The role of dealer demonstration in the adoption of electric vehicles

Abstract

A major concern that customers face when considering buying an electric vehicle is the uncertainty about its ability to cover their mobility needs. While an electric vehicle’s rated range is publicly known, the range it realistically achieves for a given customer is not, as it depends on idiosyncratic driving factors that customers can fully understand only through hands-on experience. Dealer services like extended test drives can alleviate customers’ concerns and have the potential to increase electric vehicle adoption. However, not all dealers adopt such demonstration practices and their environmental implications are not straightforward. In this paper, we develop a game-theoretic model to investigate when a dealer should offer demonstration services and whether doing so can increase electric vehicle adoption and reduce the environmental impact. We consider a car dealer who procures conventional and/or electric vehicles from a manufacturer and sells them to customers with heterogeneous mobility needs. Customers are a priori uncertain about the electric vehicle’s ability to meet their mobility needs; the dealer can provide demonstration services to mitigate the range uncertainty. We find that the dealer should offer demonstration as the electric vehicle range increases, but may refrain from offering demonstration when the production cost of electric vehicle decreases. In addition, offering demonstration leads to greater electric vehicle adoption only when the electric vehicle technology is less developed. Interestingly, even when offering demonstration leads to greater electric vehicle adoption, it may also lead to greater total usage emissions. We further examine the manufacturer’s Corporate Average Fuel Economy (CAFE) compliance and show that dealer demonstration can compromise the manufacturer’s ability to comply with the CAFE regulation while promoting electric vehicle adoption. Perhaps unexpectedly, to comply with a more stringent CAFE standard, the manufacturer may raise the wholesale price of the electric vehicle and, in the presence of demonstration, the electric vehicle adoption may be lower.

Keywords

Car Dealers Demonstration Services Electric Vehicles Environmental Impact Extended Test Drives

1. Introduction

The transportation sector is a significant contributor of greenhouse gas emissions; in the United States, it accounts for 28% of the total emissions (U.S. EPA, 2023b). Environmental groups and agencies have long advocated for the mass adoption of electric vehicles as a promising strategy to reduce emissions (U.S. EPA, 2023a). In recent years, electric vehicle adoption has attracted substantial interest from both automakers and customers. Major manufacturers of gasoline-powered vehicles, including Ford, General Motors, Volkswagen, BMW, and Nissan, are introducing fully electric options (Consumer Reports, 2023). Customers have also been expressing increasing purchase interest, with 36% of Americans recently indicating that they would seriously consider a fully electric vehicle (Consumer Reports, 2022a). Despite these promising signs, however, the adoption rate of electric vehicles is still low, and critical emission targets are projected to be missed; only 5.6% of new cars sold in the United States in 2022 were electric (Automotive News, 2023). In 2030, the adoption rate in the United States is projected to reach 25%–30% (IHS Markit, 2021), which falls short of the 50% target (The White House, 2021).

A major roadblock toward the mass adoption of electric vehicles is the customer concern over limited range. Surveys by AAA (2022a) and Consumer Reports (2022a) found more than 50% of customers to be concerned about the ability of electric vehicles to fully meet their mobility needs, making limited range one of the top reasons, besides high prices and charging logistics, preventing them from purchasing. This is despite the fact that the majority of surveyed customers understand that current electric vehicle ranges exceed 100 miles (AAA, 2022a). In fact, the Environmental Protection Agency (EPA) Fuel Economy and Environment Label (readily accessible from the “window sticker”) provides an electric vehicle’s rated range as estimated under standardized testing conditions. However, it is widely acknowledged that the range an electric vehicle realistically achieves for a given customer—its achievable range—is significantly affected by driver habits and driving conditions such as high speed, heavy acceleration, road conditions, ambient temperature, and the use of heating, ventilation, and air conditioning (HVAC); see AAA (2019); Ford (2023); Renault (2023); and U.S. EPA (2021). For example, driving at 70 mph rather than 30 mph reduces range by more than 45% (Wager et al., 2016), while HVAC use at 20 $\circ$ F than 75 $\circ$ F results in a 41% range reduction (AAA, 2019).¹ Since the impact of these idiosyncratic factors is fully revealed only after extended use, customers are a priori unsure about the range an electric vehicle can realistically achieve for them. Importantly, this uncertainty is customer-specific and cannot be ascertained from external information sources such as third-party reviews and owner forums. Although customers can theoretically learn the impact of these factors in isolation, the joint effect that determines achievable range cannot be reliably anticipated: customers’ self-knowledge of their own driving habits is imperfect (Thomas et al., 2025), and these factors interact nonlinearly (Argue, 2023).

The experiential nature of range performance suggests that providing customers with first-hand experience to better understand the extent to which an electric vehicle can meet their mobility needs is an important way to facilitate adoption.² According to the U.S. Electric Vehicle Consideration Study by J.D. Power (2022), only 11% of respondents who had no personal experience with an electric vehicle were “very likely” to consider buying one; this percentage more than tripled to 34% for those who had driven an electric vehicle before (also see AAA, 2022a; Consumer Reports, 2022b). A more recent electric vehicle driver survey finds that prepurchase range concerns drop from 48.1% to 22.8% after first-hand experience driving an electric vehicle (Plug In America, 2025). This brings to the forefront the role that car dealers play as gatekeepers of electric vehicle adoption.

In their capacity as facilitators of the entire car purchase process, from test drives to financing and vehicle delivery, car dealers are widely recognized as an essential force for the mass adoption of electric vehicles. Although a few luxury brands (most notably Tesla) sell electric vehicles directly to customers, the majority of auto manufacturers offer mass-market electric vehicles through their existing dealership networks.³ Dealerships are increasingly important as “the EV market leaves luxury niche status and enters the mainstream,” and are on track to invest $5.5 billion in electric vehicle infrastructure to provide customers with first-hand experience opportunities (National Automobile Dealers Association, 2021, 2023). Dealers also partner with nonprofit organizations to organize test drive events (Drive Electric Virginia, 2023; Smart Columbus, 2023). In addition, customers exhibit a predominant preference for buying electric vehicles at dealerships and view the first-hand experience as a major source of information (Escalent, 2021; InsideEVs, 2022).

In practice, dealers have been enabling customers to experience electric vehicles first-hand through demonstration services such as extended test drives. In contrast to traditional test drives, which typically last less than 30 min (Youngs, 2019), extended test drives span multiple days and, among other aspects of ownership, enable customers to better understand the range an electric vehicle can realistically achieve for them. For instance, customers can test drive a BMW i3 for several days using BMW’s Extended Test Drive Program (BMW of Freeport, 2023) to “see how the i3 fits [their] lifestyle” (BMW of Chattanooga, 2023). In addition, Volvo (2023) offers test drives of up to 4 days on its electric fleet through the Test Drive+ program. More examples include Charters Citroen Aldershot (2023); Hendy Group (2023); and Sherwoods Motor Group (2023). Dealerships have also been partnering with community-based organizations to hold test drive events for electric vehicles, including Chevrolet Bolt, Ford Mustang Mach-E, and Nissan Leaf (see, e.g., Clean Vehicle Empowerment Collaborative, 2023; Drive Electric Northern Colorado, 2021; Drive Green New Jersey, 2023).

Providing demonstration services is widely recognized as a promising strategy to facilitate the mass adoption of electric vehicles (Environmental and Energy Study Institute, 2021; Exro Technologies, 2023; Society of Manufacturing Engineers, 2020). In particular, empirical evidence suggests that providing test drives can increase the likelihood of electric vehicle purchases (J.D. Power, 2022; Opinion Dynamics, 2023). However, not all dealers offer demonstration services. A survey by Sierra Club (2023) shows that 66% of dealerships nationwide did not have an electric vehicle on their lots, with 45% of them indicating that they would not offer an electric vehicle even in the absence of any production or supply chain/inventory constraints. Reasons for their unwillingness to offer electric vehicles for demonstration include high infrastructure investments, for example, charging stations and staff training (Sierra Club, 2023) and the perception of lower profit margins than comparable gasoline vehicles due to the electric vehicles’ higher procurement costs (de Rubens et al., 2018, 2020).

Perhaps more importantly, the overall effect of demonstration on customers’ purchase decisions is not straightforward. Customers may find from demonstration that the electric vehicle can meet a larger fraction of their mobility needs than originally expected. Thus, some of them will prefer the electric over the conventional vehicle to benefit from its lower operating (e.g., fuel and maintenance) cost.⁴ Others, however, may confirm through the demonstration the electric vehicle’s limited ability, although perhaps not as limited as originally expected, to cover their mobility needs and, therefore, may still prefer a conventional vehicle over an electric. Additionally, demonstration does not necessarily guarantee a favorable view of the electric vehicle technology. In fact, customers may find the electric vehicle to be able to meet a smaller fraction of their mobility needs than originally expected. As a result, they may buy a conventional vehicle or entirely refrain from purchasing. Therefore, it is a priori unclear whether and when a dealer should offer demonstration.

