Turban Aronson and Liang Decision Support Systems and

Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 2 Decision-Making Systems, Models, and Support © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -1

Learning Objectives • Learn the basic concepts of decision making. • Understand systems approach. • Learn Simon’s four phases of decision making. • Learn which factors affect decision making. • Learn how DSS supports decision making in practice. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -2

Standard Motor Products Shifts Gears Into Team-Based Decision-Making Vignette • Team-based decision making – Increased information sharing – Daily feedback – Self-empowerment • Shifting responsibility towards teams • Elimination of middle management © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -3

Typical Business Decision Aspects • • • • Decision may be made by a group Group member biases Groupthink Several, possibly contradictory objectives Many alternatives Results can occur in the future Attitudes towards risk Need information Gathering information takes time and expense Too much information “What-if” scenarios Trial-and-error experimentation with the real system may result in a loss Experimentation with the real system - only once Changes in the environment can occur continuously Time pressure 4 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision Making • Process of choosing amongst alternative courses of action for the purpose of attaining a goal or goals. • The four phases of the decision process are: – Intelligence – Design – Choice – implementation © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -5

What each Phase consists of? • The Intelligence Phase consists of: - Organizational objectives. - Search and scanning procedures. - Data collection. - Problem identification. - Problem ownership. - Problem classification. - Problem statement. • The Design Phase consists of: • The Choice Phase consists of: - Solution to the model. - Sensitivity analysis. - Selection of the best (good) alternative (s). - Plan for implementation. - Formulate a model. - Set criteria for choice. - Search for alternatives. - Predict and measure outcomes. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 62 -

• Managerial Decision Making is synonymous with the whole process of management ©

Systems • A SYSTEM is a collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal • System Levels (Hierarchy): All systems are subsystems interconnected through interfaces 8 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Systems • Structure – – Inputs Processes Outputs Feedback from output to decision maker • Separated from environment by boundary • Surrounded by environment Input Processes Output boundary Environment © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -9

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban,

– Inputs: are elements that enter the system. – Processes: are all the necessary to convert or transform inputs into outputs. – Outputs: are the finished products or the consequences of being in the system. – Feedback from output to decision maker; there is a flow of information from the output component to the decision-maker concerning the system’s output or performance. Based on the outputs, the decision-maker, may decide to modify the inputs, the processes, or both. the decision-maker compares the output to the expected output and adjusts the input and possibly the processes to move close to the output targets. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 112 -

• The environment: Is composed of several elements that lie outside in in the sense that they are not inputs, output, or processes. However they affect the system’s performance and consequently the attainment of its goals. Environmental elements can be social, political, legal, physical, or economic. • The Boundary: A system is separated from its environment by boundary. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 122 -

Environmental Elements Can Be • • • Social Political Legal Physical Economical © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th and Liang Aronson, edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 2 -13

The Boundary Separates a System From Its Environment Boundaries may be physical or nonphysical (by definition of scope or time frame) Information system boundaries are usually by definition! 14 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

System Types • Closed system – – Independent Takes no inputs Delivers no outputs to the environment Black Box: is one which inputs and outputs are well defined, but the process itself is not specified. Such as transaction processing system (TPS). • Open system – Very Dependant on it environment. – Accepts inputs from the environment. – Delivers outputs to environment © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 152 -

An Information System • Collects, processes, stores, analyzes, and disseminates information for a specific purpose • Is often at the heart of many organizations • Accepts inputs and processes data to provide information to decision makers and helps decision makers communicate their results 16 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

System Effectiveness and Efficiency Two Major Classes of Performance Measurement • Effectiveness is the degree to which goals are achieved Doing the right thing! • Efficiency is a measure of the use of inputs (or resources) to achieve outputs Doing the thing right! • MSS emphasize effectiveness Often: several non-quantifiable, conflicting goals 17 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Models • • • Major component of DSS Use models instead of experimenting on the real system A model is a simplified representation or abstraction of reality. Reality is generally too complex to copy exactly Much of the complexity is actually irrelevant in problem solving 18 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Models Used for DSS • Iconic – Small physical replication of system, it may be three dimensional such as that of an airplane, car, or production line. Or two-dimensional such as photographs. • Analog – Behavioral representation of system – May not look like system Ex. Stock market charts that represent the price movements of stocks. Animations, videos, and movies. • Quantitative (mathematical) – Demonstrates relationships between systems © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 192 -

Benefits of Models 1. Time compression 2. Easy model manipulation 3. Low cost of construction 4. Low cost of execution (especially that of errors) 5. Can model risk and uncertainty 6. Can model large and extremely complex systems with possibly infinite solutions 7. Enhance and reinforce learning, and enhance training. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th and Liang Aronson, edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 2 -21

Phases of Decision-Making • Simon’s original three phases: – Intelligence – Design – Choice • He added fourth phase later: – Implementation • Book adds fifth stage: – Monitoring © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -22

Decision-Making Intelligence Phase • • • Scan the environment Analyze organizational goals Collect data Identify problem Categorize problem – Programmed and non-programmed (p 55) – Decomposed into smaller parts (p 55) • Assess ownership and responsibility for problem resolution © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 232 -

