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Abstract

Purpose:

The research explores the consumers’ behavioural intention towards electric vehicle adoption (EVA) specifically in the Indian milieu. It examines the impact of social influence (SI), facilitating conditions (FC) and knowledge about electric vehicles (EVs) on the consumers’ perceptions, further affecting their behavioural intention to adopt EVs.

Methodology:

This empirical research extends the traditional Technology Acceptance Model (TAM) within the Stimulus-Organism-Behaviour-Consequence (SOBC) framework to predict the consumers’ behavioural intention to adopt EVs. A cross-sectional analysis of data collected from 288 respondents is done employing partial least squares-based structural equation modelling.

Findings:

The findings of the research ratified all the hypothesised relationships establishing the sufficiency of the proposed framework. Outcomes emphasised that SI, FCs and knowledge about EVs significantly contribute to shaping positive consumers’ perceptions, ultimately leading to willingness to pay a premium (WTPP) price for EVs.

Practical implications:

The results offer intriguing implications for marketers and policymakers to speed up the adoption of EVs in emerging markets. It delivers valuable insights into how people with favourable adoption intentions in developing nations (like India) are willing to pay a premium for EVs, despite a low per capita income.

Originality/Value:

The current study adds value to the existing literature as it is the first study attempting to explore the intention to adopt EVs in an emerging market by integrating TAM within the SOBC framework.

Keywords

Electric vehicles SOBC TAM adoption intention social influence facilitating conditions product knowledge

Introduction

Greenhouse gas (GHG) emissions emitted from fuel combustion in the industrial and transportation sectors are the primary contributor to environmental pollution (Boo & Tan, 2024). This study focuses on the transportation sector as vehicles are crucial to people’s daily lives. Moreover, this sector accounted for 14% of GHG emissions in 2022, making it the second-highest emitter globally (Statista, 2024b). To be precise, road vehicles are the major source of transportation emissions, comprising 12% of global transportation GHG emissions (Statista, 2024b). As concerns regarding environmental issues are increasing, the demand and development of eco-friendly products are also escalating (Higueras-Castillo et al., 2021). To address the persistent environmental challenges posed by road transportation, electric vehicles (EVs) are universally acknowledged as a promising innovation (Huang & Ge, 2019). In comparison to conventional fuel-powered vehicles, EVs can cut emissions of CO₂ by 30%–50% thereby, boosting fuel efficiency by 40%–60% (Jaiswal et al., 2022). EVs are characterised as vehicles that rely on energy derived from the electrical grid either partially or fully for their propulsion (She et al., 2017). Therefore, this study includes all the variants of EVs as the research subject including battery, hybrid or plug-in EVs following Boo and Tan (2024).

Talking about India, the third-largest automotive market in the world (Invest India, 2024), the adoption rate of EVs hit 6.4% in 2023, recording the sales of 1.5 million EVs, depicting a remarkable growth compared to a decade ago (Statista, 2024a). Although electric vehicle adoption (EVA) is still at its nascent stage, it is steadily gaining traction. The gradual pace of EVA can be attributed to the competitiveness issues faced by EVs against traditional fuel-powered automobiles. Prime issues include consumers’ reluctance towards innovative technology, high upfront cost, absence of charging facilities and insufficient knowledge about EVs (Riley, 2019). According to IBEF (2024), EVA is projected to expand from USD 3.21 billion in 2022 to USD 113.99 billion by 2029 in the Indian market, reflecting a compound annual growth rate (CAGR) of 66.52%.

Being a prominent area contributing towards sustainability, it has captured the interest of numerous scholars who have conducted various studies in this domain. For instance, Wang et al. (2018) studied how knowledge about EVs (PK) affects consumers’ EVA intention utilising Technology Acceptance Model (TAM) in China. Besides that, Jaiswal et al. (2022) also investigated the influence of PK on consumers’ intention to adopt EVs employing an extended version of TAM in India. Manutworakit and Choocharukul (2022) explored the influence of facilitating conditions (FC) and social influence (SI) on consumers’ behavioural intention to adopt BEVs among Thailand car owners by expanding the UTAUT model. In addition, Khazaei and Tareq (2021) also analysed the influence of FC and SI on consumers’ adoption intention towards BEVs in Malaysia employing UTAUT 2 model. In contrast, this study examines how PK, FC and SI shape consumers’ perceptions of pro-environmental vehicles covering all variants of EVs, ultimately influencing their intention towards EVA in India by integrating TAM within the Stimulus-Organism-Behaviour-Consequence (SOBC) model.

The current study addresses three research gaps. First, while existing literature has explored various factors influencing EVA but FC and PK coupled with SI as an external stimulus is overlooked in the early adoption stages for novel technologies (Lu, 2014). The study reconciles this knowledge gap by examining the specific interplay of SI, FC and PK. Second, no study has yet explored the influence of SI, FC and PK on consumers’ perception towards EVA as well as its subsequent association with behavioural intentions further leading to their willingness to pay a premium (WTPP). Present research closes this gap by exploring the interplay of these variables by incorporating the SOBC paradigm in the realm of EVA. Third, it explores the emerging EV market of India, bridging a pivotal research gap in a geographical context. As pointed out by Singh et al. (2020), the research exploring EVA is more prevalent in developed countries compared to developing countries. To bridge this significant knowledge and geographical gap, the present study outlines the subsequent research questions (RQ):

RQ1: How does stimulus (SI, FC, PK) influence consumer perception towards EVs?

RQ2: How does consumer perception towards EVs influence their behavioural intention to adopt EVs?

RQ3: How behavioural intention to adopt EVs is associated with the WTPP?

By addressing these questions, the study offers some pioneering theoretical contributions. First, it enriches the EVA literature by illustrating the influence of stimuli in shaping consumer perceptions leading to WTPP employing the SOBC paradigm. It offers a pioneering addition to the existing body of work by exploring the perception-intention-WTPP interconnection in the EVA domain. Second, it provides a comprehensive understanding of EVA by integrating TAM with the SOBC framework adhering to Duong (2024), who mentioned that relying solely on a single theory is insufficient to offer a holistic knowledge of eco-friendly behaviours. To the extent of the authors’ acumen, no study to date examined the EVA extending TAM within the SOBC framework.

