本文为荷兰特文特大学（作者：Alexander Kinski）的硕士论文，共77页。

研究目的：

汽车行业面临需求波动加剧的局面，但仍依赖过去时的预测方法。本论文旨在研究互联网搜索在预测德国和美国新车销量方面的解释力差异。对购车过程进行了检查，并评估了在数据集中实施时间滞后的效果，以增加互联网数据的价值。客户购买新车的决策过程说明了在线搜索信息和最终购车决策之间的时间差。

研究方法：

为了研究Google搜索查询与新车销售数据之间的关系，我们估计了多种线性回归模型。

研究发现：

这项研究发现，在两个国家，汽车模型的互联网搜索与汽车销售数据之间存在显著的正相关关系，准确率高达68.5%。时滞的实现极大地提高了包含互联网数据的预测模型的有效性和准确性，并开辟了新的研究可能性。本文强调了调整搜索查询数据以预测经济变量的价值和必要性，同时也提高了研究者和实践者不要盲目依赖互联网数据的意识。研究结果表明，顾客旅程的长度取决于车型、价格以及民族文化的影响。

学术贡献：

本论文首先通过研究互联网上的数据预测模型来提高预测的准确性。

实践贡献：

研究结果鼓励汽车行业的决策者利用定制的搜索引擎数据，观察人们对特定车型的兴趣，并加强各国新车销售预测和需求规划。

Purpose The automotive industry is faced with increased demand volatility but still relies on outdated forecasting approaches. The thesis aims to investigate differences in the explanatory power of internet searches to predict new car sales in Germany and the United States with the tool Google Trends. The car buying process is examined and the effect of implementing a time lag within the dataset is assessed to increase the value of internet data. The customer decision journey towards buying a new car illustrates the time lag as the time between the online search for information and the final car purchase decision. Methodology Several linear regression models were estimated to investigate the relationship between Google Search queries and new car sales data. Findings The study found a significant and positive relationship between internet searches for car models and the car model sales data in both countries with an accuracy of up to 68.5%. The implementation of a time lag highly improved the validity and the accuracy of prediction models that include internet data and opens up new research possibilities. The thesis stresses the value and the necessity to adjust search query data to predict economic variables but raises the awareness of researchers and practitioners not to rely blindly on internet data. The outcomes suggest that the length of the customer journey depends on the car model, the price and is influenced by the national culture. Academic Contributions The thesis contributes to the Google Trends literature by examining differences in the prediction accuracy of search queries across countries for the first time and by improving prediction models that include internet data. Practical Contributions The results encourage decision-makers in the automotive industry to use tailored search engine data as a possibility to observe people′s interests for particular car models and to enhance new car sales forecasting and demand planning across countries.

1.  引言

2.  理论框架

3.  谷歌搜索趋势作为互联网数据来源

4.  研究设计

5.  结果

6.  讨论与展望

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