How the Effectiveness of Decision Support Systems Depends on the Effectiveness of People - Literature review Example

Support, Not Substitution: How the Effectiveness of DSS Depends on the Effectiveness of People Introduction The assertion that “Decision Support Systems are developed to support, not replace, human decision-making” is so widely accepted among researchers and commentators that it could be considered an axiom. “Support”, after all, is part of the name. But the support a DSS offers to a decision-maker or an organisation does not pass in one direction only. Successful use of the system can improve the system itself, so that with continued use it becomes increasingly more productive. This paper begins with a brief explanation of the need for decision support systems in business, and an explanation of the key features and characteristics of decision support systems. An overview of the ways in which decision support systems, management decision-makers, and organisations all affect each other follows. Specific examples of human decision-making processes are offered with evidence from the research literature. Finally, the impact of management integration with a decision support system and the ways that some of the social and organisational variables can be managed is discussed. From all this, it will be clearly seen that a DSS cannot be a replacement for the decision-making process, but is an effective supporting tool to be used in decision-making. The Need for Decision Support Systems 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’ 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) Iandoli, Klein, and Zollo (2009: 70) point out that the rapid growth of “online peer production communities” like Linux and Wikipedia demonstrate new, remarkable capabilities for gathering and sharing information. There are, however, a number of characteristics of group deliberation that prevent the information from being used effectively to solve problems. Social pressures may prevent group members from sharing all the relevant information they have. The information may also be affected by convergence and error amplification, because it is gathered in a sequential manner; early contributors are able to set the course of the discussion. (Iandoli, Klein, & Zollo, 2009: 71) In other words, instead of all possible alternatives being presented with equal weight for their pros and cons to be examined and debated, a group deliberation often results in one early proposition to be argued, and later alternatives do not receive as much attention. Although these observations were made in connection with online “wiki”-type groups, their relevance to business settings such as virtual teams is obvious. A decision support system is needed to effectively organize and use the information. The Conceptual Foundations of Decision Support Systems 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) Holsapple and Whinston (1996: 144-145) identify five key characteristics of a decision support system: 1. A DSS is relevant to the decision-maker’s circumstances, and offers specific alternatives for various situations. This means simply that there are very few, if any, decision support systems that can be used “off-the-shelf”; in order to be effective, a DSS needs to be specific to the kind of business and information for which it is need. 2. A DSS has the ability to gather and process descriptive and prescriptive information. A DSS should be able to handle both statistical data, such as sales records, production figures, and financial data, and also other kinds of information, such as policies and procedures. The program should be able to integrate all these different kinds of information into useful outputs. 3. A DSS can present information in a variety of ways. 4. A DSS can select different subsets of information as directed. The DSS should be able to work with different groups of information from the complete set of data that is available. 5. A DSS is interactive, and allows the user to be flexible in terms of choosing knowledge-management tasks and their sequence. The DSS should allow the user to make the final decision as to how to use the output of the program. Even though the decision model is considered more thorough and accurate than what can be achieved by a human decision-maker alone, the key characteristics of the model-maker – the decision support system – require and support human intervention at different points in the process. Humans decide what information should be used, in what combination, how it should be presented, and ultimately, how to apply the final output. In essence, the support of human decision-making rather than its replacement is part of the very nature of the DSS. Mutual Effects in the Use of Decision Support Systems Numerous researchers have studied decision support systems in various applications and from different perspectives. In virtually every case, decision support systems have been shown to function more effectively when they are made part of a decision-making process, rather than being applied as the process itself. The following section summarises some of the key findings of the research, much of which has been done very recently. DSS, Knowledge Management, data mining, and similar information-management systems all have the objective of finding, organising 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 recognise 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 organisational factors, then determine the way that the interpretation is shared with others and put to use within the business organisation. (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 organisations think and regard information. The first way in which human thinking affects the decision support system is the manner in which it is initially accepted and integrated into the business processes. Studies in Marketing management and Customer Relationship Management, two areas where DSS can be effectively applied, have found that the biases and expectations of managers greatly affect the value of the system once it is installed. If the manager does not understand the capabilities of the DSS to fully integrate into all the enterprise’s operations, there is a tendency to focus its application too much on one side of the business or the other – either the operational side or the customer service side. The system itself does not provide the integration; this has to come from the manager-user. (Love, et al., 2009: 461) Similarly, managers often have a different concept of a “decision model” in their minds than that which is built into the DSS. (Kayande, et al., 2006:1) Unless the manager can correctly conceptualise how the DSS works and what factors determine the results and recommendations it produces, he is likely to employ it improperly, and consequently be disappointed with its outcome. The “natural” way managers make decisions also plays a big part in how well they use decision support systems. The recommendation of a DSS for