This study investigates whether there are performance and self-efficacy differences between users based on their age when performing complex information technology tasks. This experimental study seeks to validate some research from cognitive science, which indicates that individuals exhibit a significant decrease in cognitive function in their late 20s and early 30s. A field experiment was created to compare how business professionals perform a series of related BI tasks of varying difficulty on laptops and tablet computers while working at their company sites. Results showed that subjects under 30 had statistically significant higher accuracy as measured by total correct, spent significantly less time in completing all tasks and had significantly higher levels of self-efficacy as measured by confidence. This is the first study to investigate whether there is an age performance gap in completing complex IT tasks on different computing devices using a controlled experiment with actual workers as the subjects.

In their ground-breaking experiments, Newell and Simon1 observed that subjects did not proceed linearly but moved back and forth between decision phases. This phenomenon has been observed subsequently but has not been investigated experimentally. In this follow-up study, the impact of recursion is examined in an experiment to solve a variety of standard business intelligence tasks of increasing complexity. The actions of the subjects (managers) were analyzed and coded to reflect Simon’s decision-making phases: Intelligence, Design, and Choice. This study demonstrates that recursion does occur but does not improve accuracy and time (with one exception), nor lead to increased self-efficacy or confidence. The level of recursion was found, generally, to be unaffected if the task was more complex. Subjects assigned laptops as compared to tablets used more recursion. It is anticipated that this study will help researchers gain an improved understanding of recursion and the future design of DSSs.

With chronic labor shortages in STEM-related industries, much research has focused on how to get more women and minorities interested in STEM careers. The most recent studies seem to indicate that the actual gap in user performance between genders has narrowed, although women tend to have less self-efficacy. However, all these previous studies involved student subjects in an educational context. This research uses a quasi-experimental field study with actual managers to test whether there are differences between genders when performing a variety of tasks of differing complexity on different computing devices and whether there are differences in self-efficacy. Subjects’ performance was measured by question accuracy and time taken to complete a task, while self-efficacy was measured by self-assessed confidence. The results support recent studies indicating that the gender gap in performance is minimal in accuracy with no differences in time spent, while the gap in self-efficacy has remained.

What specific activities do users engage in when using decision support software to solve complex problems and how do they unfold? This paper describes an experimental study where managers solved decision tasks of varying complexity on different computing devices. A video recording of their actions while using an assigned computing device (laptop or tablet) was created, then coded and analyzed using Simon’s three phase model of decision making. Differences in time spent during the Intelligence, Design and Choice phases were found and analyzed to determine if time spent could explain task performance. This study was the first to conduct an experiment with mid-level managers in the field to provide a more detailed description of how Simon’s model of decision-making occurs in practice. This research demonstrates that Simon’s model can be used to analyze decision processes and complements the work done by cognitive/neuroscientists on decision-making.

This paper determines whether the “sequential difficulty effect” or SDE holds for complex information technology tasks. The SDE hypothesizes that users will perform worse on an easy task when given a difficult task first, as compared to when subjects are given an easy task first followed a difficult one. The experiment compares how business professionals perform a series of related BI tasks of varying difficulty. The task sequence was controlled in the experiment. The results did not support the SDE and indicate that the SDE might not be generalizable to more complex tasks. One significant difference in overall user performance was found for those who were presented with the most difficult task first and who successfully completed it. This finding supports the concept of “resource engagement” whereby the cognitive resources employed for the difficult task carry over into the following tasks for certain individuals.

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.