A study of repurchase intention on satisfaction variables in the automotive sector

Introduction

In the dynamic landscape of the automotive sector, where technological advancements, environmental concerns and shifting consumer preference continually reshape the industry, understanding and responding to customer satisfaction are paramount for automotive businesses. This necessitates a keen awareness of market dynamics and an ability to adapt swiftly to changing consumer demands. The automotive industry is multifaceted, encompassing various sub sectors such as manufacturing, distribution, retail, aftermarket services and technology development. This complexity offers researchers a rich landscape to explore, with opportunities to investigate diverse topics ranging from supply chain management and consumer behavior to sustainability initiatives and technological innovation. Understanding consumer behavior and preferences is essential for automotive companies seeking to develop and market products that resonate with target audiences. Research in this area can delve into consumer attitudes, purchasing motivations, brand perceptions and the adoption of new technologies, providing actionable insights for product development, marketing strategies and customer engagement efforts. The automotive sector is subject to dynamic market forces influenced by factors such as economic conditions, technological advancements, regulatory changes, and shifting consumer preferences. Focusing on researching consumer buying preference can provide valuable insights into industry trends and help businesses tailor their products and services to meet customer demands effectively. Market research acts as a cornerstone for businesses aiming to thrive in today's competitive landscape. By delving into consumer preferences, market research provides invaluable insights that guide decision-making across all aspects of business operations. Firstly, understanding consumer buying preferences allows businesses to tailor their products and services to precisely match what their target audience desires. Whether it's identifying the features consumers prioritize, the pricing structures they find appealing, or the quality standards they Expect, market research provides the roadmap for product development and refinement. This ensures that businesses are delivering offerings that not only meet but exceed consumer expectations, fostering satisfaction and loyalty. Furthermore, market research serves as a strategic tool for staying ahead of the competition. By monitoring consumer trends, analyzing competitor offerings and identifying gaps in the market, businesses can position themselves as leaders in innovation. Whether it's through product enhancements, new service offerings, or disruptive business models, market research empowers businesses to anticipate and respond to shifting consumer preferences proactively. By incorporating consumer feedback into every aspect of their operations, businesses can foster a customer-centric culture that prioritizes continuous improvement and adaptation. This iterative approach ensures that businesses remain agile and responsive to evolving consumer needs, positioning them for sustained success in the marketplace. In an era characterized by rapid technological advancements and an abundance of choices, businesses must gain deep insights into what drives consumer choices. This knowledge empowers companies to tailor their products, services and marketing strategies to meet evolving customer expectations. By figuring out the factors that influence purchasing decisions, from personal preferences and demographics. Organizations can not only enhance customer satisfaction but also make customers happier, keep them loyal to their brand and stay ahead in a busy market. In essence, grasping consumer buying preferences is the compass that guides businesses toward long-term success and sustainable growth. In any business, varying consumer preference is a huge challenge in the sustainable growth of the business. Nowadays companies must apply more effort to have a good market share and maintain their position in the industry in which they operate. In the dynamic landscape of the automotive industry, understanding the factors influencing repurchase intention has become vital for manufacturers and marketers. Using a comprehensive questionnaire and combining traditional survey methodologies with machine learning techniques, this study contributes to the evolving field of consumer preference research. It is a valuable guide for businesses seeking to thrive in the competitive automotive landscape. Machine Learning has revolutionized how businesses approach customer satisfaction, offering powerful tools to analyze data and extract actionable insights. Integrating machine learning techniques into customer satisfaction surveys holds immense promise for companies like HONDA in the automotive industry, where consumer preferences play a pivotal role in shaping success. By leveraging statistical analysis techniques, we can delve deeper into the dataset collected through HONDA's customer satisfaction survey, uncovering hidden correlations and trends. This allows us to not only gain a deeper understanding of customer preferences but also tailor survey questions more effectively to capture nuanced insights. Our study represents a cutting-edge application of machine learning techniques in the realm of customer satisfaction research, demonstrating how innovative approaches can enhance the accuracy and utility of survey data. We aim to provide HONDA with actionable insights that drive informed decision-making and ultimately elevate the customer experience.

