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Management Support Systems and DecisionMaking


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Title: Management Support Systems and DecisionMaking

Management Support Systems and Decision-Making
Supporting Managers with Information Systems
Models and Methods for Management Support
  • To understand how computers support managers, it
    is necessary to understand what managers do.
  • It is difficult to produce a standard job
    description for all managers.

Fundamental Functions of Management
  • The traditional description of what managers do
    was first characterized by French industrialist
    Henri Fayol in his 1916 classic, Administration
    Industrielle et Generale. Fayol considered the
    manager's job as a composite of four separate
  • Planning
  • Controlling
  • Leading
  • Organizing

Fundamental Functions of Management - defined
  • Planning - establishing goals and selecting the
    actions needed to achieve them over a specific
    period of time.
  • Controlling - measuring performance against the
    planned objectives and initiating corrective
  • Leading - inducing the people in the organization
    to contribute to its goals
  • Organizing - establishing and staffing an
    organizational structure for performing business

Mintzbergs Studies of Managers
  • Myth 1 The manager is a reflective
    systematic planner.
  • Fact Study after study shows managers
    work at an unrelenting pace, that their
    activities are characterized by brevity, variety,
    and discontinuity, they are strongly oriented
    toward action, and dislike reflective activities.
  • Myth 2 The effective manager has no regular
    duties to perform.
  • Fact Managerial work involves
    performing a number of regular duties, including
    ritual and ceremony, negotiations, and processing
    of soft information that links the organization
    with its environment

Mintzbergs Studies of Managers
  • Myth 3 The senior manager needs aggregated
    information, which a formal management
    information system best provides.
  • Fact Managers strongly favor verbal
    media, telephone calls, and meetings over
  • Myth 4 Management is, or at least is quickly
    becoming, a science and a profession.
  • Fact The managers' programs - to
    schedule time, process information, make
    decisions, and so on-remain locked deep inside
    their brains.

Classic Study of Managerial Work
  • The classic study of managerial work was done by
    Mintzberg, who divided the managers roles into
    three categories
  • 1. Interpersonal roles
  • 2. Informational roles
  • 3. Decisional roles

Management Roles
  • Interpersonal Roles
  • Figurehead, Leader, Liaison
  • Informational Roles
  • Monitor, Disseminator, Spokesman
  • Decisional Roles
  • Entrepreneur, Disturbance Handler, Resource
    Allocator, Negotiator

Mintzberg The Nature of Managerial Work
Formal Authority and Status
Interpersonal Roles
Informational Roles
Decisional Roles
Mintzbergs Management Roles
  • Interpersonal Roles
  • Figurehead - Carries out a symbolic role as head
    of the organization, performing duties of a legal
    or social nature.
  • Leader - In the most widely recognized managerial
    duty, the executive is responsible for motivating
    and "activation" of subordinates, as well as
    staffing, training, promoting.
  • Liaison - Develops and maintains a personal
    network of external contacts who provide
    information and favors.

Mintzbergs Management Roles
  • Informational Roles
  • Monitor - Seeks and receives a wide variety of
    special information (much of it current) to
    develop a thorough understanding of the
    organization and the environment. In this role,
    the executive serves as the nerve center of
    internal and external information about the
  • Disseminator - Transmits information received
    from outsiders or subordinates to other members
    of the organization. Some information is
    factual, some involves interpretation and
    integration of diverse value positions of
    organizational influencers. All information is
    to guide subordinates in decision making.
  • Spokesman - Communicates information to outsiders
    on the organization's plans, policies, actions,
    results, etc. serves as the expert on the
    organization's industry.

Mintzbergs Management Roles
  • Entrepreneur - Searches the organization and
    environment for opportunities and initiates
    "improvement projects" to bring about change
    supervises design of certain projects as well.
  • Disturbance Handler - Responsible for corrective
    action when the organization faces important,
    unexpected disturbances.
  • Resource Allocator - Allocates organizational
    resources of all kinds-in effect the making or
    approval of all significant organizational
  • Negotiator - Represents the organization in major

IS and Mintzbergs Roles
Information Support for Management
  • Early information systems mainly supported the
    informational roles.
  • The purpose of recent information systems is to
    support all three roles.
  • We will explore the information support required
    for all roles, beginning with the decisional
  • The success of management depends on the
    execution of managerial functions such as
    planning, organizing, leading, and controlling.
    To carry out these functions, managers engage in
    the continuous process of making decisions.