The research also offers several practical insights for marketers, government authorities and policymakers. First, by examining the influence of FC and PK, it offers actionable insights for the government to create more effective educational campaigns about EVs and ensure investments in infrastructure upgrade plans for speedy uptake of EVs. Second, understanding consumers’ perceptions of the usefulness and ease of use of EVs will assist manufacturers in refining product development to better align with customer expectations. Third, the intention-WTPP association will help the policymakers to design effective financial and non-financial incentives to pace up the penetration of EVs in the Indian market.

Theoretical Framework and Hypotheses Formulation

A Unified Model Based on TAM and SOBC Model

The SOBC model advocated by Davis and Luthans (1980) rooted in Social Learning Theory (Bandura, 1977) is a hybrid model of Stimulus Organism Response (SOR) theory (Mehrabian & Russell, 1974) and Antecedent Behaviour Consequence (ABC) theory (Skinner, 1963) which helps in understanding the complicated human behaviour. It explores how intrinsic and extrinsic cues acting as stimulus (S) trigger the internal state of the organism (O) prompting their behavioural reactions (B) which ultimately result in observable or hidden consequences (C) (Talwar et al., 2021). The framework analyses the consequence of behavioural responses of an individual when exposed to external or internal stimuli affecting their beliefs, attitudes, and thoughts. Therefore, previous literature has used it to explore consumer behaviour in various contexts like Whelan et al. (2020) for social media overload; smartwatch usage (Saheb et al., 2022); online purchase intention during COVID-19 (Awal et al., 2023), and food wastage behaviour (Islam et al., 2024).

Since EVs are perceived as innovative technology (Shanmugavel & Michael, 2022) and individual’s behavioural intentions to adopt EVs are influenced by various psychological factors. TAM is one of the widely used cognitive frameworks which helps to comprehend the intention of an individual to use an innovative technology (Jaiswal et al., 2021) and is extensively applied in the realm of EV adoption (Adu-Gyamfi et al., 2024); influence of tourism on EVA (Prakhar et al., 2024) and EVA in paratransit owners (Hull et al., 2024).

Although, few prior studies have applied TAM within the SOBC framework (Saheb et al., 2022; Lennita et al., 2024) it has not been utilised in the domain of EVA. Therefore, this research outlined a conceptual framework (Figure 1) reasoned on TAM and SOBC to encompass the progression of stimulus to consequence indicating how SI, FC and PK acting as stimuli activate organisms incorporating perceived ease of use (PEOU) and perceived usefulness (PU) which are the two widely used antecedents of behavioural intentions to adopt any technology of TAM framework to investigate the EV adoption intention as behavioural response resulting into WTPP for EVs as a consequence.

Figure 1.

Conceptual Framework.

Stimuli and Internal State of Organism

The framework states that stimulus would induce the internal state of an individual resulting in certain behaviour and in this study SI, FC and PK are introduced as stimuli in the context of EVA.

Social Influence

SI reflects the degree to which people within a peer group influence mutual behaviour (Lu, 2014). According to Axsen et al. (2013), it is considered to occur when a person’s feelings, thoughts or actions are affected by the opinions and approvals of referents. Williams et al. (2015) noted that 86 of 110 studies signified a favourable relationship between SI and the perception of consumers towards novel technologies. In developing nations, such as India, where joint families and socio-economic reliance are prevalent, SI is prominent in framing purchase decisions (Ray et al., 2019). Notably, with pro-environmental innovations, like EVs, individuals often lack prior opinions for their new features. For instance, an individual’s perception of EV price is significantly influenced by their friends’ opinions (Zhang et al., 2011) and Abbasi et al. (2021) observed that social factors significantly influence the EVA intention of Malaysian consumers. Therefore, it is incorporated in the proposed model to anticipate its influence on EVA.

According to Lu et al. (2005), SI shapes the perception of an individual of how easy it will be to use a technology before they experience it. Moreover, SI also affects an individual’s perception of the usefulness of novel technologies (Lewis et al., 2003). Since EVs are green and innovative technologies (Bhat et al., 2022) offering societal functional benefits like reducing GHG and fuel requirements (Axsen et al., 2013), individuals will be more inclined to embrace the EVA considering their ecological and personal usefulness. Thus, the following hypotheses are proposed:

H₁: SI is positively associated with PEOU.

H₂: SI is positively associated with PU.

Facilitating Conditions

FC means the essential technical and managerial backing provided to individuals to enable the use of the given technology, system, or novel product (Venkatesh et al., 2003). FC here refers to an individual’s beliefs about the resources and assistance available for EVA including the charging facilities, infrastructure, and the overall number of operational stations (Wang et al., 2023). Practical usage of any technology is contingent on the presence of necessary infrastructure facilities (Sahoo et al., 2023). In the case of EVs, the limited driving range (Jain et al., 2022) and the scarcity of charging stations are significant barriers to improving the FC required for speedy uptake of EVs (She et al., 2017).

Although previous literature has mentioned that EVA intention is associated with FC (Bhat et al., 2022; Singh et al., 2023) but the findings are inconsistent for instance, Manutworakit and Choocharukul (2022) found no effect of FC on EVA intentions whereas Jain et al. (2022) found FC to influence EVA intentions positively and Wang et al. (2023) found moderating effect of FC on EVA intentions. Therefore, to address these inconsistencies, this study expands the existing knowledge of FC with the individual’s internal state such as PU and PEOU towards EVA.

The presence of FC increases the likelihood of adoption of novel technology with less perceived effort and more PU (Dwivedi et al., 2017). It is also affirmed by the existing studies in various contexts such as Chen and Aklikokou (2020) for e-government adoption, Ebadi and Raygan (2023) for mobile-assisted language learning. Therefore, the following hypotheses are formulated:

H₃: FC is positively associated with PEOU.

H₄: FC is positively associated with PU.

Knowledge about EVs

Knowledge regarding the product is pivotal in deciphering the individual’s decision-making mechanism and comprehending their behaviour (Kaplan, 1991; Liu et al., 2018). PK includes zero-carbon emissions aspect, no reliance on fossil fuels, required charging duration and presence of charging infrastructure, etc (Tu & Yang, 2019). Prior studies show that PK significantly predicts the purchase intention of a pro-environmental product (Qian & Yin, 2017; Uddin & Khan, 2018). Since EVs are also a pro-environmental product, PK has a significant influence on adoption intention (Wu et al., 2019). According to Berliner et al. (2019), insufficient PK is one of the prime causes behind lower acceptance rates of EVs. Furthermore, existing research has noted the importance of an individual’s knowledge regarding eco-friendly products as a cognitive factor that influences the cognitive state of individuals (Laroche et al., 2001; Uddin & Khan, 2018). Therefore, to comprehend the various facets of EV adoption, exploring the connection of knowledge about EV with PEOU and PU is highly imperative. PEOU in the context of EVs refers to the capacity of an individual to understand the operation and usage of EVs with minimal effort (Jaiswal et al., 2022). Knowledgeable individuals are more likely to perceive EVs as convenient to use, as they have prior knowledge of various performance attributes.