Literature review

Chang, T., and Hsiao, W. (2011) [1] studied how Car Infotainment Systems (CIS) influence consumer car purchase intentions. A survey of 319 consumers, analyzed using Structural Equation Modeling, showed that CIS value was linked to its utility and ability to enhance driving safety, increasing purchase intentions. Conversely, high prices and perceived risks reduced CIS value and purchase likelihood. The findings suggested that auto manufacturers should prioritize IT integration in new models and recognize CIS as crucial for future car development, particularly with the rise of electric power. Nezakati, H., et al. (2011) [8] contributed the research to academicians, practitioners, and policymakers by enhancing their understanding of consumer purchasing behavior, particularly towards imported cars in Malaysia. Marketing managers gained insights into consumer attitudes, aiding in strategy development. Local car producers, such as PROTON and PERODUA, benefited by understanding consumer behavior regarding price, quality, value, and perception to create effective marketing strategies. The study concluded that respondents had a strong belief system favoring imported cars, as analyzed using Multiple Regression. Menon, B., and Raj, J. V. P. (2012) [7] analyzed how Globalization and liberalization, coupled with the entry of foreign manufacturers, significantly transformed India's automobile industry in the early 1990s. Prominent global car manufacturers established joint ventures in India, leveraging the Foreign Direct Investment policy, and captivated Indian consumers with advanced, reliable products. This shift turned the market from seller-driven to buyer-driven, influencing personal preferences and purchasing patterns. Our study aimed to model the consumer purchase behavior of passenger car owners in Kerala, contributing valuable insights for manufacturers and dealers to tailor their marketing strategies. Despite extensive global research, little empirical work has focused on India's automobile sector. Shende, V. (2014) [12] found that the automobile industry was highly lucrative due to increased disposable income and easy finance availability, which drove high-volume car segments. The author noted that competition intensified with new entrants and global brands like Porsche, Bentley, and Ferrari entering the Indian market. This research helped car manufacturers understand customer expectations and market offerings. The author analyzed consumer behavior across all car segments, revealing that disposable income was the primary driver for car purchases, with value for money, safety, and comfort being top priorities. Brand image significantly influences perceived quality, especially in the executive and premium segments. The study emphasized the need for manufacturers to align strategies with consumer behavior to overcome market stagnation and cultivate future demand. Patikar, E. T. D. G., and Thangasamy, E. emphasized the critical role of marketing in facilitating the movement of commodities from production to consumption within the global business landscape. Consumers are depicted as seeking optimal surplus, while marketers strive for profit maximization. This dynamic necessitates continuous research into consumer behavior to guide product planning and development strategies. In India, the author noted the extensive exploration of consumer decision-making by researchers, particularly concerning durable goods. These studies provided valuable insights into how consumers select, purchase, and dispose of products, enabling firms to craft effective marketing strategies. Younus, S., et al. (2015) [15] investigated factors influencing purchase intention, including customer knowledge, perceived value, and celebrity endorsement. Findings from 100 questionnaire responses revealed significant relationships between these variables and purchase intention. Regression analysis showed positive correlations between independent variables and purchase intention. While perceived value had a limited impact, celebrity endorsement and product packaging were identified as key drivers, emphasizing the importance of effective marketing strategies in shaping consumer behavior. Panuju, A. Y. T., et al. (2020) [9] investigated that manufacturing sustainable products, particularly vehicles, was crucial for achieving global responsible consumption and production. A study in Indonesia explored the link between sustainable automotive designs and young consumers' purchase intentions, revealing the significant effects of sustainability dimensions on preferences. While social criteria like health and safety were prioritized, economic factors were less influential, likely influenced by assumptions of financial stability. Nevertheless, environmental considerations showed promise for future importance, suggesting an emerging awareness of sustainability among young consumers in developing nations. Jung, J., et al. (2021) [4] presented a study on electric vehicles (EVs) in the Korean automobile market based on a survey conducted in February 2018. The study aimed to empirically analyze factors affecting Korean consumers' preferences for EVs, considering their attitudes toward environmental issues and vehicle attributes. Using a hybrid choice model with responses from 1,251 participants, the study found that consumers who favored EVs were sensitive to vehicle pollution and valued future automobile benefits over aesthetics. Key factors for successful EV adoption included purchase price, charger subsidies, and charging infrastructure. The study suggested that government policies and technological developments emphasizing these factors could accelerate EV adoption in Korea.

Research gap

Numerous studies have explored various aspects of consumer behavior, including the factors that influence initial purchasing decisions. But there is a noticeable lack of studies focusing on repurchase intention within the automotive sector. For automotive companies like Honda, understanding the specific factors that influence repurchase intention is critical for developing strategies to retain customers and encourage repeat purchases. By focusing on the unique aspects of the automotive sector and incorporating both quantitative and qualitative methods, this research can provide actionable insights that directly benefit the industry.

