Towards a Theoretical Framework to Explain Root Causes of Errors in Manually Acquired Data

The aim of this paper is to investigate how organisations can improve the quality of their manually acquired data. This is achieved by adopting a grounded theory approach to analyse findings from an exploratory case study. The study concludes that organisations can improve the quality of their manually acquired data by increasing the intention of the data producers to input data of good quality and/or by improving the tasktechnology fit. This work contributes to the literature in two ways. First, we refine the theory of planned behaviour so that it can serve as a basis for initiatives to improve the quality of manually acquired data. Second, our theoretical framework demonstrates that the task-technology fit construct can not only be used to explain how the errors in the output of an information system affect the performance of a task for which this output is used, but is also relevant to explain some of the root causes of errors in the input of an information system.