Additionally, according to Liu et al. (2018) knowledge regarding the features and benefits provided by the technology or product are related to the PU of the technology or product. Past literature also showed this in various contexts such as Wang and Hazen (2016) for renewable energy products and Pagiaslis and Krontalis (2014) for green products. Along similar lines, individuals possessing PK have a high likelihood to perceive EVs as beneficial for them as well as for society. Rooted on the viewpoints discussed above, the following hypotheses are posited:

H₅: PK is positively associated with PEOU.

H₆: PK is positively associated with PU.

Organism’s Internal State and Behavioural Response

The paradigm postulated that the emotional and cognitive state of an individual influences the behaviour and in the current study, PEOU and PU are incorporated as organisms.

Perceived Ease of Use

PEOU refers to, ‘the extent to which a user perceives that using the target system will be free of physical and mental effort’ (Davis, 1986, p.136). It means the usage of a technology with minimal effort. The efforts of individuals are a limited resource that would be allocated to multiple tasks (Radner & Rothschild, 1975) and any technology or product that saves effort is considered user-friendly and has a high likelihood to get adopted due to increased perception of easiness in operation (Davis, 1989; Jaiswal et al., 2022). Previous research has shown a significant positive influence of PEOU on behavioural intention to adopt the concerned technology in different domains (Kaplan, 1991; Fagan et al., 2012; Wu et al., 2019).

In this study, PEOU refers to the ease of using EVs compared to conventional vehicles, including factors like battery charging, vehicle handling, etc. According to Wolff and Madlener (2019), the gradual adoption of EVs into everyday lives requires various adaptations to daily routines, especially the charging process compared to the refuelling of conventional vehicles, acceleration, etc. Thus, it is imperative to investigate whether individuals perceive it easy to adopt EVs along with these adaptations. While prior research has explored the impact of PEOU on consumer intention to adopt EVs, results have been inconsistent. For instance, Chen (2016) found an insignificant effect for electric bikes. In comparison, Lieven et al. (2011) and Wang and Dong (2016) found a significant positive influence. Hence, this study tries to address these discrepancies by expanding the existing knowledge on the relationship of PEOU of EVs with their adoption intention towards EVs. Hence, based on the abovementioned discourse, the following hypothesis is postulated:

H₇: PEOU is positively associated with the adoption intention towards EVs.

Perceived Usefulness

PU refers to, ‘the degree to which a person believes that using a particular system would enhance his or her job performance’ (Davis, 1989, p.320). Simply, it is the perception of benefits an individual anticipates getting by adopting the novel technology (Shanmugavel & Michael, 2022). In various research contexts, it is acknowledged as a significant factor influencing the behavioural intentions of individuals (Fagan et al., 2012; Wang & Dong, 2016). In the realm of EVs, it means the degree to which a person perceives that he can accomplish the task swiftly and efficiently with EVs compared to conventional vehicles which might be because of the technical variations in the engines. According to Wang and Dong (2016), it can be addressed as the perceived travel efficiency expected by an individual while using an EV. Considering EVs as innovative and pro-environmental products, Jaiswal et al. (2022) provided the operational measures of PU of EVs incorporating energy efficiency by reducing the demand for fossil fuels (White & Sintov, 2017), eco-friendly as these reduce carbon emissions (Han et al., 2017). Thus, if individuals perceive EVs as useful, they are more likely to adopt them. Thus, we suggest the following hypothesis:

H₈: PU is positively associated with the adoption intention towards EVs.

Behaviour Response and Consequence

In the present research, adoption intention is introduced as behavioural response and WTPP as the consequence.

Adoption Intention Towards EVs and WTPP

According to Venkatesh et al. (2003), intention signifies an individual’s determination to carry out a particular action and it serves as a predictor of the inclination of an individual towards using a technology in case of adoption of a technology. In the case of behavioural intentions to purchase or adopt, it indicates the readiness of an individual to purchase or adopt a particular product or technology. Yet it need not mandatorily be supported by the propensity to pay a higher price compared to the variants available (Gam et al., 2010). Although some studies have considered both these variables as one, as both are the outcomes of value propositions of products and technologies. A slight difference between them is that intention ranks the individual’s preferences for acquiring a product or technology (Liu et al., 2006) whereas WTPP analyses the impact of these values via monetary perspective (Stefani et al., 2006). Behavioural intention to adopt EV leading to WTPP is the ultimate decision-making factor of the current study’s framework. Chatterjee and Kumar (2017) highlighted that willingness to pay more is affected by the unique value effect, which accentuates the distinct features of the product or technology. According to Sukhu et al. (2017), managers must know the greatest amount a consumer is willing to spend to formulate an effective pricing strategy and predict the demand for a product with effectiveness. Previous research in the realm of green and clean technology found that the WTPP price is influenced by the adoption intention of individuals such as Farzin et al. (2023) for eco-fashion, Selem et al. (2023) for digital concierge services. Therefore, in the current study the following hypothesis is postulated:

H₉: Adoption intention towards EVs is positively associated with the WTPP towards EVs.

Measurement Scale and Data Collection

Measurement Scales

The questionnaire comprised seven constructs including adapted measurement items of prior validated scales. Items of SI were adopted from Bhat et al. (2022), FC was assessed utilising the four items of Wang et al. (2023) and items measuring PK and PU were extracted from Wang et al. (2018). Items for PEOU were adapted from Wu et al. (2019). To measure adoption intention three items were utilised from Han et al. (2017) and three items were adapted from Zhang et al. (2020) to assess WTPP price. Each item is rated on a five-point Likert scale, with one representing ‘strongly disagree’ and five representing ‘strongly agree’. Comments were taken from three professors regarding the content and clarity of the questionnaire and minor modifications were incorporated as per the suggestions. After that, a pilot survey was undertaken by distributing the questionnaire to 25 respondents who were aware of the EV technology out of which 20 completed the survey. It was conducted through face-to-face engagement, employing convenience sampling. Cronbach’s alpha values exceeded 0.7 (Hair et al., 2018) for all constructs, confirming the reliability of the scale. Drawing on the insights from the pilot survey, minor adjustments were incorporated in the statements to enhance the relevance of the questionnaire. Thereafter, the scale was used for the final data collection.

