利用神经数据预测神经营销中的总体消费者行为:理论、衡量标准、进展与展望

IF 4.4 3区 管理学 Q2 BUSINESS
Xiaoqiang Yao, Yiwen Wang
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引用次数: 0

摘要

在过去十年中,利用神经数据预测消费者总体选择的领域备受关注,为研究人员和从业人员带来了巨大的发展前景。然而,人们对这一新兴领域还缺乏全面的了解。本文旨在通过总结现有研究,包括相关理论、指标、进展和未来方向,来弥补这一不足。我们首先介绍了神经营销领域中的神经预测概念。然后,我们深入探讨了利用神经数据预测总体选择的理论,包括情感-整合-动机框架、额叶不对称和受试者间相关性。随后,我们回顾了用于预测市场行为的各种指标,包括自我报告指标、行为指标和神经指标,并介绍了相关研究的主要发现。此外,我们还探讨了这一领域的优缺点。这种方法的优点包括能够对消费者行为进行有效预测,并增强对消费者偏好和选择的洞察力,而缺点则包括成本相对较高、样本规模限制、生态有效性问题以及与反向推理相关的挑战。总之,未来的研究应优先考虑将不同类型的数据与机器学习技术相结合,提前预测营销活动的结果。此外,深入探讨成功预测背后的心理和认知过程也能提高预测的准确性和有效性。本综述为该领域的研究人员和从业人员提供了系统的概述,为未来的研究工作和行业应用提供了宝贵的见解和指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using neural data to forecast aggregate consumer behavior in neuromarketing: Theory, metrics, progress, and outlook

The field of using neural data to forecast aggregate consumer choice has garnered attention in the past decade, holding substantial promise for both researchers and practitioners. However, a comprehensive understanding of this emerging field is lacking. This paper aims to bridge that gap by summarizing existing research, encompassing relevant theories, metrics, progress, and future directions. We begin by introducing the concept of neuroforecasting within the field of neuromarketing. We then delve into theories that leverage neural data for forecasting aggregate choice, including affect-integration-motivation framework, frontal asymmetry, and inter-subject correlation. Subsequently, we review various metrics, including self-reported, behavioral, and neural metrics employed to forecast market-level behavior, presenting key findings from relevant studies. Furthermore, we examine the strengths and weaknesses of this field. Advantages of this approach include its ability to offer effective predictions of consumer behavior and provide enhanced insights into consumer preferences and choices, while its weaknesses encompass relatively high cost, sample size constraints, issues of ecological validity, and challenges related to reverse inference. In conclusion, future research should prioritize integrating diverse data types with machine learning techniques to forecast the outcomes of marketing campaigns in advance. Additionally, a deeper exploration of the psychological and cognitive processes underlying successful predictions can augment predictive accuracy and effectiveness. This review provides a systematic overview for researchers and practitioners in this field, offering valuable insights and guidance for future research endeavors and industry applications.

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来源期刊
CiteScore
7.30
自引率
11.60%
发文量
99
期刊介绍: The Journal of Consumer Behaviour aims to promote the understanding of consumer behaviour, consumer research and consumption through the publication of double-blind peer-reviewed, top quality theoretical and empirical research. An international academic journal with a foundation in the social sciences, the JCB has a diverse and multidisciplinary outlook which seeks to showcase innovative, alternative and contested representations of consumer behaviour alongside the latest developments in established traditions of consumer research.
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