电子旅游中的消费者行为:探索机器学习在旅游研究中的新应用

IF 1 Q3 HOSPITALITY, LEISURE, SPORT & TOURISM Investigaciones Turisticas Pub Date : 2023-07-17 DOI:10.14198/inturi.24629
Adrián Mendieta-Aragón, Teresa Garín-Muñoz
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引用次数: 0

摘要

数字市场改变了经济主体的互动方式,也改变了游客的行为。此外,新冠肺炎大流行表明,有必要不断监测数字消费者行为的演变及其影响因素,因为它们是随着时间推移而演变的动态因素。本文分析了数字不平等现象,验证了影响游客预订在线旅游服务的主要因素。这项研究使用了一组微观数据,分别有69752和23779个观察结果,来分析2016-2021年期间从西班牙国家统计局的居民旅行调查中获得的住宿和交通服务的预订模式。这篇文章证实了在线消费者档案和旅行特征的变化。最相关的发现之一是旅游服务在线承包方面的代沟缩小。然而,仍然存在数字不平等,例如地区不平等以及其他基于游客教育水平和收入的不平等。还强调,不同类型的旅行,根据目的地、住宿或交通类型,通过数字购买渠道预订的倾向不同。大数据源的可访问性和机器学习模型的最新进展也使分析数字消费者行为的方法不断发展,必须纳入旅游研究。本研究比较了不同方法在电子旅游背景下的预测性能。特别是,我们评估了使用机器学习技术解释电子旅游中消费者行为的潜在预测能力,并将其作为基准,将其与使用传统统计方法获得的结果进行比较。所选择的预测评估指标表明,逻辑回归统计模型优于多层感知器神经网络的预测能力,并且呈现出非常接近随机森林算法实现的最大预测能力的值。
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Consumer behaviour in e-Tourism: Exploring new applications of machine learning in tourism studies
Digital markets have altered how economic agents interact and have changed the behaviour of tourists. In addition, the COVID-19 pandemic has shown that it is necessary to constantly monitor the evolution of digital consumer behaviour and the factors that influence it, as they are dynamic elements that evolve over time. This paper analyses digital inequalities and validates the main factors influencing tourists to book online tourism services. This research uses a set of microdata with 69,752 and 23,779 observations to analyse the booking mode of accommodation and transportation services, respectively, obtained from the Resident Travel Survey of the National Statistics Institute of Spain during the period 2016-2021. The article confirms variations in the online consumer profile and in the trip's characteristics. One of the most relevant findings is the narrowing of the generational gap in the online contracting of tourist services. However, there are remaining digital inequalities, such as regional inequalities and others based on the education level and income of tourists. It is also highlighted that different types of trips, depending on the destination, the type of accommodation or transport have a different propensity to be booked through digital purchase channels. The accessibility to big data sources and recent advances in machine learning models have also made the methodologies for analysing digital consumer behaviour evolve and must be incorporated into tourism studies. This study compares the predictive performance of different methodologies in the context of e Tourism. In particular, we evaluate the potential predictive power that could be obtained using machine learning techniques to explain consumer behaviour in e-Tourism and use it as a benchmark to compare it with the results obtained using traditional statistical methods. The selected predictive evaluation metrics show that the logistic regression statistical model outperforms the predictive power of the Multilayer Perceptron neural network and presents values very close to the maximum predictive power achieved by the Random Forest algorithm.
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来源期刊
Investigaciones Turisticas
Investigaciones Turisticas HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
1.50
自引率
16.70%
发文量
36
审稿时长
24 weeks
期刊最新文献
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