Machine-driven Analytics and Contest-driven Approaches for Idea Generation

Sammanfattning

Ideas are sources of creativity and innovation, and there is an increasing demand for innovation. The significance and abundance of data are also increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. The accumulated data could be used to generate ideas, yet the manual analysis of textual information is affected by bias and subjectivity. Furthermore, even though machine-driven analytics techniques could be employed to process these data to generate and evaluate ideas, it is a relatively new area. Moreover, it is also possible to stimulate innovation by utilizing contest-driven idea generation and evaluation. However, the measurement of contest-driven idea generation processes needs to be supported to manage the process better. Besides, post-contest challenges are hindering the development of ideas to viable artifacts. This thesis aims to support idea generation and evaluation processes. To this end, this thesis presents two artifacts that support machine-driven analytics and contest-driven approaches by following a mixed-method research methodology. The results of this study are, therefore, categorized into two major groups, supporting idea generating and evaluation through machine-driven analytics and contest-driven techniques.

This thesis proposes machine-driven analytics and data sources that could guide the choice of idea generation strategies, including heuristics. This thesis also proposes two models designed to support the process of idea generation and evaluation by following the design science approach using three proof-of-concept experiments utilizing datasets about self-driving cars. The first model is proposed to support technical people engaged in data analytics operation to generate ideas. The second model, which is built on the top of the first model, is proposed to serve as a framework and a guideline to both technical and business people. The beneficiaries of these two artifacts are practitioners in data and knowledge engineering, data mining project managers, and innovation agent. Innovation agents could be incubators, contest organizers, consultants, innovation accelerators, and industries. This thesis's results indicate that ideas could be generated using machine-driven analytics. The most widely used data source, according to literature for idea generation, is patent. Yet, social media, scholarly literature, web pages, online idea management systems such, databases could also provide useful datasets.

The second group of the result is related to contest-driven idea generation and evaluation. In this investigation, two artifacts are presented. The first artifact, a method to design and refine digital innovation contest measurement models, is proposed to support idea

generation and evaluation activities. Ideas generated through contests remain on organizers' and participants' shelves due to post-contest challenges hindering ideas from entering markets. To deal with post-contest challenges hindering developers from entering the market, i.e., developing viable artifacts from ideas, a framework of barriers to idea development is proposed. The beneficiaries of these artifacts are innovation agents.

For the future, a review of the state-of-the-art of idea generation tools will serve innovation agents, knowledge engineers, and other stakeholders interested in innovation to advance in their endeavors. Future projects could develop a platform to explore and exploit unstructured data using machine learning, visual analytics, network analysis, and bibliometric for supporting idea generation and evaluation activities. It is possible to adapt and integrate the proposed method to design and refine contest-driven idea generation in developer platforms to serve as part of an embedded idea management system. Future research could also adapt the framework of barriers constraining ideas development for requirement elicitation in post-contest digital service development.