Firms face challenging analytical tasks at the advent of a growing amount of unstructured big data (BD). These data lead to radical shifts in their analytical strategies and market insights. Yet, the particular types of analytical methods remain in the literature still loosely scattered. This work stresses the unstructured BD analytics, first by capturing their unique characteristics and then by proposing a model for diagnosis of the analytical methods related to unstructured data (UD) inside the firms. We focus on five interrelated research aspects, by: explaining the essence of UD with the firms' environment; identifying and classifying the most important analytical methods in organizations to better understand UD; developing a conceptual model along with measures; and diagnosing the extent to which the unstructured analytical methods, beside the structured analytics, relate with firm performance (FP). Finally, this model is investigated from perspective of the two-communities theory in reference to data scientists and marketing researchers within the organizational environment. A model is tested on the basis of complementary analytical strategies: confirmatory and multigroup factor analyses and structural equation modeling, for which data (N = 356) were collected from international online survey. Results confirm a high level of adequacy of the conceptual model and superiority of unstructured over the structured analytics leading to FP, while the scalar invariance testing proves minor differences between groups in reference to two of the analytical methods.

中文翻译：

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企业非结构化大数据分析方法：概念模型、测量和感知

随着越来越多的非结构化大数据 (BD) 的出现，公司面临着具有挑战性的分析任务。这些数据导致他们的分析策略和市场洞察力发生根本变化。然而，特定类型的分析方法仍然在文献中松散分散。这项工作强调了非结构化 BD 分析，首先通过捕获它们的独特特征，然后提出一个模型来诊断与公司内部非结构化数据 (UD) 相关的分析方法。我们专注于五个相互关联的研究方面，通过： 用公司环境解释 UD 的本质；识别和分类组织中最重要的分析方法，以更好地理解 UD；开发概念模型和措施；并诊断非结构化分析方法的程度，除了结构化分析之外，还与公司绩效 (FP) 相关。最后，参考组织环境中的数据科学家和营销研究人员，从两个社区理论的角度对该模型进行了研究。在互补分析策略的基础上测试模型：验证性和多组因素分析以及结构方程建模，其中数据（N  = 356) 来自国际在线调查。结果证实了概念模型的高度充分性和非结构化比结构化分析的优越性，导致 FP，而标量不变性测试证明了参考两种分析方法的组之间的细微差异。