Graphical interfaces allow users to issue commands using pull-down menus, icon toolbars, and keyboard shortcuts. Menus and icon toolbars are easier to learn, whereas keyboard shortcuts are more efficient. It would seem natural for users to migrate from the use of easy-to-learn menu and icon methods to the more efficient method of keyboard shortcuts as they gain experience. To investigate the extent to which this transition takes place, 251 experienced users of Microsoft Word were given a questionnaire assessing their choice of methods for the most frequently occurring commands. Contrary to our expectations, most experienced users rarely used the efficient keyboard shortcuts, favoring the use of icon toolbars instead. A second study was done to verify that keyboard shortcuts are, indeed, the most efficient method. Six participants performed common commands using menu selection, icon toolbars, and keyboard shortcuts. The keyboard shortcuts were, as expected, the most efficient. These studies show that even experienced users are inefficient in their use of graphical interfaces. One possible way to improve user efficiency is for training programs to provide a roadmap for users to make the transition from using pull-down menus and clicking icon toolbars to issuing keyboard shortcuts.

This article presents a system for predicting the icon a user will select from an icon toolbar, based on command use frequency and mouse trajectory. The system differs from previous systems in 2 important ways: First, the prediction system does not initiate any action. Instead, it predicts where the mouse is moving and subtly "suggests" a command for the user to verify. Second, the system takes into account the relative likelihood of commands being used when making its predictions. Initial testing suggested that a system that only predicted the most frequently used icon choices was better than one that predicted all choices. A study with 12 test users using a mouse and 10 using a trackpad found substantial benefits of this "limited prediction system." The system resulted in a mean reduction in time to issue a command of 41% for trackpad users and 25% for mouse users. Trackpad users, but not mouse users, preferred the prediction system to the traditional way of pointing. These results suggest that a prediction system such as the one described here has the potential to reduce the time and effort required to issue commands. The utility of the system appears to be especially great in laptops and other devices that use trackpads as their primary pointing devices.

This article addresses a key question in the application of Card, Moran, and Newell's (1983) keystroke-level model to software in which users specify a command by working through a system of hierarchical menus. For example, to insert a row in Lotus 1-2-3®, the user makes three menu choices: W for worksheet, I for insert, and R for row. In the keystroke-level model, it is assumed that a time-consuming mental operation precedes each command. The question in the application of the keystroke-level model to hierarchical menu systems is whether the keystrokes WIR in the previous example constitute the execution of three commands and thus require three mental operations or whether WIR acts as a single command and requires only one mental operation. Data were collected from four highly experienced Lotus 1-2-3 users as they went about their day-to-day work. Strong evidence that only one mental operation is involved in choosing from a hierarchical menu system was obtained. We hypothesize that the discrepancy of our results from the data of others is due to the fact that our subjects were more experienced. The implications of our findings for the design of menus is discussed.

The ability to predict users' commands in Lotus 1-2-3 was investigated. Keystroke data was collected from four subjects in their normal work environment, with over 2700 commands being analysed. The data was used to construct transitional probability matrices that were then used to predict commands based on their immediate predecessors. Approximately half of the users' commands were correctly predicted in this manner. Also of interest was how often the users' commands would be either the most likely or the second most likely command given the previous commands. When the previous two commands were used to derive two predictions, the probability that the users' next command would be one of the two predicted commands was 0.81. The average probability of command repetition was 0.33. It was concluded that users appear to be extremely predictable in the Lotus 1-2-3 environment, and this predictability could serve as the foundation for an interface that aids the user by providing easy access to the next most likely command to be issued and by adapting the probabilities of command use over time.

An in-depth study of the influence of problem structure on the computational efficiency of the primal simplex transportation algorithm is presented.The input for the study included over 1000 randomly generated problems with 185 different combinations of the number of sources, the number of destinations, and the number of variables.Objective function coefficients were generated using three different probability distributions to study the effects of variance and skewness in these parameters.Every problem was solved using three different starting procedures, and the following data were collected for each problem: (1) time required to obtain an optimal solution; (2) time required to obtain an initial basic solution; (3) number of artificial variables in the initial basic solution; (4) number of basis changes; (5) average time to perform a change of basis; (6) average number of basic variables in the "stepping stone path" in each change of basis; (7) average number of variables considered before selecting one to enter the basis.These measures of performance provide numerous insights into the effects of problem parameters on computation time.