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Authors: Veena Bansal and Shubham Shukla

Affiliation: Indian Institute of Technology Kanpur, Kanpur-208016, India

Keyword(s): Big Data Analytics Adoption, Affordance Theory, Adoption and Usage, Adoption Framework.

Abstract: This research explores big data analytics adoption in organisations using affordance theory. Big data analytics are a set of tools and techniques that help companies to get useful business insights from the data. Adoption of big data analytics is a challenging task. Affordance theory has been used to study usage and effect of information technology. In this work, we have modified the affordance theory framework to study adoption of big data analytics. The framework takes into account characteristics of the technology, the goal and characteristics of the organisation. Organisation achieve different outcomes based on their goals and characteristics. We have used case study method to verify efficacy of the adopted framework. The results clearly show that the framework is effective in studying the adoption of big data analytics.

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Paper citation in several formats:
Bansal, V. and Shukla, S. (2021). Exploring Big Data Analytics Adoption using Affordance Theory. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-509-8; ISSN 2184-4992, pages 131-138. DOI: 10.5220/0010509801310138

@conference{iceis21,
author={Veena Bansal. and Shubham Shukla.},
title={Exploring Big Data Analytics Adoption using Affordance Theory},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2021},
pages={131-138},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010509801310138},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Exploring Big Data Analytics Adoption using Affordance Theory
SN - 978-989-758-509-8
IS - 2184-4992
AU - Bansal, V.
AU - Shukla, S.
PY - 2021
SP - 131
EP - 138
DO - 10.5220/0010509801310138