Research methodology 4.1 Research objectives

1. To know the customer's satisfaction level toward HONDA 2. To explore demographic factors affecting repeat purchase intention 3. To find the satisfaction variables that have a significant positive correlation with Overall customer satisfaction 4. To analyse the repeat purchase intention of customers 5. To predict future purchase behavior based on past interactions

Latent variables used in study

A latent variable is a variable that cannot be directly observed or measured but is inferred from observed variables. It represents an underlying construct or concept that influences or drives the observed variables. The importance of latent variables in understanding consumer preferences lies in their ability to capture complex relationships and underlying drivers that may not be immediately apparent from direct observations. By identifying and analyzing latent variables, researchers and businesses can gain deeper insights into the factors shaping consumer preferences. This allows for more accurate modeling, prediction and understanding of consumer choices, enabling businesses to develop targeted marketing strategies, improve product offerings and enhance overall customer satisfaction. Ultimately, uncovering latent variables provides a more nuanced understanding of consumer preferences, helping businesses stay competitive and responsive to changing market dynamics. For each latent variable, we included two to three related questions and calculated the mean response to represent the overall score for that variable. Below are the latent variables used in the study.

The hypothesis used in the study

Hypothesis for the relation between satisfaction variables and overall customer satisfaction  H1a: there is a positive and significant correlation between "price satisfaction" and "overall customer satisfaction".  H1b: there is a positive and significant correlation between "after sales service satisfaction" and "overall customer satisfaction".  H1c: there is a positive and significant correlation between "product quality satisfaction" and "overall customer satisfaction".  H1d: there is a positive and significant correlation between "product performance satisfaction" and "overall customer satisfaction".  H1e: there is a positive and significant correlation between "brand image-satisfaction" and "overall customer satisfaction".  H1f: there is a positive and significant correlation between "responsiveness of employees" and "overall customer satisfaction".  H1g: there is a positive and significant correlation between "delivery satisfaction" and "overall customer satisfaction".

Hypothesis for analysing distribution of overall customer satisfaction across repeat-buyers and non-buyers H2: "Overall Customer Satisfaction" is different across

Repeat-Buyers and Non-Buyers.

The questionnaire

The questionnaire design for this study was informed by a comprehensive review of relevant literature, providing theoretical underpinnings and was also influenced by established practices observed in surveys conducted by HONDA. Emphasizing a robust foundation, the questionnaire employed 11-point Likert scales to capture nuanced responses from participants, facilitating a detailed exploration of satisfaction levels across various dimensions. Drawing from identified satisfaction variables, each aspect was carefully addressed with two to three tailored questions, allowing for a comprehensive assessment of respondents' perspectives. The utilization of a Likert scale ranging from 0 to 10 offered a spectrum for respondents to express their satisfaction levels, enabling a more nuanced understanding of their attitudes and experiences. This approach aimed to elicit detailed feedback, providing valuable insights for analysis and interpretation in the study.

5. Sampling procedure  The respondents were selected using a convenient sampling technique.  75 customers were given the questionnaire, and 59 respondents gave their opinion which accounts for a 78.66% response rate.  However, 9 respondents were kept as holdouts to test the model hence the survey was conducted on 50 respondents. 2 indicates that the customers were mostly satisfied with the "Showroom-Environment Satisfaction" with (𝑚𝑒𝑎𝑛 = 9.0), followed by delivery with (𝑚𝑒𝑎𝑛 = 8.97) and "Responsiveness of Employees" with (𝑚𝑒𝑎𝑛 = 8.73).  The customers were least satisfied with the "After Sales Service" and Overall Satisfaction" of customers was (𝑚𝑒𝑎𝑛 = 8.73).

Descriptive statistics

Reliability test

 The reliability of the measurement model is measured by looking at the value of Cronbach's alpha. Cronbach's alpha measures the lower limit value of the reliability of a construct. A Cronbach's alpha value higher than 0.7 is considered satisfactory. If the value of Cronbach's alpha is between 0.6 to 0.7 it is considered moderately satisfactory and if it is higher than 0.8 it is considered highly satisfactory.

 For the reliability test, all variables are reliable because Cronbach's alpha value is above 0.7 except "Delivery Satisfaction" and "After Sales Service Satisfaction" which are moderately reliable.

Normality test

The results of the data are not completely normally distributed, because as per Shapiro-Wilk's test three of our variables have (𝑝 -𝑣𝑎𝑙𝑢𝑒 > 0.05) this means they are normally distributed, but all other variables have (𝑝 -𝑣𝑎𝑙𝑢𝑒 < 0.05) hence they are not normally distributed.

