Accelerating Electric 3-Wheeler Adoption Through Experiential Trials: Insights and Learnings from Amritsar, Punjab

Abstract

Three-wheelers (3Ws—autos or auto-rickshaws) occupy a unique yet salient and substantive position within the context of India’s urban mobility. They provide critical first-and-last-mile connectivity, fill public transit coverage gaps, boost local and urban agglomeration economies, and are a major income source for millions. Their value and utility are especially pronounced in rapidly emerging Tier-II cities such as Amritsar. The city’s 7500-strong diesel 3W (d3W) fleet is the backbone of its transportation network but also contributes to air pollution. Though Amritsar’s favorable policies to transition the d3W fleet to electric (e3W) have reduced purchase costs by 40–60%, barriers remain. This study investigates the influence of the e3W user experience through a first-of-a-kind three-day pilot trial for ~300 d3W drivers. By leveraging a pre- and post-intervention framework combining surveys and trip diaries, this study evaluated how direct exposure influences adoption intentions, perceptions, and the social dynamics underpinning decision-making. In total, ~6% of participants switched to e3Ws following the trial, and there was a 20% drop in “don’t know” answers regarding charging duration and range. The results show non-random and meaningful shifts in attitudes, a greater awareness of range and charging times, improved views on charging convenience and vehicle safety, and air quality benefits.

1. Introduction

India faces the compounding challenges of rapid urbanization, motorization, and pollution. As the world’s fastest-growing major economy and the third-largest automotive market, these dynamics present significant hurdles for the nation, which is also home to the largest population globally [1,2,3]. Cities across India are unprepared and under-equipped to handle this confluence. This phenomenon is acutely pronounced in emerging Tier-II and Tier-III cities characterized by unplanned expansion, a heavy reliance on personally owned vehicles, and underdeveloped or non-existent public transit. In such cities, as well as across large swathes of urban India, the intermediate public transit (IPT) [4,5] segment of three-wheelers (3Ws), namely rickshaws and autos, plays a pivotal role. These cost-effective, flexible, and on-demand services improve accessibility and provide first-and-last-mile connectivity [6,7]. They are often the main income source for millions of workers, from drivers to mechanics from low-income backgrounds with little to no formal education [8]. Furthermore, due to its informal and unorganized nature, the IPT segment is a dominant contributor to local and agglomeration economies [9,10].

According to official statistics [11], approximately 300 million vehicles were registered between 2008 and 2023, of which 3Ws constitute 8 million or ~3% (Figure 1); however, they contribute 10% of road transport emissions [12] and up to 25% of emissions from all motorized trips [13]. Referring to Figure 2, across the five vehicle classes of e-rickshaws (with cart and goods) and e-autos (goods, passenger, and personal), the latter represents ~80% (6.6 million) of the stock of 8 million 3Ws. Diesel 3W (d3W) vehicles make up the largest share (~44%) by fuel type, followed by petrol (26%) and CNG (15%). Electric rickshaws (e-rickshaws) constitute roughly ~18% of 3W stock.

Both e-rickshaws and e-auto (e3Ws from hereon after) cater to the same use-case and compete, but e-rickshaws (1.45 million) outnumber e3Ws (~115,000) (Figure 1 and Figure 2).

E-rickshaws have gained popularity due to their affordability and utility in urban areas; however, their reliance on lead-acid batteries poses range (~ 70 km), charging duration, battery lifetime, performance, long-run operation and maintenance costs, and durability downsides [14,15]. E-rickshaws are also known to present serious safety [16] and perception challenges for passengers [17]. Only a fraction of e-rickshaws on the road are formally registered [7] due to lax enforcement. Cities across India are beginning to regulate their operations through permits and specification standards [18,19,20,21,22]. E-autos do not have any of these operational, safety, or reliability drawbacks. Powered by lithium-ion batteries, which are more durable, energy-efficient, and reliable, e3Ws have a longer range (~120 km–150 km minimum), offering a smoother, safer, and more comfortable ride for both the driver and the passengers [23].

From a total cost of ownership (TCO) perspective, e3Ws (2.5 INR/km) are 15–35% (2.9–3.3 INR/km) cheaper than their compressed natural gas (CNG), diesel, or petrol variants [24]. It is also estimated that their life cycle greenhouse gas (GHG) emissions could be 33–60% lower than those of CNG and diesel variants [25]. Despite these favorable techno-economics, the supportive policy environment [26], the co-benefits of reducing air pollution by 15–20% [27], and the ability to substitute trips taken on conventional internal combustion engine (ICE) 3W variants [10], at the end of 2023, e3Ws constituted just ~2% of all 3Ws and ~8% of the electrified 3W market [11,28]. This discrepancy warrants a deeper examination beyond and/or in addition to techno-enviro-economic considerations.

This paper examines how direct user experience shapes perceptual shifts, attitudinal changes, and adoption intention towards e3Ws and enables us to better understand the peer and social influences possibly undergirding this transition. The novelty of this study lies in presenting a first-of-its-kind pilot in India that assesses the impact of real-world experience on adoption decisions and explores the social dynamics of e3W transition. This approach moves beyond conventional survey-based analyses and provides a more grounded understanding of user behavior.

1.1. Literature Synthesis

This section reviews the existing literature on EV uptake, focusing on the challenges, frameworks, and experiential insights relevant to the Indian context. Starting with broad categorization of major barriers, insights gleaned from contemporary studies based on stated preference surveys and choice experiments are summarized. Particular attention has been devoted to studies that examine the impact of short-duration experiences on EV uptake. In major markets, EV adoption, post-purchase behavior, and utilization studies are well-documented, relying diverse data sources from cross-sectional surveys to multi-year observational data collected via on-board telematics [29,30,31,32,33,34,35]. Since India lacks a similarly comprehensive body of research, the geographical scope of the relevant literature reviewed was expanded wherever necessary and apropos.

1.1.1. E3W Adoption Barriers in India

At the outset, it is worth mentioning a core set of challenges symptomatic of structural inefficiencies, which are not necessarily unique to e3Ws but relevant to the entire IPT segment. The authors in [6] elaborate on this issue, citing governance gaps, weak integration with city transport, limited attention to market and sustainability, poor inter-departmental coordination, and minimal stakeholder engagement. A review of the existing studies reveals four main categories of barriers: economic, operational, perceptual, and policy-related [36,37]. The upfront cost still remains unaffordable, aggravated further by limited financing due to low creditworthiness [38]. Lack of home charging, inadequate public charging infrastructure, and long recharging times exacerbate range anxiety [39], given the on-demand, ad hoc and flexible nature of first-and-last-mile mobility demand [6]. Drivers and fleet operators are not even fully informed about e3W technological capabilities, environmental benefits, and incentives [40]. Operational reliability, regulatory oversight, congestion, and safety perceptions are some of the other impediments to e3W uptake [41,42,43].

