Authors:
Hans-Friedrich Witschel
;
Marco Peter
;
Laura Seiler
;
Soyhan Parlar
and
Stella Gatziu Grivas
Affiliation:
FHNW University of Applied Sciences and Arts Northwestern Switzerland and Switzerland
Keyword(s):
Case Model, Recommender Systems, Decision Support Systems, Digital Transformation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Learning Organization & Organizational Learning
;
Symbolic Systems
;
Tools and Technology for Knowledge Management
Abstract:
In this work, we develop a case model to structure cases of past digital transformations which act as input data for a recommender system. The purpose of that recommender is to act as an inspiration and support for new cases of digital transformation. To define the case model, case analyses, where 40 cases of past digital transformations are analysed and coded to determine relevant attributes and values, literature research and the particularities of the case for digital change, are used as a basis. The case model is evaluated by means of an experiment where two different scenarios are fed into a prototypical case-based recommender system and then matched, based on an entropically derived weighting system, with the case base that contains cases structured according to the case model. The results not only suggest that the case model’s functionality can be guaranteed, but that a good quality of the given recommendations is achieved by applying a case-based recommender system using the
proposed case model.
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