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首页> 外文期刊>Journal of management information systems >Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content
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Generating Business Intelligence Through Social Media Analytics: Measuring Brand Personality with Consumer-, Employee-, and Firm-Generated Content

机译：通过社交媒体分析生成商业智能：使用消费者，员工和企业生成的内容来衡量品牌个性

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摘要

Social media platforms provide an enormous public repository of textual data from which valuable information can be extracted. We show that firms can extract business intelligence from social media data bearing on an important business application, measuring brand personality. Specifically, we develop a text analytics framework that integrates different distinct sources of social media data generated by consumers, employees, and firms, to measure brand personality. Based on Elastic-Net regression analyses of a large corpus of social media data, including self-descriptions of 1,996,214 consumers who followed the sample of brands on social media, 312,400 employee reviews of the brands' firms, and 680,056 brand official tweets, we develop a brand personality model that achieves prediction accuracy as high as 0.78. Among key insights, we find that the profile of individuals who choose to associate with brands on social media is an important predictor of brand personality; this provides the first real-world evidence for a consumer identity-brand personality link. We also identify a link between an organization's internal corporate environment as perceived by employees and brand personality as judged by consumers. We further illuminate the practical implication of our predictive model by building a cloud-based information system that allows managers and analysts to explore and track personality of their own brands and their competitors' brands.
机译：社交媒体平台提供了巨大的文本数据公共存储库，可以从中提取有价值的信息。我们证明，公司可以从社交媒体数据中提取重要商业应用程序上的商业智能，从而衡量品牌个性。具体来说，我们开发了一个文本分析框架，该框架整合了由消费者，员工和公司生成的社交媒体数据的不同截然不同的来源，以衡量品牌个性。基于对大量社交媒体数据进行的Elastic-Net回归分析，包括对自述社交媒体上的品牌样本的1,996,214名消费者的自我描述，对品牌公司的312,400名员工评论以及680,056条品牌官方推文，我们开发了品牌个性模型，可实现高达0.78的预测准确性。在关键见解中，我们发现选择在社交媒体上与品牌建立联系的个人形象是品牌个性的重要预测因素。这为消费者身份-品牌个性链接提供了第一个现实世界的证据。我们还确定了员工感知的组织内部公司环境与消费者判断的品牌个性之间的联系。我们通过构建一个基于云的信息系统进一步阐明我们的预测模型的实际含义，该信息系统使管理人员和分析人员能够探索和跟踪自己品牌和竞争对手品牌的个性。

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