1.1.2. Conceptual and Analytical Frameworks

India-centric studies have mainly relied on stated preference surveys and choice experiments to evaluate EV adoption determinants. Using data from 327 respondents from 57 auto dealerships across various Indian cities, the extended Theory of Planned Behavior (TPB) model incorporating perceived environmental benefits and government incentives as additional covariates is applied in [44]. The authors report that attitudes, subjective norms, and perceived behavioral control significantly influenced consumers’ EV adoption intentions [44]. Findings from an online survey of ~170 respondents in Bengaluru using snowball sampling revealed that charging infrastructure adequacy, attitudes towards the environment, financial barriers, and awareness (EV technology and/or policy) influence EV adoption likelihood [45]. Interestingly, these findings are from a Nordic socio-technical nexus study [46]. A combined social media sentiment analysis and logistic regression of ~250 respondents in Bengaluru is presented in [47]. The authors report that gender, age, income levels, environmental consciousness, and the fixed and variable costs of owning an EV, as well as performance, are significant predictors of EV adoption, whereas education, employment, and subsidies were surprisingly not found to be influential [47].

1.1.3. Direct-User Experience and EV Adoption

The body of work on EV adoption has progressed from stated and revealed preference surveys to observational studies in the form of targeted demonstrations, test drives, and pilots enabling direct EV user experience. These serve as a small-scale precursor or test-bed to refine research design and policy implementation before wider roll-out. With the exception of technical feasibility studies—e-rickshaws in a couple of Indian metros [48] and retrofitted freight vehicle movement on highways [49]—experiential studies are extremely rare in India. These pilots help us to understand the influence of EV experience on awareness, attitude, range anxiety, and purchase intentions, and such studies are more prevalent in mature EV markets [29,30,31,32,35,50,51,52,53].

Using a discrete choice experiment, the authors in [32] report that “brief interactions” with EVs spanning minutes enhanced the perceived value of environmental benefits and technical specifications (range, vehicle size), while exerting limited impact on longer-term sustained behavioral shift and purchase intention in Irish drivers. Similar findings were reported in a German study based on a 24-hour test drive and online surveys [31]. Over a six-month period with 79 participants, a Danish trial observed heightened appreciation of EVs’ driving comfort, reduced fuel costs, and zero tail-pipe emissions operation [54]. A randomized control trial involving ~4200 participants in Switzerland revealed that neither direct experience nor information provision substantially influenced purchase intention [55].

Evidence from short-duration pilots in mature markets shows that exposure can improve awareness and perception of performance with reduced range anxiety. Since such experiential work is rare in India, these pilot trials provide a valuable opportunity to understand how experience interacts with user perception and peer influences and can inform both research and policy.

1.2. Research Objectives

Insights on EV adoption in India or elsewhere are largely drawn from cross-sectional or panel surveys of participants from prospective buyers, manufacturers, and dealers to expert focus groups [45,56,57,58,59]. Pilots facilitating first-hand exposure to EVs have the added benefit of capturing experiential, subjective, technological, knowledge, and other contextual dimensions that may influence adoption [60]. Systematically understanding EV perceptions, feasibility, awareness, and purchase intentions based on user experiences is uncommon in India, if it occurs at all. This study aims to address this gap by presenting the outcomes of a first-of-its-kind pilot trial of 300 d3W drivers who participated in a three-day e3W trial in the city of Amritsar, Punjab, using a pre- and post-intervention framework. We aim to accomplish this by answering the following research questions:

• Does direct exposure to e3Ws affect drivers’ purchase intention?
• What is the impact of direct user experience on perceptions of e3Ws’ performative, operational, and functional attributes?
• How does the trial’s effect manifest along with symbolic and socio-dynamics such as pride, peer influence, norms, and expectations?

1.3. City of Amritsar as a Microcosm for Studying E3W Transition

Amritsar, a prominent city in Punjab, is recognized for its industrial significance, rich history, and cultural heritage. The district’s administrative center lies 217 km northwest of Chandigarh and 455 km northwest of New Delhi. It is a densely populated city of 1.4 million residents over 170 km [61]. About 19 million tourists (almost two-thirds of the state’s total tourist footfall) visited Amritsar in 2021 [62]. The city’s transportation system is highly dependent on IPT due to insufficient public transit—a shortfall of 655 buses based on government guidelines and Level of Service (LoS) benchmarks [63]. Given its public transit infrastructural inadequacy—only 0.2 buses per 1000 population against a recommended 1.2 [64] and an ineffective Bus Rapid Transit System (BRTS) [65]—the city’s transportation network is heavily dependent on its 7500-strong d3W fleet [66]. The city also faces air quality problems with vehicular emissions contributing to 30% of PM10 pollution [67].

The RAAHI (“Rejuvenation of Auto-Rickshaw in Amritsar through Holistic Intervention”) scheme is implemented under the City Investments to Innovate, Integrate, and Sustain (CITIIS) Program through a partnership between Agence Française de Développement (AFD), the European Union (EU), and the Government of India (GoI), [68,69]. Under this scheme, on top of the federal subsidy under the Faster Adoption and Manufacturing of (Hybrid) and Electric Vehicles (FAME) initiative [70], an additional INR 140,000 (~USD 1640) was provided—a purchase subsidy of INR 125,000 (~USD 1460) and INR 15,000 (USD 175) for scrapping their d3W. Empaneled banks are obligated under the GoI’s Priority Sector Lending mandate [71], capping the interest rate at 9.9%, to promote EVs. This scheme effectively lowered the purchase cost from INR 350,000 to INR 450,000 (USD 4000–USD 5300), reducing it by anywhere between 40 and 60%. Together with the subsidized interest rates, this scheme significantly alleviates purchase cost barriers and enhances the affordability of e3Ws.

Amritsar and similar mid-sized cities struggle with scarce resources, informal transport systems, and lax law enforcement. The reliance on 3Ws as a primary mode of transportation provides a concentrated platform for evaluating factors impeding e3W transition. Amritsar represents an ideal case study for understanding e3W adoption dynamics in mid-sized Indian cities. Amritsar’s profile represents a substantial portion of India but remains underrepresented in mobility research.

2. Materials and Methods

The research methodology employs a pre- and post-comparative framework consisting of survey questionnaires, a three-day hands-on pilot trial, and a daily driving logbook. A baseline assessment was conducted in the summer of 2023 to understand the existing landscape of d3W operations and driver perspectives. The study followed an exploratory sequential mixed-methods approach, beginning with focus group discussions (FGDs) and semi-structured interviews (SSIs). The baseline survey was administered to 533 d3W drivers using a mobile-based application by a professional survey agency. The survey collected data about socio-economic characteristics, vehicle ownership and usage patterns, work routes and hours, income and expenditure, awareness and knowledge of e3Ws, preferences for e3Ws, attitudes and perceptions, and behavioral tendencies that might influence decision-making. Additionally, respondents were asked about their willingness to participate in a hands-on pilot trial of e3Ws. The baseline survey revealed that nearly 85% of respondents (449 out of 533) expressed interest in participating in the trial. This subset of respondents formed the initial sampling pool for pilot recruitment. Further information about the baseline and preliminary investigations is presented in [72,73].

2.1. Sampling Strategy and Recruitment

Sampling for the pilot was purposive and convenience-based, leveraging informal networks within the driver community. Peer recommendations and referrals from Pradhans (local community leaders, authority figures closely connected with the driver community) played a vital role in recruitment. In the absence of an official database or registry of all d3W drivers, this approach was adopted as a practical solution considering feasibility and resource constraints. While this may limit the generalizability of the findings, it was deemed the most effective way to engage a representative and motivated subset of drivers for the pilot trial. Nearly 90% of the participants were recruited from a handful of key locations as depicted in Figure 3.

