Towards a Model of Information Systems User Competency

The ability to utilize information systems (IS) effectively is becoming a necessity for business professionals. However, individuals differ in their abilities to use IS effectively, with some achieving exceptional performance in IS use and others being unable to do so. Therefore, developing a set of skills and attributes to achieve IS user competency, or the ability to realize the fullest potential and the greatest performance from IS use, is important. Various constructs have been identified in the literature to describe IS users with regard to their intentions to use IS and their frequency of IS usage, but studies to describe the relevant characteristics associated with highly competent IS users, or those who have achieved IS user competency, are lacking. This research develops a model of IS user competency by using the Repertory Grid Technique to identify a broad set of characteristics of highly competent IS users. A qualitative analysis was carried out to identify categories and sub-categories of these characteristics. Then, based on the findings, a subset of the model of IS user competency focusing on the IS-specific factors – domain knowledge of and skills in IS, willingness to try and to explore IS, and perception of IS value – was developed and validated using the survey approach. The survey findings suggest that all three factors are relevant and important to IS user competency, with willingness to try and to explore IS being the most significant factor. This research generates a rich set of factors explaining IS user competency, such as perception of IS value. The results not only highlight characteristics that can be fostered in IS users to improve their performance with IS use, but also present research opportunities for IS training and potential hiring criteria for IS users in organizations.

SAMPLING OF CORN TO ASSESS BIRD DAMAGE: A Preliminary Review of a Comprehensive Study

"How large a sample is needed to survey the bird damage to corn in a county in Ohio or New Jersey or South Dakota?" Like those in the Bureau of Sport Fisheries and Wildlife and the U.S.D.A. who have been faced with a question of this sort we found only meager information on which to base an answer, whether the problem related to a county in Ohio or to one in New Jersey, or elsewhere. Many sampling methods and rates of sampling did yield reliable estimates but the judgment was often intuitive or based on the reasonableness of the resulting data. Later, when planning the next study or survey, little additional information was available on whether 40 samples of 5 ears each or 5 samples of 200 ears should be examined, i.e., examination of a large number of small samples or a small number of large samples. What information is needed to make a reliable decision? Those of us involved with the Agricultural Experiment Station regional project concerned with the problems of bird damage to crops, known as NE-49, thought we might supply an ans¬wer if we had a corn field in which all the damage was measured. If all the damage were known, we could then sample this field in various ways and see how the estimates from these samplings compared to the actual damage and pin-point the best and most accurate sampling procedure. Eventually the investigators in four states became involved in this work1 and instead of one field we were able to broaden the geographical base by examining all the corn ears in 2 half-acre sections of fields in each state, 8 sections in all. When the corn had matured well past the dough stage, damage on each corn ear was assessed, without removing the ear from the stalk, by visually estimating the percent of the kernel surface which had been destroyed and rating it in one of 5 damage categories. Measurements (by row-centimeters) of the rows of kernels pecked by birds also were made on selected ears representing all categories and all parts of each field section. These measurements provided conversion factors that, when fed into a computer, were applied to the more than 72,000 visually assessed ears. The machine now had in its memory and could supply on demand a map showing each ear, its location and the intensity of the damage.