The paper “ Effective Application of Decision Support Systems in Group- and Team-Based Enterprises" is a forceful example of a research proposal on management. As a business grows in size and complexity, the information available to and needed by executives and managers increases in a similar fashion. Every aspect of the business’ s activities is in some respect variable and interconnected. Information is an asset, and since the smart objective for any business is to use its assets productively information must be used as productively as well, and in order to manage the complexity of the available data, a decision support system is often required.
(Wild & Griggs, 2008: 493) Decision support systems generate models to present information and decision scenarios, usually in a format of “ if-then-else” : “ If” Condition A exists, “ Then” Outcome A is to be expected, “ Else” (i. e., if Condition A does not exist) Outcome B is to be expected. (Holsapple & Whinston, 1996) Models are generally more effective than ‘ expert’ knowledge gained from education or experience for a number of reasons. Models are not subject to social biases or pressures.
Models also integrate evidence uniformly and objectively from one situation to another, whereas humans do not always do so. Models are also not subject to human emotions, boredom, or fatigue. (Van Bruggen, Smidts, & Wierenga, 2000: 807) DSS, Knowledge Management, data mining, and similar information-management systems all have the objective of finding, organizing and processing the data needed for constructing a decision in any given situation. Once the decision model is developed, however, only half the job is done; it still must be applied properly in order to achieve the desired result. In order to apply it properly, the decision-maker must recognize that the decision support system has both forward and backward effects.
Human thinking abilities – deduction, inference, presumption, etc. – determine the effectiveness with which the model generated by the decision support system is interpreted. (Birchall & Giambona, 2008: 247, 250, 256-257) Human thought, as well as social, economic, and organizational factors, then determine the way that the interpretation is shared with others and put to use within the business organization. (Mohamed, Stankosky, & Mohamed, 2009: 278) Thus, the output of decision support systems is affected by human inputs, and in turn, the DSS itself affects the way decision-makers and organizations think and regard information. With those ideas in mind, Holsapple and Whinston (1996: 144-145) identify five key characteristics of a decision support system: A DSS is relevant to the decision maker's circumstances and offers specific alternatives for various situations. A DSS has the ability to gather and process descriptive and prescriptive information. A DSS can present information in a variety of ways. A DSS can select different subsets of information as directed. A DSS is interactive and allows the user to be flexible in terms of choosing knowledge-management tasks and their sequence. One interesting problem with either a strictly human-generated decision or one recommended by a DSS is that forward-thinking is required to get from “ now” to the “ future” outcome suggested by the decision.
This is a different cognitive process than backward thinking, and not one that all managers can do with equal skill. An alternative made possible by a DSS, however, is to treat the recommendation – the potential future – as the present circumstance and to work backward through the steps to reach it.
(Rollier & Turner, 1992: 1) This more closely resembles the “ natural” decision-making process and can lead to greater success, but requires a DSS that can organize and present the information; without the DSS, it would be much more difficult and take far longer. This is another example of how a DSS only supports rather than replaces human decision-making and is also an example of how the DSS has an effect on the manager’ s thinking processes.