“Exploring factors of service adoption using SERVQUAL paradigm: Its impact on millennials’ adoption of services in the self-drive rental sector”

The self-drive rental sector has witnessed exponential growth in recent years due to rising demand for long and short-distance drives among millennials. This study aims to investigate the quality of services in the self-driving rental sector and its impact on customer adoption or rejection of service in India. The conceptual framework was developed using the SERVQUAL model and other important factors affecting consumers’ service adoption. A quantitative research method was deployed, and data were gathered through a survey method using a structured questionnaire (based on a 5-point Likert scale). The sample size comprised 385 respondents, 23-38 years old millennials (with 69% of males and 31% of females). The population sample was chosen from Delhi, Mumbai, and Bangalore, India. The data were collected in March 2023. The factor and regression analyses were applied along with chi-square and SEM analyses to test the research hypotheses. The results indicated that the absence of low prices (42%), customer assistance (28 %), and security issues is responsible for consumer rejection. The factors leading to dissatisfaction are the absence of consumer schemes and discounts, a lack of staff interaction and assistance, and poor service quality. The brands must focus on the negative impact arising from the absence of these factors and effectively address the areas of improvement to regain customer trust and garner customer loyalty.


INTRODUCTION
The importance of service quality has escalated significantly because service providers have realized that they must provide more excellent value to the customer if they want to stay in business.As competition is increasing tremendously, giving customers different alternatives, the quality of service is the priority for the organization, especially when trying to maximize the value and satisfaction for their customers.
Car and bike rental services have significantly evolved and diversified so that today, this sphere represents a world industry governed by rules and national and international legislation.These self-drive rental car and bike companies filled the gap for people who loved traveling far-off places but did not own a car or a bike.Thus, today, there are multiple self-drive rentals, making it one of the fastest-growing industries in India.This is due to increased internet penetration, smartphones, and applications, which enabled easy booking options for self-riders and renters.Zymo, Avis India, Zoom Car, and Ola are prominent companies for self-drive rental cars in India.Undoubtedly, bike and car renting is booming in the country.
While many empirical studies have been conducted earlier to study the factors leading to consumer satisfaction, no study has been conducted integrating multiple factors of services that can lead to consumer dissatisfaction impacting their acceptance of service, particularly in the self-drive rental sector.Therefore, there is a so-called critical gap on this issue among Generation Y, the study of which will allow companies to align their marketing strategies accordingly to turn dissatisfaction into satisfaction, which will lead to repeat services.