Executive Activities and Information Support
  • Handling Disturbances (42) - A disturbance is
    something that happens unexpectedly and demands
    immediate attention, but it might take weeks or
    months to resolve.
  • Entrepreneurial Activity (32) - activities
    intended to make improvements that will increase
    performance levels. Improvements are strategic
    and long term in nature.
  • Resource Allocation (17) - Allocating resources
    within the framework of the annual and monthly
    planning tasks and budgets
  • Negotiations (3) resolve conflicts and disputes,
    either internal or external.
  • Other Activities (6)

Introduction to Decision-Making
  • A basic understanding of decision making is
    essential because most information systems are
    designed to support decision making in one way or
  • We will survey some models and concepts of
    decision making and methods for deciding among
  • We will look at their relevance to information
    systems design.

Decision MakingPhases
  • Herbert A. Simon (1960) proposed the most famous
    model of the Decision-Making process.
  • 1. Intelligence
  • 2. Design
  • 3. Choice
  • Some models of decision making include a 4th
    step Implementation.
  • There is a flow activities from one phase, to the
    next. At any time there may be a return to a
    previous phase.

Simons ModelFlowchart of Decision Process
Intelligence Phase
  • Searching the environment for conditions calling
    for decisions
  • Data inputs obtained, processed, examined for
    clues to identify problems or opportunities
  • Identify problems for opportunity situations
    requiring design and choice.
  • Scanning the environment, intermittently or
    continuously, is important.
  • Organizational objectives
  • search and scanning procedures
  • data collection
  • problem identification
  • problem classification
  • problem statement

Examples of the Intelligence Phase
  • Air traffic controller continuously scanning to
    detect problems in air space.
  • Each time you start your car, there is a
    conscious or unconscious scanning (listening,
    checking gauges, etc.).
  • Marketing executive makes periodic visits to key
    customers to review possible problems and
    identify new customer needs.
  • A plant manager reviews daily scrap report to
    check for quality control problems.
  • An executive reads the industry trade paper to be
    aware of events and changes in the environment.

Summary Intelligence Phase
  • Intelligence activities result in dissatisfaction
    with the current state or identification of
    potential rewards from a new state.

Design Phase
  • Inventing, developing, and analyzing possible
    courses of action
  • This involves processes to understand the
    problem, to generate solutions and test solutions
    for feasibility
  • Formulate a model.
  • Set criteria for choice.
  • Search for alternatives
  • Predict and measure outcomes

Choice Phase
  • Select an alternative from those available
  • Select and implement a choice
  • Solution to the model
  • sensitivity analysis
  • selection of best (good) alternatives(s)
  • plan for implementation (action)

Comment on Simons Model
  • Simons Model does not go beyond the choice
  • There are no steps for implementation, or
    feedback from the results of the decision.
  • Although Simons model is the most famous, others
    have adapted it.
  • Our textbook provides a similar model

(No Transcript)
Alter Textbook Model
  • Decision-making is represented as a
    problem-solving process preceded by a separate
    problem-finding process.
  • Problem-solving is the use of information,
    knowledge, and intuition to solve a problem that
    ha previously been defined.

An Alternative Model Rubenstein and
  • 1. Recognition of problem or need for decision
  • 2. Analysis and statement of alternatives
  • 3. Choice among the alternatives
  • 4. Communication and implementation
  • 5. Follow-up and feedback of results

Slades Model of Decision Making
Identify Problem
Identify Alternatives
Choose Usual Action
Evaluate Alternatives
Choose Among Alternatives
Generate New Alternatives
Effect Choice
Abandon Problem
  • All models indicate the same basic ideas
  • 1. Problem finding - Identify situations where
    problems need to be solved.
  • 2 Problem formulation - clearly state the
  • 3. Alternative Generation
  • 4. Evaluate Outcomes.
  • 5. Choice
  • 6. Implement
  • 7. Evaluate..

Problem Finding I
  • It is the difference between existing state and
    the desired state
  • The problem finder usually has an idea of the
    desired state ( a model)
  • Compared with the reality and differences noted
  • A Problem exists when there is a major difference

The role of models in decision-making
  • A major characteristic of decision-making is the
    use of models.
  • A model is a simplified representation or
    abstraction of reality.
  • It is usually simplified because reality is too
    complex to copy.
  • Basis idea is that analysis is performed on a
    model rather than on reality itself.