The environmental implications of offering demonstration services are also not well understood. Nonprofit organizations that collaborate with dealers to offer demonstration have typically recognized such services as an effective way to facilitate electric vehicle adoption, touting environmental benefits. For example, Drive Electric Northern Colorado (2021) proclaims its extended test drive program as “designed to achieve widespread deployment of electric vehicles,” presuming it to be “good for the environment”; also see Clean Vehicle Empowerment Collaborative (2023) and Evolve Houston (2023). Underlying these assertions is the belief that demonstration always resolves customers’ uncertainty in favor of the electric vehicle and therefore, guarantees the displacement of conventional vehicles by electric ones. Furthermore, these claims use electric vehicle adoption as the proxy of environmental impact and do not account for the total emissions from the usage of both electric and conventional vehicles, which may differ across customers.

Dealer demonstrations may also have significant implications for the manufacturer, who is subject to the Corporate Average Fuel Economy (CAFE) standard. The CAFE regulation requires the combined fuel economy of a manufacturer’s entire fleet to meet a minimum standard. Since the fuel economy rating is much higher for an electric vehicle than a comparable conventional vehicle, the CAFE compliance critically hinges on the ratio between conventional and electric vehicle sales. A dealer demonstration shifts this ratio by shaping customer preferences between different vehicle types and influencing the manufacturer’s wholesale pricing decisions. As the CAFE standard further tightens, it is important to understand how dealer demonstrations affects the manufacturer’s compliance.

The objective of this paper is to examine the economic and environmental implications of dealer demonstration. To this end, we consider a car dealer who procures a conventional gasoline and/or an electric vehicle from the auto manufacturer and sells them to customers with heterogeneous total mobility needs arising from heterogeneity in trip frequency and uncertainty in the length of trips. Compared to the conventional vehicle, the electric vehicle has a lower per-mile operating cost, but its designed range (i.e., the range as specified based on the technical characteristics of the car) is limited. The electric vehicle can meet customers’ total mobility needs by a fraction, which is customer-specific and a priori uncertain; we refer to this fraction as achievable range. Customers hold a prior belief about the electric vehicle’s achievable range, which they update based on the impression they receive from the demonstration service. We characterize the impact of demonstration on dealer profitability, the electric and conventional vehicle sales, the manufacturer’s CAFE compliance, and the resulting environmental impact from all vehicles sold. Our results offer several insights for dealers, auto manufacturers, policymakers, and nonprofit organizations.

We first analyze the dealer’s decision to offer demonstration. We find that the lower production cost of the electric vehicle relative to the conventional vehicle may disincentivize the dealer from offering demonstration. This is because demonstration deters purchases from high-mobility-needs customers after receiving an unfavorable impression. We then characterize the impact of offering demonstration on electric vehicle adoption. We find that offering demonstration leads to greater adoption only when the electric vehicle technology is less developed. Otherwise, the dealer offers demonstration solely as a price discrimination instrument that improves profits while reducing electric vehicle adoption.

Next, we study the environmental implications of offering demonstration. One would expect that if demonstration leads to greater electric vehicle adoption, it would also lead to lower usage emissions because electric vehicles have lower per-mile emissions. However, we show that offering demonstration may lead to higher electric vehicle adoption, but also higher usage emissions. Finally, we examine the impact of dealer demonstration on the manufacturer’s CAFE compliance. We find that offering demonstration can compromise the manufacturer’s ability to comply with the CAFE standard while promoting electric vehicle adoption. Paradoxically, even when it increases the CAFE level, offering demonstration may lead to greater emissions. To comply with a more stringent CAFE standard, the manufacturer may raise the wholesale price of the electric vehicle and, in the presence of demonstration, the electric vehicle adoption may be lower.

2. Related literature

Our paper contributes to the literature on environmentally sustainable operations (for recent reviews, see, Atasu et al., 2020; Sunar and Swaminathan, 2022) and in particular, the emerging stream of studies on green technology adoption. In this stream, Cohen et al. (2016) consider the impact of demand uncertainty in consumer subsidies to facilitate the adoption of a green technology product. Chemama et al. (2019) compare the effectiveness of subsidy commitment versus flexibility when the supplier can defer production to a later period. Murali et al. (2019) examine the effects of a mandatory environmental quality standard and a voluntary external certification on green product development among competing firms differentiated in their environmental claim credibility. In a supply chain context, Kalkanci and Plambeck (2020) and Kraft et al. (2020) study the role of information disclosure in incentivizing supplier practices to reduce the environmental and social impacts from production; for discussions on how modern supply chains can operationally address emerging environmental and social issues, see Erhun et al. (2021). Our paper is also broadly related to research on sustainable business models, for example, leasing (Agrawal et al., 2012), servicizing (Agrawal and Bellos, 2017; Bellos et al., 2017), and modularity (Agrawal et al., 2021; Agrawal and Ülkü, 2013).

Closer to our context, some papers in this stream investigate how innovative business strategies can facilitate electric vehicle adoption. Lim et al. (2015) consider that customers underestimate an electric vehicle’s range and overestimate its depreciation rate, and study how these limitations can be addressed through battery leasing and enhanced charging. Avci et al. (2015) analyze the operations of battery switching stations wherein customers can swap a depleted battery with a fully charged one on a pay-per-use basis. Shi and Hu (2024) examine the profit and environmental implications of a flexible battery leasing model, which enables customers to up/downgrade battery capacity after vehicle purchase. Other studies consider electric vehicle subsidies (Ma et al., 2019; Shi et al., 2022; Yu et al., 2022), fleet management (Abouee-Mehrizi et al., 2021; He et al., 2021), and vehicle-grid integration (Qi et al., 2022; Wu et al., 2022).

The papers in this stream assume that customers perfectly know the electric vehicle range, whereas in practice, the range that can be realistically achieved is customer-specific and a priori unknown. Our paper complements this literature by incorporating customer uncertainty over the achievable range and by investigating the car dealer’s role in facilitating the resolution of range uncertainty through demonstration services. To the best of our knowledge, we are the first to analyze the role of dealers and their offering of demonstration services in facilitating electric vehicle adoption and reducing emissions.

Our paper is also related to the literature on firm activities that facilitate customer resolution of product value uncertainty prior to purchase. Lewis and Sappington (1994) analyze a monopolist’s optimal pricing and information supply whereby customers learn about their personal fit with the product. They find that supplying information facilitates price discrimination and the optimal strategy is either no or full information. Johnson and Myatt (2006) generalize these results to any firm actions that increase the dispersion of consumer valuations. Ghosh and Galbreth (2013) analyze the strategies of two competing firms around disclosing information about the quality of their products to consumers who may miss such disclosures due to inattention and who may subsequently decide to conduct their own costly search for quality information. Boleslavsky et al. (2017) study the Bertrand competition between an established and an innovative firm whose product value is uncertain and can be learned through demonstration. They find that offering demonstration enables the innovative firm to charge a high price and target customers drawing a favorable impression, thereby softening competition. Feldman et al. (2019) consider the pricing and quality decisions of a firm selling a product with uncertain quality to customers who can learn about it through online reviews. In the context of omnichannel retailing, Gao and Su (2017) and Gao et al. (2022) examine the informational role of physical showrooms in resolving customer value uncertainty to avoid costly returns of products purchased online. These papers focus on a firm offering a single product. In contrast, our focus is on car dealers, who sell both conventional and electric vehicles. Given that an electric vehicle has a lower operating cost, whereas a limited and a priori uncertain achievable range, offering demonstration may lead some customers who would otherwise buy a conventional vehicle to buy an electric, and vice versa. Therefore, in our context, the economic and environmental implications of offering demonstration depend on its effect on the demand and usage of both electric and conventional vehicles.

3. Model

We consider a sequential game in which a car dealer procures a gasoline and/or an electric vehicle from the manufacturer and sells it to customers with heterogeneous total mobility needs. Compared to the gasoline vehicle, the electric vehicle has a lower per-mile operating cost; however, it may not fulfill a customer’s total mobility needs; the extent of fulfillment (achievable range, hereafter) is a priori uncertain as it depends on idiosyncratic driving factors. For the electric vehicle, after incurring a lump sum cost, the dealer can provide demonstration services, which enable customers to better understand the range they can realistically achieve. To ease exposition, we will refer to the manufacturer as “it,” the dealer as “she,” and a customer as “he.”

3.1. Customer and vehicle characteristics

We consider a unit mass of customers with heterogeneous total mobility needs. We consider that each customer drives a total number of $f$ trips throughout a vehicle’s useful life. Customers are heterogeneous in their trip frequency and we assume that $f \sim Uniform [0, F]$ . Moreover, each trip has a priori an uncertain length $ℓ \sim Uniform [0, 2 L]$ ; thus, trips can be long or short, with an average length of $L$ . We normalize $L = F = 1$ without loss of generality. We denote a customer’s total mobility needs from all trips taken over a vehicle’s useful life by $θ$ , which is then given by $θ \equiv f E [ℓ] = f$ . Accordingly, the total mobility need of customers is heterogeneous, and thus, given by $θ \sim Uniform [0, 1]$ . Note that while we capture heterogeneity in trip frequency $f$ in our main model, we assume that customers are homogeneous in the distribution of their trip lengths, which allows us to obtain analytically tractable and clear insights. In Section 6, we generalize our model to consider heterogeneity in trip lengths and verify the robustness of our key results.

