新車車主重複購買汽車保險行為之研究

Abstract

汽車所有人購買汽車保險常是為了轉移因發生交通事故而造成體傷或財損的風險。在大部分國家，車體損失保險是最昂貴的保單，也是產險公司主要收入來源。無論如何，新車車主傾向於在前幾年購買車體損失險，但為減少保費負擔，經常在往後幾年減少保障範圍或不再購買車體險，而導致產險公司的保費收入明顯減少。本研究之動機是希望建立模式架構以深入了解消費者選擇汽車保險之行為。

本研究的模式架構分為兩大部分：第一部分為車體損失險保單的選擇；第二部分為非車體保單（例如第三人責任險及其他附加險）的選擇。本研究的重點放在第一部分，探討投保人每年持續選擇車體險種的問題。研究方法採用離散選擇模式，替選方案包含車體險種及持續投保相同險種的年數。離散選擇模式考慮多項羅吉特模式(multinomial logit model)、巢式羅吉特模式(nested logit model)、及成對組合模式(paired combinatorial logit model)。

本研究收集台灣某家產險公司被保險人數年的資料，驗證所建立的模式架構。研究結果顯示，重複購買汽車保險之行為受年齡、汽車為國產/進口及汽車汽缸量大小所影響。巢式羅吉特模式不僅在概念上或透過統計檢定，皆比多項羅吉特模式適用於分析消費者重複選擇行為。雖然成對組合羅吉特模式比多項羅吉特模式與巢式羅吉特模式更具有彈性，但此模式在方案數很多的情況時，校估相當困難，且不易得出理想的結果。本模式架構之發展有助於了解投保人重複選擇汽車保險之行為，且校估結果可提供保險公司修訂現有的汽車保單或研擬新的行銷策略，以吸引消費者重複投保。

Car owners purchase appropriate automobile insurance policies (AIP) to provide coverage for property damages and personal injuries incurred by traffic accidents. Physical damage coverage, the most expensive policy, is the major source of incomes for non-life insurance companies in most countries today. However, new car owners are likely to purchase physical damage coverage in the first few years and then downgrade their insurance by purchasing reduced coverage or not buying any physical damage coverage in the subsequent years. As such, premium revenues for non-life insurance companies will be substantially reduced. The study is motivated by the importance of developing a modeling framework to gain insights into the insured’s choice for AIP.

The research develops a model system that consists of two components: the first component is the decision to select different types of physical damage coverage; the second component is the choice of non-physical damage coverage involving third party liability as a basic protection with additional coverage. This study focuses on the first component and, particularly, explores repeated choices of different types of physical damage coverage. A discrete choice modeling framework including the choice of physical damage coverage type and the number of consecutive years that the insured has purchased the same type of coverage is further developed. Various discrete choice models including multinomial logit, nested logit, and paired combinatorial logit are attempted.

The proposed modeling framework is empirically illustrated using a panel data provided by a non-life insurance company in Taiwan. The results indicate that the repeated choices of AIP are influenced by age of the driver, vehicle make, and engine capacity. The nested logit model statistically rejected the multinomial logit model, which demonstrates the statistical and structural superiority of the nested logit model in analyzing the insured’s repeated choices. Although the paired combinatorial logit model is more flexible than the multinomial logit and nested logit models, estimation of such model becomes very difficult when the number of alternatives gets large. The model framework developed in the study has improved our understanding of the repeated choices of AIP, and the estimation results have provided valuable implications for the insurer to modify existing automobile insurance policies or to develop marketing strategies so as to enhance repurchase intention of the insured.