Our study contributes to the research on human error during IS use by studying the antecedents of the omission errors that occur during routine instances of computerized work. While attention lapses have been identified as the main mechanism leading to omission errors, we still know little about how such lapses come about during post-adoptive system use. To address this limitation, we draw our theoretical insights from theories of attention and prospective memory to illustrate how the different forms of system use carry the potential to explain patterns of human error. Accordingly, we distinguish between two forms of use history that can consist of features that are either related or unrelated to the execution of a focal task and examine their effects on the frequency of omission errors. We also examine the interaction effects of task variation on the aforementioned relationship. Our hypotheses are tested by analyzing log data associated with the use of a newly introduced mobile application in the context of a sailing sports event. Our results indicate that restricting one's system use on related task features reduces omission errors, whereas a use history based on unrelated task features produces the opposite effects. Further, task diversity positively moderates the relationship between a use history of unrelated features and omission errors, but has no significant moderating effect on the relationship between a use history of related features and omission errors. Our findings hold a number of implications for the literature on human error, and these are discussed alongside with the implications of our study for practitioners and system design.
This is the author’s version of a work that was accepted for publication in Decision Support Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Decision Support Systems, 130, 2019 DOI: 10.1016/j.dss.2019.113225