Customers maximize their net utilities by choosing among three options: (i) buy a conventional vehicle at price $p_{g}$ , (ii) buy an electric vehicle at price $p_{e}$ , or (iii) refrain from buying a vehicle; in this case, customers meet their mobility needs through their outside option (e.g., public transportation) and derive a nominal utility, which we normalize to zero. Customers who buy a vehicle derive a gross utility $v$ per unit of mobility need (e.g., mile) met by the vehicle.⁵ They also incur a per-mile operating (e.g., fuel and maintenance) cost denoted by $k_{g}$ and $k_{e}$ for the conventional and electric vehicle, respectively. We assume that the operating cost is lower for an electric vehicle than a gasoline vehicle (AAA, 2022a, 2022b), that is, $k_{e} < k_{g}$ , and that the gross utility exceeds the operating costs, that is, $v > k_{g}$ , to avoid trivial cases in which no customers buy a vehicle; both assumptions are verified to hold with calibrated data in online E-Companion C. We also assume that the conventional vehicle can cover all trips due to the extensive refueling infrastructure (i.e., the dense network of gas stations). Thus, a customer with total mobility needs $θ$ derives a net utility $U^{g} (θ) = (v - k_{g}) θ - p_{g}$ from buying a conventional vehicle.

In contrast, an electric vehicle may not cover trips longer than a certain distance due to shorter range, less extensive refueling infrastructure, and longer refueling duration than a comparable gasoline vehicle. Let $x < 2 L = 2$ denote the maximum distance an electric vehicle can cover under standard conditions, which we refer to as the designed range to emphasize its dependence on the vehicle’s design characteristics, such as battery capacity, weight, aerodynamics, and rolling resistance (see National Research Council, 2015: 19). In practice, customers can observe the designed range from the rated range published on the vehicle’s EPA Fuel Economy and Environment Label.

We draw an important distinction between an electric vehicle’s designed versus achievable range. We define achievable range as the extent to which an electric vehicle can meet a customer’s total mobility needs under idiosyncratic conditions. Electric vehicles typically achieve lower-than-designed ranges under, for example, high-speed driving, heavy acceleration, and extensive HVAC use. Since these factors are specific to each customer at the time of driving, their combined effects on range can be ascertained only through hands-on experience with an electric vehicle.⁶

In light of these practical observations, we consider that the electric vehicle’s achievable range, as denoted by $\tilde{x}$ , is ex-ante unknown and can take either a high value equal to the designed range $x$ , or a low value equal to $ς x$ where $ς \in (0, 1)$ . Customers are uncertain about the achievable range and expect it to be high with probability $α \in (0, 1)$ and low with probability $1 - α$ .⁷ This prior captures information consumers have from external sources (e.g., online reviews) before demonstration. We consider rational expectations, that is, after purchase, customers experience a high achievable range $\tilde{x} = x$ with probability $α$ and low achievable range $\tilde{x} = ς x$ with probability $1 - α$ . Customers who experience an achievable range $\tilde{x}$ use the electric vehicle for all trips no longer than $\tilde{x}$ miles; for longer trips, they resort to the outside option and obtain zero net utility (see Lim et al., 2015 for a similar formulation). Therefore, in the absence of demonstration, a customer expects to obtain a net utility $U^{e} (θ) = (v - k_{e}) f (α \int_{0}^{x} (ℓ / 2) d ℓ + (1 - α) \int_{0}^{ς x} (ℓ / 2) d ℓ) - p_{e} = (α + (1 - α) ς^{2}) (x / 2)^{2} (v - k_{e}) θ - p_{e}$ from buying an electric vehicle, where $k_{e} \in (0, k_{g})$ denotes the per-mile operating cost and $p_{e}$ the selling price of the electric vehicle.⁸ To further simplify this and subsequent utility functions, we define $r \equiv (x / 2)^{2}$ , $τ \equiv ς^{2}$ , and obtain $U^{e} (θ) = (v - k_{e}) (α + (1 - α) τ) r θ - p_{e}$ . This suggests that the range limitation can be equivalently formulated in terms of drivers’ total mobility needs $θ$ , with the electric vehicle meeting a fraction $r$ versus $τ r$ of these needs under a high versus low achievable range. For ease of exposition, we henceforth use $r$ and $τ r$ and refer to $r$ as range.

3.2. Demonstration services

The uncertainty over the achievable range may prevent customers from purchasing an electric vehicle. The dealer can influence this decision by providing demonstration services (e.g., extended test drives) that enable customers to better understand the range an electric vehicle can achieve under their idiosyncratic driving conditions.⁹ To capture the impact of demonstration services on customers’ purchase decisions, we adopt the established framework of demonstration for innovative products with “deal-breaking” attributes (Boleslavsky et al., 2017). Specifically, we assume that a customer, who after purchase experiences a low achievable range, draws from the demonstration an unfavorable impression with probability $δ \in (0, 1]$ , and a favorable one with probability $1 - δ$ ; hence, $P (unfav | \tilde{r} = τ r) = δ$ and $P (fav | \tilde{r} = τ r) = 1 - δ .$ In other words, demonstration accurately reveals a low achievable range with probability $δ$ , which, therefore, represents the informativeness of demonstration. We also consider that a customer, who after purchase experiences a high achievable range, always draws a favorable impression from demonstration as he never encounters a deal-breaking attribute; hence, $P (fav | \tilde{r} = r) = 1$ and $P (unfav | \tilde{r} = r) = 0$ . For example, customers with high-speed driving and/or extensive HVAC use experience a low achievable range from daily usage (i.e., $\tilde{r} = τ r$ ), and may be able to infer this from an extended test drive upon observing fast battery discharge. Those without such driving habits experience a high achievable range (i.e., $\tilde{r} = r$ ) from daily usage and therefore, do not observe fast battery discharge during the test drive.

Based on the impression from the demonstration, customers update their beliefs over $\tilde{r}$ following Bayes’ rule; hence, $P (\tilde{r} = τ r | unfav) = 1$ and $P (\tilde{r} = r | fav) = α / (1 - (1 - α) δ) \equiv α^{'} > α .$ In words, customers drawing an unfavorable impression expect the electric vehicle to achieve a low range with certainty, whereas customers drawing a favorable impression expect a high achievable range with probability $α^{'}$ , which is increasing in the level of demonstration informativeness $δ \in (0, 1]$ . Specifically, when $δ = 1$ , we have $α^{'} = 1$ and demonstration is fully informative—customers drawing a favorable versus unfavorable impression expect high versus low achievable range with certainty. We analyze the economic and environmental implications of offering demonstration at any informativeness level (full or partial) in the paper. In online E-Companion H.1, we numerically examine how improvements in demonstration informativeness affect these implications.

We use the subscript $ψ \in {f, u}$ to distinguish between the cases where a customer draws a favorable ( $ψ = f$ ) versus an unfavorable ( $ψ = u$ ) impression. The corresponding net utilities that customers expect to derive after the purchase of an electric vehicle are given by $U_{f}^{e} (θ) = (v - k_{e}) (α^{'} + (1 - α^{'}) τ) r θ - p_{e}$ and $U_{u}^{e} (θ) = (v - k_{e}) τ r θ - p_{e}$ . To simplify notation, let $q_{g} \equiv v - k_{g}$ , $q_{e} \equiv (α + τ (1 - α)) (v - k_{e}) r$ , $q_{f} \equiv (α^{'} + τ (1 - α^{'})) (v - k_{e}) r$ , and $q_{u} \equiv τ (v - k_{e}) r$ . Customers’ net utilities can thus be simplified as $U^{g} (θ) = q_{g} θ - p_{g}$ , $U^{e} (θ) = q_{e} θ - p_{e}$ , and $U_{ψ}^{e} (θ) = q_{ψ} θ - p_{e}$ for $ψ \in {f, u}$ , where $q_{g}$ , $q_{e}$ , and $q_{ψ}$ can be seen as the effective, as perceived by the customer, qualities of the corresponding mobility options.

3.3. Problem formulation

The sequence of events is as follows. First, the dealer decides whether to invest $C_{d}$ and offer demonstration services. Second, the manufacturer sets wholesale prices of the electric and conventional vehicles, as denoted by $w_{e}$ and $w_{g}$ , respectively. Third, the dealer procures vehicles from the manufacturer and determines the selling prices $p_{e}$ and $p_{g}$ . Fourth, customers arrive and choose to buy a conventional versus electric vehicle or entirely refrain from buying.¹⁰ Notably, the dealer decides the demonstration strategy before procuring and selling vehicles, because offering demonstration requires substantial resources and lead time for staff training, developing extended test drive programs, and upgrading service and information infrastructure. In online E-Companion H.6, we consider an alternative setting, where the dealer decides the demonstration strategy after the manufacturer sets wholesale prices. We numerically find that all our structural results continue to hold, with demonstration being relatively more attractive as the manufacturer can better incentivize it by setting a high electric vehicle wholesale price.

