The automobile industry is experiencing an extreme transformation navigated by digitalization, developing consumer expectations and a move from conventional dealership models to mixed touchpoints strategies. Nowadays, consumers not only get involved through physical interactions while considering a vehicle purchase, instead they get involve in a multi-channel decision making process engaging both digital and physical brand touchpoints. The touchpoints like ranging from virtual test drivers, mobile apps to showroom visits and online configurations serve as crucial moments that build perceptions and influence consumer behaviour (Verhoef et al., 2015).

In spite of increased investments towards touchpoint, consumers skepticism towards brands insists remains a barrier for successful conversions in automobile industry. To increase purchase behaviour, brand skepticism featured by mistrust of marketing messages and distrust in brand authenticity can help in reducing consumer confidence (Obermiller & Spangenberg, 1998). Therefore, to understand physical and digital touchpoints collaborate with consumer skepticism to influence purchase behaviour alter to critical for automobile marketers.

The study examines how digital touchpoints, physical touchpoints and brand skepticism influence purchase intentions in the Indian automobile industry. To understand the effects of these variables on consumer behaviour, Structural equation modelling through smart pls will be used to develop and test theoretical model. The research provides insights towards omni channel strategies and resistance towards psychological factors in high engaging purchase like automobile.

Digital Touchpoints (DT) and Purchase Intention (PI)

DT surrounds the range of brand interactivity that happens through online platforms including social media, websites, mobile apps, virtual showrooms, email campaigns and online configurations. Contradictory to automobile industry, the touchpoints empower consumers to access thorough information about products, getting involved through virtual experiences (Lemon & Verhoef, 2016) and appraise brand propositions at their convenience. 

Prior studies have shown that the attainability and customization provided by digital touchpoints considerably intensifies user experience which influences perceived brand value and increase trust among consumers (Grewal et al., 2017) (Rawal, N., Vakil, S., Wadia, A. (2025). To positively influence purchase behaviour among consumers, high engaging products like automobiles, well planned digital touchpoints can help bridge information gap and ease comparison shopping (Crosman, 2020). However, Digital involvement plays a crucial role in pre purchase stages where consumers depend on virtual cues to make informed decisions (Verhoef et al., 2015). Therefore, we assume that:

H1: Digital touchpoints (DT) have a positive and significant effect on purchase intention (PI).

Physical Touchpoints (PT) and Purchase Intention (PI)

In spite of digital shift, PT remains essential in sectors like automobiles where consumers seek physical assurance before committing to a high cost purchase (Becker et al., 2020). This includes in a dealership interactivity, personal consultations, test drives, vehicle exhibitions and brand related retail environment. PT help shape the emotional and sensory aspects of consumers experience which are little difficult to replicate virtually. Physical presence allows customers to authenticate the choices through interactivity, inspection and re assurance especially in contexts where reliability, safety and after sales support matters Grewal et al. (2017). To reduce post purchase dissonance, the personal connection established through face to face sales staff can further enhance credibility. Therefore, we assume that:

H2: Physical touchpoints (PT) have a positive and significant effect on purchase intention (PI).

Brand Skepticism (BS) and Purchase Intention (PI)

BS refers to the consumers pre disposition to doubt the credibility and truthfulness of communication through brand (Obermiller & Spangenberg, 1998). In order to reluctant the purchase part in automobile sector and diminished brand trust, consumers may question exaggerated claims (Forehand & Grier, 2003) where advertisement can often emphazise performance, safety and innovation.

To make people less responsive towards digital and physical touchpoints engagement efforts, high levels of skepticism can underline the persuasive effect of marketing messages (Mohr et al., 1998). In context to India, access to peer reviews and growing awareness or expert comparisons and alternative brands can denote skepticism. Therefore, consumers may think to commit to take decision related to purchase particularly if they perceive a mis alignment between claims and actual product performance. Therefore, we assume that:

H3: Brand Skepticism (BS) has a negative and significamt effect on purchase intention (PI).

