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.

Yield potential and resource-use efficiency of maize systems in the western U.S. Corn Belt

Maize demand for food, livestock feed, and biofuel is expected to increase substantially. The Western U.S. Corn Belt accounts for 23% of U.S. maize production, and irrigated maize accounts for 43 and 58% of maize land area and total production, respectively, in this region. The most sensitive parameters (yield potential [YP], water-limited yield potential [YP-W], yield gap between actual yield and YP, and resource-use efficiency) governing performance of maize systems in the region are lacking. A simulation model was used to quantify YP under irrigated and rainfed conditions based on weather data, soil properties, and crop management at 18 locations. In a separate study, 5-year soil water data measured in central Nebraska were used to analyze soil water recharge during the non-growing season because soil water content at sowing is a critical component of water supply available for summer crops. On-farm data, including yield, irrigation, and nitrogen (N) rate for 777 field-years, was used to quantify size of yield gaps and evaluate resource-use efficiency. Simulated average YP and YP-W were 14.4 and 8.3 Mg ha-1, respectively. Geospatial variation of YP was associated with solar radiation and temperature during post-anthesis phase while variation in water-limited yield was linked to the longitudinal variation in seasonal rainfall and evaporative demand. Analysis of soil water recharge indicates that 80% of variation in soil water content at sowing can be explained by precipitation during non-growing season and residual soil water at end of previous growing season. A linear relationship between YP-W and water supply (slope: 19.3 kg ha-1 mm-1; x-intercept: 100 mm) can be used as a benchmark to diagnose and improve farmer’s water productivity (WP; kg grain per unit of water supply). Evaluation of data from farmer’s fields provides proof-of-concept and helps identify management constraints to high levels of productivity and resource-use efficiency. On average, actual yields of irrigated maize systems were 11% below YP. WP and N-fertilizer use efficiency (NUE) were high despite application of large amounts of irrigation water and N fertilizer (14 kg grain mm-1 water supply and 71 kg grain kg-1 N fertilizer). While there is limited scope for substantial increases in actual average yields, WP and NUE can be further increased by: (1) switching surface to pivot systems, (2) using conservation instead of conventional tillage systems in soybean-maize rotations, (3) implementation of irrigation schedules based on crop water requirements, and (4) better N fertilizer management.