Inferring comprehensible business/ICT alignment rules

Authors:
Bjorn Cumps;David Martens;Manu De Backer;Raf Haesen;Stijn Viaene;Guido Dedene;Bart Baesens;Monique Snoeck
Affiliations:
Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium and Department of Operations and Technology Management, Vlerick Leuven Gent Management School, B ...;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium and Department of Business Administration and Public Management, Hogeschool Gent, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium and Department of Business Administration and Public Management, Hogeschool Gent, Belgium and De ...;Department of Economics & Management, Hogeschool-Universiteit Brussel, Belgium and Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium and Department of Operations and Technology Management, Vlerick Leuven Gent Management School, B ...;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium and Department of Economics and Business, University of Amsterdam Business School, The Netherlan ...;School of Management, University of Southampton, United Kingdom and Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Belgium
Venue:
Information and Management
Year:
2009

Citing 12
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Abstract

We inferred business rules for business/ICT alignment by applying a novel rule induction algorithm on a data set containing rich alignment information polled from 641 organisations in 7 European countries. The alignment rule set was created using AntMiner+, a rule induction technique with a reputation of inducing accurate, comprehensible, and intuitive predictive models from data. Our data set consisted of 18 alignment practices distilled from an analysis of relevant publications and validated by a Delphi panel of experts. The goal of our study was to describe practical guidelines for managers in obtaining better alignment of ICT investments with business requirements. Our obtained rule set showed the multi-disciplinary nature of B/ICT alignment. We discuss implication of the alignment rules for practitioners.