The 'Dark Side' of Big Data Analytics - Uncovering Path Dependency Risks of BDA-Investments

Abstract

Recently, information systems (IS) literature has shown an increasing interest in Big Data and Analytics (BDA) to gain competitive advantage. The predominant literature focuses on operational effectiveness and how companies use historical information and uncover hidden patterns to differentiate from competition. This paper addresses how the prevailing line of reasoning is limited and how strategic risks from companies’ BDA-applications are neglected. Drawing on the theory of path dependency and resource-based view, it aims to expand the hitherto strongly IT-capability-oriented view of competitive advantages from BDA, in particular through greater in-volvement in current strategic research and by disclosing previously underexposed risk dimensions. A qualitative research shall be conducted to explore possible strategic risk dimensions associated with BDA-investments in greater detail. To reconstruct the process of BDA-investment and capability-building of firms in the maritime logistics sector, a qualitative process study seems appropriate to explore constitutive features of path formation and detect early indicators for path dependency.

Thomas Wrona

Professor & MLE-Vizesprecher

Machine learning & strategic management, Machine learning & competitive advantages, Strategic risks of machine learning, Machine learning, self-reinforcing mechanisms & strategic path dependency

Pauline Reinecke

Doktorandin

Machine learning & strategic management, Machine learning & competitive advantages, Strategic risks of machine learning, Machine learning, self-reinforcing mechanisms & strategic path dependency