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A purchase decision support model considering consumer personalization about aspirations and risk attitudes
Journal of Retailing and Consumer Services ( IF 10.4 ) Pub Date : 2021-08-26 , DOI: 10.1016/j.jretconser.2021.102728
Yongming Song 1 , Guangxu Li 2 , Tie Li 2 , Yanhong Li 3
Affiliation  

Ranking alternative products to help consumers make better purchase choices is a valuable research topic. Most previous decision support models cannot be well applied to heterogeneous consumers. This paper focuses on establishing a personalized interactive model to assist consumers make better buying decisions with less effort. For the alternative products provided by consumers, we collect online reviews and parameter configurations of alternative products and then obtain the fusing evaluative information. As consumers are dominated by bounded rationality, they only provide partially key attribute weights, based on which, we construct an optimizing model to obtain the optimal attribute weights of customers for products. Then, a satisfaction function is proposed by uniting aspiration levels and risk attitudes of consumers and a compensatory decision rules is established to rank and recommend the brands to consumers. Finally, practicability of this study is illustrated with a real car purchase case. Through the case study, it can be seen that the proposed decision support model generates a personalized list of alternatives based on consumer's own utility function about risk attitudes, aspiration levels, and preferences for product attributes, which further confirms that the proposed model can capture the personalized needs of consumers. Theoretical and managerial implications of this model as well as advantages are further illustrated.



中文翻译:

考虑消费者愿望和风险态度个性化的购买决策支持模型

对替代产品进行排名以帮助消费者做出更好的购买选择是一个有价值的研究课题。大多数以前的决策支持模型不能很好地应用于异构消费者。本文着眼于建立个性化的交互模型,帮助消费者以更少的努力做出更好的购买决策。对于消费者提供的替代产品,我们收集替代产品的在线评论和参数配置,然后获得融合的评价信息。由于消费者受有限理性支配,他们只提供部分关键属性权重,我们在此基础上构建优化模型以获得客户对产品的最优属性权重。然后,结合消费者的期望水平和风险态度提出满意度函数,并建立补偿决策规则,对品牌进行排名和推荐给消费者。最后,通过一个真实的购车案例说明了本研究的实用性。通过案例研究可以看出,所提出的决策支持模型根据消费者自身的风险态度、愿望水平和产品属性偏好的效用函数生成了个性化的备选方案列表,这进一步证实了所提出的模型可以捕捉到消费者的个性化需求。进一步说明了该模型的理论和管理意义以及优势。本研究的实用性通过一个真实的汽车购买案例来说明。通过案例研究可以看出,所提出的决策支持模型根据消费者自身的风险态度、愿望水平和产品属性偏好的效用函数生成了个性化的备选方案列表,这进一步证实了所提出的模型可以捕捉到消费者的个性化需求。进一步说明了该模型的理论和管理意义以及优势。本研究的实用性通过一个真实的汽车购买案例来说明。通过案例研究可以看出,所提出的决策支持模型根据消费者自身的风险态度、愿望水平和产品属性偏好的效用函数生成了个性化的备选方案列表,这进一步证实了所提出的模型可以捕捉到消费者的个性化需求。进一步说明了该模型的理论和管理意义以及优势。这进一步证实了所提出的模型可以捕捉到消费者的个性化需求。进一步说明了该模型的理论和管理意义以及优势。这进一步证实了所提出的模型可以捕捉到消费者的个性化需求。进一步说明了该模型的理论和管理意义以及优势。

更新日期:2021-08-26
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