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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.12.2013 | Original Article

A novel methodology for evaluating the risk of CRM projects in fuzzy environment

verfasst von: A. Keramati, S. Nazari-Shirkouhi, H. Moshki, M. Afshari-Mofrad, E. Maleki-Berneti

Erschienen in: Neural Computing and Applications | Sonderheft 1/2013

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Abstract

According to obtained reports, most of customer relationship management (CRM) projects fail. Thus, a structured approach is needed to identify, prioritize and evaluate risks in CRM projects. This helps project managers of CRM implementation to find out how to treat such risks. The nature of CRM is multi-dimensional, and it is one of the most complicated innovations in organizations. In this paper, because of multi-dimensional nature of CRM projects, fuzzy analytic hierarchy process is used to evaluate CRM risk factors based on active project managers’ judgments as experts in organizations which sale CRM software or provide service and consult in Iran. The analyses are performed by the judgments of CRM experts using a questionnaire including 49 pair-wise comparisons. In Iranian organizations, based on experts’ judgments, in the first life cycle phases of CRM, “changes in the stakeholders and top management” has the highest importance among risk factors in CRM projects. According to our experts’ beliefs, management permanence is one of the most important factors for IS projects failure in Iran which is resulted from Iranian organizations’ culture. In the next life cycle phases of CRM, the risk factors such as general factors of classification “dynamic assessment and monitoring” have the lowest priority.
Vorheriger Artikel Adaptive fuzzy tuning of PID controllers
Nächster Artikel Superior neuro-fuzzy classification systems

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Metadaten
Titel
A novel methodology for evaluating the risk of CRM projects in fuzzy environment
verfasst von
A. Keramati
S. Nazari-Shirkouhi
H. Moshki
M. Afshari-Mofrad
E. Maleki-Berneti
Publikationsdatum
01.12.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1216-7

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