For a given demonstration and pricing strategy, customers maximize their net utilities by deciding whether to buy a conventional versus electric vehicle or entirely refrain from buying. The dealer maximizes her profit by setting the vehicle prices and, when offering electric vehicles, whether to provide demonstration. Let $π_{d} (p_{g}, p_{e})$ and $π_{n} (p_{g}, p_{e})$ denote dealer profits under a given price pair $(p_{g}, p_{e})$ with and without demonstration, respectively; her pricing problems are hence given by:

\begin{aligned} \underset{p_{g}, p_{e} ⩾ 0}{maximize} π_{n} (p_{g}, p_{e}) \\ = (p_{e} - w_{e}) D_{e}^{n} (p_{g}, p_{e}) + (p_{g} - w_{g}) D_{g}^{n} (p_{g}, p_{e}), \end{aligned}

(1)

\begin{aligned} \underset{p_{g}, p_{e} ⩾ 0}{maximize} π_{d} (p_{g}, p_{e}) \\ = (p_{e} - w_{e}) D_{e}^{d} (p_{g}, p_{e}) + (p_{g} - w_{g}) D_{g}^{d} (p_{g}, p_{e}) - C_{d}, \end{aligned}

(2)

where

D_{g}^{d}

(

D_{g}^{n}

) and

D_{e}^{d}

(

D_{e}^{n}

) denote the demand of the gasoline and electric vehicles when demonstration services are (not) offered, respectively. Let

p_{g}^{n} (w_{g}, w_{e})

and

p_{e}^{n} (w_{g}, w_{e})

denote their optimal selling prices when demonstration services are not offered, and

π_{n}^{*} (w_{g}, w_{e})

the optimal dealer profits. Similarly, we use

p_{g}^{d} (w_{g}, w_{e})

and

p_{e}^{d} (w_{g}, w_{e})

to denote the optimal selling prices and

π_{d}^{*} (w_{g}, w_{e})

the optimal dealer profits when demonstration services are offered.

The manufacturer’s problem, at a given dealer demonstration strategy, is to determine the wholesale prices to maximize its profits from producing and selling the vehicles to the dealer as follows:

\begin{aligned} \underset{w_{g}, w_{e} ⩾ 0}{maximize} π_{n}^{m} (w_{g}, w_{e}) \\ = (w_{e} - c_{e}) D_{e}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})) \\ + (w_{g} - c_{g}) D_{g}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})), \\ \underset{w_{g}, w_{e} ⩾ 0}{maximize} π_{d}^{m} (w_{g}, w_{e}) \\ = (w_{e} - c_{e}) D_{e}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})) \\ + (w_{g} - c_{g}) D_{g}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})), \end{aligned}

where we denote the electric and conventional production costs by

c_{e}

and

c_{g}

, respectively, and use the subscript

m

to indicate the manufacturer’s profits. Notice that the manufacturer sets the wholesale prices taking into account their impacts on the dealer’s prices, which further determine the demand for the conventional and electric vehicles. Let

w_{g}^{n}

and

w_{e}^{n}

denote the optimal wholesale prices in the absence of demonstration, and

w_{g}^{d}

and

w_{e}^{d}

the optimal wholesale prices in the presence of demonstration. The dealer determines whether to offer demonstration by comparing

π_{n}^{*} (w_{g}^{n}, w_{e}^{n})

and

π_{d}^{*} (w_{g}^{d}, w_{e}^{d})

; thus, her optimal profit is

Π^{*} = max {π_{n}^{*} (w_{g}^{n}, w_{e}^{n}), π_{d}^{*} (w_{g}^{d}, w_{e}^{d})}

We assume $c_{g} < q_{g} = v - k_{g}$ to rule out trivial cases in which the conventional vehicle is unprofitable to sell, and similarly, $c_{e} < q_{e}$ for the electric vehicle. In addition, we assume that the electric vehicle’s operating cost $k_{e}$ is sufficiently low, while the achievable range for customers receiving an unfavorable impression (parameterized by $τ$ ) is sufficiently limited; see online E-Companion A for details. These assumptions allow us to focus on the interesting cases where the electric vehicle’s operating cost advantage and limited (achievable) range disadvantage are such that demonstration is salient in influencing customers’ purchase decisions.

4. Analysis

In this section, we characterize the subgame perfect equilibria (see online E-Companion B for proofs). We first consider customers’ optimal purchase decisions for a given demonstration and pricing strategy.

In the absence of demonstration, customers a priori expect a higher effective quality of an electric vehicle than a conventional vehicle due to the former’s lower operating cost, that is, $q_{e} > q_{g}$ . Thus, they buy the electric vehicle if their total mobility needs are high, buy the conventional vehicle if their total mobility needs are intermediate, and refrain from buying a vehicle if their total mobility needs are low; see Figure 1 for an illustration.

Figure 1.

Customers’ purchase decisions for given prices in the absence of demonstration. Note. All $Θ$ -thresholds are functions of prices $(p_{g}, p_{e})$ and are characterized in \DIFadd{online} E-Companion~B.1. Although we illustrate the case of $0 < \underline{Θ} < \bar{Θ} < 1$ , we have analyzed all cases, e.g., $0 < \underline{Θ} = \bar{Θ} < 1$ such that only electric vehicles are sold.

In the presence of demonstration, customers draw from it an impression and update their belief regarding the electric vehicle’s achievable range. Customers who receive a favorable impression infer the achievable range more likely to be high and expect an even higher effective quality of the electric vehicle (i.e., $q_{f} > q_{e} > q_{g}$ ). As a result, they buy the electric vehicle if their total mobility needs are high, buy the conventional vehicle if their total mobility needs are intermediate, and refrain from buying a vehicle if their total mobility needs are low; see Figure 2(a). Customers who receive an unfavorable impression infer the achievable range to be low with certainty and expect the effective quality of the electric vehicle to be lower than that of the conventional vehicle (i.e., $q_{u} < q_{g}$ ). As a result, they buy the conventional vehicle if their total mobility needs are high, buy the electric vehicle if their total mobility needs are intermediate, and refrain from buying a vehicle if their total mobility needs are low; see Figure 2(b). Notice that customers’ purchase decisions are jointly determined by their mobility needs and impression from demonstration. For example, customers with high total mobility needs buy the electric vehicle if they draw a favorable impression and buy the conventional vehicle if they draw an unfavorable impression.

Figure 2.

Customers’ purchase decisions for given prices in the presence of demonstration. (a) Favorable-impression customers and(b) unfavorable-impression customers.Note. All $Θ$ -thresholds are functions of prices $(p_{g}, p_{e})$ and are characterized in \DIFadd{online} E-Companion~B.1. Although we illustrate the case of $0 < {\underline{Θ}}_{f} < {\bar{Θ}}^{f} < 1$ and $0 < {\underline{Θ}}_{u} < {\bar{Θ}}^{u} < 1$ above, we have analyzed all possible cases.

4.1. Customer purchase decisions in equilibrium

Based on the optimal purchase decisions presented above, we derive the demand functions for the electric and conventional vehicles, substitute them into the dealer and manufacturer profits, and solve for their optimal (selling and wholesale) prices, with and without demonstration separately. Below, we present how offering demonstration affects customers’ purchase decisions in equilibrium.

When the production cost of the electric vehicle is prohibitively high (i.e., $c_{e} ⩾ q_{f} - q_{g} + c_{g}$ ), the dealer never finds it profitable to sell electric vehicles at the equilibrium wholesale prices set by the manufacturer, and therefore, the demonstration has no impact on customers’ purchase decisions. In the remainder of the paper, we focus on the nontrivial case of $c_{e} < q_{f} - q_{g} + c_{g}$ . To disentangle the different effects of offering demonstration, we present the equilibrium purchase decisions of customers who in the absence of demonstration would buy versus not buy an electric vehicle, separately.

Lemma 1
In the absence of demonstration, customers purchase an electric vehicle if and only if their total mobility needs are high (i.e., $θ > {\bar{θ}}^{n}$ ) and the electric vehicle production cost is not too high (i.e., $c_{e} < {\bar{c}}_{e}$ ). In the presence of demonstration, customers who otherwise would not buy an electric vehicle (i.e., those with $θ ⩽ {\bar{θ}}^{n}$ ), now purchase one if they receive a favorable impression and their total mobility needs are not too low (i.e., $θ > {\bar{θ}}^{d}$ ). The price of the electric vehicle is higher in the presence of demonstration.

Due to the lower operating cost, customers a priori expect a higher effective quality of the electric vehicle than the conventional vehicle (i.e., $q_{e} > q_{g}$ ). Therefore, if the electric vehicle is not too costly to produce (i.e., $c_{e} < {\bar{c}}_{e}$ ), the manufacturer and the dealer set the wholesale and selling prices, respectively, such that customers with high total mobility needs (i.e., $θ > {\bar{θ}}^{n}$ ) buy an electric vehicle to benefit from its lower operating cost. In the presence of demonstration, customers who receive a favorable impression infer a greater likelihood of a high achievable range (i.e., $α^{'} > α$ ) and thus, expect an even higher quality of the electric vehicle (i.e., $q_{f} > q_{e}$ ). This allows the dealer to not only charge a higher price but also sell electric vehicles to customers with lower total mobility needs (i.e., $θ \in ({\bar{θ}}^{d}, {\bar{θ}}^{n})$ ), who would otherwise buy a conventional vehicle or refrain from purchasing.

Having characterized the positive effect of a favorable impression on electric vehicle adoption, we now investigate the effect of an unfavorable impression.
Lemma 2
In the presence of demonstration, customers who in its absence would purchase an electric vehicle, do not purchase one if they receive an unfavorable impression.