Data Collection

A cross-sectional survey was conducted in Delhi NCR employing both online and offline means applying a non-probabilistic convenience sampling technique due to pragmatic constraints. Individuals who knew about EVs were chosen as subjects of the study. After eliminating the invalid responses (like uniform responses for all the items or missing data), 288 valid responses were retained for further analysis which seemed sufficient for multivariate analysis according to Hair et al. (2010) who recommended 10:1 as a sample to variable ratio. Table 1 demonstrates the sociodemographic profile of the respondents. The majority of respondents in the current study belong to a younger demographic, considering current trends in EVA. Moreover, the buying decision for new vehicles is predominantly being shaped by the young and educated adults within the family (Young & Hinesly, 2012).

Table 1.

Respondents’ Demographic Profile.

Measure	Item	N	Percentage (%)
Gender	Male	130	45.0
Gender	Female	158	54.7
Age	18–30	205	70.9
	31–40	53	18.3
	41–50	19	6.6
	51–60	8	2.8
	More than 60	3	1.0
Income	Less than 25,000	87	30.1
	25,001–50,000	86	29.8
	50,001–75000	37	12.8
	More than 75,000	78	27.0
Education	Upto 12^th	34	11.8
	Bachelor degree	88	30.4
	Master degree	130	45.0
	Ph.D	36	12.5
No. of vehicles owned by family	0	31	10.7
	1	92	31.8
	2	97	33.6
	More than 2	68	23.5
No. of E-vehicles owned by family	0	223	77.2
	1	59	20.4
	2	3	1.0
	More than 2	3	1.0

Data Analysis

The proposed research model was analysed using partial least square structural equation modelling (PLS-SEM). PLS is acknowledged as a second-generation method with several advantages, including less restrictive presumptions about non-normal data distribution, adaptability in handling intricate relationships across constructs and efficiency in identifying the primary driver of construct outcomes (Hair et al., 2012). Using PLS-SEM, the measurement and structural models can be assessed concurrently.

Measurement Model

The following criteria are used to evaluate the measurement model: reliability, discriminant validity and convergent validity. The several assessment components for reflective measurement models are listed in Table 2.

Table 2.

Measurement Model Analysis Results.

Construct	Items	Outer Loadings	Cronbach’s Alpha	CR	AVE	VIF
Social influence	SI1	0.770	0.831	0.862	0.748	1.518
	SI2	0.899				2.527
	SI3	0.919				2.620
Facilitating conditions	FC1	0.881	0.869	0.874	0.719	3.124
	FC2	0.851				2.690
	FC3	0.795				1.741
	FC4	0.861				2.108
Knowledge about EVs	PK1	0.900	0.873	0.882	0.796	2.652
	PK2	0.875				2.179
	PK3	0.902				2.305
Perceived ease of use	PEOU1	0.934	0.900	0.901	0.834	3.432
	PEOU 2	0.903				2.667
	PEOU 3	0.903				2.676
Perceived usefulness	PU2	0.888	0.860	0.878	0.782	2.586
	PU3	0.924				2.819
	PU3	0.838				1.792
Adoption intention	AI1	0.895	0.874	0.875	0.798	2.309
	AI2	0.903				2.513
	AI3	0.882				2.258
Willingness to pay premium	WTPP1	0.929	0.889	0.910	0.818	3.083
	WTPP2	0.923				2.867
	WTPP3	0.860				2.214

Using Cronbach alpha and Composite reliability (CR), the observed variables’ reliability was assessed. All of the latent construct values were over the minimal threshold level of 0.70, as indicated by Cronbach alpha and CR, showing a reasonable level of reliability for the scales. The convergent validity was assessed using the factor loadings for the reflective constructs and the average variance extracted (AVE). The results are shown in Table 2. Each indicator showed factor loading of more than 0.7 and AVE values between 0.719 and 0.834, which are higher than the 0.5 criterion. These standards demonstrate that the constructs load well on corresponding latent variables and that the final model exhibited convergent validity.

To compute multicollinearity, the variance inflation factor (VIF) must be assessed in addition to validity and reliability before structural model analysis. A cutoff value of 5.0 for multicollinearity was proposed by Hair et al. (2014). The VIF results for each construct showed that there were no collinearity problems between the constructs in this study, as they were all below the threshold value of 5.0

The Heterotrait-Monotrait ratio technique (HTMT) as recommended by Henseler et al. (2015) and the Fornell-Larcker criterion of cross-loading indicators (Hair et al., 2017) were used to evaluate the discriminant validity of the measurement model. Discriminant validity assessment ensures that in contrast to other constructs and their indicators, reflective constructs and their indicators have significant relationships (Hair et al., 2017). Table 3 shows that all of the reflective constructs show adequate or satisfactory discriminant validity (Fornell & Larcker, 1981), where the square of AVE (diagonal) is greater than the correlations (off-diagonal).

Table 3.

Discriminant Validity (Fornell-Larcker Criterion).

	AI	FC	PEOU	PK	PU	SI	WTPP
AI	0.894
FC	0.242	0.848
PEOU	0.464	0.377	0.913
PK	0.288	0.362	0.380	0.892
PU	0.374	0.346	0.464	0.478	0.884
SI	0.302	0.332	0.447	0.420	0.416	0.865
WTPP	0.546	0.339	0.208	0.180	0.114	0.147	0.904

Table 4 shows that the HTMT values for each variable are less than 0.85. As a result, the constructs differ from one another empirically.

Table 4.

Heterotrait-monotrait Ratio of Correlations (HTMTs).

	AI	FC	PEOU	PK	PU	SI	WTPP
AI
FC	0.275
PEOU	0.524	0.427
PK	0.321	0.419	0.425
PU	0.429	0.393	0.531	0.540
SI	0.366	0.389	0.509	0.493	0.482
WTPP	0.609	0.385	0.232	0.202	0.123	0.194

Furthermore, before testing the structural model its fitness was examined. SmartPLS 4.0 produced the goodness of fit results with an NFI value of 0.769, which is less than the recommended standard of 0.95. According to Hu and Bentler (1999), the SRMR value should be less than 0.08. The findings of the study indicated a good model fit, with an SRMR value of 0.064.

