Effective Application of Decision Support Systems in Group- and Team-Based Enterprises - Research Proposal Example

Area of Research and Focus Provisional Project Title: “Effective Application of Decision Support Systems in Group- and Team-Based Enterprises.” Keywords: Corporate communication, strategic planning, supply chain, operations Background 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) 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, 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. With those ideas in mind, 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. 2. A DSS has the ability to gather and process descriptive and prescriptive information. 3. A DSS can present information in a variety of ways. 4. A DSS can select different subsets of information as directed. 5. 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 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. 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 other words, DSS systems are designed to work on a “best option” premise – they analyse data and present the one (or sometimes more than one) solution that is most likely to achieve a desired result. Any type of group or team decision process is designed to work on a “consensus” premise. The problem of gathering and organising information (what the DSS does automatically) is the same whether the decision is being made by a single manager or a team, that is to say, in most businesses it is too much for humans to do with complete efficiency. So any kind of decision-maker (a single person or a group) can benefit from the technical advantage of a DSS – except that a DSS works in a way which is the opposite of how groups or teams make decisions. While this limitation is recognised in the research literature, very little if any study has been done to develop a DSS model or framework that would be effective in a team setting. Aims and Objectives Decision Support Systems (DSS) are useful information management systems for managers in making operational or strategic decisions. 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. In organisations that have a group- or team-based structure and decision-making process, however, DSS has not been demonstrated to be effective. The objective of this research, therefore, is to investigate why DSS does not seem to work well in flat-architecture enterprises, and to develop a model framework for a team-based DSS. The development of an actual Decision Support System, which would require extensive computer programming expertise as well as the cooperation of a business enterprise willing to test it in real-world conditions, is outside the scope of this study. The developed model, however, will be suitable for further development and study in future research. Review of the Literature Many of the resources that will be valuable to this research have already been noted in this proposal, but there are a number of others that should be highlighted as well. A large part of the relevance of this proposed research lies in the fact that DSS is constantly evolving and becoming more sophisticated, but doing so in a way that seems to clash with the growing trend towards flatter, more collaborative organisational structures. Developing a conceptual model that allows enterprises to take advantage of both of these trends is the objective of this project. Two significant areas of development in DSS are in data mining and intelligent agents. Foster, McGregor, and El-Masri (2005) examine what are called Intelligent Decision Support Systems (IDSS) in the context of their application to medical diagnosis and treatment. The advantage of IDSS, and its possible application to a group-oriented system, is that instead of presenting one or more definitive decision choices, the outputs are characterised in terms of probabilities of success, given that there are a large number of unaccountable variables involved in treating patients, who may respond in completely unexpected ways. This leaves the final choice to the physician as to which treatment course to pursue; essentially the IDSS manages the quantifiable variables, allowing the physician to apply his instincts and experience more efficiently. In a group application, the place of the physician’s ‘instincts and experience’ might be taken by the group’s consensus, to which everyone contributes using their own instincts and experience. The conceptual basis for intelligent agents is explained by Wang and Wang (2005), who define IA based on four key characteristics of a business process or task: 1. The task requires a number of decisions: The ultimate, final decision representing the problem to be solved requires that a series of lesser decisions be made on the way to arriving at the final solution. 2. The decisions are interdependent: The decisions may occur sequentially or simultaneously, but each decision has some effect on all the other decisions. 3. The environment of the decisions changes as a result of the decisions, and can also change on its own: The conditions that exist at the beginning of the problem change as the subordinate decisions are made, so that the final decision is not based on the original set of conditions, but on the last set of conditions. 4. The decisions are made in real-time: The decisions all work in a ‘forward’ direction; if a decision is incorrect, it has already changed the environment so that it is impossible to back-track to the exact conditions before the incorrect decision and try a different option that was rejected at that time. The essential point here is that IA allows a DSS to work with information that changes in response to intermediate inputs, such as the order in which data is processed, or partial decisions in one direction or another. Some of the literature already summarised as well as others provides information on Data Mining (DM). DM is another improvement over systems used for less-refined DSS (such as OLAP), because it is not limited by the need for a specific and exact question or hypothesis to be asked. (Hedelin & Allwood, 2002) DM instead can search for trends or patterns, which can suggest the hypothesis or question after the fact, in a manner of speaking. In this way, DSS using DM is a more flexible and thorough system than a DSS which does not use DM. In general, a system using DM requires less human intervention and analysis, depending on how specific and detailed the problem put to the system is at the beginning; less detail, of course, would require greater analysis. DM employed in this manner, however, assumes a static set of conditions; patterns and trends do not change, nor does the underlying environment change as a result of decision selections. When IA is added, the dynamic nature of the data environment is taken into account. Decisions – such as those in the medical field, for example – where a degree of uncertainty about the outcome cannot be avoided can be offered more consistently as a set of alternatives with varying levels of confidence. This provides a richer