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This work identifies the types and eminent advantages of the collaboration tools including the significance of decision making collaboration tools and how the future decisions embody teeming result. A comparative study of these tools is made in order to accentuate their advantages and disadvantages. This endeavors to be a collaboration tool alike GDSS, useful to understand what opportunities will the collaboration tools open up in future for business decision making.
Index Terms - Collaboration Software Tools, Decision Making Tools, GDSS, Electronic Meeting Tools, Virtual Team, Communication.
Teamwork and decision making have always been a rich area to compile the importance not only for just the size of the team or an organization but also as collaboration of the member's effects the decision and action. As the success of action depends on the decision, it is significant to understand whose participation effects as contribution in the final decision. This type of decision making has got the familiarity with the name collaborative decision making.
Collaboration software tool is basically a technology designed to make group works effective, flexible, time saving and increase productivity. Though e-mailing service can project a group contribution but it can create unusual delays and misunderstandings, which can result a negative impact. The concept of "Groupware" came into reality with the development of these collaboration software tools with the integration of 3 key areas known as: communication, collaboration and coordination .
Team coordination and communication is not only limited to sharing; it has significance on making decisions. A survey report on 2006 states that 36% of company's performance was due to a company's collaboration index which impacts more than twice of a company's strategic orientation and even more than five times than the impact of market and technological turbulence influences .
For the collaborative decision making GDSS (Group Decision Support System) is another tool and system. This system allows getting the anticipated result with numerous facilities and features.
There are two different dimensions for collaboration tools which are based on time and place of interaction facts .
Based on time the collaboration tools are knows as synchronous or real-time and asynchronous tools. Synchronous tools require immediate or least response time. For messaging Messenger, ICQ can be good examples of synchronous tool. Video communication or conference attending system, Table sharing (eg Smartboard), Group Decision Support System (GDSS), Chat Message System, Multiplayer Gaming are some sorts of synchronous or real-time tools. Asynchronous tools require no short time response. The e-mailing, blogs with comments section and discussion forums, calendar (Groupware) are considered as asynchronous tools.
Based on the place of interactions collaboration tools can be of two types known as: collocated or face to face and non-collocated or distance. Collocated tools depicts the users having the advantage of doing tasks together in one place, however non-collocated tools allow users to work from different places.
GDSS can be defined as a system consists of computer software, hardware, meetings procedures and facilitation which support groups being a part of collaborative work . Many GDSSs usually has hardware implementation as electronic meeting rooms which may contain few numbers of networked workstations with large displays and audio video equipment . There is a possibility of this type of GDSS requiring a skilled operator though many others assume group members must know about the usage .
GDSS supported meetings are basically comprised of 3 major components known as: The facility, the facilitator and the meeting application .
There are few characteristics of GDSS:
GDSS is basically used for complex decision making. It is not justified to use or invests for this type of system if there are only 2 to 3 people.
To choose the right GDSS it is important to keep the idea of decision task type, group size and location of members of the group in mind.
The decision making process model can be divided into 3 phases: pre-decision, decision and post-decision .
In this phase the participants usually are supposed to share the problem understanding and indentify the objective. As all of the user's knowledge, experience, and background are not similar this phase helps to attain the common representation. The pre-decision phase helps to describe the limits and boundaries of the problem from all perspectives expressing the expectation of the group, defining the role of the stakeholders, identifying the technology to be implemented and creating an agenda.
In this phase there are four steps: idea generation, idea organization, idea evaluation, idea selection.
Idea Generation: In this step the group produces idea first individually then with the whole group. This allows the group to individually and collectively generate ideas and compare the ideas to get the best one.
Idea Organization: This step helps to organize in order to increase the understanding and visibility of the ideas. This step also helps to find out and merge the similar ideas and eliminate redundancy.
Idea Evaluation: This step gives the means, standard deviations which help to get a visual result to the idea's performance.
Idea Selection: This is the step where the idea is finalized from random unorganized ideas. The selected idea should be context based and make sense to everyone in the team. The participants also need to be notified about every modification and concerning the decision made, every personnel including direct and indirect participants should be informed if any changes occur.
This phase allows the decision monitoring consisting essentially realization of action planning in order to implement the decision. The decision monitoring contains the information of the people corresponding to their role and job with calendar on which the progress will be shown time by time.
The architecture of the collaborative decision making can be divided into two types: Human-System Cooperation and Mediated Human-Human Cooperation .
Human-System Cooperation: The human is the decision maker in here. So the system plays the cooperative role with the human who has the system access and the system acts as the human expectation. So basically it means this system require a set of rules that structure the cooperative action must be defined between the decision maker and the system. This type of cooperation is useful when the individual problems resolution phase made by each decision maker.
Mediated Human-Human Cooperation: Though group decision making is basically human to human interaction; there is a system which collects all the plans shared. There is also a probability of this system's not being suitable for every usage. On that case decision makers use traditional way without technology to avoid any sort of inconveniences.
Based on the cooperation types collaborative decision making process architecture has been presented here. This architecture gives particular place to the session facilitator.
Individual decisions are made using specific cooperative decision support systems called DM-DSS. The facilitator also has a system for facilitating named F-DSS. An F-DSS is particularly useful for inexperienced facilitators.
A collaborative decision making process is based on the techniques and tools supporting ideas generation (brainstorming, ponder etc), idea organization (classification, categorization etc.), evaluation and idea selection (negotiation, finalization, reporting etc.).
An agenda builder helps the facilitator to prepare decision making meeting in advance by specifying the objectives and steps.
The organizational memory is basically the database containing all data (users, agenda, dates, generated ideas, evaluations, decisions etc.) conducted by a group which are more likely related to the previous sessions.
A communication manager allows participants to interact with each other. Web applications have this type of support. This is why most of the GDSS is developed in web version.
Collaboration tools are becoming more and more flexible now a days. Earlier the collaboration tools were enterprise based; wherever the future collaboration tools will be mostly web based social software with social interaction, such as opinion polls, blogs, forums, community chats and social networking. Also the use of organizational memory will conjugate data analysis for predictions. The adoption of gathering, sharing and communicating via social network platforms has already become familiar to users. Putting analytical tools for decision making with the access of flexibility to analyze data any way an individual user will like to, can conquer an organization with an ease.
Many authors talked about an expert system approach which is capable of developing facilitation skills [10, 11]. For a better result driven system or tool it is important to have expertise and experience in planning meetings and building agendas as well. An interaction meta-model i.e. an expert system with expertise group and task can ease up the work as Management Information System (MIS), Data Mining and Online Analytical Processing (OLAP) .
So the overall future collaboration tools will collect data from past meetings, from user interactions, informal knowledge records, engagement of stakeholders and integration of work processes. This set of data is usually stored in organizational memory store. Then the data will be analyzed by sorting and fetching for any need and decision making. The data will be directly shared to the system with interaction meta-model for further analysis and decision making.
There are more ways to enhance decision making and support with data mining and analysis technologies. Automated decision making can be integrated to a real-time system. Collection of data from various web-based collaboration tools and putting them in organizational memory for data analysis and mining to get decisions can also help to forecast data. Knowledge management and information integration technologies with the integration of collaboration tools will help to sort data and here for any type of complicated problem, an expertise interaction meta-data model process from qualitative to quantitative.
Finally, there are people thinking the collaboration tools going all web with automated decision support system will increase the usefulness of collaboration tools and social software. Moreover, integration of collaboration tools with social networking can be a good place for knowledge share and collective intelligence. The data export to integrate with the expertise system for predicting and automated decision making will be a guide to future decision making with collaboration tools.
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