The city’s major auto stands, including the bus stand, the railway station, Sangam cinema, Ranjit Avenue, Chheharta, the Golden Temple, Mirakot, the city center, and Majitha Road. Among these, the city’s main bus and train station and Sangam cinema (which is near the bus stand) serve as the primary transit hubs. Other notable points of interest from which drivers were recruited include Ranjit Avenue and the city center (commercial and shopping areas) and Chheharta, the Golden Temple, Majitha Road, Durgiana Temple, Rajasansi, and the airport. These are high footfall locations proximal to places of worship, tourist attractions, and serving commuter travel demand.

2.2. Data Acquisition Techniques

2.2.1. Questionnaire: Pre- and Post-Intervention

The questionnaires were designed to assess drivers’ perceptions, attitudes, and operational patterns before and after their exposure to e3Ws. Appendix A summarizes the scope of questions and response choices. Informed consent was obtained from all participants, clearly communicating the voluntary nature of the trial, the study’s purpose, confidentiality, and data privacy.

Pre-Intervention Only

Captures basic information about the driver and their d3W such as

• Demographics and socio-economics: Details on age, gender, marital status, household size, education level, and type of residence.
• Vehicle use, operation, and ownership: Stand location, license details, and vehicle ownership and financing information, where relevant, vehicle age, daily operation (including idle time), and yesterday’s driving data including distance traveled, operating expenses, and revenue.
• Auxiliary information: Whether they had previously test-driven an e3W and the information channels through which they learned about the trial.

Themes common to pre- and post- intervention: Constructs common to pre-and post-intervention surveys are summarized below:

• Perception of e3Ws: Drivers rated e3Ws on performance, comfort, safety, charging costs/time, and their confidence in meeting daily needs, trip rates, and revenue compared to d3Ws. Participants’ knowledge of e3Ws was assessed by questions about practical aspects like charging time (hours) and range per charge (km). General perceptions surrounding air quality, the importance of switching to e3Ws, and how it contributes to improving air quality were also collected.
• Adoption intention: Assessed drivers’ readiness and willingness to transition to e3Ws, capturing both immediate interest and longer-term preferences. Drivers rated their likelihood of purchasing a new auto followed by the likelihood that it would be an e3W including a timeline for their purchase.
• Social dimensions: Explored if and to what extent peer dynamics, community perceptions, and the role of Pradhans influenced drivers’ attitudes toward e3W adoption. Drivers rated their anticipated pride in owning and operating an e3W, reflecting its symbolic value within their social networks. Questions investigated how likely peers at each stand were to adopt e3Ws and how this affected individual adoption decisions. Additional data on social and peer network influence included perceptions’ of the Pradhan’s support, likelihood of switching if the Pradhan adopts, willingness to recommend to peers, and expected peer opposition.

Most of these were assessed using a structured Likert scale and dichotomous (yes/no) choices depending on the context. Refer to Appendix A for additional details.

Post-Intervention Only

Drivers rated their overall satisfaction with the trial and provided open-ended comments on charging, range, and driving comfort. These qualitative inputs were systematically text-mined and recoded into charging and driving attributes. Each open-ended unstructured response was analyzed and classified as positive, negative, or neutral based on specific keywords. Examples include but not limited to

• Positive: ‘convenient’, ‘affordable’, ‘fast’, ‘good’, ‘sufficient’, ‘comfortable’, ‘smooth’.
• Negative: ‘too long’, ‘not enough’, ‘short’, ‘not reliable’, ‘hard’, ‘cramped’, ‘difficult’.

2.2.2. Daily Trip Diary or Driving Logbook

A detailed daily logbook or trip diary in a conventional pen-and-paper format was maintained throughout the trial. The logbook recorded the origin and destination (OD) of each trip categorized by purpose—shared rides, private trips (“Salam”), or empty runs—beginning with the first trip of the day and concluding with the drop-off on the last day of the trial. Driving metrics, such as odometer reading battery state of charge (SoC) at the start and end of each trip, depth of discharge (DoD), and remaining range, were noted at the start and end of each day to track the cumulative distance traveled and energy consumed. Self-reported daily earnings and notable trip use-cases such as trip to the Wagah border or school rides were also collected. Since the logbook was introduced a few weeks into the trial, the above data was not collected and was unavailable for the first 26 participants.

2.3. Pilot Design and Implementation

Trial participants parked their d3Ws at a secure facility provided by the Amritsar Municipal Corporation. It also served as an overnight charging location, ensuring batteries were fully charged before the next day’s use. The trial began with a mandatory orientation and practical training session as well as a detailed walkthrough of the e3W’s operation, charging process, and functionalities. After completing their daily trips, drivers returned the e3W to the same location and retrieved their d3W for their commute back home. Post-trial survey was administered immediately after the end of the trial on the 3rd day.

2.3.1. Eligibility and Sample Validity

Eligibility for participation in the trial was determined by residency within Amritsar, and ownership or rental of a d3W. Participants who drove less than 100 km over the entire three-day pilot duration were excluded from the study. This cutoff was selected based on the self-reported daily distance driven by d3W yesterday (before the trial). The final sample size of 297 is used for subsequent analysis. Participants were categorized based on their referral sources: 109 drivers were recruited directly through Pradhans, peers referred 157, and the remaining participants were recruited via other informal networks.

2.3.2. Trial Duration Considerations

The trial duration was guided by the following heuristic:

• “Long enough” to experience core aspects like charging, range, and driving.
• “Short enough” to maintain participant engagement and minimize dropout rates.
• “Practical” from resource requirement and logistical perspectives.

The pilot design factored a 35% attrition rate from the subset of baseline respondents interested in participating in the pilot (449 respondents from the baseline study expressed interest in the pilot and with a 65% retention rate, giving ~300 pilot participants), derived from EV pilot trial studies [33,55,74,75] elaborated in the literature review section, resulting in a size of approximately 300 participants. The 100 km cutoff represents a compromise between trial length, resource limitations, and achieving adequate real-world EV experience for participants. The 3-day trial period allows participants sufficient time to integrate the vehicle into their typical daily travel patterns without significantly altering their habitual driving patterns.

2.3.3. E3W Vehicle Specifications

The trial included a selection of top-selling e3W models from Amritsar, and their key attributes are summarized in

Table 1. Specifications and features of e3W used for the pilot trial [69].

Attributes	EV1	EV2a	EV2b
	Cost and Performance
On-Road Price, INR (₹), lakhs (1)	3.65	3.37	3.26
Max Power, kW	4.5	8	7.4
Max Torque, Nm	36	42	29
Gradeability (%)	29	12.7	20
Maximum Speed, kmph	50	40	43
	e-Powertrain
Range, km	178	141	150
Battery Capacity, kWh	8.9	7.37	7.5
Charging Time, minutes	270	230	225
Efficiency, kWh/100 km	6.2	5.8	5.8
Cycle Life	1000	2000	1000
	Transmission and payload
Transmission	Automatic	Direct Drive	Integrated Differential
GVW/GCW (2)	708	350	713
Curb Weight, kg	362	387	413
	Dimensions, mm
Length	2635	2769	2700
Width	1300	1350	1370
Height	1700	1757	1725
Clearance	170	142	163
Wheelbase	2274	2073	1920

Notes: (1) Price excludes subsidies. For reference, INR 1 is roughly USD 1200 assuming an exchange rate of INR 83 = USD 1; (2) GVW is gross vehicle weight, GCW is gross combination weight.