Theoretical Framework

The research involves Stimulus Organism Response (SOR) Model which represents that environmental stimuli i.e brand tourchpoints effects internal evaluations i.e. skepticism which then influence behavioural responses i.e. purchase intention (Mehrabian & Russell, 1974). In the study, DT and PT reflects as external stimuli and BS reflects as an internal psychological state that regulates consumers eventual decision to buy an automobile. Prior studies have addressed there variables differently, there is a lack of research that incorporates all these variables into a emphirical model using SEM ie. Structural equation modelling, mainly in automobile industry in Indian context. The study targets to fill this gap by investigating how DT and PT brand strategies along with BS influence PI. Figure 1 represents the theoretical model of the study.

Figure 1: Theoretical Model

A quantitative research design has been adopted by the study to empirically examine the impact of DT, PT, BS on PI. A cross sectional survey method was applied to collect data from consumers who are potential automobile consumers in Gujarat, India. The theoretical framework referents SOR model where DT, PT and BS represents external stimuli and consumer pre dispositions which influences behavioural responses i.e. PI.

A non probability purposive sampling method was used to reach out towards consumers who have recently purchased or either shown any intention to purchase any automobile withing the next 12 months. Adult consumers are comprised as target population residing in Gujarat, which is a significant and growing market for purchases in automobile in India. A structures questionnaire was used to gather information through online and offline, to make sure broader reach and participation.  250 responses were collected and analyzed for the study. The sample size exceeds the structural equation modelling recommended threshold, which normally requires minimum 200 responses for vigorous model estimation (Hair et al., 2019). All variables constructs were measures using 7 point likert scale. To make sure the reliability and validity of the constructs, the study adapted and modified the established measurement scales from prior studies. The table 1 represents the variables, their meanings and sources of prior literature, from where measurement scales are taken and modified.

Table 1: Variables

Data Analysis

The study has applied structural equation  modelling through smart PLS ( Partial least squares) because of its prediction oriented research and suitability for exploratory models. First step will be to check reliability and validity for that measurement model evaluation will be assessed which includes cronbach’s alpha value, Average variance extracted and composite reliability values followed with structural model evaluation for hypothesis testing through path coefficients, t-values and significance levels. Table 2 represents the demographic profile of respondents

Table 2: Demographic Profile

Out of 250 respondents, the demographic profile reveals fair balanced distribution maong gender with 56.6% male and 43.2% female respondents. The demographic profile represents the gender composition in automobile purchase commonly observed where male consumers are more represented. The largest proportion of participants fall under age group 25-34 years with 35.2% followed with 18-24 years age group with 24%. It represent how young to early middle aged adults people are typically connected with first time or inspiring vehicle purchase. 35-44 years of age group accounts for 22.4% denoting mid career professionals who consider upgrades or family vehicles. Rest 18.4% are 45 years and above age group denoting lower purchase activity towards older age group.

49.2% of respondents were postgraduates with respect to educational qualifications followed by 38.8% who were undergraduates. This represents who relatively educated participants sample were collected who suggest informed decision making and higher livelihood of involvement with both DT and PT during their purchase process.  54% respondents were working professionals, playing a dominating role with the income levels required for automobile buying. 28% were students including aspiring buyers or influencers in household decisions. 10& and 8% are contributed business owners and homemakers respectively. 38.4% respondents earn monthly between Rs. 25001 and Rs 50000, while 23.2% of respondents were below Rs 25000 and 21.6% of respondents were between Rs. 50001 and Rs 75000 range. Remaining 16.8% of respondents were earning above Rs. 75000 denoting a majority of participants from middle income segment, an important target demographic for mid range model in automobile industry. Therefore, the demographi profile indicates a young, working and middle income respondents, defining a suitable part to explore purchase intention in automobile sector particularly in context to the influence of brand touchpoints and BS.

