Key factors and processes for digital government success

Abstract

Digital government projects and initiatives are complex endeavors. Technology by itself is a source of complexity, as its impacts, benefits and limitations are not always fully understood by project leaders or researchers. This is particularly the case when the implemented technology is not yet widely adopted. Moreover, digital government projects regularly involve the participation of a wide range of diverse stakeholders both in public and private organizations who need to reach agreement on project goals, objectives, and means [2]. Further, project goals and activities are usually constrained by institutional arrangements such as laws and regulations, and several context-related factors such as specific economic situations, demographic conditions, or broader social technology adoption and access [1,3,4]. Given this complexity, it is not uncommon that digital government projects are abandoned before completion or fail delivering the expected benefits. Some experts estimate an 85% failure rate for digital government projects [11]. In 2009 alone, government spending in technology worldwide was about 428.38 billion US dollars, thus failure rates for these kinds of projects are a major concern [10]. In this way, understanding reasons for project success or failure is an important research and practical problem. In order to understand success and failure rates, researchers in digital government and more in general, researchers in information systems and information technologies have used two different approaches: a factor approach and a process approach [9]. The factor approach involves the use of correlational statistics to identify the “key success factors”, that is to say, the most important variables in determining the success of a project [5,6]. Such factors involve variables as technology, organizational, institutional or contextual variables. On the other hand, process approaches, which frequently involve case studies, offer a complementary way to understand project success [9]. From this approach, key variables interact among themselves in a series of processes that evolve dynamically over time, and these processes are as important as the key variables to fully understand success [7,8].