Tugrul U. Daim

Date of Award


Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Technology Management


Engineering and Technology Management

Physical Description

1 online resource (xii, 309 pages)




Big data projects are facing an alarmingly high percentage of failure, with severe consequences related to the cost of this type of projects, the waste of resources and efforts in doing it, and the competitive disadvantage caused by the lack of big data analytics capabilities in comparison with competitors. Furthermore, there is a decent amount of research on the main challenges facing big data projects. However, there is a lack of research on how to leverage on this knowledge to evaluate an organization's readiness for a big data project, more specifically, how to systematically assess an organization's current status against known reasons that might cause a big data project to fail. Hence, identifying shortcomings that need to be addressed before the project start, to reduce chances of failure for that project.

Therefore, the primary goal of this research is to help any organization, which is planning to transform to the big data analytics era, by providing a systematic and comprehensive model that this organization can use to better understand what factors influence big data projects. Also, the organization's current status against those factors. Finally, what enhancements are needed in the organization's current capabilities for optimal management of factors influencing an upcoming big data project. However, big data applications are vast and cover many sectors, and while most of the factors influencing big data projects are common across sectors, there are some factors that are related to the specific circumstances of each sector. Therefore, this research will focus on one sector only, which is the smart city sector, and its generalizability to other sectors is discussed at the end of the research.

In this research, literature review and experts feedback were used to identify the most critical factors influencing big data projects, with focus on smart city, Then, the HDM methodology was used to elicit experts judgment to identify the relative importance of those factors. In addition, experts' feedback was used to identify possible statuses an organization might have regarding each factor. Finally, a case study of four projects related to the City of Portland, Oregon, was conducted to demonstrate the practicality and value of the research model.

The research findings indicated that there are complicated internal and external, sometimes competing, factors affecting big data projects. The research identified 18 factors as being among the most important factors affecting smart-city-related big data projects. Those factors are grouped into four perspectives: people, technology, legal, and organization. Furthermore, the case study demonstrated how the model could pinpoint shortcomings in a city's capabilities before the project start, and how to address those shortcomings to increase chances of a successful big data project.

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