a particular situation is a kind of scenario, a possible future that will occur under a certain set of circumstances. The recommendation is based on information from the past, or in other words, backward thinking. That closely matches the natural way people to make choices, only people do it with the inputs of experiences from similar circumstances in the past rather than strictly through quantitative data. The 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 backwards 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 organise 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. Decision-Making in Complex and Time-Critical Applications In a business such as marketing, decisions that need to be made often do not fit neatly into single moments, but are continuous. For example, market or buyer trends must be constantly monitored, and reactions to them must be constantly adjusted. In these instances, some of the data inputs used for making decisions, such as behaviour of customers, might be hard to quantify, and might be perceived differently by different decision-makers. (Cassie, 1997: 296) For that reason, managers often rely on instinct to make decisions. They have a decision-making process, but rather than it being manifested by a sequence of deliberative steps, it is rather a moment judgment; the length of the moment is related to the experience and skill of the manager, and how many simplifying strategies of eliminating higher-risk alternatives and reducing the number of cues he employs. (Hedelin & Allwood, 2002: 127) The result is almost always a workable decision, but it might not always be the best decision. Decisions support systems, particularly those used in marketing, take into account the value of management judgment in making decisions and are built to encourage a high level of human interaction in developing the output. (Van Bruggen, Smidts, & Wierenga, 2000: 804) The “retroactive scenario” process proposed by Rollier and Turner (1992) is just one example of the interactive function of a DSS. What the DSS does to support the manager’s decision-making capability is to quickly organise and process those data pieces that can be quantified, leaving the manager free to apply his knowledge and experience to the other less-quantifiable parts. Impact on Organisations The careful support of the manager’s decision-making abilities by the decision support system is important because once the decision or solution is created, the organisation must implement it. The guidance of management is critical for a couple reasons: First, the acceptance of the DSS by the rest of the organisation depends on the enthusiasm and confidence the management expresses for it. Second, the abilities of the group strongly impact the successful implementation of the decision. In a study conducted with the US Navy, the adoption and use of knowledge management systems was negatively affected by the constant replacement of personnel, an ordinary feature of military life. Evidently, extensive experience and contextual analytical ability – as demonstrated by personnel of higher rank with several years’ experience – is a key to the success of any kind of information-management system. (Wild & Griggs, 2008: 501) Even decision support systems that are designed for groups and recognise the likelihood of variables in group experience and skills cannot completely account for them. Group DSS works best as a form of ‘funnelling technology’, sorting out and combining individual inputs to develop a majority view. (Iandoli, Klein, & Zollo, 2009: 72) But this again may be simply a workable decision, and not necessarily the best decision; some degree of human insight and judgment will always be required. Sometimes that can take the form of additional processes; studies have shown that DSS and other information-management systems are most effective when other initiatives such as Business Process Re-engineering and Total Quality Management – ideas that rely heavily on the proper application of human talents – are applied at the same time. (Loukis, Pazalos, & Georgiou, 2009: 568) Once a way has been found to make the implementation of DSS recommendations most effective in the organisation, however, every subsequent use of the system will be more productive; the acceptance of DSS becomes part of the management and organisation’s body of experience and knowledge, and successful application provides better data for future use. Conclusion The complexities of business are many, and to successfully manage them, the decision-maker must not only consider vast amounts of descriptive data and prescriptive policy and procedural information, but also the variable and sometimes intangible aspects of human behaviour, group dynamics, and market forces. In order to gather the appropriate data, organise it a usual fashion, determine its correlations and effects, and then choose a sound course of action, a decision support system can be a valuable and productive tool. The key characteristics of a DSS, however, automatically require human interaction: Humans determine the context in which the DSS will be used, determine the information which it will gather, decide which data should be used in particular circumstances, and finally decide the best sequence and manner in which to implement the recommended course of action. The manager must understand and have confidence in the capabilities of the DSS, not only to make the best use of it, but also to effectively direct its use by the organisation. In return, the DSS can help the manager focus on qualitative judgments, and can organise decision processes in a more natural way. Effective use of the DSS by managers and organisations in turn improves the information that is input into the DSS, so that every subsequent application of the system will be increasingly accurate and effective. Just as its name suggests, a Decision Support System supports the human decision-making process rather than replacing it. Managers and organisations are often disappointed by the results of using a DSS because they do not fully understand how it works and how it should be employed. To be most productive, a DSS must be fully-integrated in the enterprise, not only in the processes and procedures, but as a part of the mental processes of the people within the organisation. When regarded and used in this fashion, a DSS can be a highly-effective tool. Works Cited Birchall, D.W., and Giambona, G. (2008) “The impact of ICT on the work patterns of managers and their organisations”. EuroMed Journal of Business, 3(3): 244-262. [Internet] Emerald: www.emeraldinsight.com/10.1108/14502190810906428. Cassie, C. (1997) “Marketing decision support systems”. Industrial Management & Data Systems, 97(8): 293-296. [Internet] Emerald: www.emeraldinsight.com/10.1108/02635579710195000. Hedelin, L., and Allwood, C.M. (2002) “IT and strategic decision making”. Industrial Management & Data Systems, 102(3): 125-139. 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