A classification of success factors for decision support systems

1998 ◽  
Vol 7 (1) ◽  
pp. 53-70 ◽  
Author(s):  
Paul N. Finlay ◽  
Morteza Forghani
Author(s):  
Nalika Ulapane ◽  
Nilmini Wickramasinghe

The use of mobile solutions for clinical decision support is still a rather nascent area within digital health. Shedding light on this important application of mobile technology, this chapter presents the initial findings of a scoping review. The review's primary objective is to identify the state of the art of mobile solution based clinical decision support systems and the persisting critical issues. The authors contribute by classifying identified critical issues into two matrices. Firstly, the issues are classified according to a matrix the authors developed, to be indicative of the stage (or timing) at which the issues occur along the timeline of mobile solution development. This classification includes the three classes: issues persisting at the (1) stage of developing mobile solutions, (2) stage of evaluating developed solutions, and (3) stage of adoption of developed solutions. Secondly, the authors present a classification of the same issues according to a standard socio-technical matrix containing the three classes: (1) technological, (2) process, and (3) people issues.


2014 ◽  
Vol 8 (3) ◽  
pp. 1364-1371
Author(s):  
Mohammed A. I. Ayoub

Web-based decision support systems are increasingly used over the past years. However, few studies have been conducted on evaluation of web-based decision support systems especially in the field of online shopping. This paper attempts to explore the critical success factors that influence decision making satisfaction in online shopping context by providing a conceptual model for this purpose. Although there are various factors which contribute in making online shopping decisions but this study focuses on five factors i.e. web site quality, data quality, knowledge management, decision making satisfaction, and perceived net benefit. Also, this research will use existing models that explain and predict information systems success. However, these success models need to be updated to recurrent industry developments since the updating existing IS success models, a better understanding of web-based DSS practitioner success can be achieved.


2015 ◽  
Vol 61 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Ján Tuček ◽  
Róbert Sedmák ◽  
Andrea Majlingová ◽  
Maroš Sedliak ◽  
Susete Marques

Abstract Project COST Action FP 0804 - FORSYS summarizes European experiences in developing and applying decision support systems for forest management. This paper introduces FORSYS methodology for the classification of current forest management problems and for the description of existing decision support systems. The paper identifies the general forestry planning problems that need to be solved in Slovakia, lists the DSS tools available in Slovakia and evaluate their ability for addressing the identified problems. Finally, the research needs and gaps in this field were identified. A comparison of the situation regarding decision support in Slovakia and both in Europe and neighbouring countries (Austria, Hungary) is introduced in order to justify the identified needs. The paper is focused on the overview of models, methods and knowledge management techniques which are available in Slovakia now. We found out that the Slovak decision support research follows the state in Europe with a significant time delay and a lack of adequate instruments for addressing the contemporary planning problems exists. Consequently, there is a strong need for the development and application of computer-based tools to support decision-making problems in forest management.


1996 ◽  
Vol 26 (12) ◽  
pp. 2099-2108 ◽  
Author(s):  
David A. MacLean ◽  
Wayne E. MacKinnon

The accuracy of aerial sketch-mapping estimates of spruce budworm (Choristoneurafumiferana (Clem.)) defoliation was evaluated from 1984 to 1993 in 222–325 sample plots in spruce (Picea sp.)–balsam fir (Abiesbalsamea (L.) Mill.) stands in New Brunswick. Operational aerial defoliation estimates were used, wherein all productive forest in known budworm infestation zones was surveyed each year from small aircraft with flight lines 2–5 km apart, and rated in classes of nil (0–10%), light (11–30%), moderate (31–70%), and severe (71–100%). Aerial defoliation estimates were compared with ground-based binocular estimates of current defoliation for an average of 10 trees/plot (range 5–20). Overall, 56% of plots were correctly rated by aerial sketch mapping in four classes (nil, light, moderate, and severe), with 37% of the plots underestimated and 7% overestimated. The predominant error (26% of plots) was rating defoliation as nil (0–10%) from the air when it was actually light (11–30%). This error was deemed not important in terms of predicting tree response, since data from the literature indicated that defoliation less than 30% did not cause tree mortality, although if continued, it would reduce growth. Using three defoliation classes (by combining nil and light, 0–30%), 82% of the plots were correctly classified by aerial sketch mapping. The probability of correct aerial classification of defoliation was significantly affected by defoliation class, weather conditions prior to and during observation flights, and the defoliation class × weather interaction. It was concluded that aerial sketch mapping of spruce budworm defoliation is a viable technique that can be used for both surveys and decision support systems that estimate forest response to budworm outbreaks and management activities.


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