An unfavorable impression reduces the attractiveness of the electric vehicle to customers who now perceive it to have a lower effective quality than the conventional vehicle. That is, although in the absence of demonstration these customers expect a higher effective quality of electric vehicle than conventional vehicle (i.e., $q_{e} > q_{g}$ ), they now perceive a lower quality due to inferring a low achievable range (i.e., $q_{u} < q_{g}$ ). Furthermore, as shown in Lemma 1, the dealer prices the electric vehicle higher in the presence of demonstration. Therefore, customers with high total mobility needs (i.e., $θ > {\bar{θ}}^{n}$ ) who would buy an electric vehicle in the absence of demonstration, now buy a conventional vehicle or refrain from purchasing, should they receive an unfavorable impression.

In what follows, we build on Lemmas 1 and 2 to explore the implications of offering demonstration for the dealer’s profitability, the electric vehicle adoption, and the total emissions. We graphically illustrate our results based on calibrated data (see online E-Companion C for details).
4.2. When should the dealer offer demonstration?

The dealer’s decision of whether to offer demonstration is affected by its impact on the demands of electric and conventional vehicles as derived in Section 4.1, as well as its impact on the manufacturer’s wholesale pricing, as characterized in the next lemma.

Lemma 3
In the presence of demonstration, the manufacturer sets a higher wholesale price for the electric vehicle (i.e., $w_{e}^{d} > w_{e}^{n}$ ), but maintains the same wholesale price for the conventional vehicle (i.e., $w_{g}^{d} = w_{g}^{n}$ ).

In the presence of demonstration, customers receiving a favorable impression expect a higher effective quality. This allows the dealer and the manufacturer to charge higher selling and wholesale prices, respectively. Notably, regardless of demonstration, the wholesale and selling prices are not affected by customers receiving an unfavorable impression, as they do not buy an electric vehicle due to expecting a lower effective quality than the conventional vehicle.

The manufacturer maintains the same wholesale price of the conventional vehicle because demonstration does not affect its effective quality (and therefore, selling price). Although the electric vehicle is more attractive to customers receiving a favorable impression, potentially cannibalizing conventional vehicle sales, the dealer does not reduce the conventional vehicle price because he profits more by increasing the electric vehicle price and maintaining the conventional vehicle price.

Based on these results, we next analyze the dealer’s decision of whether to offer demonstration to maximize her profits.
Proposition 1
The dealer should offer demonstration if and only if $C_{d} < {\bar{C}}_{d} (r, c_{e}, c_{g}, δ)$ , which is increasing in $c_{e}$ if and only if $c_{e} ⩽ {\bar{C}}_{e}^{d} (r, c_{g}, δ)$ , and is decreasing in $c_{g}$ if and only if $c_{g} ⩾ {\bar{C}}_{g}^{d} (r, c_{e}, δ)$ . If $c_{e} ⩾ c_{g} + q_{e} - q_{g}$ , then ${\bar{C}}_{d} (r, c_{e}, c_{g}, δ)$ is increasing in $r$ and $δ$ .

The dealer should offer demonstration if the associated cost $C_{d}$ is below a threshold ${\bar{C}}_{d}$ , which depends on vehicle production costs $c_{e}$ and $c_{g}$ , designed range $r$ , and demonstration informativeness $δ$ . As electric vehicle production becomes more efficient (i.e., $c_{e}$ decreases), offering demonstration first becomes more attractive and then less attractive to the dealer; see Figure 3(a). The former part may be expected because a lower $c_{e}$ makes the electric vehicle more profitable to sell. More interestingly, when $c_{e}$ is sufficiently low, further reductions make offering demonstration less attractive to the dealer. Due to the already low $c_{e}$ , the dealer finds it optimal to sell a large number of electric vehicles even in the absence of demonstration. Thus, the profit loss from customers receiving an unfavorable impression from demonstration and switching to buying a conventional vehicle is more pronounced. As $c_{e}$ further decreases, electric vehicle sales are even higher without demonstration, which amplifies the profit loss and makes demonstration less attractive to the dealer.

Figure 3.
When to offer demonstration services? (a) Impact of $r$ and $c_{e}$ ( $c_{g} = $ 15, 900$ ) and (b) impact of $r$ and $c_{g}$ ( $c_{e} = $ 23, 000$ ).Note. The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, C_{d} = $ 88$ , and the dashed line represents the current value of vehicle production cost.

The above result also shows that as the conventional vehicle production cost $c_{g}$ increases (e.g., due to regulatory disincentives), offering demonstration initially becomes more attractive to the dealer and then less attractive. This is similar to the effect of a lower $c_{e}$ , as a higher $c_{g}$ also makes an electric vehicle relatively more attractive compared to the conventional vehicle. Interestingly, we find that for the calibrated values representing current conditions, a higher $c_{g}$ will have a different effect than a lower $c_{e}$ . As can be seen from Figure 3(b), a higher $c_{g}$ (from the current value of $15,900) will make offering demonstration less attractive. However, as can be seen from Figure 3(a), a lower $c_{e}$ (from the current value of $23,000) will make offering demonstration more attractive as long as the $c_{e}$ is not too low (i.e., higher than $20,920). If the reduction in $c_{e}$ is sufficiently large, offering demonstration will become less attractive due to a lower $c_{e}$ .

Proposition 1 also shows that offering demonstration becomes more attractive to the dealer when the designed range $r$ or demonstration informativeness $δ$ improves. Intuitively, an improved designed range or more informative demonstration makes the electric vehicle even more attractive to customers who receive a favorable impression as they perceive a higher effective quality $q_{f}$ . Note that while we can analytically prove this result only for $c_{e} ⩾ c_{g} + q_{e} - q_{g}$ , we find that this result holds for calibrated data even when $c_{e} < c_{g} + q_{e} - q_{g}$ .
4.3. The effect of offering demonstration on electric vehicle sales

We now examine the impact of offering demonstration on the sales of electric and conventional vehicles. To focus on the more interesting cases, we henceforth assume $C_{d} < {\bar{C}}_{d}$ such that demonstration is offered in equilibrium. The next proposition characterizes the effect of offering demonstration on electric vehicle adoption and how it depends on the state of electric vehicle technology as proxied by $c_{e}$ and $r$ , where a lower $c_{e}$ and/or higher $r$ imply a more developed technology.

Proposition 2
Offering demonstration leads to greater electric vehicle adoption if and only if $c_{e} ⩾ A (r, c_{g}, δ)$ which is increasing in $r$ and $c_{g}$ .

The above result shows that offering demonstration leads to greater electric vehicle adoption when the electric vehicle technology is less developed, that is, when $c_{e} ⩾ A (r, c_{g}, δ)$ , which is increasing in $r$ . Under these conditions, electric vehicle sales are low in the absence of demonstration. With demonstration, customers who draw a favorable impression expect the electric vehicle to achieve a high range with a higher likelihood. Given its lower operating cost than the conventional vehicle, customers with high total mobility needs prefer an electric vehicle leading to greater adoption.

When the electric vehicle technology is more developed (i.e., $c_{e} < A (r, c_{g}, δ)$ ), offering demonstration leads to lower electric vehicle adoption even when increasing dealer profits; see shaded region in Figure 4. This is counterintuitive because a favorable impression persuades more customers to purchase the electric vehicle as its designed range improves. However, with a more developed electric vehicle technology, the manufacturer and the dealer raise the wholesale and selling prices to capitalize on the customers’ higher perceived quality, which reduces electric vehicle adoption. Moreover, demonstration dissuades customers who draw an unfavorable impression from buying an electric vehicle; the number of such customers is larger when the electric vehicle is already attractive in the absence of demonstration due to a more developed electric vehicle technology.

Figure 4.
Does offering demonstration services increase or decrease electric vehicle adoption $D_{e}$ ? (a) $c_{e} = $ 15, 900$ and (b) $c_{e} = $ 16, 377$ .Note. The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, C_{d} = $ 0$ , and the dashed line represents the current value of vehicle production cost.

Proposition 2 also examines the impact of $c_{g}$ and shows that offering demonstration leads to greater electric vehicle adoption only when $c_{g}$ is low. An increase in $c_{g}$ , driven by stronger regulatory disincentives, leads to a higher selling price of the conventional vehicle, making the electric vehicle more attractive by comparison and increasing electric vehicle adoption, regardless of whether demonstration is offered. However, the adoption increases less in the presence of demonstration. This is because customers who receive a favorable impression perceive the electric vehicle as having higher effective quality and become less sensitive to vehicle prices. As such, an increased price appeal of the electric vehicle due to a higher $c_{g}$ promotes electric vehicle adoption less than it does in the absence of demonstration.¹¹

Our results highlight the evolving economic and environmental implications of demonstration services as the electric vehicle industry matures. Under current conditions, when electric vehicle and demonstration technologies are costly or limited (i.e., high $c_{e}$ , low $r$ , or low $δ$ ), demonstration plays a pivotal role in improving dealer profitability and facilitating electric vehicle adoption. However, as electric vehicles and demonstration technologies improve, dealers may use demonstration primarily for price discrimination, thereby reducing electric vehicle adoption. Moreover, regulators should exercise caution in introducing production disincentives for conventional vehicles. Although such disincentives promote electric vehicle adoption in the absence of demonstration, they can induce dealers to offer demonstration services and adjust the selling price of the electric vehicle, which eventually leads to lower adoption in equilibrium.

