Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE credits
Purpose: The purpose of this thesis is to understand the role of big data in the innovation process and specify the conditions or scenarios where big data and innovation can join forces.
Research design, approach and method: The theoretical framework used is based on the innovation framework with big data from Kusiak (2015) and it is validated by three propositions:
1) Big data analytics (BDA) gives the possibility to discover new insights, such as increase profitability, expansion of customer base and market growth, hence it is a contributor to innovation.
2) Firms are investing or plan to invest massively in big data analytics, in terms of money and time, in order to have a competitive advantage.
3) Big data analytics is a general purpose technology (GPT), thus can bring value across different industries.
The study is explorative using a qualitative approach primarily validated by interviews and supported by one exploratory survey. A total of four individuals were interrogated through semi-structured and two through unstructured interviews. Regarding the cross-sectional on-line survey, the response rate was 29% where 15 questionnaires were filled out.
Findings: The outcome of the study is that:
BDA gives the possibility to discover new insights hence BDA is a contributor to innovation if an evolving process is in place and that humans are interacting with the results.
Firms are investing or plan to invest massively in BDA in order to have a competitive advantage is partially supported as there is a lack of financial figures and order of magnitude.
BDA is a general purpose technology thus can bring value across different industries (e.g. Insurance and metallurgy) is supported by the empirical findings.
In conclusion, big data analytics can play a role in the innovation process in three different phases: data storage, data analysis and innovation knowledge. It is reflected as: BDA can trigger innovation, BDA can be innovation and different sources of data can contribute to insights in a BDA system