LITERATURE REVIEW AND HYPOTHESES
The self-drive rental sector is a relatively new business model.So, the literature available is limited.Verma et al. (2022) used sentiment analysis to learn more about the feelings of Indians who use self-drive rental cars on social media and to evaluate the connection between demographics and car rental preferences.Sing and Rakshit (2020) opine that customers evaluate prices and compare prices between various providers of services.Therefore, marketing managers must track their nearest competitors' pricing and advertising strategies to create a competitive advantage over the others (Shaw et al., 2022) Nevertheless, these conditions are not constant and vary between the two environments.In the offline context, there is no statistically significant effect on satisfaction with recovery, despite this association being significant among the online group, along with compensation and explanation.
Online customer reviews are a notable form of brand information customers generate.Adverse feedback leads to the most intense states of arousal.The degree of service failure impacts both frustration and referential cohesion (Fox et al., 2018).This comprehension is essential in contemporary society, propelled by user-created material accessible through a vast number of consumer submissions on the internet.Investigating online customer reviews is crucial for marketing academics and other researchers due to internet reviews' substantial role as a source of user-generated content.The analysis proves that 83% of the customers prefer payment through cash.There are complaints regarding the payment method as there is sometimes non-availability of cash, and people do not get the exact change, which further creates a problem.
Consumer loyalty to Airbnb services is primarily related to the quality of service and visibility on social media and offered authentic experiences (Lalicic & Weismayer, 2018).However, perceived economic benefits do not influence the degree of loyalty.According to Kalla and Purohit (2017), the first aspect is the company's core business, i.e., usability and user engagement.The second aspect is customer interaction and involvement, with active customer inclusion in the service process.The third aspect is the relevant distinction to provide the consumers with an additional benefit that no one provides.
In contrast to developed nations, Indians are less relaxed using online taxi services (Sharma & Das, 2017).With the billing and payment of online cab services, they lack trust.Even compared to Western countries, women are not comfortable riding alone in cabs in India.The study could have been done across various individuals apart from students alone, so the results cannot be generalized as it is not done on a pan-generation basis.The determinants taken into account, such as stimulus generation, knowledge quest, and quality of service, significantly affect customer loyalty, which defines consumer behavior for travel planning purposes toward e-tourism services (Bajpai & Lee, 2015).Perceived service recovery mediated the relationship between satisfaction and relationship intention following service recovery (Kruger et al., 2015).A direct and indirect relationship exists between intention and satisfaction following service recovery.Hence, cell phone network providers should not only prioritize their efforts in building relationships with customers who intend to form a connection but also provide service recovery that is perceived positively by these customers.This approach would result in higher satisfaction levels after the service recovery process.
South Koreans exhibited a greater propensity to express dissatisfaction through complaint actions than Americans.However, the two groups had no substantial differences in their evaluations of efforts to rectify the service-related issues.Americans have been anticipating equitable treatment in administering cases involving speech and private complaints, focusing on fairness in distribution, procedures, and human interactions.On the other hand, South Koreans expressed their desire to voice complaints, which they believe will lead to fair treatment regarding both the distribution of resources and the procedures followed.Thus, future researchers should employ comparable sample sizes for the two distinct cultures in order to obtain more accurate and dependable findings (Gi Park et al., 2014).
Gera (2011) proves the extensive presence of direct and indirect impacts on prospective behavioral intentions stemming from service quality, value expectations, and overall satisfaction.The study also recognizes that the key factors contributing to positive behavioral outcomes are the primary aspects of service quality, including product awareness, empathy, efficiency, and trust.Perceptions of agent service quality, contentment, and value substantially impact the likelihood of recommending the service.Li and Hitt (2010) indicate the significance of companies in their overall marketing strategy to account for these price effects.Companies' dimensional ratings are more closely associated with product value ratings than product quality ratings.Ansari and Riasi (2016) depicted how expectations of justice affect client satisfaction as they lodge a complaint and undergo rehabilitation.Moreover, because it is risky for businesses to perceive service recovery as an opportunity to delight customers, companies can also use a constructive strategy to maintain their customers.Kim et al. (2010) implied that the system provides a starting point for extending the thinking on processes for addressing customer complaints.The study also gives hospitality managers insight into dealing with unhappy customer encounters.
Akhter (2010) brought a critical point, i.e., how the services' characteristics positively affect overall satisfaction.The overall satisfaction then positively impacts the purpose of buying, which positively impacts the actual repurchase.Subsequently, happy customers prefer to buy back from the same service provider, so it will be difficult for competitors to lure these rivals with their deals.Then lastly, different customer groups weigh service attributes differently in determining overall satisfaction.According to Lin (2010), the smaller the gap between service quality expectation and perception, the more consumers will show their loyalty to post-purchase conduct, while the more significant the difference, the more complaints will be made and transferred to other brands by more customers.Therefore, establishing a bank's uniqueness and distinguishing ability should also consider the development of a brand image, the enhancement of goodwill, and even the improvement of public relations.The paradigm of knowledge, process, quality, and purpose provides a comprehensive basis for understanding and reviewing service failure and recovery attempts (McCole, 2003).In addition to implementing concepts bor-rowed from SERVQUAL, the model combines dis-confirmation, equity, prospect, and attribution theory.The study derived a model that will help businesses to understand the standards of eservice and achieve high retention of clients, customer loyalty, and profitability.Practitioners often do not need to segment based on gender or age to achieve service efficiency (Shankar et al., 2022).
Globally, many studies have been done to understand the effect of rental services on consumer behavior.However, considering all the above factors, more research needs to be done on the self-drive rental sector.
This study aims to define the critical factors in the self-driving rental sector and their impact on customer adoption or rejection of service in India.The following hypotheses were developed to study the relationships between the variables based on the literature review and theoretical background (Figure 1): H1: A significant relationship exists between consumer expectations of services and their satisfaction.
H2: An adverse effect is seen on consumers due to poor service quality, leading to higher consumer dissatisfaction.
H3: There is a significant impact of brand attributes, technology, and price on consumer adoption of services.
H4: There is a significant association between SERVQUAL model variables and consumer service adoption.