Pounds Categories of Models - Expectations
against which reality is measured
  • Historical - expectation based on extrapolation
    of past experience.
  • Planning - the plan is the expectation
  • Inter-organizational - Models of other people in
    the organization (e.g. superiors, subordinates,
    other departments, etc.)
  • Extra-organizational - models where the
    expectations are derived from competition,
    customers, professional organizations, etc.

Another classification of models
  • Iconic Models
  • Analog Models
  • Mathematical Models
  • Mental Models

Iconic and Analog Models
  • Iconic (scale) models - the least abstract model,
    is a physical replica of a system, usually based
    on a different scale from the original. Iconic
    models can scale in two or three dimensions.
  • Analog Models - Does not look like the real
    system, but behaves like it. Usually
    two-dimensional charts or diagrams. Examples
    organizational charts depict structure,
    authority, and responsibility relationships maps
    where different colors represent water or
    mountains stock market charts blueprints of a
    machine speedometer thermometer

Mathematical Models
  • Mathematical (quantitative) models - the
    complexity of relationships sometimes can not be
    represented iconically or analogically, or such
    representations may be cumbersome or time
    consuming.A more abstract model is built with
  • Note recent advances in computer graphics use
    iconic and analog models to complement
    mathematical modeling.
  • Visual simulation combines the three types of

Mental Models
  • People often use a behavioral mental model.
  • A mental model is an unworded description of how
    people think about a situation.
  • The model can use the beliefs, assumptions,
    relationships, and flows of work as perceived by
    an individual.
  • Mental models are a conceptual, internal
    representation, used to generate descriptions of
    problem structure, and make future predications
    of future related variables.
  • Support for mental models are an important aspect
    of Executive Information Systems. We will discuss
    this in depth later.

Problem Formulation
  • There is always the danger of solving the wrong
  • Here, you try to clarify the problem so that you
    work on the right problem
  • Frequently, the process of clearly stating the
    problem is sufficient in other cases, reduction
    of complexity is needed.
  • Some strategies to use for reducing complexity
    and formulating a manageable problem are shown in
    the next slide

Problem Formulation Strategies
  • Determine problem boundaries (I.e. what is
    clearly part of the problem)
  • Examine changes that precipitated the problem
  • Break it down into smaller sub-problems
  • Focus on controllable elements
  • Relate to a previously solved class of problems,
    an analogy situation.

Alternative Generation
  • A significant part of the process of
    decision-making is the generation of alternatives
    to be considered in the choice phase.
  • This is a creative task and can be taught
  • Can be enhanced by aids such as
  • scenarios
  • brainstorming
  • analogies
  • checklists, etc
  • Requires Knowledge of the problem and its
    boundaries (domain knowledge), as well as
    motivation to solve the problem.

Decision-Making Concepts
Decision Making Concepts
  • Decisions differ in a number of ways.
  • The differences affect the alternative generation
    process, and how a final choice will be made.
  • The differences can also affect how information
    systems and information technology can support
    the process at any one of the stages.
  • Four dimensions of decision types
  • I. Knowledge of Outcomes
  • II. level of structure/programmability
  • III. criteria for the decision
  • IV. level of decision impact

Decision Making Concepts IKnowledge of Outcomes
  • Outcome - what will happen if a particular
    alternative or course of action is chosen
  • Knowledge of outcomes is important with multiple
  • Three types of knowledge with respect to outcomes
    are usually distinguished
  • Certainty
  • Risk
  • Uncertainty

Knowledge of OutcomesThree Types
  • Certainty
  • Complete and accurate knowledge of outcome of
    each alternative. There is only one outcome for
    each alternative.
  • Risk
  • Multiple outcomes for each alternative and a
    probability can be assigned to each
  • Uncertainty
  • Multiple outcomes for each alternative and a
    probability cannot be assigned to each

Decision-Making Under Conditions of Certainty
  • If the outcomes are known and the values of the
    outcomes are certain, the task of the
    decision-maker is to compute the optimal
    alternative or outcome.
  • Are we rational decision makers?
  • There is ongoing argument pro and con
  • People are said to be limited rationalists
  • We might look for a limited number of
    alternatives and decide

  • A rational decision maker is expected to decide
    on the optimal alternative or outcome
  • The optimal alternative is one that is related to
    some optimization criteria such as minimize cost,
    for example
  • Thus the rational decision maker chooses the one
    that has the minimum cost
  • Consider purchasing two products that are
    identical in all respects and appear equal in
  • All other things being equal, the rational
    decision maker chooses the one with the lower
  • Rare, since all things are rarely equal