Figure 5.
Equilibrium effect of offering demonstration on electric vehicle adoption $D_{e}$ and usage emissions $E M_{U}$ . (a) $c_{e} = $ 15, 900$ and (b) $c_{e} = $ 16, 377$ .Note. The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, C_{d} = $ 0, η_{U} = 0.319$ lb/mile, $γ_{U} = 3.03$ , and the dashed line represents the current value of vehicle production cost.
4.4. The effect of offering demonstration on emissions

Next, we analyze how the dealer’s demonstration strategy affects emissions from the usage of all (electric and conventional) vehicles sold.¹² Usage emissions (henceforth, emissions) include tailpipe emissions as well as emissions associated with fuel generation (i.e., gasoline and electricity for conventional and electric vehicles, respectively). Let $η_{U}$ denote the per-mile emissions driven on an electric vehicle and $γ_{U}$ denote the emission multiplier of the conventional vehicle, that is, its per-mile emissions are $γ_{U} η_{U}$ . Following the literature (see, e.g., Avci et al., 2015), we assume the per-mile emissions to be higher for conventional than for electric vehicles (i.e., $γ_{U} > 1$ ) due to different energy sources (i.e., fossil fuels vs. electricity) and to be negligible for public transportation, which we have assumed to be customers’ outside option. The total emissions under a given demonstration strategy, as denoted by $E M_{U}^{d}$ ( $E M_{U}^{n}$ ) in the presence (absence) of demonstration, are given by:

\begin{aligned} E M_{U}^{d} = & η_{U} Ω_{e}^{d} ({\underline{Θ}}_{f}, {\bar{Θ}}_{f}, {\underline{Θ}}_{u}, {\bar{Θ}}_{u}) + γ_{U} η_{U} Ω_{g}^{d} ({\underline{Θ}}_{f}, {\bar{Θ}}_{f}, {\underline{Θ}}_{u}, {\bar{Θ}}_{u}), \end{aligned}

(3)

\begin{aligned} E M_{U}^{n} = & η_{U} Ω_{e}^{n} ({\underline{Θ}}_{H}, {\bar{Θ}}_{H}, {\underline{Θ}}_{L}, {\bar{Θ}}_{L}) + γ_{U} η_{U} Ω_{g}^{n} ({\underline{Θ}}_{H}, {\bar{Θ}}_{H}, {\underline{Θ}}_{L}, {\bar{Θ}}_{L}), \end{aligned}

(4)

where we use

Ω_{m}^{d}

(

Ω_{m}^{n}

) to denote the total usage for each vehicle type

m \in {e, g}

in the presence (absence) of demonstration. We obtain their expressions by aggregating the per-customer usage over all purchasing customers as specified by the

Θ

thresholds above. Then, we substitute the expressions into (3)–(4) to characterize the total emissions under optimal prices and analyze how the emissions are influenced by the dealer providing demonstration services. We relegate technical details to online E-Companion B.8 and we present the main results below. For expositional parsimony, we assume

γ_{U} > {\underline{γ}}_{U}

, which is verified to hold in our calibrated model using real-life data.¹³

Lemma 4

When offering demonstration leads to lower adoption of electric vehicles (i.e., when $c_{e} < A (r, c_{g}, δ)$ ), it also leads to greater emissions.

As it is expected, emissions increase when the demonstration diverts customers from electric to conventional vehicles due to receiving an unfavorable impression. Hence, it may be tempting to assume that greater electric vehicle adoption implies lower total emissions. In what follows, we show that this is not necessarily true.

Proposition 3

When the dealer’s offering of demonstration leads to greater adoption of electric vehicles (i.e., when $c_{e} ⩾ A (r, c_{g}, δ)$ ), it also results in lower emissions if and only if $c_{e} ⩾ E (r, c_{g}, δ)$ which is increasing in $r$ and $c_{g}$ .

As illustrated in Figure 5, when offering demonstration leads to greater electric vehicle adoption, it also leads to lower emissions only under a less developed electric vehicle technology (i.e., $c_{e} ⩾ E (r, c_{g}, δ)$ , where $E (r, c_{g}, δ)$ increases in $r$ ). Otherwise, offering demonstration leads to greater emissions despite promoting electric vehicle adoption. This happens when demonstration expands car ownership by leading customers who would otherwise refrain from purchasing a vehicle to buy an electric one resulting in greater emissions. This is driven by the fact that the demonstration mitigates customer uncertainty over the achievable range, increasing the effective quality of the electric vehicle as perceived by customers who receive a favorable impression.

Perhaps more interestingly, we also find that the demonstration may lead to greater emissions even when it does not expand car ownership.¹⁴ To understand the intuition, recall that in the absence of demonstration, customers with more total mobility needs prefer the electric vehicle due to a lower operating cost, whereas customers with less total mobility needs prefer the conventional vehicle as the operating cost benefit is outweighed by range concerns. Thus, in the presence of demonstration, a customer diverted from the electric to the conventional vehicle, due to receiving an unfavorable impression, imposes a larger amount of emissions from greater usage than the amount reduced by a customer diverted in the opposite direction due to receiving a favorable impression. Additionally, with a more developed electric vehicle technology, electric vehicle sales are higher in the absence of demonstration. As a result, with the demonstration more customers will be diverted from the electric to the conventional vehicle, and therefore, emissions are more likely to increase.

Proposition 3 also examines the impact of the conventional vehicle production cost $c_{g}$ and shows that offering demonstration leads to lower emissions only when $c_{g}$ is low, that is, when $c_{e} ⩾ E (r, c_{g}, δ)$ . Intuitively, a lower $c_{g}$ enables the dealer to reduce the selling price of the conventional vehicle and expand sales to customers with more total mobility needs. This enhances the emission reductions due to customers switching from conventional to electric vehicles after receiving a favorable impression, while mitigating the emission increases due to customers switching oppositely after receiving an unfavorable impression. Overall, offering demonstration leads to lower emissions.¹⁵

5. The effect of demonstration on manufacturer’s CAFE compliance

Previously, we analyzed the economic and environmental implications of dealer demonstrations with endogenous wholesale prices by the manufacturer. In practice, auto manufacturers are also subject to regulations that require their CAFE level, defined as the production-weighted harmonic mean fuel economy, to meet a predetermined standard. In our model, the manufacturer’s CAFE level $C F = (D_{e} + D_{g}) / (D_{e} / e_{e} + D_{g} / e_{g})$ , where $e_{e}$ and $e_{g}$ are the fuel economy ratings of the electric and conventional vehicles, respectively, with $e_{e} > e_{g}$ .¹⁶ A dealer demonstration affects the manufacturer’s CAFE level by influencing both $D_{e}$ and $D_{g}$ , and below we explore the effect of offering demonstration on the manufacturer’s CAFE compliance in two aspects. In Section 5.1, we examine its effect on the equilibrium CAFE level to derive insights into how the manufacturer may adjust productions of other vehicles in the fleet and/or trade CAFE credits to achieve overall CAFE compliance. In Section 5.2, we analyze the optimal wholesale prices that maximize manufacturer profits and meet the CAFE standard for the electric and conventional vehicles under consideration. This guides the optimal wholesale pricing strategies jointly shaped by dealer demonstrations and CAFE regulation.

Figure 6.

Equilibrium effect of offering demonstration on electric vehicle adoption $D_{e}$ , CAFE level, and usage emissions $E M_{U}$ .Note. The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, C_{d} = $ 0, η_{U} = 0.319$ lb/mile, $γ_{U} = 3.03, e_{g} = 35 MPG, e_{e} = 111$ MPGe, and the dashed line represents the current value of vehicle production cost.

For expositional convenience, we will use the ratio $D_{e} / D_{g}$ as the CAFE level, and $D_{e} / D_{g} ⩾ R$ the CAFE constraint. It is without loss of generality since $C F$ increases if and only if $D_{e} / D_{g}$ increases.

5.1. The effect of offering demonstration on the CAFE level

Since the CAFE level is effectively the ratio of electric to conventional vehicle sales, the impact of offering demonstration on the CAFE level depends on its effect on both electric vehicle sales (as derived in Proposition 2) and conventional vehicle sales, which we analyze next.

Proposition 4
Offering demonstration leads to greater conventional vehicle sales if and only if $c_{e} ⩽ G (r, c_{g}, δ)$ , which is increasing in $r$ and $c_{g}$ , and satisfies $G (r, c_{g}, δ) ⩾ A (r, c_{g}, δ)$ .

As expected, when offering demonstration leads to lower electric vehicle sales (i.e., $c_{e} ⩽ A (r, c_{g}, δ)$ ), it also leads to greater conventional vehicle sales. More interestingly, even when offering demonstration leads to greater electric vehicle sales, conventional vehicle sales still increase when $c_{e} \in (A (r, c_{g}, δ), G (r, c_{g}, δ))$ . In this case, conventional vehicle sales increase because customers who receive an unfavorable impression from the demonstration switch from electric to conventional vehicles. Although this lowers electric vehicle sales, offering demonstration leads to greater electric vehicle sales overall through market expansion. Specifically, in the absence of demonstration customers with less total mobility needs do not purchase any vehicle, but in the presence of demonstration, they may purchase an electric one after receiving a favorable impression.