METHOD
The study is quantitative.The primary data were collected by a questionnaire survey, which has both open-and close-ended questions from the respondents, in order to understand the impact of service on choosing a particular brand of self-drive rental car or bike.The quantitative method is conducted with students and working crowd consumers.It is also a descriptive study where responses are collected by using the convenience method under non-probability sampling.The questionnaire is based on a 5-point Likert scale for better analysis.
The sample size for this study is 385, and the respondents are millennials between the ages of 23 and 38.The population sample was chosen from Delhi, Mumbai, and Bangalore city.The data were collected in March 2023.The margin of error for the study is 5, while the confidence level is 95%.The formula used is where n is the sample size, N is the total population size, and e is the margin of error.The tools of IBM SPSS software were used for this study.Reliability, factor analysis, regression, and structural equation modeling (SEM) were used.

Figure 1. Proposed conceptual framework
The SERVQUAL model, alternatively called the GAP model, assesses service quality by capturing consumer expectations.Therefore, service quality is vital in distinguishing a product and establishing a competitive edge for any brand.Organizations compete by strategically presenting service options to attract and satisfy consumer expectations and views of service.SERVQUAL defines service quality as the disparity between a customer's anticipated level of service and their actual impression of the service provided.The disparity between expectations and perceptions is called the gap, ultimately shaping a customer's service quality assessment.
According to the theory of planned behavior, when individuals perceive the suggested conduct as beneficial (attitude) and have confidence that others would engage in the behavior (subjective norm), it leads to a stronger intention (motivations).It increases the likelihood of them actually performing the behavior.It the factor of perceived behavioral control, which also contributes to the purchase intention of a customer.The model finally establishes a direct relationship between the intention of a customer and their final behavior.Thus, this model consists of four major elements: • Attitude: It is the behavioral belief and outcome evaluators to know the favorable or unfavorable mindset towards a situation.
• Subjective Norm (S.N.):It is the normative beliefs, and data collection was done through well-structured motivations to influence decisions.
• Behavioral Intentions (B.I.):It is the intention or the sampling method chosen is a nonprobability propensity of the user to engage in a particular behavior.
• Actual Behavior (A.B.):It is the actual behavior.