Decision Making under Risk
  • Risk is when multiple outcomes of each
    alternative is possible and a probability of
    occurrence can be associated with each
  • In such cases, the general rule is to pick the
    one that has the highest expected value

RiskExpected Value
  • Which would you choose?
  • Action 1 offers 1 probability of a gain of
    15,000, or
  • Action 2 that offers 50 probability of a gain of
  • Solution use Expected Value
  • Expected value is defined as the product of the
    outcome and the probability of the outcome
  • Expected value outcome x probability

RiskExpected Value (contd.)
  • Action 1 Expected Value 0.01 x 15,000 150
  • Action 2 - Expected Value. 0.5 x 400 200
  • Action 2 has the higher expected value
  • The rational decision maker chooses the strategy
    that has the higher expected value
  • OK strategy if the probability is known

Decision Making Under Uncertainty
  • Uncertainty is the situation where the outcomes
    are known, but the probabilities are unknown
  • One solution is to somehow assign the
    probabilities and then convert it to a problem
    under risk.
  • Other decision rules are to minimize regret and
    to use the maximum and minimum criteria. We will
    look at these later.
  • Uses Bayesian decision theory which recommends
    maximizing subjective expected utility, and on
    decision analysis which uses decision trees,
    payoff matrices, and influence diagrams to
    implement Bayesian Decision Theory.

Decision-Making Concepts II Programmed vs. Non
Programmed Decisions
  • We have reviewed this with the Gorry and Scott
    Morton Paper discussed earlier
  • Programmed Decisions - those that can be
    pre-specified by a set of rules or decision
  • Non-programmed Decisions - those that do not have
    any pre-established decision rule or procedures

Criteria for Decision-Making III Normative vs.
Descriptive Models
  • Normative or Prescriptive - a model of decision
    making that tells the decision maker how to make
    a class of decisions. These have been developed
    by economists, management scientists, etc.
    Examples Linear programming, game theory,
    capital budgeting, statistical decision theory.
  • In normative models the criterion for selecting
    among alternatives is maximization or
    optimization of either utility or expected value.

Criteria for Decision-Making III Normative vs.
Descriptive Models
  • Descriptive - a model of decision making that
    describes how decision makers actually make
    decisions. They are used primarily by behavioral
  • Descriptive models introduce the concept of
  • These two models introduce the Rational Approach
    as well as behavioral approaches.

Criteria for Decision-Making IV Level of
Decision Impact
  • What are the consequences of the Decision?
  • Will the consequences affect choice?
  • What are the consequences under conditions of
    certainty, risk, or uncertainty?

Management Support Systems and Decision-MakingPar
t II
Views or Models ofIndividual and
Organizational Decision-Making
Views or Models of Decision-Making
  • The Rational Manager View
  • The Satisficing, or Process-Oriented View
  • The Organizational Procedures View
  • The Political View
  • The Individual Differences View
  • The Garbage Can Model

The Rational Manager View
  • Oldest Theory to be proposed and studied in
    detail - it is a normative model.
  • Based Heavily on Theory of Economic Man developed
    in economics and applied to management.
  • Assumes organizational actors have complete
    knowledge of a decision scenario, and complete
    knowledge of their preferences.
  • An exhaustive search is made of all possible

Rational Manager - 2
  • Consequences of alternatives are evaluated in
    terms of known preferences.
  • An optimal choice can be selected.
  • Proponents of cost-benefit analysis adopt this
  • Model is highly normative, and has little
    descriptive support in true form.
  • It is impractical and over-idealized.
  • Influenced all other views of decision-making.

The Satisficing Viewpoint
  • Simon was among first to attack the Rational
  • Most decision situations provide limited
    knowledge on some aspect of the problem.
  • Impractical to think of generating all possible
    relevant alternatives for a situation.
  • The bounded rationality of the human mind would
    make all of this information unassimable.
  • Simon argues we tend to satisfice, or settle for
    a choice after a moderate amount of search.

Satisficing View - 2
  • Since search is not exhaustive, heuristics or
    rules of thumb are used to identify solutions
    that are good enough most of the time.
  • Heuristics reflect bounded rationality, i.e. a
    compromise between the demands of the problem,
    and the capabilities and commitment of the
  • Simons Model has had wide discussion.
  • His model, called Administrative Man, is a
    rejection of the Economic Man theory.

