Abstract
The creation of an innovation needs new
input, new ideas which are not easy to be acquired. Meanwhile in the era of
internet people express their mind, their feeling and their judgement about
anything in the digital world. Among others the crowd (those people) gives also
comments about a physical object (hardware) or digital object (software). Some
of the comments may give a direction for a new innovation. The usage and
extraction of these information (comments) for the elicitation of product
requirement for a new innovation is an object of research works and there are
already methods developed by scholars. After studying the state of the art we
come up with an idea to enhance the performance of the existing methods. We
develop a process chain based on machine learning for an automated requirement
elicitation based on crowd input optimized by a specific domain knowledge,
applied for e-commerce. We focus on the aspects related to digital disruption
as well as agile management in innovation. The crowd input gained from a
digital big data available in twitter from the past 5 years until now. We
analyze the digital consumer behavior by extracting this data. By using an
optimized specific domain knowledge, we extract product requirement for a new
innovation in the domain of e-commerce. We compare our method with the
available state of the art methods and show better performance of our approach.
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IJBMS by IPRPD is licensed under a Creative Commons Attribution 4.0 International License.