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Nowadays, competition is getting tougher as market shrinks because offinancial crisis of the late 2000s. Organizations are tensely forced toleverage their core competencies to survive through attracting more customersand gaining more efficacious operations. In such a situation, diversifiedcorporations which run multiple businesses have opportunities to getcompetitive advantage and differentiate themselves by executing horizontalstrategy. Since this strategy completely engages a number of business units ofa diversified corporation through resource sharing among them, any effort toimplement it will fail if being not supported by enough information. However,for successful execution of horizontal strategy, managers should have reliableinformation concerning its success probability in advance. To provide such aprecious information, a three-step framework has been developed. In the firststep, major influencers on successful execution of horizontal strategy havebeen captured through literature study and interviewing subject matter experts.In the second step through the decision making trial and evaluation laboratory(DEMATEL) methodology, critical success factors (CSFs) have been extracted frommajor influencers and a success probability assessment index system (SPAIS) hasbeen formed. In the third step, due to the statistical nature (multivariate anddistribution free) of SPAIS, an artificial neural network has been designed forenabling organizational managers to forecast the success probability ofhorizontal strategy execution in a multi-business corporation far better thanother classical models.

Forecasting the successful execution of horizontal strategy in a  diversified corporation via a DEMATEL-supported artificial neural network - A  case study