The Key Drivers and Barriers towards Big Data Usage Adoption in the Automotive Industry
(2015) INFM10 20151Department 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/5467216
- author
- Henzelmann, Johannes LU and De Beer, Merijn LU
- supervisor
- organization
- course
- INFM10 20151
- year
- 2015
- 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}}, language = {{eng}}, note = {{Student Paper}}, title = {{The Key Drivers and Barriers towards Big Data Usage Adoption in the Automotive Industry}}, year = {{2015}}, }