Driving Consumer Choices: The Influence of Advertising Campaigns on Automobile Purchases in Uttarakhand
Abstract
This study examines how consumer buying decisions in Uttarakhand, India’s automotive industry is influenced by advertising campaigns. The goal of the study is to pinpoint the essential components of advertising campaigns that affect the choices and preferences of consumers. A structured questionnaire was given to 132 respondents utilizing a quantitative method, with an emphasis on topics, such as celebrity endorsements, emotional appeals, and the efficacy of digital advertising. The results show that customer preferences are greatly influenced by advertising efforts, and that brand awareness and preferences are mostly shaped by digital platforms. Engaging, and emotional ads leave a lasting effect and promote favourable brand attitudes. Furthermore, the study shows that consumer variables, like gender, money, and education, have a big impact on how people see car ads. Notably, the efficiency of different advertising methods was found to be influenced by gender disparities. The study emphasizes the significance of customized advertising that appeals to local sensitivities, like highlighting eco-friendliness and fuel efficiency. The findings provide marketers with important information to improve consumer interaction and advertising tactics in the Uttarakhand car market, which will eventually increase sales and brand loyalty.
References
Alsharif, A. H., Salleh, N. Z. M., & Al-Zahrani, S. A. (2022). Consumer Behaviour to Be Considered in Advertising: A Systematic Analysis and Future Agenda. Behavioural Sciences, 12(12), 472. https://doi.org/10.3390/bs12120472
Aslam, M. (2019). Introducing Kolmogorov–Smirnov Tests Under Uncertainty: An Application to Radioactive Data. ACS Omega, 5(1), 914-917. https://pubs.acs.org/doi/10.1021/acsomega.9b03940
Bagga, T., & Gupta, D. (2014). Internet Marketing by Automobile Industry: Special Reference of Indian Counterparts. International Journal of Computer Applications, 97(6), 9-16. https://doi.org/10.5120/17009-7219
Bansal, S., & Malik, G. (2015). The Impact of Factors Influencing and Promotion Strategies of Automobile Companies on Consumer Purchase Decisions. ANVESHAK: International Journal of Management, 4(1), 164–187. https://doi.org/10.15410/aijm/2015/v4i1/59899
Chatzi, A., & Doody, O. (2023). The One-Way ANOVA Test Explained. Nurse Researcher, 31(4). https://doi.org/10.7748/nr.2023.e1885
Chen, S. X., Li, J., & Zhong, P. S. (2019). Two-sample and ANOVA tests for High Dimensional Means. The Annals of Statistics, 47(3), 1443-1474.
Di Biase, R. M., Fattorini, L., & Marchi, M. (2018). Statistical Inferential Techniques for Approaching Forest Mapping. A Review Of Methods. Annals of Silvicultural Research, 42(2), 46-58. https://dx.doi.org/ 10.12899/asr-1738
Diedenhofen, B., & Musch, J. (2016). Cocron: A Web Interface and R Package for the Statistical Comparison of Cronbach's Alpha Coefficients. International journal of internet science, 11(1), 51-60.
Emerson, R. W. (2022). ANOVA Assumptions. Journal of Visual Impairment & Blindness, 116(4), 585-586. https://doi.org/10.1177/0145482X221124187
Francis, G. & Jakicic, V. (2023). Equivalent statistics for a One-Sample T-Test. Behavior Research Methods, 55(1), 77-84. https://doi.org/10.3758/s13428-021-01775-3
Gerald, B. (2018). A Brief Review of Independent, Dependent and One Sample T-Test. International Journal of Applied Mathematics and Theoretical Physics, 9(2), 50-54. https://doi.org/10.11648/j.ijamtp.20180402.13
Giombi, K., Viator, C., Hoover, J., Tzeng, J., Sullivan, H.W., O’Donoghue, A.C., Southwell, B.G. & Kahwati, L.C. (2022). The Impact of Interactive Advertising on Consumer Engagement, Recall, and Understanding. Plos One, 17(2). https://doi.org/10.1371/journal.pone.0263339
Kaushik, M., & Mathur, B. (2014). Data Analysis of Students Marks with Descriptive Statistics. International Journal on Recent and Innovation Trends in computing and communication, 2(5), 1188-1190.
Kellner, J., & Celisse, A. (2019). A One-Sample Test for Normality with Kernel Methods. Bernoulli, 25(3), 1816-1837. https://doi.org/10.3150/18-BEJ1037
Kim, J., & Seo, B. S. (2013). How to Calculate Sample Size and Why. Clinics in Orthopedic Surgery, 5(3), 235-242. http://dx.doi.org/10.4055/cios.2013.5.3.235
Klomberg, B., & Cohn, N. (2022). Picture Perfect Peaks: Comprehension of Inferential Techniques in Visual Narratives. Language and Cognition, 14(4), 596-621. https://doi.org/10.1017/langcog.2022.19
KPMG. (2020). Digital Transformation in India’s Automobile Industry. KPMG India Report.
Lall, A. (2015, October). Data Streaming Algorithms For The Kolmogorov-Smirnov Test. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 95-104). IEEE. https://doi.org/10.1109/BigData.2015.7363746
Lou, Y., Yuen, S. Y., & Chen, G. (2018, July). Evolving Benchmark Functions Using Kruskal-Wallis Test. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1337-1341). https://doi.org/10.1145/3205651.320825
Luiz, A. J. B., & Lima, M. A. De. (2021). Application of the Kolmogorov-Smirnov Test to Compare Greenhouse Gas Emissions Over Time. Brazilian Journal of Biometrics, 39(1), 60–70. https://doi.org/10.28951/rbb.v39i1.498
Majeske, K. D., Menk, D. M., & Serocki, J. S. (2011). An Economic Impact Model for Evaluating the Automobile Purchase Decision. International Journal of Business Insights & Transformation, 4(2).
Mendeş, M., & Yiğit, S. (2013). Comparison of ANOVA-F and ANOM tests with regard to type I error rate and test power. Journal of Statistical Computation and Simulation, 83(11), 2093-2104. https://doi.org/10.1080/00949655.2012.679942
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive Statistics and Normality Tests for Statistical Data. Annals of Cardiac Anaesthesia, 22(1), 67-72. https://doi.org/10.4103/aca.ACA_157_18
Ren, W. L., Wen, Y. J., Dunwell, J. M., & Zhang, Y. M. (2018). pKWmEB: integration of Kruskal–Wallis Test with Empirical Bayes Under Polygenic Background Control for Multi-Locus Genome-Wide Association Study. Heredity, 120(3), 208-218. https://doi.org/10.1038/s41437-017-0007-4
Schrepp, M. (2020). On the Usage of Cronbach's Alpha to Measure Reliability of UX Scales. Journal of Usability Studies, 15(4), 247-258
