This paper examines an important source of the value of decision support systems (DSS), namely, decision flexibility. It proposes a framework for systematically analyzing, understanding, and possibly quantifying flexibility resulting from the use of DSS in decision situations with time constraints, based on real options theory. The proposed framework is compared with anecdotal evidence of DSS value from the MIS literature. This framework has interesting implications for DSS developmen

Kumar, Ram L.

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 1997 Proceedings Americas Conference on Information Systems(AMCIS)8-15-1997DSS Value and Time-Constrained DecisionMakingRam L. KumarUniversity of NC at CharlotteFollow this and additional works at: http://aisel.aisnet.org/amcis1997This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 1997 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationKumar, Ram L., "DSS Value and Time-Constrained Decision Making" (1997). AMCIS 1997 Proceedings. 101.http://aisel.aisnet.org/amcis1997/101DSS Value and Time-Constrained Decision Making  Ram L. Kumar  Information & Operations Management  University of NC at Charlotte, Charlotte, NC 28223 Abstract This paper examines an important source of the value of decision support systems (DSS), namely, decision flexibility. It proposes a framework for systematically analyzing, understanding, and possibly quantifying flexibility resulting from the use of DSS in decision situations with time constraints, based on real options theory. The proposed framework is compared with anecdotal evidence of DSS value from the MIS literature. This framework has interesting implications for DSS development.  1. Introduction Investments in DSS and Executive Information Systems (EIS) are often justified on the basis of intuition regarding qualitative benefits (Belcher and Watson, 1992), (Hogue and Watson, 1983), though attempts have been made to quantify some intangible benefits resulting from specific applications (Belcher and Watson, 1992). The need for a disciplined analysis and quantification of costs and benefits resulting from IT investment has been emphasized in earlier research (Bacon, 1992), (Belcher and Watson, 1992). A theory to facilitate better understanding of the relationship between decision support and performance has been identified as an important need (Konsysnski, Stohr, and McGee, 1992). The objective of this research is to provide a framework for analysis and enhanced understanding of an important type of benefit resulting from DSS investments that has not been analyzed in earlier research, namely flexibility in the context of time-constrained decision making. Flexibility refers to the ability to respond easily to changing conditions. Ease of responding to change could be measured by factors such as speed of response and/or cost of response. Different factors may make sense in different contexts. The proposed framework uses real options theory (Dixit and Pindyck, 1994; Trigeorgis, 1996) to model time-constrained decision making, and has several managerial implications.  2. Real Options The concept of options, though general has been extensively studied in the context of financial instruments. A financial option is basically a right (but not a binding requirement) to engage in a financial transaction (usually to buy or sell a financial asset). A real option, on the other hand is an option (a right but not an obligation) to trade in a real asset. There is a growing body of literature which emphasizes the fact that many investment decisions are in fact real options (Trigeorgis, 1995; Trigeorgis, 1996). In the MIS literature, concepts from real options theory have been used in the context of evaluating investments in new information technologies (Dos Santos, 1991; Kumar, 1996).  3. Time-Constrained Decision Making and DSS Value  Each decision scenario is modeled as being triggered by some event (or the last of a series of events) such as competitive action, or change in customer preferences. Decisions are modeled as being made in response to these events (reactive decision making) or in anticipation of the future based on these events that have occurred (proactive decision making). Making a decision is analogous to exercising an option (for example, an option to buy a certain financial asset within some time constraint). Figure 1 illustrates this scenario.  Some time elapses after the triggering event before a decision maker realizes (or has enough information to realize) that the decision maker has an option that may be exercised. This elapsed time is referred to as the information gathering and analysis phase.  Let t(event) denote the time at which the triggering event (or last of a series of triggering events) occurs. Let t (option) denote the time at which the decision maker realizes that an option exists (or the time at which the option is acquired). Let T denote the time at which the option expires. In other words, the option needs to be exercised by time T. In practice, T would be influenced by factors such as competition or customer requirements. Each decision scenario is modeled as consisting of an option to invest an uncertain amount C (expected present value of discounted cash flows) in exchange for an uncertain value or benefit V(expected present value of discounted cash flows).   Figure 1. A Decision Scenario with Time Constraints The following example from (Belcher and Watson, 1993) illustrates a scenario of the type described in Figure 1 : " one commodity trading group asserted that an EIS application enhanced its ability to analyze market supply and demand conditions. A set of graphs had been developed, that compared inventories with prices for each commodity. The graphs were updated as soon as new date became available. The automated graphing made it possible to instantly identify new areas of opportunity. During the initial use of this application, the trading group spotted a marketing region in which inventories were decreasing steadily while prices remained level. This condition gave early warning of a price rise because low supplies usually predict high prices. The traders' quick response to this anticipated price increase generated considerable, calculable savings for the company. " In this example, the decision support system can be viewed as generating an option to trade and thus make profits.  