Department of Construction Management and Real Estate, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio av. 11, LT-10223 Vilnius, Lithuania.
Department of Computing, Faculty of Engineering, Imperial College London, South Kensington Campus, London SW7 2BU, UK.
Int J Environ Res Public Health. 2020 Mar 27;17(7):2244. doi: 10.3390/ijerph17072244.
The implementation of advertising for green housing usually involves consideration of individual differences among potential buyers, their desires for residential unit features as well as location impacts on a selected property. Much more rarely, there is consideration of the arousal and valence, affective behavior, emotional, and physiological states of possible buyers of green housing (AVABEPS) while they review the advertising. Yet, no integrated consideration of all these factors has been undertaken to date. The objective of this study was to consider, in an integrated manner, the AVABEPS, individual differences, and location impacts on property and desired residential unit features. During this research, the applications for the above data involved neuromarketing and multicriteria examination of video advertisements for diverse client segments by applying neuro decision tables. All of this can be performed by employing the method for planning and analyzing and by multiple criteria and customized video neuro-advertising green-housing variants (hereafter abbreviated as the ViNeRS Method), which the authors of this article have developed and present herein. The developed ViNeRS Method permits a compilation of as many as millions of alternative advertising variants. During the time of the ViNeRS project, we accumulated more than 350 million depersonalized AVABEPS data. The strong and average correlations determined in this research (over 35,000) and data examination by IBM SPSS tool support demonstrate the need to use AVABEPS in neuromarketing and neuro decision tables. The obtained dependencies constituted the basis for calculating and graphically submitting the ViNeRS circumplex model of affect, which the authors of this article developed. This model is similar to Russell's well-known earlier circumplex model of affect. Real case studies with their related contextual conditions presented in this manuscript show a practical application of the ViNeRS Method.
绿色住宅广告的实施通常需要考虑潜在购房者的个体差异、他们对住宅单元特征的需求以及所选物业的位置影响。然而，很少有考虑绿色住宅潜在购买者（AVABEPS）在审查广告时的唤醒和效价、情感行为、情绪和生理状态。迄今为止，还没有综合考虑所有这些因素。本研究的目的是综合考虑 AVABEPS、个体差异以及位置对房产和理想住宅单元特征的影响。在这项研究中，上述数据的应用涉及神经营销和通过应用神经决策表对不同客户群体的视频广告进行多标准检查。所有这些都可以通过使用规划和分析方法以及多标准和定制视频神经广告绿色住宅变体（简称 ViNeRS 方法）来完成，本文作者已经开发并在此处提出。开发的 ViNeRS 方法允许编制多达数百万个替代广告变体。在 ViNeRS 项目期间，我们积累了超过 3.5 亿份去身份识别的 AVABEPS 数据。本研究中确定的强相关和平均相关（超过 35,000 个）以及 IBM SPSS 工具的数据检查支持了在神经营销和神经决策表中使用 AVABEPS 的必要性。获得的依赖关系构成了计算和图形化提交本文作者开发的 ViNeRS 情感双因素模型的基础。该模型类似于 Russell 早期著名的情感双因素模型。本文呈现的实际案例研究及其相关情境条件展示了 ViNeRS 方法的实际应用。
