Menu

ICICKM Mini Tracks

ICICKM Mini Track Submission Topics

Click on the below links to jump to a specific ICICKM Mini Track

Submit your Abstract to an Academic Conference

Mini Track Call for Papers on Data-driven business transformation

Prof. Dr. Olaf Jacob

Dr James Njenga

Mini Track Co-chairs: Prof. Dr. Olaf Jacob, Neu-Ulm University of Applied Sciences, Germany and Dr James Njenga, University of the Western Cape, South Africa

ICICKM 2018 Mini Track Call for Papers on Data-driven business transformation

Businesses, irrespective of their size and shape, are being affected by the immense pervasiveness of information and communication technologies, and the growing need to use data for decision making, and ultimately new types of knowledge for advancement. Businesses are under immense pressure to use data, and the improved technologies to develop new or adapt existing business models from the insights and resultant knowledge the data provides. Adopting data-driven digital transformations is important, but there has been a lag because of, amongst others, the inability to use the vast amount of data, and the subsequent inability to use the intelligence from the data to design new business models.

We invite submissions to this mini-track that focus on the role of data in the business transformation and new business models formulation. The research could offer fresh theoretical and empirical insights on how businesses could use data in the business model formulation and digital transformation. By digital transformation, we mean the inevitable changes in the businesses as a result of the pervasiveness of information and communication technologies.

We invite submissions on topics that include, but are not limited to:

  • Role of data in the business transformation
  • New business models formulation
  • Types of business transformation and the role data plays in each]
  • Changes in the businesses as a result of smart use of data
  • How data protection changes the way businesses run
  • Differences between using data and using traditional knowledge for decision making