This scenario can be modeled as an option to exchange an asset of uncertain value (C) for another asset of uncertain value (V) within some time frame t ( t(option)- t(event) ). Several option valuation models are available depending on assumptions about the nature of uncertainty and the nature of the underlying asset for the option. One possible option valuation model is based on (Margrabe, 1978), and has been used in earlier MIS research (Dos Santos, 1991; Kumar, 1996). The option value (OV) is given by   where ,   and,   is the cumulative standard normal density function, and . and denote the standard deviations of the rate  of change of and respectively (standard deviation of the percentage change in and over unit time) and is the correlation coefficient between and . The term t denotes the time available for exercising the option (T- t(option)). The comparative statics of this model (Margrabe, 1978; Dos Santos, 1991; Kumar,. 1996) are summarized as follows: 1. Increases in B increase  2. Increases in C decrease  3. Increases in ( ) increase the value of . If =0 (C is known exactly), then increases in increase OV. Similarly, if is known exactly, then increases in increase OV.  4. Increases in t (time available for exercising the option) increase option value.  In terms of figure 1, in this example, t(event) might be the occurrence of a decreasing price trend while prices remained constant. In the absence of a decision support system this pattern of inventory and market changes might not have been observed at all or might have been observed too late to act on. In other words t(optiont) might have been too large (possibly greater than T). The use of a decision support system resulted in a small t(option), and this in turn resulted in a large t (T-t(option)). Also, exercising the option to trade results in a certain expected profit (B -C).  In general, use of the DSS could generate new options or modify the value of existing options. In the example above, it may be possible that the option to trade existed even without the DSS. Use of the DSS merely altered the parameters of the option, (for example, by increasing t ) and consequently increased the value of the option. The value of a unit increase in t would depend on the application of the DSS. The same increase in t would have a higher value in the presence of a higher degree of uncertainty regarding costs and benefits ( ). For example, the value of using DSS in fast-paced , time-constrained, decision making such as financial trading could be high. Decision support systems add value to time-constrained decision environments by reducing C, increasing V, or more importantly increasing t (by reducing the time required for the information gathering and analysis phase). Major DSS features that increase t include telecommunication links for rapid gathering of data, drilldown features for identifying relevant data, and presentation features such as graphs which facilitate early recognition of options. Keen (1981) discusses the fact that fast response to events (anticipated or unexpected) is a benefit perceived by DSS users and provides several examples.  4. Conclusions and Future Research  Thus, the real options paradigm provides a useful framework for analyzing the value of flexibility (ability to respond quickly to change) resulting from DSS use. Thinking of decision scenarios in terms of options might help to identify high value applications and/or important features of applications.  Though this paper has used the option to exchange risky assets (Margrabe, 1978), other valuation paradigms may be appropriate depending on the actual application. Smith and Nau (1995) discuss the fact that decision theory could complement real options theory in situations where the assumptions of real options theory are strongly violated. Valuations of real options under a different set of assumptions than those used in deriving financial option pricing results is an important area where further research is required.  Estimation of model parameters may be difficult in some situations. However, use of the real options framework is an improvement over merely using net present value calculations, and has been used in capital budgeting decisions (Kemna, 1993; Trigeorgis, 1995). It is also possible to view any decision situation as consisting of a portfolio of real options. Modeling and valuation of such portfolios of real options and the role of DSS in augmenting the value of such portfolios is an interesting area of future research.  References  C.J. Bacon, The Use of Decision Criteria in Selecting Information Systems/Technology Investments, MIS Quarterly 16, No. 3, (1992), pp. 335-350.  L.W. Belcher, and H.J. Watson, Assessing the Value of Conoco's EIS, MIS Quarterly (1993), pp. 239-253.  A. Dixit, and R. Pindyck, Investment Under Uncertainty (Princeton University Press, Princeton, New Jersey, 1994).  B. Dos Santos. Justifying investments in new information technologies, Journal of Management Information Systems, Vol. 7, No. 4 (1991), pp. 71-86.  J.T. Hogue, and H.J. Watson, Management's Role in the Approval and Administration of DSS, MIS Quarterly, Vol. 7, No. 2 (1983), pp. 15-26.  P.G.W. Keen, Value Analysis-Justifying Decision Support Systems, MIS Quarterly, Vol. 5, No. 1(1981), pp. 1-14.  B.R. Konsysnski, E.A. Stohr, and J.V. McGee, Review and Critique of DSS. In E. Stohr, and B. Konsysnski, (Ed.), Information Systems and Decision Processes ( IEEE Computer Society Press, Los Alamitos, CA 1992), pp. 7-27  A. Kemna, Case studies on real options, Financial Management, Vol. 22, No. 3, (1993), pp. 259-270.  R. Kumar, A Note on Project risk and Option Values of Investments in Information Technologies. Journal of Management Information Systems, Vol. 13, No. 1, (1996), pp. 187-193.  W. Margrabe, The value of an option to exchange one asset for another, Journal of Finance, XXXIII, (1978), pp. 177-186.  J.E. Smith, and R.F. Nau, Valuing Risky Projects: Option Pricing Theory and Decision Analysis, Management Science, Vol. 41, No. 5 (May, 1995), pp. 795-816.  L. Trigeorgis, (Ed.), Real Options in Capital Investment,( Praeger, Westport, CT , 1995).  L. Trigeorgis, Real Options (MIT Press, Cambridge, MA, 1996).   

DSS Value and Time-Constrained Decision Making

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1455&amp;context=amcis1997

DSS Value and Time-Constrained Decision Making

Abstract

Similar works

Full text

Available Versions

AIS Electronic Library (AISeL)