RESULTS AND DISCUSSION
The reliability test was performed to analyze whether the questionnaire given to the respondents was reliable.Reliability refers to the degree to which the effects of a test remain accurate if the measurements are repeated several times.Hence, before starting the analysis, it is vital to test the reliability of the data.Therefore, the study performs the reliability test, i.e., Cronbach's alpha, for all the scale variables (Table 1).The dataset is measured using a value called Cronbach's Alpha.Cronbach's Alpha value explains whether the dataset can be practically accepted here.Here, the value is .929,which is above .5.Hence, the value shows that the dataset is excellent and reliable and can be used for factor analysis.The dataset is 92.9% reliable.Factor analysis helps one understand the various factors that affect the consumer toward service adoption.It is a valuable tool for investigating variable relationships for complex concepts.The KMO value (Table 2) indicates the suitability of the variables used for factor analysis.The obtained result is 0.829, surpassing the required threshold of 0.5.Bartlett's test is used to check if a correlation exists between the variables and the customer satisfaction levels.
Regarding H1, the obtained p-value of 0.000 is smaller than the predetermined significance level of 0.05.Therefore, the variables have a substantial correlation, and an identity matrix does not represent this connection.The rotated components matrix (Table 3) displays the factor loadings assigned to each statement, component-wise.Two factors were identified with 6 and 4 statements, respectively.The first component can be named as security, as the customers are majorly concerned with the security of the payments that they make online or whether they are driving a car or riding a bike; they want first t o make sure that it is secure enough to take the service and also while booking, the applications which are using the personal information of the consumers are keeping the data secure.
The second component is the convenience factor, where the consumer is looking for a convenient option to take the services anytime or per their requirement.The consumers are also concentrating on convenience or flexibility in the delivery of the services, i.e., whether the cars are available, so is it providing any convenient packages for the customers.Thus, these factors can only be responsible for service adoption, leading to the repeated purchase of service.Otherwise, the absence of these factors will lead to consumer dissatisfaction, resulting in service rejection.
Factor analysis is done to identify the factors responsible for consumers' dissatisfaction with selfdrive rental services.Brands need to encounter these factors as they can be why some brands operate well, and others do not.So, after understanding the factors, the brands can work on improving the factors that will come out from this analysis and thus solve the issues the consumers face during their service process.The study accepts H1.The Kaiser-Meyer-Olkin (KMO) test assesses the suitability of the data for factor analysis, choosing factors leading to consumer satisfaction (Table 4).
The test assesses the sampling sufficiency for each variable included in the model.If the KMO value is observed to be .868,then the data are deemed statistically significant and sufficient for factor analysis.Suppose the values are less than 0.05 of the significance levels, indicating that factor analysis may be helpful with the data.An identity matrix is a matrix in which all diagonal elements are 1, and all off-diagonal elements are 0. So, this study's significance value is 0.000 (0.000 < 0.05), i.e., less than 0.5; therefore, data is valuable and significant for factor analysis.In Table 5, the first component is no consumer schemes and discounts, which tells that the brands are not looking to reduce the prices and are not even giving any additional discounts or offers.It will result in consumers moving to dif-ferent brands as they are dissatisfied with the prices and cannot find any additional rewards to stay loyal to one brand.The second component is the lack of staff interaction and assistance, which is a significant concern for consumers because whenever they are facing any issue, they want the brands to solve and support their issues.However, when there is not enough interaction, the consumers will not be satisfied and thus will stop taking the services.
The third component, the most critical component, is poor service quality due to inefficiency in operation.When there is a decline in the quality and the quality is not improved further by brands, consumers are forced to move and try other brands as they are dissatisfied with the services of a particular brand.Thus, the study accepts H2.The model summary (Table 6) shows that 74.1% of these factors impact the change or help decide the dependent variable.Since the Durbin-Watson is close to 2, it can be inferred that the variables dataset is positive for regression.A correlation coefficient of 0.861 shows a positive linear relationship between independent and dependent variables.R square is 74.1%, which means that if there is any variation in the independent variable, there is a 74.1% change in the dependent variable.Adjusted R square tells that if there is any addition of a new independent variable, a variation of 73.4% in the correlation between the dependent and independent variables exists.Also, the adjusted R-value is less than R square, and it is also seen that Durbin-Watson's value is near 2, i.e., 2.078, which shows that there is positive autocorrelation.
The significance level in ANOVA (Table 7) is .000,which is less than .05.Thus, this model is a good fit and H3 is accepted.
Confirmatory factor analysis (CFA) is a hypothesis-testing approach to factor analysis for which an appropriate model for accuracy can be tested based on the factors considered.Also, by using structural equation modeling, the model fit of the conceptual model can be determined.As for consumer data, many variables have been taken under consideration.The CFA has been used identify the most contributing variables, which will help extract the variables based on the regression estimates responsible for the performance.
The outputs from the CFA for the data are: Chisquare = 12607.957,degrees of freedom = 560, probability level = .000.The probability level is less than 0.05, which shows the appropriateness of hypothesis testing in the model; thus, the study can indeed accept H4, with positive degrees of freedom and a minimum level of chi-square achieved that this model is fit statistically.There is a high dependency on SERVQUAL variables on service adoption.Also, it is clear that they are interlinked with each other, and thus, through the confirmatory factor analysis, one variable is dependent on the other variable in order to have service adoption by the consumers.
The study accepts H4 based on the probability level and chi-square test.The research identified that SERVQUAL variables like reliability, empathy, assurance, responsiveness, tangibility, and pricing components are closely related, leading to service adoption.Thus, these factors are crucial and can significantly impact consumers' purchase intentions.
From Table 8, there is a high dependency of all the SERVQUAL variables on each other, as the value should be more than .5, and a high association between these SERVQUAL variables, as most of the values are near .8,which is pretty high.Thus, these variables and their high correlation are responsible for consumer service adoption.A value of the RMSEA of about .03 to .08 with a confidence interval would indicate a close fit of the model with the degrees of freedom.However, the value is more than 0.05 but closer to it, so this model can be accepted with degrees of freedom.The number of males using the self-drive rental services of cars and bikes was found to be 69%, and female users account for only about 31% out of the total number of 385 respondents.The respondents' average disposable income is between 20,000 and 80,000 per month, and most consumers, 79%, have completed their master's degree.The analysis reveals that the % of respondents who use self-drive cars and bikes is 47%, while 35% of people drive self-drive cars alone and take its services.The reliability of the entire sample was high at 0.929.
It was also observed that the expectations from consumers for self-drive service mainly rely on 42% for low prices, and 28% of them also wanted convenience in getting service delivery.In contrast, consumers showed very little concern toward more vehicle options.Hence, the first and most crucial factor leading to consumer satisfaction from self-drive rental services is security, as the customers are majorly concerned about the security of the payments that they make online.
The second most crucial component observed through this factor analysis test is the convenience factor, where the consumer is looking for convenient options to take the services anytime as per their requirement.Thus, these factors affect the millennials' minds, and the presence of these factors can result in their overall satisfaction, which further leads to repeated purchases of services.
The first prominent factor concerning dissatisfaction is the absence of consumer schemes and discounts.This tells that if the brands are not looking to reduce prices, they should focus more on giving an additional offer that attracts the consumers.The second essential component is a lack of staff interaction and assistance, which can result in consumer dissatisfaction.This is a significant concern for consumers as whenever they are facing any issue, they want the brands to solve their issues, and if this is absent, this might result in consumer rejection of service.Lastly, poor service quality is the third and most crucial factor that depicts consumer dissatisfaction.It occurs due to inefficiency in the service operation and the decline in quality improvements.Hence, consumers are forced to try other brands as they are dissatisfied with the services of a particular brand.
The tests also determined that brand image and quality play a significant role, as it is through goodwill and brand trust that will result in consumer service adoption.Technological improvements in the services and an easy and smooth booking process significantly impact consumer service adoption.Thus, there is a strong association between brand attributes, price, and technology concerning consumer service adoption of self-driving cars or bikes since the p-value is less than the significance level (0.05) with regard to the variables taken in the tests.
Finally, through the SEM analysis, it is found that there is a strong association between the SERVQUAL factors.Thus, the relationship between them will finally lead to consumer service adoption.So, through SEM, the SERVQUAL factors are critical and can significantly impact con- sumers' purchase intention as the probability level is less than 0.05, i.e., 0.000, which shows the appropriateness of hypothesis testing in the model.
This study is constrained due to its focus being limited to an urban population residing in metropolitan cities within a developing market.
Consequently, the study is limited to a single area, thus making it impossible to validate the generalization of results.Hence, this paper's reach might be extended to several locations and contrasted to comprehend these aspects comprehensively on a global scale.Moreover, the study may be subject to bias due to the uneven distribution of profession kinds.Nevertheless, there is considerable potential for further investigation to explore the correlation between demographic characteristics and variations in their patterns of service use.Though the study is confined to the self-drive rental sector, this has opened up a new sense of understanding and a deeper perspective into this sector.Thus, this will help design new strategies to attract new consumers and make them repurchase the services.Also, more variables can be found, leading to consumer service adoption.

