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Big data analytics and firm performance: Findings from a mixed-method approach

机译：大数据分析和公司绩效：混合方法的发现

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Big data analytics has been widely regarded as a breakthrough technological development in academic and business communities. Despite the growing number of firms that are launching big data initiatives, there is still limited understanding on how firms translate the potential of such technologies into business value. The literature argues that to leverage big data analytics and realize performance gains, firms must develop strong big data analytics capabilities. Nevertheless, most studies operate under the assumption that there is limited heterogeneity in the way firms build their big data analytics capabilities and that related resources are of similar importance regardless of context. This paper draws on complexity theory and investigates the configurations of resources and contextual factors that lead to performance gains from big data analytics investments. Our empirical investigation followed a mixed methods approach using survey data from 175 chief information officers and IT managers working in Greek firms, and three case studies to show that depending on the context, big data analytics resources differ in significance when considering performance gains. Applying a fuzzy-set qualitative comparative analysis (fsQCA) method on the quantitative data, we show that there are four different patterns of elements surrounding big data analytics that lead to high performance. Outcomes of the three case studies highlight the inter-relationships between these elements and outline challenges that organizations face when orchestrating big data analytics resources.
机译：大数据分析已被广泛视为学术界和商业界的突破性技术发展。尽管发起大数据计划的公司越来越多，但是对于公司如何将此类技术的潜力转化为业务价值的理解仍然有限。文献认为，要利用大数据分析并实现绩效提升，公司必须开发强大的大数据分析功能。尽管如此，大多数研究都是在这样的假设下进行的：企业构建其大数据分析功能的方式的异质性有限，并且无论背景如何，相关资源的重要性都相似。本文借鉴了复杂性理论，并研究了资源配置和上下文因素，这些因素导致大数据分析投资带来了性能提升。我们的实证研究采用了混合方法，使用了来自希腊公司的175位首席信息官和IT经理的调查数据，并进行了三个案例研究，结果表明，根据具体情况，大数据分析资源在考虑绩效提升时的重要性也有所不同。在定量数据上应用模糊集定性比较分析（fsQCA）方法，我们显示大数据分析周围有四种不同的要素模式，这些要素可以带来高性能。这三个案例研究的结果突出了这些要素之间的相互关系，并概述了组织在编排大数据分析资源时面临的挑战。

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