. EV1 emphasizes range and speed, providing 178 km per charge and maximum speed of 50 kmph. EV2a, while compact and offering the highest power output of 8 kW, has a shorter range of 141 km and lower gradability. EV2b, on the other hand, provides a balance with a 150 km range and an integrated differential transmission.

Regarding size, EV1 had the smallest footprint, suggesting ease of handling in tighter spaces. EV2a is slightly broader, with a higher passenger or cargo capacity than EV1. EV2b, being the largest of the three, emphasizes robustness and load-bearing capability. EV1 has the longest wheelbase and highest ground clearance. Conversely, the shorter wheelbase of EV2b allows for tighter turn radii, making it advantageous for navigating congested traffic and narrow streets, which are common in Amritsar. EV2a, with an intermediate wheelbase, balances maneuverability and ride stability, perhaps catering to diverse use-cases.

EV1 was treated as a standalone model to highlight its bigger battery (longer range) and higher top-speed capabilities; EV2a and EV2b were grouped together as EV2 due to comparable range, cost, and top speed. This was performed to avoid over-representing marginal differences between models and to achieve a balanced sample.

2.4. Statistical Techniques

Diverse quantitative methods were employed to analyze changes in perception, adoption intention, awareness, and performance before and after the intervention. Given the nature of our data collection using Likert-style responses, we condensed the categories into three main response groups—negative, neutral, and positive (e.g., highly unlikely and unlikely as negative; likely and highly likely as positive)—to address sparsity, wherever necessary and applicable.

Three primary tests for statistical significance were utilized: (i) the likelihood ratio (LR) test for evaluating the probability of observing response patterns under different theoretical models; (ii) the Pearson chi-square (Chi-Sq) test for assessing broader associations between categorical variables; (iii) Fisher’s exact test for analyzing specific cell comparisons in contingency tables.

Frequency distribution tables derived from pre- and post-trial survey responses were examined at both the cell and sample levels. The cell-level analysis examined specific transitions within contingency tables, such as shifts from neutral to positive perceptions, using Fisher’s exact test to check whether this shift was meaningful or non-random. The sample-level analysis evaluated broader distributional changes between pre- and post-intervention responses using LR and Chi-Sq tests to understand the intervention’s overall impact [76].

The marginal homogeneity test assessed the consistency of row and column marginal distributions pre- and post-intervention, identifying changes to the structural response patterns not captured by cell-level or overall distribution analysis. Two non-parametric tests were also employed as a secondary measure for validation and to ensure consistency: Wilcoxon signed-rank and the sign tests [77].

2.5. Sample Profile

Sample characteristics are listed in

Table 2. Distribution of household size, marital status, residence type, age, and education.

Vehicle Age, Years	1 to 5	6 to 10	11 to 15	≥15	(∑ N = 297)
Household Size (hh_size)
4 or more	9 (3%)	182 (61%)	74 (25%)	18 (6%)	283 (95%)
3	<1%	4 (1%)	<1%	2 (<1%)	7 (2%)
2	<1%	<1%	<1%	0	<1%
1	<1%	0	0	0	<1%
Subtotal	9 (3%)	186 (63%)	74 (25%)	20 (7%)	297 (100%)
Marital Status (marital_status)
Married	10 (3%)	166 (56%)	73 (25%)	18 (6%)	267 (90%)
Unmarried	<1%	21 (7%)	6 (2%)	2 (<1%)	29 (10%)
Subtotal	10 (3%)	187 (63%)	79 (27%)	20 (7%)	297 (100%)
Residence Type (residence_type)
Own	11 (4%)	184 (62%)	68 (23%)	18 (6%)	281 (95%)
Rented	0	2 (<1%)	11 (4%)	2 (<1%)	15 (5%)
Subtotal	11 (4%)	186 (63%)	79 (27%)	20 (7%)	297 (100%)
Driver Age (driver_age)
18–25	<1%	16 (5%)	2 (<1%)	0	19 (6%)
26–40	6 (2%)	114 (38%)	39 (13%)	13 (4%)	172 (58%)
40–55	3 (1%)	45 (15%)	34 (11%)	6 (2%)	88 (30%)
Above 55	<1%	11 (4%)	4 (1%)	<1%	17 (6%)
Subtotal	9 (3%)	186 (63%)	79 (27%)	19 (6%)	297 (100%)
Education Background (edu_bg)
Secondary school	7 (2%)	98 (33%)	45 (15%)	9 (3%)	159 (54%)
Higher secondary	3 (1%)	37 (12%)	16 (5%)	6 (2%)	62 (21%)
Primary (up to 5th)	<1%	35 (12%)	13 (4%)	4 (1%)	52 (18%)
No formal schooling	<1%	17 (6%)	5 (2%)	<1%	24 (8%)
Subtotal	10 (3%)	187 (63%)	79 (27%)	19 (6%)	297 (100%)

and

Table 3. Summary of vehicle ownership, loan status, work hours, and idle time.

Vehicle Age, Years	1 to 5	6 to 10	11 to 15	≥15	(∑ N = 297)
Vehicle_ownership
Own	10 (3%)	167 (56%)	68 (23%)	17 (6%)	262 (88%)
Rent	<1%	20 (7%)	11 (4%)	3 (1%)	35 (12%)
Subtotal	10 (3%)	187 (63%)	79 (27%)	20 (7%)	297 (100%)
Vehicle on Loan
No	9 (3%)	110 (37%)	53 (18%)	15 (5%)	187 (63%)
Yes	2 (<1%)	77 (26%)	26 (9%)	5 (2%)	110 (37%)
Subtotal	11 (4%)	187 (63%)	79 (27%)	20 (7%)	297 (100%)
Work Hours (work_hours), hours
3–6	3 (1%)	49 (17%)	25 (8%)	5 (2%)	83 (28%)
6–9	6 (2%)	97 (33%)	32 (11%)	7 (2%)	141 (47%)
9–12	2 (<1%)	41 (14%)	23 (8%)	<1%	73 (25%)
Subtotal	11 (4%)	187 (63%)	79 (27%)	20 (7%)	297 (100%)
Idle Time (idle_time), hours
1–3	7 (2%)	139 (47%)	44 (15%)	15 (5%)	205 (69%)
3–6	4 (1%)	42 (14%)	32 (11%)	5 (2%)	83 (28%)
6–9	0	<1%	<1%	<1%	9 (3%)
Subtotal	11 (4%)	187 (63%)	79 (27%)	20 (7%)	297 (100%)

. It must be clarified that the absence of a comprehensive database of the d3W driver community implies that accounting for nuances specific to smaller sub-groups is practically infeasible and nearly impossible. Nevertheless, the diversity of participants ensures reasonable representation across socio-demographics, vehicle ownership, financing, and fleet age.