CONCLUSION
This study aims to investigate the quality of services in the self-driving rental sector and its impact on consumer satisfaction in India.The findings indicate a few critical factors that significantly influence consumers concerning the adoption or rejection of service.These included customer support, low prices, a smooth booking process, discounts and cashback, and payment options.Rotated component matrix for two components leading to consumer satisfaction demonstrated 6 and 4 statements accordingly.For the first component, the highest value is secure to give personal information for services (0.908).For the second component, the highest value is convenience, and flexible delivery time is crucial (0.816).
Rotated component matrix for three components leading to consumer dissatisfaction demonstrated 3, 4, and 4 statements accordingly.The first component is no consumer schemes and discounts (0.813); the second component is the lack of staff interaction and assistance (0.847); the third component, the most critical, is poor service quality due to inefficiency in operation (0.578).Along with these factors, the SERVQUAL model dimensions were also considered essential to service adoption.SERVQUAL factors are critical and can significantly impact consumers' purchase intention as the probability level is less than 0.05, i.e., 0.000.Thus, this study extensively examined the attributes to understand these variables' importance in consumer service adoption.So, companies must play with cues and attract consumers through improved services.Hence, every company should give importance to these factors so that customers not only get impacted by the services for the first time but also repeat the services offered by the brands, eventually leading to more sales and profitability.

Table 1 .
Reliability statistics

Table 2 .
KMO and Bartlett's test for factors leading to consumer satisfaction

Table 3 .
Rotated component matrix for factors leading to consumer satisfaction Note: Extract Method: Principal Component Analysis.

Table 4 .
KMO and Bartlett's test for factors leading to consumer dissatisfaction

Table 5 .
Rotated component matrix for factors leading to consumer dissatisfaction

Table 6 .
Model summary for brand attributes leading to service adoption Note: Dependent variable: Adoption of self-drive rental services.

Table 7 .
ANOVA for brand attributes leading to service adoption Note: Dependent variable: Adoption of self-drive rental services.

Table 8 .
Correlations for SEM analysis