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https://doi.org/10.32657/10356...
Doctoral thesis . 2019 . Peer-reviewed
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Living innovation laboratory model design and implementation

Authors: Zheng, Yuting;

Living innovation laboratory model design and implementation

Abstract

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published that discuss and define the model and architecture of LIL. There are three characteristics of LIL: user centered design, co-creation, and context awareness, which make it distinguished from test platform and other innovation approaches. LIL consists of five phases: initialization, preparation, formation, development, and evaluation. However, the current LIL has some drawbacks. Firstly, LIL relies on user requests, which usually results in incremental innovation, instead of disruptive innovation. Unlike incremental innovation that only incrementally improves the existing market, disruptive innovation can create new markets. Secondly, current LIL cannot allow the co-creation team to detect those user needs that users may not notice by themselves. Thirdly, LIL co-creation team is usually a targeted group of users, developers and industry party, which may not capture the global needs and thus limits the creativity. In order to cope with the drawbacks of LIL, we propose a new generation of LIL that is LIL 2.0. Its characteristics include unobtrusive user involvement (UUI), massive co-creation (MCC), and predictable context awareness (PCA). UUI helps to discover the hidden user needs. MCC makes the co-creation team more diverse. PCA makes the innovation proactive and forward-looking. Thus, LIL 2.0 has higher chance to produce disruptive innovation in an effective and efficient way with lower business risk. Some advanced concepts, such as big data, crowd sourcing, crowd funding and crowd testing, can facilitate UUI, MCC and PCA and eventually help to build LIL 2.0. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To demonstrate the feasibility of LIL Goal Net Model, it was applied to two real world cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully based on LIL goal net methodology. The two projects were evaluated by phenomenography, which is a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency. MASTER OF ENGINEERING (SCE)

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Keywords

:Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling [DRNTU], :Engineering::Computer science and engineering::Information systems::Models and principles [DRNTU]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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bronze