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Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the accounting students have a slightly higher score for deep learning compared to surface learning. Moreover, high intrinsic motivation and extrinsic motivation have a significant positive influence on deep learning. Next, deep learning leads to higher academic performance; surface learning on the other hand leads to lower academic performance. The effect of deep learning on performance still holds, when we control for time spent, gender and ability. Consequently we can conclude that a deep learning approach is much more than "simply" spending a lot of time on studying.

Time estimates are an important part of many costing systems. As they may be subject to estimation error and hence result in measurement error in product and service costs, it is important to understand the sources of this estimation error as well as mechanisms to mitigate them. We performed a computer-based lab experiment to test the joint impact of task interruption and interactive time estimation on the accuracy of time estimates. As predicted, we find that the negative impact of task interruption on time estimation accuracy is mitigated when participants are allowed to interactively discuss their time estimates before making a final time estimate alone. We explain our findings through the underlying effects of cognitive load and confidence. Hence, managers may improve the accuracy of their time-based costing systems by enabling an interactive time estimation process to offset the detrimental effect of task interruption. [ABSTRACT FROM AUTHOR]