Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
4-1-2021 12:00 AM
End Date
9-1-2021 12:00 AM
Description
Due to the growing importance of data-driven innovation, multiple streams of literature that offer varying definitions and frameworks for using data and analytics in innovation have emerged. This eventually resulted in synonymously used terminology and overlapping concepts leading to a lack of clarity and transparency. This paper investigates different aspects and variations of existing classification approaches, such as taxonomies, around data-driven innovations, and related fields. For this purpose, a systematic literature review was conducted. The resulting 30 publications were synthesized along the concepts type of study objects, type of output investigated as well as type of value dimension influenced by data and analytics. The review underlines the importance of connecting the different literature streams (e.g. data-driven or analytics business model innovation, or Analytics-as-a-Service) which emerged in recent years and hence developing a common language and knowledge basis around data-driven innovation.
Realizing Value with Data and Analytics: A Structured Literature Review on Classification Approaches of Data-Driven Innovations
Online
Due to the growing importance of data-driven innovation, multiple streams of literature that offer varying definitions and frameworks for using data and analytics in innovation have emerged. This eventually resulted in synonymously used terminology and overlapping concepts leading to a lack of clarity and transparency. This paper investigates different aspects and variations of existing classification approaches, such as taxonomies, around data-driven innovations, and related fields. For this purpose, a systematic literature review was conducted. The resulting 30 publications were synthesized along the concepts type of study objects, type of output investigated as well as type of value dimension influenced by data and analytics. The review underlines the importance of connecting the different literature streams (e.g. data-driven or analytics business model innovation, or Analytics-as-a-Service) which emerged in recent years and hence developing a common language and knowledge basis around data-driven innovation.
https://aisel.aisnet.org/hicss-54/os/org_issues_in_bi/5