and more detailed set of information for experts (such as doctors) to assess and make a choice. (Foster, McGregor, & El-Masri, 2005: 4) These decisions then become the basis for a better set of organisational knowledge, which improves the decision process in subsequent problems. (Zhang, 2009) IA-driven DM in decision support systems is not a perfect solution in all cases, however. For environments that involve many kinds of different work tasks and dynamic, fast-operating information paths, IA is a good solution because it works to identify patterns of logic in complex business activities. (Wang & Wang, 2005: 9) Good examples of the kinds of enterprises where IA can be helpful are the medical field, financial businesses, and complex manufacturing operations that rely on long, interconnected supply chains. In less-complex environments where there are relative few work tasks and the movement of information is reasonably straightforward, however, an IA system may be unnecessarily complex and actually slow down work processes. The other significant negative characteristic about IA in relation to its use by business enterprises is, ironically, its sophistication. Studies have shown that the acceptance of business process and information technology is inversely proportional to its complexity and the ease with which it can be understood by users. (Hedelin & Allwood, 2002, and Kim & Trimi, 2007) Because of the thoroughness with which they can find and retrieve information, DM systems, and particular IA-driven DM systems, can be misused to gather private and secured information. (Mraovic, 2008) In addition, IA-enhanced DSS systems require a high degree of human expertise and intervention, particularly in applications where recommended decisions are expressed in terms of probabilities or degrees of uncertainty, such as in the medical field. The problems these factors can cause are obvious. If managers do not understand the capabilities of the system and lack a clear understanding of how it operates, they will have less confidence in using it. If the manager is not confident and committed to the system, he will not have much success in promoting its adoption and use throughout the rest of the organisation. The confidence factor applies to the security concerns as well; users will either be hesitant to use the system to its full capabilities, or attempt to impose security safeguards that may interfere with its proper function. And finally, if the users do not understand that the system is an additional tool to use in their decision-making process, rather than a substitute for it, there is a strong likelihood it will render unsatisfactory results. Methodological Approach The methodological approach will essentially be survey-based, and will consist of several steps. One: identifying a number of firms that use different common types of DSS programs and describing their decision-making processes in model form. Two: identify a number of team-oriented firms – Internet-based businesses or businesses which use virtual teams are likely candidates – and compare their existing information-management and decision-making processes with the firms in the first group. Three: the aspects that both groups have in common will then be used to develop the team-based DSS model framework. Based on the research literature, a survey applicable to both types of firms will be developed which will gather key data needed, such as: form of internal organisation, planning and operational decision-making communications pathways, type of DSS used (if any) and the methodology the system employs, and degree of reliance on decision support systems, either computer or human, which can be measured in one way by the number of system-recommended decisions that are adopted. Survey data can be organised and analysed using a relatively simple program such as Excel, SPSS, or SalStat. References 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. Foster, D., McGregor, C., and El-Masri, S. (2005) “A Survey of Agent-Based Intelligent Decision Support Systems to Support Clinical Management and Research”. [Internet] University of Western Sydney, June 2005. http://www.diee.unica.it/biomed05/pdf/W22-102.pdf. Hedelin, L., and Allwood, C.M. (2002) “IT and strategic decision making”. Industrial Management & Data Systems, 102(3): 125-139. [Internet] Emerald: www.emeraldinsight.com/10.1108/02635570210421318. Holsapple, C.W., and Whinston, A.B. (1996) Decision Support Systems: A Knowledge Based Approach, 10th ed. Los Angeles: West Group. Iandoli, L., Klein, M., and Zollo, G. (2009) “Enabling On-Line Deliberation and Collective Decision-Making through Large-Scale Argumentation: A New Approach to the Design of an Internet-Based Mass Collaboration Platform”. International Journal of Decision Support System Technology, 1(1): 69-92. Kim, S., and Trimi, S. (2007) “IT for KM in the management consulting industry”. Journal of Knowledge Management, 11(3): 145-155. [Internet] Emerald: http://www.emeraldinsight.com/10.1108/13673270710752162. Mohamed, M., Stankosky, M., and Mohamed, M. (2009) “An empirical assessment of knowledge management criticality for sustainable development”. Journal of Knowledge Management, 13(5): 271-286. [Internet] Emerald: www.emeraldinsight.com/10.1108/13673270910988105. Mraovic, B. (2008) “Relevance of Data Mining for Accounting: Social Implications”. Social Responsibility Journal, 4(4): 439-455. [Internet] Emerald: http://www.emeraldinsight.com/10.1108/17471110810909858. Rollier, Bruce, and Turner, Jon A. (1992) “Creativity in Strategic Planning: The Influence of Temporal Perspective”. NYU Information Systems Working Paper no. IS-92-04, January 1992. [Internet] SSRN: http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=1288482. Van Bruggen, Gerrit H., Smidts, Ale, and Wierenga, Berend. (2001) “The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems”. European Journal of Marketing, 35(7/8): 796-816. [Internet] Emerald: http://www.emeraldinsight.com/10.1108/EUM0000000005726. Wang, M., and Wang, H. (2005) “Intelligent Agent Supported Business Process Management”. Proceedings of 38th Hawaii International Conference on System Science. [Internet] CiteSeer: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.2470. Wild, R., and Griggs, K. (2008) “A model of information technology opportunities for facilitating the practice of knowledge management”. VINE: The journal of information and knowledge management systems, 38(4): 490-506. [Internet] Emerald: www.emeraldinsight.com/10.1108/03055720810917732. Zhang, Z. (2009) “Personalising Organisational Knowledge and Organisationalising Personal Knowledge”. Online Information Review, 33(2): 237-256. [Internet] Emerald: http://www.emeraldinsight.com/10.1108/14684520910951195. Research Centres: This research topic has application in several management areas, but would be categorised most appropriately within the focus of the Information Systems Evaluation & Integration (ISEing) Research Centre. 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