Big data analytics capability and contribution to firm performance: the mediating effect of organizational learning on firm performance

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.