Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.
Smuts, H. and Smith, A. (2021), "Collaboration of Human and Machine for Knowledge Work: An Organisational Transformation Framework for Data-driven Decision-making", Lee, Z.W.Y., Chan, T.K.H. and Cheung, C.M.K. (Ed.) Information Technology in Organisations and Societies: Multidisciplinary Perspectives from AI to Technostress, Emerald Publishing Limited, Leeds, pp. 25-59. https://doi.org/10.1108/978-1-83909-812-320211002
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