Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12164/133
Title: Strategic Directions for Big Data Analytics in E-Commerce with Machine Learning and Tactical Synopses: Propositions for Intelligence Based Strategic Information Modeling (SIM)
Authors: Samuel, Jim
Kashyap, Rajiv
Betts, Stephen
Issue Date: 2018
Publisher: North American Business Press
Rights: © 2018 Journal of Strategic Innovation and Sustainability
http://rightsstatements.org/vocab/InC/1.0/
URI: http://hdl.handle.net/20.500.12164/133
Type: journal article
Citation: Samuel, J., Kashyap, R., & Betts, S., (2018) ‘Strategic Directions for Big Data Analytics in E-Commerce with Machine Learning and Tactical Synopses: Propositions for Intelligence Based Strategic Information Modeling (SIM)’ Journal of Strategic Innovation and Sustainability, 13(1), pp. 99-106.
Abstract: E-commerce has seen tremendous growth in big data and the continued acceleration in growth of information facets. There has been a significant scaling up of information quantity, granularity, complexity, equivocality and variety. Data analytics tools and techniques (DATT) such as machine learning and artificial intelligence have been widely leveraged to gain competitive advantage and such resources are readily available. However, there has been a lack of clarity surrounding, what we term as 'strategic information modeling' (SIM). Our research presents propositions to provide contextual clarity to the rapidly expanding Big Data environment and also an articulation of the emerging informational challenges in e-commerce. Our analysis provides insights into the potential role of SIM and SIM generated competitive advantages and concludes with e-commerce relevant propositions for an optimal path towards SIM and machine learning.
Subject: Electronic commerce
Big data
Information modeling
Machine learning
Artificial intelligence
Appears in Collections:Faculty Collection
Marketing and Management Sciences Department

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