Location

260-057, Owen G. Glenn Building

Start Date

12-15-2014

Description

Today’s enterprises often face heterogeneous application landscapes. Many of those companies struggle with effective and efficient accomplishment of enterprise application integration (EAI), which results in significant time and budget overruns. As regards EAI project management, a major reason for failure is considered to be underestimation of effort. The underestimation has been found to be an aftermath of applying estimation methods that do not account for all relevant factors influencing EAI project effort. We therefore explore factors affecting the effort of such projects in this study. Applying Repertory Grid, we conduct 22 semi-structured expert interviews. 91 factors influencing the effort of EAI projects in nine categories emerge from these interviews. We provide an extensive overview of effort-influencing factors and their classification, which can be used as a checklist in EAI projects. Future research can additionally use our findings as basis for development of more accurate effort estimation models.

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Dec 15th, 12:00 AM

Factors Influencing the Effort of EAI Projects – A Repertory Grid Investigation

260-057, Owen G. Glenn Building

Today’s enterprises often face heterogeneous application landscapes. Many of those companies struggle with effective and efficient accomplishment of enterprise application integration (EAI), which results in significant time and budget overruns. As regards EAI project management, a major reason for failure is considered to be underestimation of effort. The underestimation has been found to be an aftermath of applying estimation methods that do not account for all relevant factors influencing EAI project effort. We therefore explore factors affecting the effort of such projects in this study. Applying Repertory Grid, we conduct 22 semi-structured expert interviews. 91 factors influencing the effort of EAI projects in nine categories emerge from these interviews. We provide an extensive overview of effort-influencing factors and their classification, which can be used as a checklist in EAI projects. Future research can additionally use our findings as basis for development of more accurate effort estimation models.