Task-Technology Fit in Data Warehousing Environments: Analyzing the Factors that Affect Utilization

Abstract

Enterprise data warehouses can be an expensive investment in both time and resources. The promise of data warehouses is improved decision making through user empowerment and enablement of sophisticated decision support tools. Have organizations been able to achieve this goal? The theory of tasktechnology fit defines a model that suggests that for a technology to be utilized it must meet the needs of a user. When the technology meets the users’ needs and provides features that support the fit of the requirements of the task, performance impacts will result. When an organization commits the time and resources necessary to develop an enterprise data warehouse, their expectation is that there will be a high tasktechnology fit and that performance impacts will result in an improved organization. This study extends prior task-technology fit research to provide an evaluation of task-technology fit in data warehouse applications. The focus of the study is to examine the factors that contribute to the task-technology fit specifically within data warehouse environments. Six organizations that have implemented a data warehouse were surveyed. The sample population consisted of users of large-scale data warehouses within the insurance industry.