2.5.1. Socio-Economics and Demographics

All participants are male, reflecting the male-dominated nature of this occupation in the city. About 90% of drivers are married and 95% live in households of four or more members, underscoring the possible prevalence of extended families. More than half (58%) of drivers sampled are 26–40 years old, followed by 40–55 years (30%). A relatively smaller proportion of younger drivers aged 18–25 years and drivers older than 55 years were sampled—6% each. About half (54%) have completed secondary schooling (6th to 10th grade), while 21% attained higher secondary levels (11th to 12th grade). About 8% have no formal schooling. In terms of asset ownership, 97% of d3W drivers own their homes and only a minuscule 3% rent.

2.5.2. Vehicle Ownership and Utilization

Vehicles used by d3W drivers were predominantly owned outright (88%), with only 12% relying on rentals. Among vehicle owners, 63% have vehicles that are not on loan, indicating a level of financial independence. However, 37% of the drivers have financed their vehicles, highlighting a reliance on credit. Drivers with newer vehicles (1–5 years old) on loans form a very small fraction (0.7%). The age of these vehicles skews older—63% are 6–10 years old, followed by 11–15 years old (27%). Only 4% operate relatively newer vehicles (1–5 years), and 7% rely on vehicles older than 15 years. Drivers aged 26–40 years, the most economically active group, dominate ownership across all vehicle age categories. The most common daily duty cycle pattern reported is 6 –9 h of operation and 1 –3 h of idle time.

3. Results

Between February and May 2024, 297 participants completed roughly 6300 trips (for the first 26 participants, the trip rate was not collected as the logbook was not included yet, and hence it was imputed with the average of the available responses) over 890 driving days, accumulating 75,500 zero-emission kilometers while consuming 4.73 MWh of electricity. The distribution of drivers between EV1 and EV2 showed a split of 42% (N = 124 drivers) and 58% (N = 173 drivers), respectively. EV1 and EV2 covered 44% and 56% of the total distance and roughly similar proportions of electricity consumption—46% (2.15 MWh) and 54% (2.57 MWh), respectively. Participants drove, on average, 85 km/day.

Assuming an average d3W fuel economy of 25 kmpl [24] and the entire d3W fleet of Amritsar being BS-VI norm [78] compliant, a very conservative assumption, over the course of the pilot trial, at least 6.7 t of CO₂ and 3000 L of diesel were avoided. Considering the weighted average grid emissions factor of 0.7 tCO₂e/MWh [79], when annualized at a fleet level, 20,000 tCO_2e and ~9 million liters of diesel could be avoided if the entire 3W fleet was electric. (Note: Annual vkt of 30,000 km—durability limit as per certification and type approvals.) Equivalent air quality benefits translate to 4 t of carbon monoxide (CO), 23 t of hydrocarbons (HCs), 23 t of nitrogen oxides (NO_x), and 6 t of particulate matter (PM) reductions annually.

3.1. Descriptive and Distributional Statistics

3.1.1. Daily Driving Distances

D3W and E3W Comparison

The CDF plot of the d3W and e3W average daily driving distance is depicted in Figure 4 shows. On average, d3Ws (µ ± σ = 98 ± 40 km) drive ~15% more than e3Ws (µ ± σ = 85 ± 21 km) with a moderate effect size (Cohen’s d = 0.43). Statistical tests confirmed significant differences (p < 0.001) across central tendency, variability, and distribution. Parametric tests (t-tests, Welch’s test) show clear differences in average driving distances, while variance-based tests (Bartlett’s, Brown–Forsythe–Levene, Kolmogorov–Smirnov) reveal disparities in variability and overall distribution. Non-parametric tests (Wilcoxon and Kruskal–Wallis) further confirmed differences in median distances. e3Ws in this pilot trial show more consistent usage patterns, while d3Ws were used more often for longer trips.

Functionality Versus Aesthetics

The e3Ws used in the pilot trial had undergone physical modifications such as the addition of external metal fenders and bigger tires to project sturdiness and durability (Figure 5). These alterations to the original design were perhaps aimed at addressing drivers’ perceptions of e3Ws as light and flimsy compared to d3Ws and e-rickshaws. While probably improving aesthetics and psycho-visual appeal, the incremental weight, drag, and rolling resistance could have lowered the energy efficiency and thereby the effective on-road range.

3.1.2. Trip Use-Cases and Origin-Destination (OD) Patterns

Trip OD data from the driving logbook was analyzed to determine the purpose and patterns of trip attraction (generation), Figure 6. The results indicate that over 90% of trips originate from the following locations: the bus stand (31%), the railway station (22%), the Golden Temple (9%), Chheharta (9%), Ranjit Avenue (7%), Nangli (5%), Majitha Road (5%), Meerakot (3%), Wagah Border (2%), Kachehri—District Court (2%), the airport (1%), Durga Temple (1%), Verka (1%), Batala Road (1%), and Ramtirth Road (1%). These locations represent a mix of significant transit nodes and cultural and religious landmarks, as well as commercial and commuting points of interest. The importance of access to economic centers is highlighted by commercial hubs such as Ranjit Avenue, Kachehri, and Verka; residential areas like Meerakot Chowk and Batala Road, meanwhile, showcase the influence of daily commutes on travel patterns.

The railway station to the bus stand (10%), Chheharta to the bus stand (8%), the railway station to the Golden Temple (7%), the bus stand to Nangli (5.4%), and Ranjit Avenue to the bus stand (5.2%) are the top five routes by share. The top 10 OD pairs collectively accounted for 25% of the total distance driven during the pilot. The average trip distance ranges from 2 km to 11 km, with an overall average of 6.3 km per trip. This suggests that most trips fall within a short-to-medium distance range. These observations reveal that major transit nodes and cultural landmarks are central to the city’s mobility patterns, while the concentration of trips around commercial hubs emphasizes the role of economic activity.

The bar chart in Figure 7 summarizes the number of trips undertaken by EV1 and EV2 across three categories or trip use-cases: empty, shared, and Salam. The overall trip data provides a comprehensive view of e3W utilization across EV1 and EV2, with a total of 5752 trips recorded. Shared trips dominate the dataset, comprising 72% (4157 trips), highlighting the primary role of e3Ws in shared transport. Salam trips (private or personal trips) make up 23% of the total (1324 trips). Empty trips, where vehicles operated without passengers, account for 5.6% of all trips (271 trips).

Of EV1’s 2411 trips, ~80% (1917) were shared, ~16% were shared, and the rest were empty trips (~5%). By comparison, EV2’s 3341 trips included 67% shared (2240), 27% Salam (893), and 6% empty (208). EV1 was primarily utilized for shared transport, demonstrating efficient operations with minimal empty trips. Conversely, EV2 shows a broader operational scope, with a larger share of Salam trips and slightly higher empty trips—in both absolute and relative terms.

3.1.3. Overall Experiential Trial Feedback Score

The e3W pilot trial received overall positive feedback (1–10 scale) from participants (µ = 8). Over 79% of participants rated their experience at 8 or above, with only ~2% rating it below 6. The relatively low standard deviation (0.94) and median absolute deviation (3.49) highlight consistency in participant ratings.

