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Is Automation the Future of Machine Learning? A Qualitative Study Exploring the Influential Factors for Adoption of Automated Machine Learning in an Organizational Context

Mahgoub, Yahia LU and Petrov, Dragomir Veselinov LU (2021) INFM10 20211
Department of Informatics
Abstract
Machine learning (ML) started as hype and an academic dream and throughout recent years has become reality for many organizations that are working towards becoming data-driven when making vital business decisions with the use of great amounts of data. Capitalizing on ML re-quires expertise which the job market is struggling to provide. This resulted in the intro-duction of the concept of automating the ML activities, namely AutoML. However, until to-day there is still little evidence of organizations adopting AutoML and a lack of understanding around the factors that influence the adoption of AutoML. Hence, this research aims to provide knowledge about what factors are critical to consider in the context of adopting the AutoML in an... (More)
Machine learning (ML) started as hype and an academic dream and throughout recent years has become reality for many organizations that are working towards becoming data-driven when making vital business decisions with the use of great amounts of data. Capitalizing on ML re-quires expertise which the job market is struggling to provide. This resulted in the intro-duction of the concept of automating the ML activities, namely AutoML. However, until to-day there is still little evidence of organizations adopting AutoML and a lack of understanding around the factors that influence the adoption of AutoML. Hence, this research aims to provide knowledge about what factors are critical to consider in the context of adopting the AutoML in an organiza-tional context. This is done so by interviewing experts on the topic through the perspectives of a conceptual TOE-framework. These are technological,
organizational and external environment. Seven factors were considered from the literature: technological readiness, benefits and barriers, size, management support, championship, compe-tition and IS support. Additional two were proposed and motivated: Data availability and Trust. The study found that all factors except championship and data availability were influential. (Less)
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author
Mahgoub, Yahia LU and Petrov, Dragomir Veselinov LU
supervisor
organization
course
INFM10 20211
year
type
H1 - Master's Degree (One Year)
subject
keywords
Automated machine learning, Machine learning, Technology adoption, TOE-framework
report number
INF21-066
language
English
id
9066276
date added to LUP
2021-10-12 16:57:28
date last changed
2021-10-12 16:57:28
@misc{9066276,
  abstract     = {{Machine learning (ML) started as hype and an academic dream and throughout recent years has become reality for many organizations that are working towards becoming data-driven when making vital business decisions with the use of great amounts of data. Capitalizing on ML re-quires expertise which the job market is struggling to provide. This resulted in the intro-duction of the concept of automating the ML activities, namely AutoML. However, until to-day there is still little evidence of organizations adopting AutoML and a lack of understanding around the factors that influence the adoption of AutoML. Hence, this research aims to provide knowledge about what factors are critical to consider in the context of adopting the AutoML in an organiza-tional context. This is done so by interviewing experts on the topic through the perspectives of a conceptual TOE-framework. These are technological, 
organizational and external environment. Seven factors were considered from the literature: technological readiness, benefits and barriers, size, management support, championship, compe-tition and IS support. Additional two were proposed and motivated: Data availability and Trust. The study found that all factors except championship and data availability were influential.}},
  author       = {{Mahgoub, Yahia and Petrov, Dragomir Veselinov}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Is Automation the Future of Machine Learning? A Qualitative Study Exploring the Influential Factors for Adoption of Automated Machine Learning in an Organizational Context}},
  year         = {{2021}},
}