Empirical Research
  • Empirical research has shown the importance of
    rationality and bounded rationality in
    organizational decision-making.
  • Rationality and bounded rationality may be viewed
    at opposite ends of a continuum with the decision
    setting playing a contingency role.
  • Threatening environments, high uncertainty,
    external control decreased rationality.
  • The more complex or turbulent the environment,
    the less rationality used.

Empirical Research - 2
  • Comprehensiveness - a desire to be rational,
    reflects how exhaustive and inclusive the
    decision process is in seeking alternatives.

Organizational Process View
  • Cyert and March extended Simons concept of
    bounded rationality to the organizational
  • Organizational Decision-Making in terms of
  • formal and informal structure of the organization
  • standard operating procedures
  • channels of communication
  • Choice is made in terms of goals, on the basis of
  • The organization is a coalition of participants
    with disparate demands, focus of attention, and
    limited ability to attend to all problems

Organizational Process View - 2
  • Bargaining among coalitions produces agreements
    which are the organizations goals.
  • Organizational expectations arise from inferences
    from available information.
  • Choice emerges as the selection of the first
    alternative that expectations identify as
    acceptable in terms of goals.
  • Choice in the short-run is driven by standard
    operating procedures.
  • Choice in the long-run driven by organizational

The Political View
  • Here decision-making is a personalized bargaining
    process among organizational units.
  • Power and Influence determine the outcome of any
  • The players act in terms of no consistent set of
    strategic objectives, but rather according to
    their personal goals, stakes, interests.
  • Organizational choice is the result of the
    pulling and hauling that is organizational

The Political View - 2
  • One must understand the realities of power and
    the compromises and strategies necessary to mesh
    interests and constraints of the players.
  • Decisions are made to enhance the winner's
    conception of organizational, group, or personal
  • Allison argued that Politics is a process or
    conflict and consensus Building.

Empirical Research - 3 (1992)
  • Procedural Rationality, or the desire to follow a
    rational approach to decision-making, is
    independent of the political process
  • Decisions can be both procedurally rational and
    political, or neither.
  • They can be independent dimensions of the
    organizational decision-making process.

The Political View - 3
  • An important sub-model is the concept of
    incremental change - because there are so many
    actors involved in an organizational decision
    setting, clear, rapid progress is rarely possible
  • The result of political bargaining and compromise
    is incremental change, i.e. decision-makers move
    to situations which are only slightly different
    from the current situation.
  • Lindbloom talked of this in The Science of
    Muddling Through (1959).
  • Muddling through is explicitly anti-utopian - it
    is the best we can do.

The Individual Differences View
  • This view focuses on the individual
    decision-maker and his/her cognitive style,
    information processing and problem-solving
  • Some individuals have specialized styles of
    decision-making which are effective in some
    contexts, and less so in others.
  • Personal rationality is subjective, and behavior
    is determined by the manner in which individuals
    process information.

Individual Differences - 2
  • In the organizational context, managers develop
    their own mental models of problems and issues.
  • Decision-making can be a mixture of rationality
    and intuition, based heavily of experience and
  • MS and OR are attractive to an analytic,
    systematic style. They may be less attractive to
    managers with a more intuitive style.
  • Personality (what a person thinks) vs. Cognitive
    Style (how a person thinks).

Cognitive Style Dimensions
  • Left brain
  • words
  • analytic
  • sequential
  • active
  • realistic
  • planned
  • Right brain
  • images
  • intuitive
  • simultaneous
  • receptive
  • imaginative
  • impulsive

Empirical Research - 4
  • Bobbit and Ford (1980) saw that executives had
    firm pre-dispositions about how the process of
    looking for ideas should unfold.
  • Executive attitudes are influences by belief
    structures and past experience - pragmatic.
  • Those with a low tolerance for ambiguity and high
    need for structure will adopt a decision process
    that has a narrow search zone.
  • Risk propensity and risk perception also play
    roles, with risk propensity dominating decision
    situations. (Sitkin and Pablo, 1992).

The Garbage Can Model
  • Proposed by Cohen, March, and Olsen in 1972.
  • Appropriate for highly complex, unstable, and
    ambiguous environments called organized
  • Decisions result from a complex interaction
    between four independent streams of events
  • problems, solutions, participants, and choice

Garbage Can Model - 2
  • The interaction of these events creates a
    collection of
  • choices looking for problems
  • issues and feelings looking for decision
    situations in which they might be aired
  • solutions looking for issues to which they might
    be the answer
  • decision-makers looking for work.
  • The four streams are independent in nature and
    interact in a random fashion.
  • A decision is made only when the four streams
    happen to interact.