Structural Model

The next stage of the PLS-SEM technique is the structural model assessment after the measurement model has been validated. A bootstrapping technique using 5000 resamples was used to assess the given hypothesis. According to Ramayah et al. (2016), the quality of the structural model is gauged by the R² value (Table 5). The coefficient of determination (R²) values for PU, PEOU, adoption intention and WTPP were 0.304, 0.282, 0.247 and 0.298, respectively.

Table 5.

R² and R² Adjusted.

	R-square	R-square Adjusted
Perceived ease of use	0.282	0.275
Perceived usefulness	0.304	0.296
Adoption intention	0.247	0.242
Willingness to pay premium	0.298	0.295

Table 6 shows the results of the structural model assessment. The results confirm H₁ by demonstrating that SI has a favourable and significant impact on PEOU (β = 0.302, t = 4.306, p < .05). Furthermore, the relationship indicates that SI significantly impacts PU (β = 0.228, t = 3.675, p < .05). Thus, H₂ is accepted. FC shows a significant impact on PEOU and PU (β = 0.213, t = 3.388, p < .05; β = 0.152, t = 2.200, p < .05). Thereby accepting H₃ and H₄. In addition, H₅ and H₆ are accepted as PK positively impacted PEOU and PU (β = 0.176, t = 2.459, p < .05; β = 0.327, t = 5.818, p < .05). PEOU and PU also positively impacted adoption intention (β = 0.371, t = 4.303, p < .05; β = 0.202, t = 2.613, p < .05). Thus, H₇ and H₈ are also accepted. Adoption intention also shows a significant impact on WTPP (β = 0.546, t = 12.539, p < .05) (H₉ supported) (Figure 2).

Figure 2.

Source: Analysis Output

Table 6.

Results of Structural Model Assessment.

	Paths	β	p Value	t-Statistics	Conclusion
H₁	Social Influence ® Perceived ease of use	0.302	***	4.306	Supported
H₂	Social Influence ® Perceived usefulness	0.228	***	3.675	Supported
H₃	Facilitating conditions ® Perceived ease of use	0.213	***	3.388	Supported
H₄	Facilitating conditions ® Perceived usefulness	0.152	***	2.200	Supported
H₅	Knowledge about EVs ® Perceived ease of use	0.176	***	2.459	Supported
H₆	Knowledge about EVs ® Perceived usefulness	0.327	***	5.818	Supported
H₇	Perceived ease of use ® Adoption intention	0.371	***	4.303	Supported
H₈	Perceived usefulness ® Adoption intention	0.202	***	2.613	Supported
H₉	Adoption intention ® Willingness to pay premium	0.546	***	12.539	Supported

Note: ^***p < .05.

Discussion and Conclusion

All the proposed associations are confirmed by the results of the study affirming the effectiveness of the proposed unified model integrating TAM and SOBC paradigm towards EVA in the Indian milieu. Findings confirmed the significant and positive association of SI with PEOU (H₁) in congruence with the prior investigation of Lu et al. (2005). H₂ proposing the significant association of SI with PU is supported in consonance with the evidence (Lu, 2014; Lu et al., 2005). It exhibits that the consumers’ perception of EVs is influenced by their social networks. Additionally, the study corroborated the findings of previous studies illustrating the favourable influence of FC on PEOU (Chen & Aklikokou, 2020; Ebadi & Raygan, 2023) and PU (Chen & Aklikokou, 2020), respectively (H₃, H₄). It entails that the presence of FC like robust charging infrastructure, will shape consumer perception towards EVs favourably. Similarly, H₅ and H₆ were supported by the results of present research replicating the prior studies affirming the positive association of PK with PEOU (Jaiswal et al., 2022) and PU (Wang et al., 2018; Jaiswal et al., 2022) respectively. It highlights the relevance of PK in shaping the consumers’ perception of effortless usage besides the usefulness of EVs. Moreover, results demonstrated a positive and significant influence of PEOU and PU on AI following the existing research works (Huang et al., 2021; Jaiswal et al., 2022) (H₇, H₈). It implies that if consumers perceive driving EVs as convenient and beneficial for them, their inclination toward its adoption will increase. Furthermore, the conclusions of Kant et al. (2024) and Zheng et al. (2022) were ratified by the current study highlighting AI as a dominant antecedent of WTPP for EVs (H₉). The prominence of these outcomes is underscored by the fact that willingness to pay signifies a favourable mindset that is on the verge of resulting in actual purchase behaviour (Fleșeriu et al., 2020).

Theoretical Implications

The research makes prominent theoretical contributions to EVA literature. First, it aims to model consumers’ behavioural intention towards EVA leading to WTPP by underscoring the relevance of the intention-WTPP association. This pathway from intention to economic behaviour is often overlooked in existing TAM-based models. By incorporating this link, the current study theoretically enriches TAM-based EVA literature on how to translate the adoption intention of consumers into their willingness to pay premium for eco-friendly technologies including EVs. Second, by incorporating the peer influence along with product-specific stimuli (PK, FC), it offers an in-depth theoretical insight into the SOBC mechanism on how social factors beyond the product features contribute to shaping consumer behavioural decisions in the context of EVA. Third, by adapting the validated frameworks (TAM & SOBC) in the field of EVs, it advances the theoretical grounding to accentuate the significance of SOBC for providing more nuanced insights on how social and product factors comprehensively contribute towards the broader adoption of eco-friendly innovations.

Practical Implications

The outcomes of the research provide some pioneering practical applications for promoting EVA. First, by emphasising the favourable influence of the social group, it suggests that marketers to capitalise on social forces by collaborating with community leaders, influencers, and satisfied customers to design social marketing campaigns that boost trust in EVs. Second, by highlighting the positive association of PK with perception, it guides marketers to develop awareness and educational campaigns to disseminate information about the environmental and economic benefits of EVs. Third, the correlation between adoption intention and WTPP offers a more nuanced perspective to marketers for the formulation of efficacious pricing strategies. By leveraging the consumers’ WTPP for eco-friendly features of EVs, marketers can effectively justify the high upfront cost in a developing country like India where the per capita income is relatively low.

