Purpose

The study examines how firms may transform big data analytics (BDA) into a sustainable competitive advantage and enhance business performance using BDA. Furthermore, this study identifies various resources and sub-capabilities that contribute to BDA capability.

Design/methodology/approach

Using classic grounded theory (GT), resource-based theory and dynamic capability (DC), the authors conducted interviews, which involved an exploratory inductive process. Through a continuous iterative process between the collection, analysis and comparison of data, themes and their relationships appeared. The literature was used as part of the data set in the later phases of data collection and analysis to identify how the study’s findings fit with the extant literature and enrich the emerging concepts and their relationships.

Findings

The data analysis led to developing a conceptual model of BDA capability that described how BDA contributes to firm performance through the mediated impact of organizational learning (OL). The findings indicate that BDA capability is incomplete in the absence of BDA capability dimensions and their sub-dimensions, and expected advancement will not be achieved.

Research limitations/implications

The research offers insights on how BDA is converted into an enterprise-wide initiative, by extending the BDA capability model and describing the role of per dimension in constructing the capability. In addition, the paper provides managers with insights regarding the ways in which BDA capability continuously contributes to OL, fosters organizational knowledge and organizational abilities to sense, seize and reconfigure data and knowledge to grab digital opportunities in order to sustain competitive advantage.

Originality/value

This article is the first exploratory research using GT to identify how data-driven firms obtain and sustain BDA competitive advantage, beyond prior studies that employed mostly a hypothetico-deductive stance to investigate BDA capability. While the authors discovered various dimensions of BDA capability and identified several factors, some of the prior related studies showed some of the dimensions as formative factors (e.g. Lozada et al., 2019; Mikalef et al., 2019) and some other research depicted the different dimensions of BDA capability as reflective factors (e.g. Wamba and Akter, 2019; Ferraris et al., 2019). Thus, it was found necessary to correctly define different dimensions and their contributions, since formative and reflective models represent various approaches to achieving the capability. In this line, the authors used GT, as an exploratory method, to conceptualize BDA capability and the mechanism that it contributes to firm performance. This research introduces new capability dimensions that were not examined in prior research. The study also discusses how OL mediates the impact of BDA capability on firm performance, which is considered the hidden value of BDA capability.

中文翻译：

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大数据分析能力及其对企业绩效的贡献：组织学习对企业绩效的中介作用

目的

该研究探讨了企业如何将大数据分析 (BDA) 转变为可持续的竞争优势，并利用 BDA 提高业务绩效。此外，本研究还确定了有助于 BDA 能力的各种资源和子能力。

设计/方法论/途径

作者利用经典扎根理论（GT）、基于资源的理论和动态能力（DC）进行了访谈，其中涉及探索性归纳过程。通过数据的收集、分析和比较之间的不断迭代过程，主题及其关系出现了。在数据收集和分析的后期阶段，文献被用作数据集的一部分，以确定研究结果如何与现有文献相吻合，并丰富新兴概念及其关系。

发现

数据分析导致开发了 BDA 能力的概念模型，该模型描述了 BDA 如何通过组织学习 (OL) 的中介影响对公司绩效做出贡献。研究结果表明，如果没有BDA能力维度及其子维度，BDA能力是不完整的，并且不会实现预期的进步。

研究局限性/影响

该研究通过扩展 BDA 能力模型并描述每个维度在构建能力中的作用，提供了有关如何将 BDA 转换为企业范围计划的见解。此外，本文还为管理者提供了有关 BDA 能力如何持续为 OL 做出贡献的见解，培养组织知识和组织能力来感知、捕获和重新配置数据和知识，以抓住数字机会，从而保持竞争优势。

原创性/价值

本文是第一篇使用 GT 来确定数据驱动型企业如何获得和维持 BDA 竞争优势的探索性研究，超越了之前主要采用假设演绎立场来调查 BDA 能力的研究。虽然作者发现了 BDA 能力的各个维度并确定了几个因素，但之前的一些相关研究表明某些维度是形成因素（例如 Lozada等人，2019 年；Mikalef等人，2019 年），而其他一些研究描述了BDA 能力的不同维度作为反射因素（例如 Wamba 和 Akter，2019 年；Ferraris等人，2019 年），2019）。因此，我们发现有必要正确定义不同的维度及其贡献，因为形成性模型和反思性模型代表了实现能力的各种方法。在这方面，作者使用 GT 作为一种探索性方法来概念化 BDA 能力及其对公司绩效的贡献机制。这项研究引入了先前研究中未检验的新能力维度。研究还讨论了OL如何调节BDA能力对企业绩效的影响，这被认为是BDA能力的隐藏价值。