The Intelligence Phase Scan the environment to identify problem situations or opportunities Find the Problem • • • Identify organizational goals and objectives Determine whether they are being met Explicitly define the problem 24 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Problem Classification Structured versus Unstructured Programmed versus Nonprogrammed Problems Simon (1977) Nonprogrammed Problems Programmed Problems 25 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

• Problem Decomposition: Divide a complex problem into (easier to solve) subproblems Chunking (Salami) • Some seemingly poorly structured problems may have some highly structured subproblems • Problem Ownership Outcome: Problem Statement 26 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision-Making Design Phase • • • Develop alternative courses of action Analyze potential solutions Create model Test for feasibility Validate results Select a principle of choice – Establish objectives – Risk assessment and acceptance – Criteria and constraints © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -27

Decision-Making Choice Phase • Principle of choice – Is a criterion that Describes acceptability of a solution approach • Normative Models – Optimization • Effect of each alternative – Rationalization • More of good things, less of bad things • Courses of action are known quantity • Options ranked from best to worse – Suboptimization • Decisions made in separate parts of organization without consideration of whole © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 282 -

• Normative Models: are those in which the chosen alternative is demonstrably the best of all possible alternatives. To find it, one should examine all alternatives and prove that one selected is indeed the best, which is what one would normally want. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -29

Normative Models • The chosen alternative is demonstrably the best of all (normally a good idea) • Optimization process • Normative decision theory based on rational decision makers 30 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

The Principle of Choice • • • What criteria to use? Best solution? Good enough solution? 31 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Selection of a Principle of Choice Not the choice phase A decision regarding the acceptability of a solution approach • • Normative Descriptive 32 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

The Modeling Process-A Preview Solution Approaches • • Trial-and-Error Simulation Optimization Heuristics 33 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Components of Quantitative Models • • • Decision Variables Uncontrollable Variables (and/or Parameters) Result (Outcome) Variables Mathematical Relationships or Symbolic or Qualitative Relationships (Figure 2. 3) 34 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Results of Decisions are Determined by the • • • Decision Uncontrollable Factors Relationships among Variables 35 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision Variables • • • Describe alternative courses of action The decision maker controls them Examples - Table 2. 2 36 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Uncontrollable Variables or Parameters • • • Factors that affect the result variables Not under the control of the decision maker Generally part of the environment Some constrain the decision maker and are called constraints Examples - Table 2. 2 Intermediate Result Variables • Reflect intermediate outcomes 37 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Rationality Assumptions • Humans are economic beings whose objective is to maximize the attainment of goals; that is, the decision maker is rational • In a given decision situation, all viable alternative courses of action and their consequences, or at least the probability and the values of the consequences, are known • Decision makers have an order or preference that enables them to rank the desirability of all consequences of the analysis 38

Descriptive Models • Describe things as they are, or how things are believed to be • These Model are Typically, mathematically based • Applies single set of alternatives • Examples: – Simulations – What-if scenarios – Cognitive map: understand issues better, focus better – and reach closure Narratives: is a story that, when told, helps a decision maker uncover the important aspects of the situation and leads to better understanding and framing. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 392 -

Descriptive Models • • • Describe things as they are, or as they are believed to be Extremely useful in DSS for evaluating the consequences of decisions and scenarios No guarantee a solution is optimal Often a solution will be good enough Simulation: Descriptive modeling technique Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Satisficing (Good Enough) • Most human decision makers will settle for a good enough solution • Tradeoff: time and cost of searching for an optimum versus the value of obtaining one • Good enough or satisficing solution may meet a certain goal level is attained (Simon, 1977) 41 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Why Satisfice? Bounded Rationality (Simon) • • • Humans have a limited capacity for rational thinking Generally construct and analyze a simplified model Behavior to the simplified model may be rational But, the rational solution to the simplified model may NOT BE rational in the real-world situation Rationality is bounded by – limitations on human processing capacities – individual differences • Bounded rationality: why many models are descriptive, not normative 42 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Predicting the Outcome of Each Alternative • • • Must predict the future outcome of each proposed alternative Consider what the decision maker knows (or believes) about the forecasted results Classify Each Situation as Under – Certainty – Risk – Uncertainty 43 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision Making Under Certainty • • Assumes complete knowledge available (deterministic environment) Example: U. S. Treasury bill investment Typically for structured problems with short time horizons Sometimes DSS approach is needed for certainty situations 44 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision Making Under Risk (Risk Analysis) • • Probabilistic or stochastic decision situation Must consider several possible outcomes for each alternative, each with a probability Long-run probabilities of the occurrences of the given outcomes are assumed known or estimated Assess the (calculated) degree of risk associated with each alternative 45 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Risk Analysis • Calculate the expected value of each alternative • Select the alternative with the best expected value • Example: poker game with some cards face up (7 card game - 2 down, 4 up, 1 down) 46 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision Making Under Uncertainty • • • Several outcomes possible for each course of action BUT the decision maker does not know, or cannot estimate the probability of occurrence More difficult - insufficient information Assessing the decision maker's (and/or the organizational) attitude toward risk Example: poker game with no cards face up (5 card stud or draw) 47 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Measuring Outcomes • • • Goal attainment Maximize profit Minimize cost Customer satisfaction level (minimize number of complaints) Maximize quality or satisfaction ratings (surveys) 48 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Scenarios Useful in • • Simulation What-if analysis 49 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Importance of Scenarios in MSS • • • Help identify potential opportunities and/or problem areas Provide flexibility in planning Identify leading edges of changes that management should monitor Help validate major assumptions used in modeling Help check the sensitivity of proposed solutions to changes in scenarios 50 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Decision-Making Choice Phase • Decision making with commitment to act • Determine courses of action – Analytical techniques ( solving a formula) – Algorithms( step-by-step procedures) – Heuristics (rules of thumb) – Blind searches( shooting in the dark, ideally in a logical way) • Analyze for robustness 512 -

