignment and shared vision across stakeholders. It is critical for ensuring interoperability between different sectors.

Quantifying these dimensions through the rubric helps to evaluate the structural coherence of India’s AAM governance network.

Implementation lens

Even where influence and connectivity exist, implementation bottlenecks can undermine progress. Following Pressman and Wildavsky (1984), the implementation lens examines how institutional structures convert policy intention into operational outcomes.

• Capacity measures the human, technical, and financial resources available for executing AAM initiatives.
• Commitment captures the depth of institutional engagement i.e whether agencies pursue AAM as a strategic priority or a peripheral mandate.
• Readiness gauges preparedness to operationalise or scale pilot projects, reflecting alignment between regulatory, infrastructural, and technological subsystems.

By scoring these attributes, the rubric identifies implementation asymmetries such as high policy enthusiasm but low technical readiness among certain stakeholders. This will help to distinguish “policy leaders” from “execution enablers” within India’s AAM ecosystem.

The integration of these three lenses transforms the stakeholder analysis from a qualitative mapping exercise into a quantitative–relational model that captures both hierarchical authority and horizontal interdependence. When combined within the rubric, these metrics create a multi-layered analytical surface that reveals how institutional strengths and coordination gaps interact. By translating institutional relationships into quantifiable dimensions, it will generate actionable insights into how India can strengthen its governance architecture and stakeholder alignment to achieve AAM realisation in a structured, evidence-based manner.

Modeling and Quantifying Stakeholder Relationships in India’s AAM Ecosystem

The rubric-based framework converts qualitative judgments on stakeholder influence, coordination, and readiness into measurable indicators. Each analytic lens i.e Salience, Network, and Implementation comprises three sub-dimensions scored on a 1–5 ordinal scale derived from expert judgment, document review, and stakeholder interviews.

The model assumes that AAM realisation in India is a function of institutional salience (influence), network cohesion (coordination), and implementation capacity (execution readiness):

Unlike countries with centralised AAM programs, India’s ecosystem involves parallel institutions with overlapping authority in airspace management, communication, energy supply, and urban development. The ecosystem is characterised by institutional complexity, cross-sectoral dependencies, uneven readiness across regulatory, infrastructural, and technological domains. This diversity makes subjective prioritisation insufficient. Therefore, a structured and transparent method is required to balance regulatory influence, network coordination, and implementation readiness.

To capture variable significance across dimensions, the Analytic Hierarchy Process (AHP) is employed to assign relative weights to each sub-criterion of the AAM Realisation Index (AAMRI). AHP transforms expert qualitative judgments into quantitative weights while ensuring logical consistency through pairwise comparisons.

Hierarchical Structure

• Goal: Prioritise stakeholders contributing to AAM realisation in India.
• Criteria Groups: Salience, Network, and Implementation.
• Sub-Criteria:
  • Salience: Power (P), Legitimacy (L), Urgency (U)
  • Network: Connectivity (Ct), Centrality (Cy), Cohesion (Ch)
  • Implementation: Capacity (Cp), Commitment (Cm), Readiness (R)

Pairwise Comparison

Experts from aviation policy, AAM operations, and urban infrastructure sectors compared sub-criteria two at a time using the Saaty 1–9 scale (Saaty, 1990).

Each 3×3 comparison matrix yields local weights after normalisation and averaging of rows. A Consistency Ratio (CR) is computed to ensure coherence; matrices with CR ≤ 0.10 are accepted (Saaty, 1990).

AHP generates relative weights (w) for the nine sub-criteria wP, wL, wU, wCt, wCy, wCh, wCp, wCm, wR. These weights will reflect expert consensus and the most influential in regulatory power, network centrality, and implementation readiness in India’s AAM trajectory. The weighted AAMRI for each stakeholder is calculated as:

Where “x_ij” is stakeholder “i’s” performance on sub-criterion “j”. This ensures that both expert-informed priorities and empirical data shape the overall influence index. This ensures logical consistency and quantified relative importance such as “Power > Legitimacy > Urgency in the early regulatory stage of India’s AAM”. This process creates a hierarchical weighting structure rather than flat averages.

Integrating Multidimensional Performance Evaluation through TOPSIS

While AHP establishes how important each stakeholder is, it does not measure how well each stakeholder performs relative to others. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) complements AHP by ranking stakeholders according to how closely they resemble an ideal performer across all weighted criteria (Hwang & Yoon, 1981). In other words, TOPSIS determines which stakeholder performs best given the AHP weights. It compares each stakeholder to:

• an ideal solution (v⁺): the best observed performance across all sub-criteria, and
• an anti-ideal solution (v^–): the worst observed performance.

The method then calculates:

• : distance from the ideal,
• : distance from the ideal,
• : closeness coefficient

Furthermore, the stakeholders are ranked by “CC_i“; higher values indicate closer alignment to the ideal AAM stakeholder profile. This integration allows for a balanced evaluation of influence, coordination, and capability, thereby identifying institutional strengths and interdependencies critical to India’s eVTOL and AAM realisation vision.

The AAM Realisation Index (AAMRI) now is the outcome of an integrated AHP–TOPSIS modeling.

This framework provides a defensible methodology to determine which stakeholders significantly influence and enable India’s AAM realisation vision, and how their interdependencies affect system outcomes. Furthermore, it enables a data-driven assessment of leadership gaps, coordination deficits, and implementation asymmetries within India’s AAM ecosystem. By quantifying power, connectivity, and readiness interdependencies, it reveals how institutional strengths can be leveraged or rebalanced to achieve coherent governance. The resulting AAMRI offers policymakers an evidence-based diagnostic of which stakeholders drive, enable, or hinder the realisation of India’s eVTOL and Advanced Air Mobility (AAM) vision.

References

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