Consumer Decision-Making in Car Purchases: Insights from a Logistic Regression Analysis
DOI:
https://doi.org/10.28918/logiclink.v2i1.10934Abstract
Indonesia, with its large population, presents significant consumer demand, particularly in the automotive sector. Consequently, numerous car brands actively invest and market their products within the country. Among the 36 car brands officially operating in Indonesia, Toyota has consistently dominated the market. This study specifically examines consumer perceptions and decision-making processes regarding Toyota car purchases using logistic regression analysis. It investigates the impact of perceived risk, price sensitivity, convenience, and customer satisfaction on consumers' purchasing choices. Using a quantitative approach, data were collected from Indonesian Toyota consumers through purposive sampling and analyzed using the logistic regression. The findings reveal that perceived risk and price significantly influence consumers' decisions to purchase Toyota cars, highlighting the importance for Toyota and other automotive brands to strategically manage risk perceptions and pricing policies to enhance consumer appeal and market share.
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References
Abernathy, W. J. (1978). The Productivity Dilemma: Roadblock to Innovation in the Automobile Industry. Johns Hopkins University Press.
Ali, Y., Mehmood, B., Huzaifa, M., Yasir, U., & Khan, A. U. (2020). Development of a new hybrid multi criteria decision-making method for a car selection scenario. Facta Universitatis, Series: Mechanical Engineering, 18(3), 357-373.
Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1–17.
Chand, M., & Avikal, S. (2015). An MCDM based approach for purchasing a car from Indian car market. In 2015 IEEE Students Conference on Engineering and Systems (SCES) (pp. 1-4). IEEE.
Cronin, J. J., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193–218.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning.
Holweg, M. (2007). The genealogy of lean production. Journal of Operations Management, 25(2), 420–437.
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
Lichtenstein, D. R., Ridgway, N. M., & Netemeyer, R. G. (1993). Price perceptions and consumer shopping behavior: A field study. Journal of Marketing Research, 30(2), 234–245.
McKinsey & Company. (2016). Automotive revolution – perspective towards 2030.
Menard, S. (2010). Logistic Regression: From Introductory to Advanced Concepts and Applications. SAGE Publications.
Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163–195.
Nunes, J. C., & Drèze, X. (2006). Your loyalty program is betraying you. Harvard Business Review, 84(4), 124–131.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(Special Issue), 33–44.
Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1), 3–14.
Roy, S., Mohanty, S., & Mohanty, S. (2018). An efficient hybrid MCDM based approach for car selection in automobile industry. In 2018 international conference on research in intelligent and computing in engineering (RICE) (pp. 1-5). IEEE.
Singh, R., Rashmi, & Avikal, S. (2019). A mcdm-based approach for selection of a sedan car from indian car market. In Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018 (pp. 569-578). Springer Singapore.
Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39–50.
Susanty, A., Akshinta, P. Y., Ulkhaq, M. M., & Puspitasari, N. B. (2022). Analysis of the tendency of transition between segments of green consumer behavior with a Markov chain approach. Journal of Modelling in Management, 17(4), 1177–1212.
Ulkhaq, M. M., Widodo, A. K., Yulianto, M. F. A., Mustikasari, A., & Akshinta, P. Y. (2018b). A logistic regression approach to model the willingness of consumers to adopt renewable energy sources. In IOP Conference Series: Earth and Environmental Science (Vol. 127, No. 1, p. 012007). IOP Publishing.
Ulkhaq, M. M., Wijayanti, W. R., Zain, M. S., Baskara, E., & Leonita, W. (2018a). Combining the AHP and TOPSIS to evaluate car selection. In Proceedings of the 2nd International Conference on High Performance Compilation, Computing and Communications (pp. 112-117).
Ulkhaq, M. M., Wibowo, A. T., Tribosnia, M. R., Putawara, R., & Firdauz, A. B. (2021). Predicting customer churn: A comparison of eight machine learning techniques: A case study in an Indonesian telecommunication company. In 2021 International Conference on Data Analytics for Business and Industry (ICDABI) (pp. 42–46). IEEE.
Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine That Changed the World. Free Press.
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