Limitations and Future Research Directions

Despite its potential to advance the EVA literature, the current study has some shortcomings that must be considered before generalising the outcomes. The cross-sectional aspect of the study limits its potential to examine the causal associations among variables. Considering this, future studies can undertake longitudinal studies to evaluate these associations over time. Moreover, the influence of cultural differences on the consumers’ perceptions and intentions to adopt EVs can be analysed by conducting a comparative study across different regions with high and low adoption rates. Furthermore, future researchers can integrate both qualitative and quantitative methods to gain detailed insights into current EV user experiences by providing a comprehensive understanding of factors affecting EV usage. Additionally, this research applied the unified model of TAM and SOBC to assess adoption intention towards EVs in a developing nation for the first time, focussing solely on direct associations but future researchers are urged to incorporate mediators and moderators like financial incentives, innovativeness, socio-economic demographics, to strengthen the conceptual model. Furthermore, current research analysed the behavioural intentions towards EVA as it is considered a dominant antecedent of actual behaviour. However extant literature has shown that behavioural intentions might not turn into actual behaviour (Hsu & Huang, 2010). To offer more robust insights, future studies can extend it by focussing on actual adoption behaviour towards EVs.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

References

Abbasi

H. A.

, Johl

S. K.

, Shaari

Z. B. H.

, Moughal

, Mazhar

, Musarat

M. A.

, Rafiq

, Farooqi

A. S.

, & Borovkov

(2021). Consumer motivation by using unified theory of acceptance and use of technology towards electric vehicles. Sustainability, 13(21), 12177.

Adu-Gyamfi

, Asamoah

A. N.

, Obuobi

, Nketiah

, & Zhang

(2024). Electric mobility in an oil-producing developing nation: Empirical assessment of electric vehicle adoption. Technological Forecasting and Social Change, 200, 123173.

Awal

M. R.

, Hossain

M. S.

, Arzin

T. A.

, Sheikh

M. I.

, & Haque

M. E.

(2023). Application of the SOBC model to study customers’ online purchase intentions in an emerging economy during COVID-19: Does gender matter? PSU Research Review, 8(2), 553–574.

Axsen

, Orlebar

, & Skippon

(2013). Social influence and consumer preference formation for pro-environmental technology: The case of a UK workplace electric-vehicle study. Ecological Economics, 95, 96–107.

Bandura

(1977). Social learning theory. Prentice-Hall google schola, 2, 101–123.

Berliner

R. M.

, Hardman

, & Tal

(2019). Uncovering early adopter’s perceptions and purchase intentions of automated vehicles: Insights from early adopters of electric vehicles in California. Transportation research part F: traffic psychology and behaviour, 60, 712–722.

Bhat

F. A.

, Verma

, & Verma

(2022). Measuring and modelling electric vehicle adoption of Indian consumers. Transportation in Developing Economies, 8(1), 6.

Boo

S. Y.

, & Tan

(2024). Electric vehicles purchase intention: the role of mediators using an extended TPB model. Journal of Contemporary Marketing Science, (ahead-of-print).

Chatterjee

, & Kumar

(2017). Consumer willingness to pay across retail channels. Journal of Retailing and Consumer Services, 34, 264–270.

10.

Chen

, & Aklikokou

A. K.

(2020). Determinants of E-government adoption: Testing the mediating effects of perceived usefulness and perceived ease of use. International Journal of Public Administration, 43(10), 850–865.

11.

Chen

S. Y.

(2016). Green helpfulness or fun? Influences of green perceived value on the green loyalty of users and non-users of public bikes. Transport Policy, 47, 149–159.

12.

Davis

F. D.

(1986). A technology acceptance model for testing new end-user information systems: Theory and results. Ph.D. dissertation, MIT Sloan School of Management, Cambridge, MA.

13.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

14.

Davis

T. R.

, & Luthans

(1980). A social learning approach to organizational behavior. Academy of Management review, 5(2), 281–290.

15.

Duong

C. D.

(2024). Using a unified model of TPB, NAM and SOBC to understand students’ energy-saving behaviors: Moderation role of group-level factors and media publicity. International Journal of Energy Sector Management, 18(1), 71–93.

16.

Dwivedi

Y. K.

, Rana

N. P.

, Janssen

, Lal

, Williams

M. D.

, & Clement

(2017). An empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2), 211–230.

17.

Ebadi

, & Raygan

(2023). Investigating the facilitating conditions, perceived ease of use and usefulness of mobile-assisted language learning. Smart Learning Environments, 10(1), 30.

18.

Fagan

, Kilmon

, & Pandey

(2012). Exploring the adoption of a virtual reality simulation: The role of perceived ease of use, perceived usefulness and personal innovativeness. Campus-Wide Information Systems, 29(2), 117–127.

19.

Farzin

, Shababi

, Shirchi Sasi

, Sadeghi

, & Makvandi

(2023). The determinants of eco-fashion purchase intention and willingness to pay. Spanish Journal of Marketing-ESIC, 27(3), 348–366.

20.

Fleșeriu

, Cosma

S. A.

, & Bocăneț

(2020). Values and planned behaviour of the Romanian organic food consumer. Sustainability, 12(5), 1722.

21.

Fornell

, & Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39–50.

22.

Gam

H. J.

, Cao

, Farr

, & Kang

(2010). Quest for the eco-apparel market: A study of mothers’ willingness to purchase organic cotton clothing for their children. International Journal of Consumer Studies, 34(6), 648–656.

23.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2010). Multivariate data analysis (7th ed.). Pearson.

24.

Hair

J. F.

, Black

W. C.

, Babin

B. J.

, & Anderson

R. E.

(2018). Multivariate data analysis (8th ed.). Cengage.

25.

Hair

J. F.

, Hult

G. T. M.

, Ringle

C. M.

, & Sarstedt

(2014). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications.

26.

Hair

J. F.

, Hult

G. T. M.

, Ringle

C. M.

, & Sarstedt

(2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.

27.

Hair

J. F.

, Sarstedt

, Ringle

C. M.

, & Mena

J. A.

(2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.

28.

Han

, Wang

, Zhao

, & Li

(2017). The intention to adopt electric vehicles: Driven by functional and non-functional values. Transportation Research Part A: Policy and Practice, 103, 185–197.

29.

Henseler

, Ringle

C. M.

, & Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.

30.

