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The Key Drivers and Barriers towards Big Data Usage Adoption in the Automotive Industry

Henzelmann, Johannes LU and De Beer, Merijn LU (2015) INFM10 20151
Department of Informatics
Abstract
Big data usage is an often-hyped phenomenon within industry and academia. The automotive
industry commonly employs BI technology to improve the quality of their decision-making.
However, recent developments within the automotive industry such as the connected car and
advanced manufacturing make big data usage increasingly important. Since the adoption of
new technologies in organizations turns out to be a complex venture, this thesis aims to unveil
the key drivers and barriers of big data adoption within the automotive industry. A theoretical
big data adoption framework is being established based on the organizational innovation
adoption framework of Frambach and Schillewaert (2002), combined with a literature review
on big data.... (More)
Big data usage is an often-hyped phenomenon within industry and academia. The automotive
industry commonly employs BI technology to improve the quality of their decision-making.
However, recent developments within the automotive industry such as the connected car and
advanced manufacturing make big data usage increasingly important. Since the adoption of
new technologies in organizations turns out to be a complex venture, this thesis aims to unveil
the key drivers and barriers of big data adoption within the automotive industry. A theoretical
big data adoption framework is being established based on the organizational innovation
adoption framework of Frambach and Schillewaert (2002), combined with a literature review
on big data. This framework is used in conjunction with expert interviews to evaluate the key
factors towards big data adoption and their significance. The analysis outcome of the
empirical data combined with the literature indicate which factors are considered as barriers
and drivers towards big data usage adoption. Furthermore, it illustrates that some factors are
more significant than others regarding the adoption process. The authors present an amended
theoretical framework towards big data usage adoption, based on the empirical data and its
discussion. (Less)
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author
Henzelmann, Johannes LU and De Beer, Merijn LU
supervisor
organization
course
INFM10 20151
year
type
H1 - Master's Degree (One Year)
subject
keywords
Big Data, Automotive Industry, Adoption drivers and barriers, Business Intelligence
report number
INF15-031
language
English
id
5467216
date added to LUP
2015-07-01 14:22:20
date last changed
2015-07-01 14:22:20
@misc{5467216,
  abstract     = {Big data usage is an often-hyped phenomenon within industry and academia. The automotive
industry commonly employs BI technology to improve the quality of their decision-making.
However, recent developments within the automotive industry such as the connected car and
advanced manufacturing make big data usage increasingly important. Since the adoption of
new technologies in organizations turns out to be a complex venture, this thesis aims to unveil
the key drivers and barriers of big data adoption within the automotive industry. A theoretical
big data adoption framework is being established based on the organizational innovation
adoption framework of Frambach and Schillewaert (2002), combined with a literature review
on big data. This framework is used in conjunction with expert interviews to evaluate the key
factors towards big data adoption and their significance. The analysis outcome of the
empirical data combined with the literature indicate which factors are considered as barriers
and drivers towards big data usage adoption. Furthermore, it illustrates that some factors are
more significant than others regarding the adoption process. The authors present an amended
theoretical framework towards big data usage adoption, based on the empirical data and its
discussion.},
  author       = {Henzelmann, Johannes and De Beer, Merijn},
  keyword      = {Big Data,Automotive Industry,Adoption drivers and barriers,Business Intelligence},
  language     = {eng},
  note         = {Student Paper},
  title        = {The Key Drivers and Barriers towards Big Data Usage Adoption in the Automotive Industry},
  year         = {2015},
}