InnoCrowd, An AI Based Optimization of a Crowdsourced Product Development

Kusumah, Indra; Rohleder, Clotilde; Salinesi, Camille

doi:10.1007/978-3-030-94335-6_19

Cited by 1 publication

(1 citation statement)

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“…This final ranking may become the input to a further iteration of the process. We enhanced our approach of InnoCrowd -an AI Based Optimization of a Crowdsourced Product Development - [6] and we adopted new ideas from formerly published studies to develop a new method for a semi-automated requirement elicitation for product innovation in data driven marketing and applied it to the e-commerce domain. This method will use the base for the data classification related to our two main aspects mentioned in the next sub chapter, e-commerce and innovation in e-commerce.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Semi-Automated Requirement Elicitation in Data Driven Marketing for Product Innovation by Using Machine Learning

Dr. Clotilde Rohleder,

Kusumah,

habil Camille Salinesi

et al. 2023

IJBMS

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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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Section: Background and Literature Reviewmentioning

confidence: 99%