Higueras-Castillo

, Guillén

, Herrera

L. J.

, & Liébana-Cabanillas

(2021). Adoption of electric vehicles: Which factors are really important? International Journal of Sustainable Transportation, 15(10), 799–813.

31.

Hsu

C. H.

, & Huang

(2010, June). Formation of tourist behavioral intention and actual behavior. In 2010 7th International Conference on Service Systems and Service Management (pp. 1–6). IEEE.

32.

L. T.

, & Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

33.

Huang

, & Ge

(2019). Electric vehicle development in Beijing: An analysis of consumer purchase intention. Journal of Cleaner Production, 216, 361–372.

34.

Huang

, Lin

, Lim

M. K.

, Tseng

M. L.

, & Zhou

(2021). The influence of knowledge management on adoption intention of electric vehicles: Perspective on technological knowledge. Industrial Management & Data Systems, 121(7), 1481–1495.

35.

Hull

, Giliomee

J. H.

, Visser

, & Booysen

M. J.

(2024). Electric vehicle adoption intention among paratransit owners and drivers in South Africa. Transport Policy, 146, 137–149.

36.

IBEF. (2024). Electric vehicle. https://www.ibef.org/industry/electric-vehicle.

37.

Invest India. (2024). India is the world’s third-largest automobile market. https://www.investindia.gov.in/sector/automobile#:~:text=India%20is%20the%20world’s%20third,Apr%202023%20to%20Mar%202024.

38.

Islam

S. A.

, Jones

R. P.

, Akhi

A. A.

, & Talukder

M. S.

(2024). Do food delivery apps influence food waste generation? A stimulus-organism-behavior-consequence (SOBC) exploration. British Food Journal, 126(2), 879–897.

39.

Jain

N. K.

, Bhaskar

, & Jain

(2022). What drives adoption intention of electric vehicles in India? An integrated UTAUT model with environmental concerns, perceived risk and government support. Research in Transportation Business & Management, 42, 100730.

40.

Jaiswal

, Kant

, Singh

P. K.

, & Yadav

(2022). Investigating the role of electric vehicle knowledge in consumer adoption: Evidence from an emerging market. Benchmarking: An International Journal, 29(3), 1027–1045.

41.

Jaiswal

, Kaushal

, Kant

, & Singh

P. K.

(2021). Consumer adoption intention for electric vehicles: Insights and evidence from Indian sustainable transportation. Technological Forecasting and Social Change, 173, 121089.

42.

Kant

, Mehta

, Jaiswal

, & Kumar

(2024). Adoption intention and willingness to pay for electric vehicles: Role of social-psychological attributes, fiscal incentives and socio-demographics. Management of Environmental Quality: An International Journal, 35(4), 945–963.

43.

Kaplan

(1991). Beyond rationality: Clarity-based decision making. In Gärling

& Evans

G. W.

(Eds.), Environment, cognition, and action: An integrated approach (pp. 171–190). Oxford University Press.

44.

Khazaei

, & Tareq

M. A.

(2021). Moderating effects of personal innovativeness and driving experience on factors influencing adoption of BEVs in Malaysia: An integrated SEM–BSEM approach. Heliyon, 7(9), e08072.

45.

Laroche

, Bergeron

, & Barbaro-Forleo

(2001). Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing, 18(6), 503–520.

46.

Lennita

, Condrobimo

A. R.

, & Candra

(2024, January). Integrating TAM and ISSM Theories to Investigate Social Media Factors on Students’ Satisfaction and Academic Performance. In 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent, Systems (ICETSIS) (pp. 1117–1122). IEEE.

47.

Lewis

, Agarwal

, & Sambamurthy

(2003). Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Quarterly, 27(4), 657–678.

48.

Lieven

, Mühlmeier

, Henkel

, & Waller

J. F.

(2011). Who will buy electric cars? An empirical study in Germany. Transportation Research Part D: Transport and Environment, 16(3), 236–243.

49.

Liu

, Bruce Sunderland

, Liu

, & George O’Neil

(2006). In vitro and in vivo release of naltrexone from biodegradable depot systems. Drug Development and Industrial Pharmacy, 32(1), 85–94.

50.

Liu

, Hong

, Zhu

, Yan

, Qi

, & Liu

(2018). Promoting green residential buildings: Residents’ environmental attitude, subjective knowledge, and social trust matter. Energy Policy, 112, 152–161.

51.

(2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134–159.

52.

, Yao

J. E.

, & Yu

C. S.

(2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268.

53.

Manutworakit

, & Choocharukul

(2022). Factors influencing battery electric vehicle adoption in Thailand—Expanding the unified theory of acceptance and use of technology’s variables. Sustainability, 14(14), 8482.

54.

Mehrabian

, & Russell

J. A.

(1974). An approach to environmental psychology. The MIT Press.

55.

Pagiaslis

, & Krontalis

A. K.

(2014). Green consumption behavior antecedents: Environmental concern, knowledge, and beliefs. Psychology & Marketing, 31(5), 335–348.

56.

Prakhar

, Jaiswal

, Gupta

, & Gupta

S. K.

(2024). Decoding tourist perceptions and behavioral intentions to use electric vehicles for sustainable tourism with the lens of technology continuance theory. International Journal of Tourism Cities, Vol. ahead-of-print No. ahead-of-print.

57.

Qian

, & Yin

(2017). Linking Chinese cultural values and the adoption of electric vehicles: The mediating role of ethical evaluation. Transportation Research Part D: Transport and Environment, 56, 175–188.

58.

Radner

, & Rothschild

(1975). On the allocation of effort. Journal of Economic Theory, 10(3), 358–376.

59.

Ramayah

, Ling

N. S.

, Taghizadeh

S. K.

, & Rahman

S. A.

(2016). Factors influencing SMEs website continuance intention in Malaysia. Telematics and Informatics, 33(1), 150–164.

60.

Ray

, Bala

P. K.

, & Dasgupta

S. A.

(2019). Role of authenticity and perceived benefits of online courses on technology based career choice in India: A modified technology adoption model based on career theory. International Journal of Information Management, 47, 140–151.

61.

Riley

(2019). The race to the electric car is just getting started. CNN Bus.

62.

Saheb

, Cabanillas

F. J. L.

, & Higueras

(2022). The risks and benefits of Internet of Things (IoT) and their influence on smartwatch use. Spanish Journal of Marketing-ESIC, 26(3), 309–324.

63.