Building on the analysis above, we now examine the impact of offering demonstration on the manufacturer’s CAFE level. When $c_{e} ⩾ G (r, c_{g}, δ)$ , offering demonstration leads to lower conventional and greater electric vehicle sales, thus a higher CAFE level. When $c_{e} ⩽ A (r, c_{g}, δ)$ , offering demonstration leads to greater conventional and lower electric vehicle sales, thus a lower CAFE level. When $c_{e} \in (A (r, c_{g}, δ), G (r, c_{g}, δ))$ , offering demonstration leads to greater electric and conventional vehicle sales. Hence, the overall impact on the CAFE level is more nuanced and is further explored below.
Proposition 5
When $c_{e} \in (A (r, c_{g}, δ), G (r, c_{g}, δ))$ , offering demonstration leads to a lower CAFE level if and only if $c_{e} ⩽ F (r, c_{g}, δ)$ , which is increasing in $c_{g}$ and satisfies $F (r, c_{g}, δ) < G (r, c_{g}, δ)$ .

The above result shows that offering demonstration leads to a lower CAFE level when the electric vehicle technology is more developed and/or the conventional vehicle production cost is high (i.e., $c_{e} ⩽ F (r, c_{g}, δ)$ which is increasing in $c_{g}$ ;¹⁷ see Figure 6). Under these conditions, the electric vehicle is relatively more attractive even in the absence of demonstration. This further implies that: (i) electric vehicle sales are already high in the absence of demonstration, so more customers switch to conventional vehicles after receiving an unfavorable impression; (ii) only customers with very low total mobility needs purchase the conventional vehicle, so they are less attracted to the electric vehicle’s lower operating cost after receiving a favorable impression. In other words, offering demonstration leads to a larger increase in conventional than electric vehicle sales, hence the CAFE level increases.
5.2. Optimal wholesale pricing under CAFE compliance

We now examine the manufacturer’s wholesale pricing decisions to ensure CAFE compliance and the resulting effect on electric vehicle adoption in equilibrium. To this end, we formulate the manufacturer’s problem as

\begin{aligned} \underset{w_{g}, w_{e} ⩾ 0}{maximize} π_{n}^{m} (w_{g}, w_{e}) \\ = (w_{e} - c_{e}) D_{e}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})) \\ + (w_{g} - c_{g}) D_{g}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})), \\ s.t. D_{e}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})) \\ ⩾ R D_{g}^{n} (p_{g}^{n} (w_{g}, w_{e}), p_{e}^{n} (w_{g}, w_{e})) \end{aligned}

in the absence of demonstration, and

\begin{aligned} \underset{w_{g}, w_{e} ⩾ 0}{maximize} π_{d}^{m} (w_{g}, w_{e}) \\ = (w_{e} - c_{e}) D_{e}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})) \\ + (w_{g} - c_{g}) D_{g}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})), \\ s.t. D_{e}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})) \\ ⩾ R D_{g}^{d} (p_{g}^{d} (w_{g}, w_{e}), p_{e}^{d} (w_{g}, w_{e})) \end{aligned}

in the presence of demonstration. The objectives

π_{n}^{m}

and

π_{d}^{m}

are the same as before, but now the manufacturer is subject to the CAFE constraint

D_{e} / D_{g} ⩾ R

First, for a given demonstration strategy, we examine how the manufacturer should adjust the wholesale prices under a more stringent CAFE standard. Since this requires a larger ratio of electric relative to conventional vehicle sales, one might expect the manufacturer to lower the wholesale price of the electric vehicle and raise the wholesale price of the conventional vehicle. However, our analysis shows that this is not always the case.

Proposition 6
For a given demonstration strategy, there exist $R_{w}$ and $R_{a} ⩾ R_{w}$ , such that: (i)
the optimal conventional vehicle wholesale price is increasing in $R$ ;
(ii)
the optimal electric vehicle wholesale price is decreasing in $R$ if and only if $R < R_{w}$ ;
(iii)
the equilibrium electric vehicle adoption is increasing in $R$ if and only if $R < R_{a}$ .

Under a more stringent CAFE standard, the manufacturer should raise the wholesale price of the conventional vehicle $w_{g}$ to reduce its sales, and lower the wholesale price of the electric vehicle $w_{e}$ only when the CAFE standard is not very stringent (i.e., $R < R_{w}$ ). As the CAFE standard tightens further, the manufacturer should instead raise $w_{e}$ and meet the CAFE standard solely by decreasing conventional vehicle sales through a higher $w_{g}$ . This is more profitable than lowering the already low $w_{e}$ , which would further erode the profit margin. Moreover, raising $w_{g}$ not only incentivizes electric vehicle purchases but also leads customers with lower total mobility needs to refrain from purchasing altogether, further decreasing conventional vehicle sales. This allows the manufacturer to meet the tighter CAFE standard while profitably raising $w_{e}$ , which leads to decreased electric vehicle adoption when the CAFE standard is sufficiently stringent (i.e., $R > R_{a}$ ).

Next, we compare results with and without demonstration to gain further insights into its moderating role in the effects of a tighter CAFE standard $R$ . As illustrated by Figure D.1(a) and (c) in online E-Companion D, threshold $R_{w}$ is higher in the presence of demonstration—it expands the range of CAFE standard wherein further tightening leads the manufacturer to lower $w_{e}$ (i.e., $R < R_{w}$ ). To see the intuition, recall from Lemma 3 that offering demonstration enables the manufacturer to raise $w_{e}$ and obtain a higher profit margin ( $w_{e} - c_{e}$ ). As a result, it can meet a tighter CAFE standard more efficiently by lowering $w_{e}$ as it does not significantly erode electric vehicle profitability.

We also find that a tighter CAFE standard leads to lower electric vehicle adoption only in the presence of demonstration. This occurs when the tighter CAFE standard leads the manufacturer to raise $w_{e}$ , for example, $R > R_{a} = R_{w} = 11.6$ in Figure D.1(c) and (d). In the absence of demonstration, however, a tighter CAFE standard always leads to greater electric vehicle adoption, even though the manufacturer raises $w_{e}$ for a wider range, for example, $R > R_{w} = 4.2$ in Figure D.1(a). This is because, for CAFE compliance, the manufacturer raises $w_{g}$ , which makes the electric vehicle relatively more attractive. Although it also raises $w_{g}$ in the presence of demonstration, the electric vehicle adoption may decrease as the CAFE standard tightens. This is because offering demonstration enhances the quality differentiation between electric and conventional vehicles—customers receiving an unfavorable impression from the demonstration will not buy an electric vehicle even when $w_{g}$ increases.
6. Heterogeneous trip length distributions

In the main model, we assumed that customers are heterogeneous in their trip frequency, but homogeneous in the distribution of their trip lengths. In particular, customers may have trips of different lengths that follow the same distribution $ℓ \sim Uniform [0, 2 L]$ , where the average trip length is given by $L$ , normalized to $L = 1$ . We now consider an extension where customers have heterogeneous trip length distributions to verify the robustness of our key results and offer new insights.

We consider two customer segments that have the same total mobility needs but differ in trip frequency and length. A fraction $λ$ of customers (referred to as high-type customers hereafter) are similar to customers in our main model; each customer takes $f \sim Uniform [0, 1]$ trips, each of which has an a priori uncertain length $ℓ \sim Uniform [0, 2]$ . The remaining $1 - λ$ fraction of customers (referred to as low-type customers hereafter) have more frequent trips that are shorter on average than the trips of high-type customers. In particular, we assume a trip frequency $f_{l} \sim Uniform [0, F_{l}]$ among these customers, with each customer’s trip length $ℓ_{l} \sim Uniform [0, 2 L_{l}]$ . Moreover, to ease comparisons, we assume that the distribution of the total mobility needs is the same across both types of customers. That is, we have that $f_{l} L_{l} = θ \sim Uniform [0, 1]$ , or equivalently $L_{l} F_{l} / 2 = E [θ] = 1 / 2$ . This implies that $F_{l} = 1 / L_{l}$ and that $L_{l} < 1 = L$ and $F = 1 < F_{l}$ , which captures that low-type customers have shorter but more frequent trips than the high type.