The open-ended feedback from the e3W pilot trial, coded into positive (green) and negative (red) sentiments, highlights distinct trends across charging and driving attributes, is shown in Figure 8. In the charging category, availability received 44 negative responses with only 2 positive mentions. (Note: This includes earthing as well. Since the responses were sparse, it was combined with the broader availability sub-attribute of charging.) Conversely, charging speed emerged as a relatively strong point, with 202 positive mentions, despite 77 negative responses. The pilot’s structured design, including guaranteed access to reliable, free charging, probably biased participant views favorably. This may not be indicative of the broader public opinion on Amritsar’s public EV charging infrastructure. Still, it alludes to the importance of reliable, accessible, available, and affordable public charging infrastructure.

Most driving attributes received overwhelmingly positive feedback, except for driver-side comfort. Acceleration (285 vs. −7) and driving speed (263 vs. −6) were highly rated, but range received both positive (167) and negative (−126) comments, indicating a possible disconnect between the theoretical or label estimated range and the effective on-road range. During this feedback collection, each participant was individually questioned about their ideal or expected range. From 136 replies, 77 (~60%) chose 200 km, while 24 selected an even longer range of 220–250 km, and 33 preferred 140–190 km.

Breaking down the ratings by vehicle type, both EV1 and EV2 received generally favorable feedback. EV1 achieved a mean rating of 8, with 55% of participants rating it an 8. EV2, on the other hand, had a slightly higher mean rating of 8.3, with almost half rating it an 8 and about one-third (31%) rating it even higher at 9. Individual EV1 and EV2 feedback largely follows the aggregate snapshot of pilot depicted in Figure 8 with a few model-specific variations, as illustrated in Figure 9.

Charging attributes for both EV1 and EV2 drew negative feedback, EV2 more so than EV1 (−29 vs. −15). Charging speed was a relatively stronger point for both, likely due to the pilot design as clarified earlier. Both EVs received mixed reviews regarding their range. Driving comfort emerged as a consistent weakness for both, particularly for EV1, which received 79 negative mentions compared to EV2’s 11. Referring to Table 1, EV1 has a higher ground clearance and wider wheel base but it is slightly shorter and narrower compared to the other models. This could be a plausible explanation for the negative feedback on the comfort of EV1.

3.2. Impact of Pilot: Pre/Post Comparative Assessment

This sub-section is organized into four parts—perceptions, awareness, social dimensions, and adoption intention—each exploring shifts observed during the trial.

3.2.1. Perceptions

General Understanding on the Role, Value and Utility of E3Ws

Roughly ~60% rated the importance of switching to e3Ws positively in both pre- and post-intervention responses. However, ~35% (N = 110) of responses remained neutral or negative. Post-intervention, 71% (N = 210) of drivers perceived e3Ws positively, with a notable decline in neutral or negative responses (N = 87). A near-unanimous 97% (N = 287) of participants agreed that e3Ws contribute to reducing air pollution. This consensus highlights a shared acknowledgment of the societal and environmental value of e3Ws.

Charging Duration and Range

Figure 10 depicts the mosaic plots for charging duration and range perceptions pre- and post-trial. Before the trial, 37% of participants indicated they “don’t know” (dnk) the charging duration. This dropped significantly to 7% post-trial, indicating a marked improvement in awareness. The distribution shows a clear consolidation around the 3 to 4 h charging duration category, which increased from ~35% to ~50% of responses. Likelihood ratio chi-square (χ² = 84.345, p < 0.0001) and Pearson chi-square (χ² = 77.707, p < 0.0001) tests confirm these changes to be statistically significant, with an R-square (U) value of 0.0533 indicating a moderate strength of association.

Pre-trial, 23% (N = 69) of participants were uncertain about the range (don’t know), while 36% (N = 80) estimated a range of 110–120 km. Post-intervention, “don’t know” responses reduced significantly to 5% (N = 15). The most common estimated range dropped to 100–110 km (N = 74) from 110–120 km (N = 80), aligning with the lower-than-expected real-world range. Improved confidence and accuracy in range estimation is reflected in the slight increase in participants (from 12%, N = 36, to 15%, N = 45) estimating 120–130 km post-trial. The likelihood ratio chi-square (χ² = 63.549, p < 0.0001) and Pearson chi-square (χ² = 59.614, p < 0.0001) tests indicate the statistically significant nature of these perceptual changes, albeit with a lower R-square (U) value of 0.0278, indicating a weaker strength of association compared to charging duration.

These results demonstrate that direct experience with e3Ws led to more informed and realistic perceptions of both charging duration and range capabilities. The significant reduction in “don’t know” responses and convergence toward actual performance parameters suggest that experiential learning could improve awareness about e3W technology and performative capabilities.

Safety

The Sankey plot of e3W safety ratings before and after the trial is illustrated in Figure 11 and highlights notable shifts in participants’ perceptions. Out of the 182 participants who rated safety as positive pre-trial, 133 (73%) remained positive post-trial. An additional 64 participants shifted from neutral to positive and 3 from negative to positive, increasing the total number of positive ratings to 200 (67%) post-trial. Pre-trial, 111 participants rated safety as neutral. Of these, 45 participants remained neutral post-trial, and only 2 participants moved from neutral to negative, representing a minimal shift. Stability is observed in both positive and negative ratings, with minimal new negative ratings emerging.

The likelihood ratio test, with a significant p-value of 0.0233, indicates a notable association between pre- and post-intervention responses for safety ratings. This suggests that participants’ perceptions of e3W safety underwent significant changes due to the intervention. The Pearson chi-square test corroborates this finding with a p-value of 0.0221. While the LR and Pearson tests provide a broad overview of distributional shifts, Fisher’s exact test revealed significant transitions from “neutral” to “positive” safety ratings, with p-values of 0.0067 and 0.0072 for these pairs. This granular insight highlights where the most substantial perceptual shifts occurred (Figure 11).

Parity with D3W

The trial significantly influenced participants’ perceptions of e3Ws’ ability to match diesel autos in terms of trips and income. Post-trial, ~75% of participants (217 out of 290) believed they could take the same number of trips and earn the same income, compared to ~67% pre-trial. Among participants who initially responded “Yes” (expecting parity), a significant majority, 187 out of 236 (79%), maintained their positive perception post-trial. Among participants who initially responded “No” (not expecting parity with diesel autos), 24 out of 54 (44%) maintained this perception post-trial, while 30 shifted to “Yes” (~56%). While a majority of participants became more optimistic post-trial, the persistence of some negative perceptions (and the shift of 49 participants from “Yes” to “No”) highlight areas for further confidence-building measures or operational clarifications.

For the stability and applicability of the chi-square test, responses categorized as “dnk” (don’t know) were excluded (N = 6). Both the LR and Chi-Sq tests yielded statistically significant results (p-value = 0.0005 and 0.0003, respectively), indicating that the changes in responses between pre- and post-trial are not by chance but reflect a meaningful shift in perceptions. The two-tailed test confirmed significant differences (p-value = 0.0008) across pre- and post-trial responses.

3.2.2. Adoption Intention

Beyond perceptions of performance and usability, an important question is whether the trial shaped participants’ willingness to adopt EVs in the future. Figure 12 shows the pre- and post-trial likelihood of e3W adoption. Before the trial, 24 participants expressed a negative perception, with only 4 (17%) retaining this stance post-trial. The majority (50%) transitioned from negative to neutral, while a notable 33% shifted directly to a positive outlook, indicating a moderate re-evaluation of their initial skepticism. For those with neutral pre-trial responses, representing 118 participants, there was significant movement post-trial. While 42% remained neutral, over half (~52%, N = 61/118) shifted to a positive stance, highlighting the intervention’s role in swaying ambivalent attitudes toward favorability.