Sahoo

A. K.

, Dash

, & Nayak

(2023). Perceived risk and benefits of e-health consultation and their influence on user’s intention to use. Journal of Science and Technology Policy Management, 15(6), 1577–1594.

64.

Selem

K. M.

, Khalid

, Raza

, & Islam

M. S.

(2023). We need digital inquiries before arrival! Key drivers of hotel customers’ willingness to pay premium. Journal of Quality Assurance in Hospitality & Tourism, 1–23.

65.

Shanmugavel

, & Micheal

(2022). Exploring the marketing related stimuli and personal innovativeness on the purchase intention of electric vehicles through Technology Acceptance Model. Cleaner Logistics and Supply Chain, 3, 100029.

66.

She

Z. Y.

, Sun

, Ma

J. J.

, & Xie

B. C.

(2017). What are the barriers to widespread adoption of battery electric vehicles? A survey of public perception in Tianjin, China. Transport Policy, 56, 29–40.

67.

Singh

, Paul

U. K.

, & Pandey

(2023). Does electric vehicle adoption (EVA) contribute to clean energy? Bibliometric insights and future research agenda. Cleaner and Responsible Consumption, 8, 100099.

68.

Singh

, Singh

, & Vaibhav

(2020). A review and simple meta-analysis of factors influencing adoption of electric vehicles. Transportation Research Part D: Transport and Environment, 86, 102436.

69.

Skinner

B. F.

(1963). Operant behavior. American Psychologist, 18(8), 503.

70.

Statista. (2024a). Penetration rate of electric vehicles in India from 2014 to 2023. https://www.statista.com/statistics/1453069/india-penetration-of-electric-vehicles/.

71.

Statista. (2024b). Transportation emissions worldwide - statistics & facts. https://www.statista.com/topics/7476/transportation-emissionsworldwide/#topicOverview.

72.

Stefani

, Romano

, & Cavicchi

(2006). Consumer expectations, liking and willingness to pay for specialty foods: Do sensory characteristics tell the whole story? Food Quality and Preference, 17(1–2), 53–62.

73.

Sukhu

, Bilgihan

, & Seo

(2017). Willingness to pay in negative restaurant service encounters. International Journal of Hospitality Management, 65, 11–19.

74.

Talwar

, Jabeen

, Tandon

, Sakashita

, & Dhir

(2021). What drives willingness to purchase and stated buying behavior toward organic food? A Stimulus–Organism–Behavior–Consequence (SOBC) perspective. Journal of Cleaner Production, 293, 125882.

75.

J. C.

, & Yang

(2019). Key factors influencing consumers’ purchase of electric vehicles. Sustainability, 11(14), 3863.

76.

Uddin

S. F.

, & Khan

M. N.

(2018). Young consumer’s green purchasing behavior: Opportunities for green marketing. Journal of Global Marketing, 31(4), 270–281.

77.

Venkatesh

, Morris

M. G.

, Davis

G. B.

, & Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3),425–478.

78.

Wang

, Ozden

, & Tsang

Y. P.

(2023). The impact of facilitating conditions on electric vehicle adoption intention in China: An integrated unified theory of acceptance and use of technology model. International Journal of Engineering Business Management, 15, 18479790231224715.

79.

Wang

, Wang

, Li

, Wang

, & Liang

(2018). Policy implications for promoting the adoption of electric vehicles: Do consumer’s knowledge, perceived risk and financial incentive policy matter? Transportation Research Part A: Policy and Practice, 117, 58–69.

80.

Wang

, & Hazen

B. T.

(2016). Consumer product knowledge and intention to purchase remanufactured products. International Journal of Production Economics, 181, 460–469.

81.

Wang

, & Dong

(2016). Determinants and policy implications of residents’ new energy vehicle purchases: The evidence from China. Natural Hazards, 82, 155–173.

82.

Whelan

, Islam

A. N.

, & Brooks

(2020). Applying the SOBC paradigm to explain how social media overload affects academic performance. Computers & Education, 143, 103692.

83.

White

L. V.

, & Sintov

N. D.

(2017). You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions. Transportation Research Part A: Policy and Practice, 99, 94–113.

84.

Williams

M. D.

, Rana

N. P.

, & Dwivedi

Y. K.

(2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488.

85.

Wolff

, & Madlener

(2019). Driven by change: Commercial drivers’ acceptance and efficiency perceptions of light-duty electric vehicle usage in Germany. Transportation Research Part C: Emerging Technologies, 105, 262–282.

86.

, Liao

, Wang

J. W.

, & Chen

(2019). The role of environmental concern in the public acceptance of autonomous electric vehicles: A survey from China. Transportation Research Part F: Traffic Psychology and Behaviour, 60, 37–46.

87.

Young

A. M.

, & Hinesly

M. D.

(2012). Identifying Millennials’ key influencers from early childhood: Insights into current consumer preferences. Journal of Consumer Marketing, 29(2), 146–155.

88.

Zhang

, Xiao

, & Zhou

(2020). Willingness to pay a price premium for energy-saving appliances: Role of perceived value and energy efficiency labeling. Journal of Cleaner Production, 242, 118555.

89.

Zhang

, Yu

, & Zou

(2011). Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV. Energy Policy, 39(11), 7015–7024.

90.

Zheng

, Liu

, Guan

, Yang

, Li

, Fahad

, & Li

(2022). Identifying intention-based factors influencing consumers’ willingness to pay for electric vehicles: A sustainable consumption paradigm. Sustainability, 14(24), 16831.

Predicting Behavioural Intention Towards Electric Vehicle Adoption in India: A Unified TAM-SOBC Framework

Abstract

Purpose:

Methodology:

Findings:

Practical implications:

Originality/Value:

Keywords

Introduction

Theoretical Framework and Hypotheses Formulation

A Unified Model Based on TAM and SOBC Model

Conceptual Framework.

Stimuli and Internal State of Organism

Social Influence

Facilitating Conditions

Knowledge about EVs

Organism’s Internal State and Behavioural Response

Perceived Ease of Use

Perceived Usefulness

Behaviour Response and Consequence

Adoption Intention Towards EVs and WTPP

Measurement Scale and Data Collection

Measurement Scales

Data Collection

Data Analysis

Measurement Model

Structural Model

Discussion and Conclusion

Theoretical Implications

Practical Implications

Limitations and Future Research Directions

Footnotes

Declaration of Conflicting Interests

Funding

References