Introducing machine-learning-based data fusion methods for analyzing multimodal data: An application of measuring trustworthiness of microenterprises

IF 6.5 1区 管理学 Q1 BUSINESS Strategic Management Journal Pub Date : 2024-03-05 DOI:10.1002/smj.3597
Xueming Luo, Nan Jia, Erya Ouyang, Zheng Fang
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Abstract

Multimodal data, comprising interdependent unstructured text, image, and audio data that collectively characterize the same source, with video being a prominent example, offer a wealth of information for strategy researchers. We emphasize the theoretical importance of capturing the interdependencies between different modalities when evaluating multimodal data. To automate the analysis of video data, we introduce advanced deep machine learning and data fusion methods that comprehensively account for all intra- and inter-modality interdependencies. Through an empirical demonstration focused on measuring the trustworthiness of grassroots sellers in live streaming commerce on Tik Tok, we highlight the crucial role of interpersonal interactions in the business success of microenterprises. We provide access to our data and algorithms to facilitate data fusion in strategy research that relies on multimodal data.
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引入基于机器学习的数据融合方法来分析多模态数据:测量微型企业可信度的应用
多模态数据由相互依存的非结构化文本、图像和音频数据组成,这些数据共同描述了同一来源的特征,视频就是一个突出的例子,为策略研究人员提供了丰富的信息。我们强调在评估多模态数据时捕捉不同模态之间相互依存关系的理论重要性。为了实现视频数据分析的自动化,我们引入了先进的深度机器学习和数据融合方法,全面考虑所有模态内和模态间的相互依存关系。我们通过实证演示,重点衡量了 Tik Tok 上直播流媒体商务中草根卖家的可信度,突出了人际互动在微型企业商业成功中的关键作用。我们提供数据和算法的访问权限,以促进依赖多模态数据的战略研究中的数据融合。
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来源期刊
CiteScore
13.70
自引率
8.40%
发文量
109
期刊介绍: At the Strategic Management Journal, we are committed to publishing top-tier research that addresses key questions in the field of strategic management and captivates scholars in this area. Our publication welcomes manuscripts covering a wide range of topics, perspectives, and research methodologies. As a result, our editorial decisions truly embrace the diversity inherent in the field.
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