We now derive customers’ utilities from purchasing an electric vehicle. We assume $2 L_{l} ⩽ ς x < x ⩽ 2 L = 2$ to focus on the interesting case wherein high- and low-type customers are sufficiently different in trip lengths and therefore, affected by the demonstration differently. In the absence of demonstration, customers expect a high achievable range (i.e., $\tilde{x} = x$ ) with probability $α$ , and a low achievable range (i.e., $\tilde{x} = ς x$ ) with probability $1 - α$ . Thus, the net utilities, as denoted by $U_{h}^{e} (θ)$ for high- and $U_{l}^{e} (θ)$ for low-type customers are

\begin{aligned} U_{h}^{e} (θ) & = (v - k_{e}) f_{h} (α \int_{0}^{x} \frac{ℓ}{2} d ℓ + (1 - α) \int_{0}^{ς x} \frac{ℓ}{2} d ℓ) - p_{e} \\ = (v - k_{e}) (α + (1 - α) τ) r θ - p_{e}, \\ U_{l}^{e} (θ) & = (v - k_{e}) f_{l} (α \int_{0}^{2 L_{l}} \frac{ℓ}{2 L_{l}} d ℓ + (1 - α) \int_{0}^{2 L_{l}} \frac{ℓ}{2 L_{l}} d ℓ) - p_{e} \\ = (v - k_{e}) f_{l} L_{l} - p_{e} = (v - k_{e}) θ - p_{e} . \end{aligned}

In the presence of demonstration, customers update their beliefs based on the impression they receive. As in the main model, they expect a high achievable range with probability

α^{'} > α

after receiving a favorable impression and zero probability after receiving an unfavorable impression; seeSection 3.2. Accordingly, we derive the utility expressions as

U_{h \cdot f}^{e} (θ) = (v - k_{e}) (α^{'} + (1 - α^{'}) τ) r θ - p_{e}

U_{h \cdot u}^{e} (θ) = (v - k_{e}) τ r θ - p_{e}

, and

U_{l \cdot f}^{e} (θ) = U_{l \cdot u}^{e} (θ) = (v - k_{e}) θ - p_{e}

There are two important observations from the above: First, low-type customers are less affected by the range limitation due to their shorter trips. Although high-type customers can use the electric vehicle only for trips within range (i.e., $ℓ ⩽ 2 L$ ), low-type customers can use the electric vehicle for all trips as they are within range, that is, $2 L_{l} ⩽ ς x$ . Aggregating over trips, the electric vehicle only meets a fraction $\int_{0}^{\tilde{x}} \frac{ℓ}{2} d ℓ = (\tilde{x} / 2)^{2} < 1$ of the high-type customers’ total mobility needs, but meets all the mobility needs of the low-type customers, under the achievable range $\tilde{x} \in {x, ς x}$ . That is, the low-type customers do not experience any utility loss, but the high-type customers face utility loss due to their longer trips being limited by the range of the electric vehicle. This implies that the effect of offering demonstration is different for low-type versus high-type customers. Second, in the presence of demonstration, the order of customers’ purchase decisions upon receiving an unfavorable impression does not switch for all customers (see online E-Companion E for details). As in our main model, high-type customers with high total mobility needs switch to preferring a conventional vehicle as they now perceive a lower quality due to inferring a low achievable range. However, low-type customers, whose trips are shorter and within range, still expect a higher quality from the electric vehicle, and continue to prefer an electric vehicle even if their total mobility needs are high.

While we can analytically characterize the customer purchase decisions under this generalization, it is analytically intractable to characterize the equilibrium wholesale prices and other outcomes. See online E-Companion E for all the technical details. Accordingly, we resort to numerical analysis to compare the equilibrium outcomes in the absence and presence of demonstration to offer insights for when the dealer should offer demonstration, and how this influences adoption and usage emissions. We find that all our structural results in Section 4 continue to hold as can be seen by comparing Figure 7 with Figures 3(a) and 5(a).

Figure 7.

When to offer demonstration services and equilibrium effect on adoption $D_{e}$ and usage emissions $E M_{U}$ .\emph{Note.} The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, c_{g} = $ 15, 900, λ = 0.9, η_{U} = 0.319$ lb/mile, $γ_{U} = 3.03$ , and the dashed line represents the current value of vehicle production cost.

We next focus on how heterogeneity in trip length distributions affects the adoption of the demonstration, and thus, the equilibrium adoption and usage emissions (see Figure 8, where the effect of greater heterogeneity can be inferred by considering a lower $λ$ ). We find that greater heterogeneity exacerbates our result that offering demonstration can lead to lower electric vehicle adoption (see Figure 8(a)). A greater heterogeneity implies more low-type customers who are less affected by range limitation. Moreover, with the demonstration, high-type customers who receive an unfavorable impression prefer the conventional vehicle to the electric vehicle, so the dealer sells the conventional vehicle to them, which leads to lower electric vehicle adoption. Similarly, a greater heterogeneity also strengthens our result that the demonstration can lead to lower emissions (see Figure 8(b)). With more low-type customers, the dealer can profitably raise the price of the electric vehicle and induce more high-type customers to purchase a conventional vehicle. Offering demonstration mitigates the dealer’s incentive to do so as high-type customers who receive a favorable impression perceive a higher effective quality of the electric vehicle and prefer it to the conventional vehicle.

Figure 8.

Equilibrium effect of the fraction of high-type customers $λ$ on electric vehicle adoption $D_{e}$ and usage emissions $E M_{U}$ with and without demonstration.Note. The figure is based on calibrated data, where $v = ⧸ c 35 /mile, k_{e} = ⧸ c 11.98 /mile, k_{g} = ⧸ c 21.38 /mile, τ = 0.35, α = 0.8, δ = 0.8, c_{e} = $ 23, 000, c_{g} = $ 15, 900, η_{U} = 0.319$ lb/mile, $γ_{U} = 3.03$ , and the dashed line represents the current value of vehicle production cost. We use different r values in the left and right panels to more clearly illustrate the impact of offering demonstration on $D_{e}$ and $E M_{U}$ .

7. Conclusions

In this paper, we formally examined the impact of offering demonstration services on dealer profits, the manufacturer’s regulatory compliance, the adoption of electric vehicles, and overall environmental impact. Our results highlight that the economic, environmental, and policy implications of offering demonstration are nuanced, and depend critically on operational characteristics, including production costs and the electric vehicle’s designed range. These findings offer a set of key managerial and policy insights.

First, offering demonstration services can simultaneously improve dealer profits, promote electric vehicle adoption, and reduce emissions when the electric vehicle technology is relatively less developed; see region I in Figure 6. This highlights the potential of community-based organizations’ ongoing initiatives in facilitating dealer demonstrations (Drive Electric Northern Colorado, 2021; Evolve Houston, 2023; Peninsula Clean Energy, 2021) to generate both economic and environmental benefits. The effectiveness of such initiatives depends on the state of electric vehicle technology. As the production cost $c_{e}$ decreases and/or the designed range $r$ increases due to larger battery capacity and improved charging infrastructure, offering demonstration becomes more likely to decrease electric vehicle adoption and increase emissions (see region IV in Figure 6).

Second, community-based organizations should exercise caution when using electric vehicle adoption as the primary metric for evaluating the environmental benefit of their initiatives that facilitate dealer demonstrations. When offering demonstration leads to greater electric vehicle adoption, it also leads to lower emissions when the electric vehicle technology is less developed, but can result in higher emissions once the technology becomes more advanced; see regions II and III in Figure 6.

Third, manufacturers should carefully consider the impact of dealer demonstrations on their CAFE level when deciding whether to facilitate demonstration by providing staff training and/or promoting extended test drive programs on their websites (Volvo, 2023). Even when dealer demonstration promotes electric vehicle adoption, it may lower the manufacturer’s CAFE level by leading to a proportionally greater increase in conventional vehicle sales (see region III in Figure 6).

Fourth, policies that disincentivize conventional vehicle production (e.g., through taxation or emission regulations) or tighten the CAFE standard have nuanced environmental implications through their interactions with dealer demonstrations. Although conventional vehicle production disincentives on their own may promote electric vehicle adoption, they can induce the dealer to refrain from offering demonstration services, which in turn can decrease adoption and increase emissions when the electric vehicle technology is less developed. Moreover, in the presence of demonstration, a more stringent CAFE standard may not lead to greater electric vehicle adoption.

To derive first-order insights into the role dealers can play in facilitating electric vehicle adoption, our model relied on simplifying assumptions, which naturally open avenues for future research. For instance, building on our analytical framework, one can further explore how dealer demonstrations could help mitigate competitive pressures from other dealers or auto manufacturers. Another promising direction is to introduce information asymmetry, for example, dealers may be better informed than customers on vehicle performance, durability, and safety features, and to examine the signaling role of demonstration services. Future research can examine other aspects that customers may learn from dealer demonstrations, such as the charging experience, driving experience, or overall lifestyle fit. Other promising directions include accounting for additional sources of customer uncertainty (e.g., travel needs or charging access) and information-provision mechanisms (e.g., product reviews and owner communities). Future research may also incorporate additional considerations, such as salesforce effort, planned obsolescence, secondary markets, consumer subsidies, or battery recycling policies that promote social welfare, as well as network effects in technology diffusion, to examine their interactions with dealer demonstrations. The impact of dealer demonstrations on the manufacturer’s ability to meet environmental regulations also highlights opportunities for negotiation and coordination between dealers and automakers.

Supplemental Material

sj-pdf-1-pao-10.1177_10591478261455540 - Supplemental material for The role of dealer demonstration in the adoption of electric vehicles

Supplemental material, sj-pdf-1-pao-10.1177_10591478261455540 for The role of dealer demonstration in the adoption of electric vehicles by Vishal V Agrawal, Ioannis Bellos and Hang Ren in Production and Operations Management

Footnotes

Acknowledgments

The authors are listed in alphabetical order of their last names. We would like to express our sincere gratitude to the Department Editor, the Senior Editor, and the two anonymous reviewers for their valuable and constructive suggestions, which greatly improved the paper. We are also sincerely grateful to Professors Atalay Atasu, Ho-Yin Mak, Şafak Yücel, and Can Zhang for their valuable and constructive feedback.

ORCID iDs

Vishal V Agrawal

Ioannis Bellos

Hang Ren

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online (doi: ).

Notes

How to cite this article

Agrawal VV, Bellos I and Ren H (2026) The role of dealer demonstration in the adoption of electric vehicles. Production and Operations Management x(x): 1–20.

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