Measurement Model

SEM has been applied as it represents a powerful multivariate method that allows for continuous assessment of measurement model indicating relationships between observed variables and latent constructs and structural model indicating relationships among latent constructs. The study involves Latent constructs such as DT, PT, BS and PI therefore, SEM was suitable as these variab;es cannot be measured directly but are related through multiple indicators. Through structural equation modelling the research make sures a comprehensive and statistical evaluation of variables reliability, validity and their relationships among key variables which influence automobile purchase behaviours.

To construct the variables used in the study, measurement model was examined through reliability, validity and multicollinearity. Cronbach’s alpha, composite reliability, average variance extracted and variance inflation factor were evaluated for each items: DT,PT,BS and PI. Table 3 represents the values and Figure 2 represents the model.

Table 3: Measurement Model

Figure 2: Model

First of all, cronbach’s alpha and composite reliability values were checked to ensure the internal consistency and reliability. The Composite reliability values were ranging from 0.796 to 0.869 representing acceptable CR for BS and DT respectively. CR values should exceed the recommended threshold of 0.70 for all constructs (Hair et al., 2019). Cronbach’s Alpha values should exceed 0.70 creteria, where DT and PT values have exceed respectively with 0.798 and 0.718. Though BS and PI cronbach’s alpha values were 0.696 and 0.695 which were marginally low than the threshold but was considered acceptable in exploratory research (Nunnally & Bernstein, 1994) particularly when incorporated through strong CR values.

AVE values were identified to check the convergent validity. AVE values should be above 0.50 value. All constructs have AVE values more than 0.50 (Fornell & Larcker, 1981), denoting a substantial proportion of variance captured by underlying latent variables in the observed indicator. AVE values ranging from 0.507 to 0.625 suggested an adequate convergent validity for measurement model.

Variance inflation factor values were investigated to test the multicollinearity of each construct. All VIF values were below the threshold 3.0, ranging between 1.78 to 2.12 denoting that multicollinearity is not a concern for the theoretical model (Hair et al., 2019).

Structural Model

After measurement model assessment, next step is to determine path coefficient, coefficient of determination and model fit through structural model assessment. The hypothesis were examined by evaluating the significance of the standardized path coefficients. Table 4 shows the relationships and their path coefficients, were  relationships were statistically significant at 0.05 level.

Table 4: Structural Model

The outcome of the study indicates a significant and positive influence of DT, PT towards PI, While a negative and significant influence of BS towards PI. Coefficient of determination R² value was determined for dependent variable to assess the explanatory power of the model. 0.349 was the R² value for PI denoting an approx. 34.9% of variance by DT, PT and BS on PI, suggesting a moderate predictive power of the model (Hair et al., 2019). Table 5 shows the R² value.

Table 5: R² Value

Standardized Root Mean Square Residual was investigated to assess the overall fit of the structural model. 0.078 was the value for SRMR for both saturated and estimated model. Henseler et al. (2015) indicates that SRMR values below 0.08 denotes a good model fit while 0.10 values or below that are generally acceptable in exploratory context. 

The research primary goal was to examine the impact of Brand skepticism, Digital touchpoints and Physical touchpoints on Purchase intentions within the automobile industry in Indian context. The model provide a lot of insights regarding the variables . Firstly, all three hypothesized relationships were found to be statistically significant. Among them, Physcial touchpoints (PT → PI) exhibited the strongest positive influence on Purchase Intention (β = 0.383, p < .001), followed by Digital touchpoints (DT → PI) (β = 0.169, p < .001), and Brand Skepticism (BS → PI) (β = 0.157, p < .01). The results align with prior studies emphasise the role of digital and physical touchpoints in reducing consumers hesitation and encouraging willingness to purchase for specially high engaging product categories like automobiles (Chen & Dhillon, 2003; Schaupp et al., 2009; Luo et al., 2022). However, the negative and significant influence from Brand skepticism underlines the persistent challenge skeptical attitudes provides to marketers in building good brand images (Forehand & Grier, 2003).

