Researchers use time-series data extensively to explain how the rate of technology adoption varies with time, but time-series data does not address the fundamental reasons for adoption. These three empirical methodologies describe the parts of agricultural technology adoption which must be understood if governments and NGOs are to craft their activities for optimum effect: Technology Adoption Technology is assumed to mean a new, scientifically derived, often complex input supplied to farmers by organizations with deep technical expertise. This coincidence should not obstruct the point that a technology is simply the application of scientific knowledge for a certain end.
This article has been cited by other articles in PMC. Abstract Objectives The objective of this study was to create a new measure for clinical information technology IT adoption as a proxy variable of clinical IT use.
The 18 clinical IT applications were analyzed across 3, acute care hospitals in the United States. After factor analysis was conducted, the clinical IT adoption score was created and evaluated.
This clinical IT score varied across hospital characteristics. Conclusions Different IT applications have different adoption patterns. In creating a measure of IT use among various IT components in hospitals, the characteristics of each type of system should be reflected.
Aggregated IT adoption should be used to explain technology acquisition and utilization in hospitals.
Introduction Studies of clinical information technology IT have increased considerably in recent years with the growing recognition of the importance of clinical IT in the context of healthcare quality and costs [ 1 - 15 ].
Previous studies have found that clinical IT could significantly increase quality and productivity, and decrease costs [ 1 - 14 ], although some studies have found a weak impact [ 916 ]. Many studies have adopted different approaches to measure IT adoption because healthcare facilities use many diverse IT systems.
However, few studies and even fewer discussions have addressed how IT adoption should be measured in various clinical settings. It is important to measure IT adoption correctly because we must clearly understand the degree of IT adoption to fully understand the level of IT dispersion in healthcare facilities.
In previous studies, IT adoption has been categorized, broadly, using two methods. One is to measure IT adoption by whether or not hospitals adopt a specific IT system. However, such studies cannot broadly explain technology acquisition and utilization because they ignore the other clinical IT systems in use.
These studies may fit well or be appropriate in studies when researchers focus on the effect of a specific IT system. The other method is to measure IT use with an aggregated score by assigning equal weight to each IT application [ 26 ].
Although this method better estimates the effect of using aggregated IT systems, it still has limitations in that the assignment of equal weight to IT systems can lead to inaccurate results. For example, adopting a basic system e. Healthcare organizations may differ in their patterns of introducing various IT systems.
Healthcare organizations with less experience in using clinical IT systems would likely invest in less expensive IT systems, and those with more experience would likely adopt more sophisticated and expensive systems. Although there is some debate on measuring the use of clinical IT by counting with equal weight as mentioned above, a measure more sensitive to the type of IT could provide an important index for the overall level of IT adoption in healthcare organizations.
We may choose a more advanced approach from the second one above when we have to measure IT adoption by showing the degree of overall IT adoption. Many IT applications are adopted in healthcare organizations and are closely related with multi-products.
Therefore, to estimate the potential impact of clinical IT systems on hospital output, it is necessary to measure IT systems aggregately by reflecting different weights for diverse IT systems. Few studies have measured clinical IT systems this way.
With the current national healthcare reform in the United States, it is imperative to measure the degree of clinical IT application aggregately in healthcare organizations.
Lacking a clear and accurate measurement of clinical IT may have precluded answering vital theoretical and policy questions related to clinical IT.New Economy Handbook: Hall and Khan November 1 Adoption of New Technology Bronwyn H.
Hall University of California at Berkeley Beethika Khan. Mesurement of Technology Adoption for Vegetablesproduction in Nainital District of Uttarakhand Essay () Received June ; Accepted Oct Measurement of Technology (Hybrid Seed) Adoption for Vegetable The results obtained revealed that the area under study was characterized by preponderances of low adoption of technology as.
Extent of Awareness and Adoption of Bio Agents in Vegetable Production in Kerala (IJSTE/ Volume 3 / Issue 01 / ) integrated pest and disease management so as to lessen the use of harmful chemicals in agriculture and to pave way for organic. 38 Evaluation of Vegetable Based Production Technology Adoption by Women Farmers in Delta State, Nigeria 1Oroka, Frank Oke & 2Ozoani, Sussan Ebele 1Department of Agronomy, Delta State University, Asaba Campus, Nigeria.
level of technology in sector s, country c, and period t, and xct denotes the overall adoption level in country c in period t, the average of the adoption levels by sector for country c in period t.
a level for the standard deviation close to in all three periods. level of technology in sector s, country c, and period t, and xct denotes the overall adoption level in country c in period t, the average of the adoption levels by sector for country c in period t. a level for the standard deviation close to in all three periods.