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Evaluation of Factors Contributing to the Repurchase Intention of the Automobile Industry Using Sparse Modeling

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Advances in Digital Science (ICADS 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1352))

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Abstract

The best competitive advantage for a company is building long-term relationships with highly satisfied customers, by understanding the factors that contribute to their repurchase intentions. Since large amount of point of sales (POS) data can be obtained in the retail industry, past research has focused on the retail industry. Recently, many studies targeting online shopping sites were reported. On the other hand, there are not many studies of durable consumer goods with a long span of replacement. This research evaluates the factors that contribute to the repurchase intention for the Japanese automobile industry. The evaluation should not depend on the parameters defined by the researcher, but on the factors from the consumer’s point of view. Therefore, in the online survey, the reason for the repurchase intention was heard by free answer and the factors were extracted by natural language processing. The most important issue in statistics is said to be variable selection in regression models because constructing statistical models using many variables may cause over-fitting of acquired data. Evaluation was performed by sparse modeling which efficiently extracts the maximum feature quantity from data by utilizing the universally inherent sparsity of high-dimensional data. The results show that the most effective factors were trust and design with the odds ratios of 1.339 and 1.163, respectively. This research is expected to be useful for improving the products and services by quantitatively clarifying the factors of repurchase intention in the automobile industry, that has been insufficient till date.

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Authors and Affiliations

  1. Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Saitama, Japan

    Takumi Kato

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  1. Takumi Kato
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Correspondence to Takumi Kato .

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  1. Institute of Certified Specialists (ICS), Perm, Russia

    Tatiana Antipova

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Kato, T. (2021). Evaluation of Factors Contributing to the Repurchase Intention of the Automobile Industry Using Sparse Modeling. In: Antipova, T. (eds) Advances in Digital Science. ICADS 2021. Advances in Intelligent Systems and Computing, vol 1352. Springer, Cham. https://doi.org/10.1007/978-3-030-71782-7_30

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