Decision-Making Implementation Phase • Putting solution to work • Vague (unknown) boundaries which include: – Dealing with resistance to change – User training – Upper management support © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -52

Source: Based on Sprague, R. H. , Jr. , “A Framework for the Development of DSS. ” MIS Quarterly, Dec. 1980, Fig. 5, p. 13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -53

Decision Support Systems • Intelligence Phase – Automatic • Data Mining – Expert systems, CRM, neural networks – Manual • OLAP • KMS – Reporting • Routine and ad hoc © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 542 -

Decision Support Systems • Design Phase – Financial and forecasting models – Generation of alternatives by expert system – Business process models from CRM, ERP, and SCM © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -55

Decision Support Systems • Choice Phase – Identification of best alternative – Identification of good enough alternative – What-if analysis – Goal-seeking analysis – May use KMS, GSS, CRM, ERP, and SCM systems © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -56

Decision Support Systems • Implementation Phase – Improved communications – Collaboration – Training – Supported by KMS, expert systems, GSS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -57

Other Important Decision. Making Issues • • Personality types Gender Human cognition Decision styles 58 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Personality (Temperament) Types • • • Strong relationship between personality and decision making Type helps explain how to best attack a problem Type indicates how to relate to other types – important for team building • Influences cognitive style and decision style 59 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Gender • • Sometimes empirical testing indicates gender differences in decision making Results are overwhelmingly inconclusive 60 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Bias and Heuristics in DSSs • Heuristics are often built through trail-and-error experience • If heuristics are well tested, they can serve as a reliable tool for reducing the search space for alternatives • Search directed by heuristics is usually less costly and more efficient than blind search • Heuristics can provide solutions close to those produced by a comprehensive blind search with regards to quality

Advantages of using heuristics in problem solving – Simple to understand – Easy to implement – Requires less time – Require less cognitive effort – Can produce multiple solutions © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -62

When to use Heuristics – The input data are limited – The computation time for the optimal solution is excessive – Problems that are being solved frequently – The efficiency of an optimisation process can be improved © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -63

Decision Styles The manner in which decision makers think and react to problems • Varies from individual to individual and from situation to situation 64 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Types of Decision styles – Directive: low tolerance of context ambiguity. Does not requires large volumes of information and verbal communication is preferable on writing methods for managers – Analytical: High tolerance of context ambiguity and requires great values of information. Not quick in taking decisions. – Conceptual: The “people person” and they tend to be thinkers rather than doers. – Behavioural: It requires low amount of input data and demonstrate a short-rang vision © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -65

The Decision Makers • • Individuals Groups 66 Decision Support Systems and Intelligent Systems,

Individuals • • May still have conflicting objectives Decisions may be fully automated 67 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Groups • • Most major decisions made by groups Conflicting objectives are common Variable size People from different departments People from different organizations The group decision-making process can be very complicated Consider Group Support Systems (GSS) Organizational DSS can help in enterprise-wide decisionmaking situations 68 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6 th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

• Technology is used to access information and data. Describe how information technology can help the teams. Information technology is used to provide immediate access to information to each team member. Information technology is used for group support group discussions directly, through technologies such as interactive chat or indirectly such as through the use of email. Information technology can also help team disseminate information through technologies such as Web portals. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -69

• Review what is meant by decision-making versus problem-solving. Compare the two, and determine whether or not it makes sense to distinguish between them. . It is a matter of definition. Some people consider decision-making as a step in problem-solving, . Some people refer to decision-making as the process of making a recommendation, whereas problemsolving includes the implementation of the recommendation. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -70

• • • Compare the normative ( standard) and descriptive approaches to decisionmaking. Normative refers to models that tell you what you should do. These are prescriptive models that usually utilize optimization. Descriptive models are those that tell you "what-if. " These are usually simulation models. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -71

• What is the impact on decision-making of giving people responsibility for their own work? Why are self-directed team members happier than workers under a traditional hierarchy? • • Responsibility for their work will allow people to feel they are truly empowered to make decisions and therefore will be more willing to do so. Selfdirected teams feel more in control of their own destiny, they have more control over their work activities. • © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7 th Edition, Turban, Aronson, and Liang 2 -72