A small fraction (6%) of neutral respondents moved toward a negative perception, suggesting some residual concerns or challenges that require addressing. For the positive group, initially comprising 155 participants, a majority (53%) retained their positive outlook post-trial. However, 42% of this group tempered their enthusiasm, transitioning to a neutral stance, potentially reflecting a more cautious optimism post-trial. Roughly 5% of positive respondents shifted to a negative perception, indicating minimal opposition among those who already had a positive or neutral disposition. A minor reduction in positive responses was more than offset by a noticeable shift from negative to more favorable categories; the negative group showed the most significant change.

The likelihood ratio test (χ² = 5.27, p = 0.26) and Pearson chi-square test (χ² = 6.28, p-value = 0.179) both indicate that while there are observable changes in the distribution of responses pre- and post-intervention, these changes do not reach classical notions of statistical significance (p < 0.05 or p < 0.1). Fisher’s exact test (p-value = 6.742 × 10⁻⁵ for one-sided, p-value = 0.1964 for two-sided) provides a more precise analysis for smaller sample sizes. The significant one-sided result suggests directional shifts in responses, though the two-sided test agrees with the other measures in not reaching statistically significant levels. When examining individual pairwise comparisons, for the “neutral” category, there is a marginal statistical indication of a difference in responses, as seen in the Pearson chi-square p-value (p-value = 0.0498). The likelihood ratio test confirms similar trends, with p-values for shifts between pre- and post-responses showing subtle distinctions.

The Gamma coefficient (0.129) suggests a weak positive association between pre- and post-intervention responses, with a relatively wide confidence interval (−0.06 to 0.32) that includes zero, while Kendall’s Tau-b (0.073) and Stuart’s Tau-c (0.061) indicate very weak rank correlation between pre- and post-responses, and the various uncertainty coefficients (all p-value < 0.01) highlight minimal predictive power between pre- and post-intervention responses.

These statistical measures collectively indicate that while the intervention produced observable shifts in participant attitudes, these changes were subtle and characterized by considerable sub-sample variation rather than systematic patterns. While individual adoption intentions are central, they are also shaped by broader social influences; therefore, the following section explores peer effects and community-level dynamics.

3.2.3. Social Dimensions

Pride and Symbolism

Pre- and post-intervention analysis of responses to the question, “How proud would you feel owning and driving an e3W in front of your peers?” shed light on the interplay between self-image, perceived image, and pride. The contingency table showed a rise in positive responses (67% to 75%) post-trial. A reduction in “neutral” responses (32% pre-trial down to 24% post-trial) accompanies the increase in positive responses, possibly moving away from ambivalence towards affirmation. However, these shifts were not found to be statistically significant (p > 0.2) according to the Wilcoxon signed-rank, sign, and marginal homogeneity tests.

Pradhan as a Facilitator

The analysis of how respondents perceived their “Pradhan” (local community leader)’s support for switching to e-autos shows meaningful shifts. The contingency table revealed an increase in “positive” responses from 42% pre-intervention to 55% post-intervention, suggesting that the trial effectively strengthened participants’ confidence in receiving support from their community leaders. Concurrently, “neutral” responses decreased slightly from 44% to 42%, and “negative” responses remained minimal, reflecting directional movement toward positive perceptions of Pradhan support. The marginal homogeneity test (Z = −2.879, p = 0.004) and sign test (Z = −4.157, p-value < 0.001) confirm the statistical significance of these shifts, with a majority of participants transitioning to more favorable categories post-intervention.

Pre-trial, 65% of participants expressed a “positive” likelihood of adoption if their Pradhan already owned an e3W, with the majority retaining this stance post-trial (58%), though some transitioned to “neutral” (13%), indicating a moderate softening of confidence. Notably, among the “neutral” pre-trial group (32%), 50 participants moved toward a “positive” stance post-trial, reflecting a net favorability gain. The “negative” category, while small, displayed some movement toward “neutral” or “positive”. Statistical validation via the sign test (p-value < 0.001) and the marginal homogeneity test (p-value < 0.05) underscores the non-random nature of these shifts. However, it must be clarified that, for methodological reasons, sparse cell counts in the contingency table required combining smaller groups to ensure the statistical validity and applicability of chi-square tests.

Collectively, these findings indicate the intervention’s dual impact: reshaping individual perceptions whilst simultaneously strengthening confidence in community-level support mechanisms.

4. Discussion

4.1. Demand-Side Policy Effectiveness

The stacked bar chart in Figure 13 provides insights into the monthly trend of e-auto deliveries from 2022 to 2024, categorized by whether a loan was required. Trends show an increase in overall deliveries over time, with significant peaks observed in March (130), June (101), and October (99) of 2024. Out of 1470 applications, 1337 e3Ws were delivered, indicating a very high application-to-delivery turnover rate (91%). However, 121 applications were rejected due to low CIBIL scores (Indian equivalent of credit rating), while 11 applicants withdrew for other unspecified reasons. A good proportion of e3Ws (~26% or 350 total) were purchased through loans, with loan-supported purchases consistently matching or exceeding non-loan purchases across most months up and until Sep 2024. Towards the end of 2023, the share of applications that do not require formal financial assistance drastically reduced. In 2023, 281 out of the total 581 e3W deliveries were availed of via loan (roughly 54%), which dropped to 7% in 2024 (49 out of 741 e3W deliveries). It is worth mentioning that the e3W pilot trial ran from January to May 2024, during which almost a quarter of all e3Ws (342 out of 1337) were delivered.

The pie chart in Figure 14 illustrates the distribution of e3Ws delivered to date under the RAAHI scheme by Original Equipment Manufacturers (OEMs). Piaggio leads with ~35%, followed by Mahindra at 24%, Atul at 18%, Bajaj at 13%, and Montra at ~10%. From 2022 to May 2023, only two major OEMs offered three trims collectively. By contrast, the current market landscape includes five OEMs offering seven trims. The expansion of offerings demonstrates the industry’s responsiveness to market demands and highlights the role of competition in fostering innovation and user-centric designs, which are essential for scaling e3W adoption.

4.2. E3W Experiential Pilot Learnings

4.2.1. Exposure as a Tool for Improving Awareness and Aligning Perceptions with Real-World

The trial demonstrated its value in recalibrating perceptions which manifested in the following ways: (i) across both charging duration and range perceptions, the “don’t know” category contracted post-trial; (ii) participants’ responses shifted toward categories that align with actual e3W performance capabilities—3–4 h for charging time and 110–130 km range; (iii) the high retention rate of positive safety ratings (73%) indicates that favorable impressions formed pre-trial were well-founded and backed by real-world experience; (iv) the highly significant chi-square results validate these shifts as non-random and attributable to the intervention.