The R square value of 0.349 for purchase intention denotes that approx. 34.9% of the variance in PI can be explained be their variables in the model. (Hair et al., 2019) indicates a moderate value reflects a meaningful level of explanatory power in behavioural research. It also recommends the presence of other factors which might not taken into account in the current model, pointing towards fruitful avenues for future explorations.

Standardized Root mean Square Residual (SRMR) was 0.078, representing a model fit situation within the acceptable threshold for a well fitted model (Henseler et al., 2015). This represents the sturdy of the structural model and supports the adequacy of the specified relationships. However, outcomes of the study validates the importance of touchpoints i.e. Digital and physical touchpoints in shaping purchase intentions in the evolving automobile industry. The variables becomes crucial strategic levers in an era, where consumers have increasingly scrutinize different brands and demand accountability. Contradictory Brand skepticism statistically significant had a relative weak influence on purchase behaviour indicating if skepticism exists, it can be mitigated through different touchpoints and strategies.

The results of the study provides insights into the body of literature on digital consumer behaviour by emphasising the psychological underlining that provides purchase decisions in the automobile sector.  To enhance sales conversions and encourage or boost up the positive consumer attitudes, they offer practical implications for brand managers denoting transparency and trust for digital and physical touchpoints.

Implications

The study provides crucial insights about how BS, DT and PT effects purchase intentions in the automobile industry in Indian context. The outcome of the results have theoretical implications by provide more advancement in consumer behaviour literature especially in touchpoints for both digital and physical for high engaging products. The research integrates and extends the Stimulus Organism Response (SOR) Model by validating he predictive power of touchpoints of both digital and physical part in the automobile industry in Indian context. As a novelty of the study it establishes Brand Skepticism as a direct and significant influence on purchase intention rather than as a mediating or moderating variable, therefore providing a fresh lens for examining negative brand perceptions.

The study underlines the importance of digital and physical touchpoints y cultivating trust and openness through these touchpoints as a practical implication. The marketers should focus on designing translucent communication strategies like disclosing warranties, pricing and policies clearly besides digital channels for automobile sector. However, building digital touchpoints through safe platforms, real time assistance, verified consumer reviews and consistent brand is essential for fostering consumer confidence and involvement. By addressing brand skepticism with credibility through credit driven storytelling, influencer partnerships and behind the scenes content can help in future to reduce resistance among skeptical consumers. Therefore, these actions can all together not only improve purchase intentions but also helps in contribution to long term brand loyalty and digital competitiveness.

Limitations and Future research directions

The research contributes a comprehensive investigation of how digital and physical touchpoint, brand skpeticism are influencing consumer purchase intentions in the automobile industry in Indian context. The findings highlight that, physical touchpoints emerged as the most significant predictor of purchase intentions, followed by digital touchpoints and brand skepticism. The outcomes underlines the increasing importance of credibility, clarity and safe digital involvement in influencing consumer behaviour, particularly for high engaging products like automobiles. Nowadays, automobile brands are increasing shifting towards digital platforms, the psychological variables play a crucial role in shaping consumer willingness to involve and convert.

However, the research includes lot of limitations. The cross sectional nature of the study restricting the ability to infer a lot of causality between variables. In addition, the model accounts for 34.9% of the variance in purchase intention, giving suggestions that other relevant variables such as consumer innovativeness, perceived value or social influence may also contribute meaningfully. The information provided by respondent is self reported, introducing the potential for common method bias and limited to single country context sample which impacts the generalization of the findings across global markets.

Future research should consider emphasizing longitudinal or experimental designs to track changes in consumer perceptions over time and to strengthen causal interpretations. To test the model applicability globally, comparative studies across different cultural or regional contexts could help validate. However, incorporating brand credibility, online involvement or affective trust can be as mediating variables could deepen understanding of the underlying mechanisms. As digital touchpoints continue to evolve especially with the emergence of AI-driven platforms and immersive experiences future studies should also explore how these new technologies influence trust-building, skepticism reduction, and ultimately, purchase behavior in the automotive sector.

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