A theoretical framework to improve the quality of manually acquired data

Abstract We present a framework for organisations to prevent errors in data entry. It states that data entry errors can be prevented by a strong intention of data producers to enter data correctly and by a high task-technology fit. Two empirical studies support the framework and demonstrate that a high task-technology fit is relatively more important than the data producers’ intention. The framework refines the theory of planned behaviour, and extends the explanatory domain of the task-technology fit construct. The empirical evidence underlines the importance of the task-technology fit construct, an often-neglected construct in information systems research.

• Publication year

  2019

• Venue

  Information Manager (The)

• Publication date

  2019-01-01

• Fields of study

  Business, Computer Science

• Identifiers
  DOI 10.1016/j.im.2018.05.014
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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