4.2.2. Downsides of Informal Financing

While formal loan-supported deliveries are well understood, it is important to note that many buyers likely relied on informal financing channels with higher interest rates, which were recorded as cash purchases. (Note: Based on a telephonic communication with a consultant working with Amritsar Municipal Corporation under a USAID project, around two-thirds may have relied on informal financing options for e3Ws. The exact count is unavailable due to the very nature of how such financing mechanisms function.) These cash-documented transactions mask the prevalence of high-interest informal loans, often the only recourse for individuals with low credit scores. This presents a paradox where formal credit barriers inadvertently push toward costly informal lending channels. Drivers dependent on e3Ws for their livelihoods, particularly those from economically vulnerable backgrounds, face persistent challenges in securing affordable credit despite the inclusion of EVs under priority sector lending (PSL).

4.2.3. Leveraging Social Effects and Authority Figure’s Influence for Scaling

The results demonstrated the intervention’s capacity to amplify the sense of pride and social validation associated with e3W ownership. The statistically significant reduction in neutral responses and the corresponding rise in positive responses underline the potential of targeted interventions to strengthen emotional and social connections with e3W adoption. The positive association between Pradhan’s perceived support and participants’ increased willingness to adopt e-autos illustrates how leadership and social modeling can amplify the acceptance of new technologies.

4.3. Recommendations

4.3.1. Overcoming Induced and Direct Barriers

• Expand driver outreach, awareness, and educational campaigns: Build on the trial’s success by conducting targeted outreach programs to educate drivers about the technological and operational benefits of e3Ws.
• Expand public charging infrastructure network: Strategically place charging stations near high-demand areas such as transit hubs and commercial zones to further reduce concerns about charging duration and range anxiety.
• Communicate the difference between type approval and real-world range: Mandate clear labeling of e3Ws’ range under realistic driving conditions, reducing reliance on anecdotal or exaggerated claims. This gap is not only technical but also informational; addressing it can improve buyer confidence and decision-making.
• Extend battery warranty: To offset the higher initial cost of the battery, prolonging the battery warranty from the typical 3 years/100,000 km to a super-warranty of 5 years/200,000 km [80] could alleviate concerns for prospective buyers.

4.3.2. Inclusive, Supportive, and Accessible Financing

Policymakers must strive to address the forces that could push interested buyers to avail themselves of high-interest informal channels over conventional banks and microfinancing institutions, subsidized interest rates notwithstanding. This is better understood through the broader lens of financial literacy. Financial literacy campaigns on available loan options, government incentives, and the risks associated with informal borrowing could be conducted. Another option is to work upstream by formalizing informal creditors through credit pooling and cooperative financing models. As an extension, forming driver cooperatives or self-help groups that can collectively negotiate better financing terms can help to address the 121 rejected loan applications due to low credit.

4.3.3. Harnessing Social Capital and Local Leadership

As insights from the baseline indicate [73], peer networks are a powerful channel to go beyond sharing peer experiences to mentoring platforms. Strengthening local associations and positioning the authority figure or Pradhan as a change agent can facilitate disseminating accurate information about e3W performance, addressing common misconceptions, and providing practical support throughout the e3W transition and post-purchase.

4.3.4. Institutional and Governance Reforms

Perhaps the most understood and accepted but rarely acknowledged or addressed need is that for structural and systematic reforms to the very basic nature of city and local planning efforts across urban India. A multi-layered policy approach harmonizing government initiatives and ensuring local context adaptation is needed for successful replication and scaling of Amritsar and RAAHI-like initiatives. At the central level, policies like FAME II have already laid the foundation for EV adoption. To maintain momentum, these must be effectively integrated with state-specific policies contextualized to local challenges and opportunities.

Cities must align e3W deployment strategies with their comprehensive mobility plans, ensuring that charging infrastructure development complements and co-evolves with urban development and travel demand patterns. This includes strategic placement of charging stations near high-demand areas and integration with public transit hubs to facilitate first-and-last-mile connectivity [81,82].

Clear incentive structures require careful calibration across multiple dimensions [83]. The evidence from state EV policies suggests that successful adoption requires a combination of fiscal and non-fiscal incentives, tailored to local economic conditions [84]. Beyond direct purchase subsidies, cities could explore operational perks like lower permit fees, preferential parking, and free public charging up to a certain timeframe like Nissan’s No Charge to Charge. (Note: With No Charge to Charge, public charging is complimentary for an unlimited number of 30 min DC Fast charges and 60 min Level 2 charges at participating locations for 24 months. Charging sessions beyond these lengths are subject to additional fees, depending on the network (sic) [85].)

Policies should encourage collaboration with multiple manufacturers to ensure a variety of trims and variants tailored to local needs. Additionally, the analysis of Amritsar’s financing mechanisms highlights the need to bridge the formal–informal financing gap.

The coordination between various stakeholders—from urban local bodies to electricity distribution companies—requires institutional mechanisms that facilitate regular communication and joint decision-making. Establishing clear protocols and jurisdictional oversight for inter-departmental coordination, particularly in areas like charging infrastructure deployment where multiple agencies have overlapping jurisdictions [86,87], is vital.

5. Conclusions

As of December, 2024, approximately 18% of Amritsar’s d3W fleet has transitioned to e3Ws under the RAAHI scheme. The direct user experience resulted in ~6% of participants switching to an e3W. The RAAHI project distinguished itself through its innovative scrappage-linked subsidy—an additional incentive over and beyond FAME and low-interest loans. However, this steep cost reduction through point-of-sale subsidy is an exception rather than the norm.

Key insights from the trial highlight the value of leveraging informal networks, with the involvement of local community leaders and early adopters as advocates. This reflects that e3W adoption is driven not only by incentives but also by social validation and experiential learning, often overlooked in conventional surveys. Addressing operational challenges, such as range anxiety, requires the development of robust charging infrastructure, particularly in areas with high demand and public awareness efforts showcasing e3Ws’ intrinsic technological, economic, and environmental benefits. Financial incentives must be designed to be both impactful and self-sustaining, addressing gaps in formal financing mechanisms while reducing reliance on informal loans.

Participants post-trial found e3W charging more convenient and safer and had a realistic understanding of vehicle range and charging times. These shifts underscore the impact of experiential learning in addressing psychological and perception barriers to adoption. The change in participant perceptions and expectations alludes to a complex reshaping of their pre-trial attitudes, accompanied by a meaningful directional shift.

The study used purposive and convenience sampling, facilitating community engagement and providing a snapshot of the d3W driver population, but potentially limiting the generalizability of the results. The lack of pre-trial logbook data for the initial cohort of participants reduced the availability of comprehensive longitudinal data. Additionally, reliance on informal networks for participant recruitment may have introduced selection bias. The trial’s short duration may not have captured long-term behavioral patterns or operational challenges such as maintenance costs or battery degradation, or sustained adoption dynamics, which warrant further investigation in future studies.

Extended trial periods in future studies could offer more robust data on vehicle durability and evolving user expectations. Telematics data collection could provide granular, real-time information on vehicle utilization, driving, and charging behavior. Future work could also explore advanced statistical models for market segmentation and multi-dimensional scaling. These sophisticated analytical approaches can enable the identification of specific user groups and tailoring targeted interventions. Such analyses could inform policy refinements and marketing strategies to accelerate e3W adoption across diverse user segments.

Overall, these insights emphasize that scaling e3W adoption requires a layered approach which combines experiential exposure, financial inclusion, supportive governance, and community-driven advocacy to move beyond cost considerations and address the social dynamics that underpin adoption decisions.