Group Decision Support Systems (GDSS)
Group Decision Support System refers to an intelligent information system designed to support group meetings and further aid discussions aimed at creating solutions to various problems or providing alternative solutions to existing problems. According to Sharda et al. (2020), a Group Decision Support System is characterized by certain basic elements such as enhanced user interaction, predictive ability as well as other features that facilitate the solution of an unstructured problem. The GDSS is a pure computerized environment that has the ability to facilitate every participating team member to go about the usual tasks. The primary goal of a GDSS involves providing various improvements to the process of decision making by groups hence making such processes precise, fast and effective through the elimination of group discussion challenges such as miscommunication, domination and conformity amongst others. Such problems are often experienced during the processes of group decision making.
There are different various technological levels that characterize the GDSS. The process level forms the elementary level of every GDSS. The primary goal of the process level is to eliminate or significantly minimize the communication barriers that often occur during group sessions. The level is created by way of electronic messaging where participants feed ideas unanimously then the intelligent element of the system through the aid of public screens, networks, idea and votes solicitation as well as displaying of the idea. All these end up in coming up with a session agenda that is agreeable to all and continuously displaying it hence acting as a constant reminder to the participants (Carneiro et al., 2019). The second level of technology is referred to as decision making support. The level is one of the most important ones in the whole GDSS set up as it aims at adding models to the GDSS as well as providing decision analysis in an environment free from noise and other forms of distractions. The level leverages its self-differentiating features such as the financial models and decision trees, models for resource allocation, social judgment models and probability assessment models to provide task gains. Finally, there is the third level of technology in the GDSS is the rules of order. The level mainly trades its guns on the control of the content of the decision-making process. The rules are mainly inclined on timing, messaging and other patterns.
GDSS exist in different types. There is the K-GDSS whose main advantage is the ability to display votes and general portability. The major strength of this type of GDSS is that it is a handset-based system hence the anonymity, which improves participation in the meetings. This type also upholds the effectiveness even if there are several meetings lined up with a large number of participants. Planning for the meetings is also easier as it also offers the means of controlling meeting sessions. The type is particularly vital where the participating group members are geographically located in different places (Gusakova&Romanova, 2019). Even though the GDSS makes it possible to implement virtual teams, the biggest problem remains to be the achievement of synchronization. The challenge is often fueled by the diversity brought about by different geographical locations which sometimes can be falling on different time zones.
The deployment of GDSS intensively utilizes two main types of technologies. They include client-server and web-based technologies. Technology has tremendously advanced, especially the IoT and web applications, in general, have contributed towards the current platforms of the web-based GDSS (Alajlouni, Mohammed Iqbal, 2016). There has been an emergence of great use of web-based applications leading to wide utilization of web-based systems in organizations across the world. The systems continue to help several companies in providing the solution to complex problems in the organization through the use of collaborative tools. The most noticeable and recent web-based GDSS advancement has been the introduction of the collaborative systems that have played critical roles in improving collective intelligence.
In contemporary society, the access of the Web-based GDSS has tremendously improved. The users have been able to access the systems from their remotely located computers via the recently developed and updated web-based browsers that are also supported by intranets of decision-making processes. The web-based GDSS come with various dimensions of collective intelligence. The first in this case is the freedom of the mind which enables for the responsible exercise of the mental authority, which includes behaviour adjustment to suit certain situations. The web-based GDSS possess central memory that all the participants can access and retrieve important information that they can use in making decisions. The web-based GDSS is also merited with sharing of knowledge hence enabling the participants to utilize little time to explore various perspectives upon which they use to arrive at the most significant talking points. They also allow for collective perception like cognitive decision models and feedback as well as other cognitive-aid structures.
References
Alajlouni, Mohammed Iqbal. (2016). THE ROLE OF WEB-BASED GROUP DECISION SUPPORT SYSTEMS IN CLARIFYING THE COLLECTIVE INTELLIGENCE: MEASURING THE OUTCOMES. 10.13140/RG.2.1.5181.8644.
Carneiro, J., Martinho, D., Marreiros, G., &Novais, P. (2019).Arguing with behaviour influence: a model for web-based group decision support systems. International Journal of Information Technology & Decision Making, 18(02), 517-553.
Gusakova, E., &Romanova, E. (2019).Using group decision support systems in the preparation of real estate development projects.In E3S Web of Conferences (Vol. 97, p. 01004).EDP Sciences.
Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.