Correlation analysis

As data is not completely normally distributed, spearman's correlation analysis was applied to know which satisfaction variables are related to overall satisfaction of the customer in Table 3. According to correlation analysis Table 4, all our variables have a positive significant relationship with "Overall Satisfaction" except "Showroom Environment Satisfaction" which has a correlation coefficient 𝑟 = 0.090 and 𝑝 -𝑣𝑎𝑙𝑢𝑒 = 0.533, which is greater than 0.05 hence the claim hypothesis H1h is not supported which means that there is no positive and significant correlation between "Showroom Environment Satisfaction" and Overall customer satisfaction. This means that if we increase "Showroom Environment Satisfaction" of the customer there will be no significant increase in "Overall Satisfaction" with the HONDA. Hence "Showroom Environment Satisfaction" is not a significant variable, but all other satisfaction variables are significant for increasing "Overall Customer Satisfaction".

Mann -Whitney u-test

To know the customers, repeat purchase intentions, a question was asked to the customers (if you want to buy a car in the future, would you buy it from HONDA or not?). The customers who replied "Yes" were coded as "Repeat-Buyers" and the customers who replied "No" were coded as "Non-Buyers". Mann Whitney U-Test was applied to know if the overall customer satisfaction was the same across independent groups (Repeat Buyers and Non-Buyers) or not 𝑝 -𝑣𝑎𝑙𝑢𝑒 the Mann Whitney U-Test came out to be 0.00 which is highly significant and supports the claim hypothesis H2 (Overall Customer Satisfaction is different across Repeat-Buyers and Non-Buyers) It is seen that more customers with higher overall satisfaction rating are in the Repeat-Buyers group and more customers with less overall satisfaction rating are in the Non-Buyers group. Also, the mean rank in the Repeat-Buyers group is higher and in the "Non-Buyers" group, it is on the lower side. Hence customers who are highly satisfied with HONDA are more likely to become repeat buyers and customers who are not very satisfied are more likely to become Non-Buyers Existing Literature such as Waluya et al.

(2019) [13] , Sharma and Singh (2021) also claimed that "Overall customer satisfaction" plays an important role in influencing customer for a Repeat purchase.

Fig 1:

Discriminant analysis

Significant variables of correlation analysis were then taken as independent variables in discriminant analysis to find the significant satisfaction variables which are important to differentiate between two groups (Repeat-Buyer and Non-Buyer) Repeat purchase intention of the customer which is a categorical variable was used as a dependent variable for discriminant analysis

Multicollinearity check Fig 2: Multicollinearity check

The correlation matrix for all independent variables shows that none of the correlations exceed 0.75. Therefore, we can confidently conclude that there is no issue of multicollinearity present.

Variance inflation factor

The Variance Inflation Factor (VIF) is a crucial tool for assessing multicollinearity in a regression model. By calculating VIF values, we can determine the extent to which the variance of an independent variable is increased due to its correlation with other independent variables. This helps identify and quantify the severity of multicollinearity issues, allowing for necessary adjustments to improve the model's reliability and interpretability. A VIF of 1 indicates no multicollinearity, 1 < VIF < 5 suggests moderate and acceptable multicollinearity, VIF ≥ 5 indicates potentially problematic multicollinearity, and VIF ≥ 10 signals severe multicollinearity, which can significantly affect regression estimates.

Results and Discussion

 The findings of the correlation analysis showed that there is a significant positive relationship between most of the satisfaction variables and overall customer satisfaction. Variables, namely "Price Satisfaction", "After Sales Service Satisfaction", "Product quality satisfaction", "Product performance satisfaction", "Brand Image Satisfaction", "Satisfaction with Responsiveness of Employees" and "Delivery satisfaction" were positively related to "Overall Customer Satisfaction".  These results suggested that an increase in each satisfaction variable is likely to lead to an increase in overall customer satisfaction with HONDA.  "Showroom Environment Satisfaction" correlation coefficient with "Overall Customer Satisfaction" is very low and the p-value also suggests that the relation between these two variables is not statistically significant.  The most important variables to increase the "Overall Satisfaction" of the customer is the "Brand Image Satisfaction" and "After Sales Service Satisfaction" hence HONDA should try to increase these satisfactions among customers to increase overall satisfaction.  HONDA needs to increase the customer's "Overall Satisfaction" as it influences the customer for a repeat purchase.  The results of the discriminant analysis suggest that "After Sales Service Satisfaction" and "Brand Image Satisfaction" are the most important variables to differentiate a customer in the category of Repeat Buyers and Non-Buyers which means that the customers who do not want to buy a car from HONDA in the future are not much satisfied by the quality of the product and brand image as there is much difference between means for these two variables in two groups of repeat-buyer and non-buyer.  The proposed discriminant model predicts the perspective of Repeat Buyers and Non-Buyers with an accuracy of 85%.

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