Garbage Can Model - contd.
  • Good decisions are made when this happens at the
    right time.
  • Solutions represent the ideas constantly flowing
    through an organization.
  • Solutions are used to formulate problems.
  • Note that managers often do not know what they
    want until they have some idea of what they can

Individual Aspects of Decision-Making
Human Expectations
  • Humans display a variety of responses in decision
    making. Some are related to individual
    differences such as cognitive style, others re
    related to expectations.
  • Role of expectations can be partially explained
  • theory of cognitive dissonance
  • commitment theory
  • theory of anticipatory regret

Theory of Cognitive Dissonance
  • propagated by Leon Festinger
  • explains behavior after a choice is made
  • Selected alternative has some negative features
    and rejected ones have some positive features
  • Decision maker has feelings of mental discomfort
    following a decision because of recognition of
  • Second-Guessing
  • Ex. Purchase of car

Cognitive Dissonance - 2
  • Customers might need to be bolstered about their
  • Hence, sales procedures follow up a sale with a
    congratulatory letter to bolster the effect of
    cognitive dissonance reduction

Theory of commitment
  • If the person knows the decision is not revocable
    (firm commitment to decision), then decision time
    increases and processes will be more careful
  • Having spent time making decision, the decision
    maker is reluctant to change it

Theory of anticipatory regret
  • The decision maker anticipates the regrets that
    might occur
  • This inhibits the decision maker from making a
    decision without contemplating the consequences
  • Can be used to lessen post-decision regret
    thinking about consequences before they happen
    reduces the psychological impact when hey happen.

Behavioral Aspects of Organizational
Behavioral Aspects of Organizational Decision
  • Many Issues Related to the Organizational
    Procedures Viewpoint and the Political Viewpoint
  • quasi-resolution of conflict
  • uncertainty avoidance
  • problemistic search
  • organizational learning
  • incremental decision making

Quasi-Resolution of Conflict
  • An organization can be considered as coalition of
    members having different goals and unequal power
    t influence organizational objectives.
  • There are conflicts among the goals of the
    various members (e.g. production, sales,
  • Conflicts need to be resolved thru
  • local rationality
  • acceptable-level decision rules
  • sequential attention to goals

Uncertainty Avoidance
  • Organizations live in uncertain environments
  • This theory assumes that organizations will seek
    to avoid risk and uncertainty at the expense of
    expected value
  • A decision maker will be willing to accept a
    reduction in the expected value in exchange for
    an increase in the certainty of the outcome

Uncertainty Avoidance - 2
  • Thus, a decision maker will choose a 90 chance
    of making 10 over a 12 chance of making 100
  • The second alternative has a higher expected
  • The decision though, is the first alternative
  • Major benefit - reduction in uncertainty

Uncertainty Avoidance - 3Legal Methods
  • Short-run feedback and reaction cycle
  • short feedback cycle allows frequent new
    decisions and thus reduce need to be concerned
    about future uncertainty.
  • Negotiated environment
  • organization seeks to control its environment
    through industry-wide conventional practices
    (sometimes just as restrictive or collusive
  • long-term supply contracts, etc.

Problemistic Search
  • Search for solutions is problem-stimulated
  • Little planned search for solutions not motivated
    by problems
  • Simple rules
  • search locally close to present symptom
  • if this fails, expand search to vulnerable areas
    before moving to other areas

Organizational Learning
  • Organizations exhibit adaptive behavior over time
  • They change their goals and revise problem search
    procedures on the basis of experience
  • Aspiration levels for goals are assumed to change
    in response to results obtained
  • Plans tend to reflect aspiration levels
  • Information systems are an important factor in
    reconciling achievement level and aspiration level

Incremental Decision Making
  • Decision making in organizations is confined to
    small changes from existing policy and procedures
  • Emphasis is on correcting or improving existing
    policies and actions
  • Emphasis on consensus
  • Called muddling through by Lindbloom

Decision Making Under Psychological Stress
Decision Making under Stress
  • based on the conflict-theory model of Janis and
    Mann (1977)
  • Decision making causes stress but here the
    characteristic is that all the alternative
    courses of action appear to have serious
    undesirable outcomes.
  • Symptoms of such conflict are
  • apprehensiveness
  • hesitation
  • vacillation
  • distress
  • Decisions are made using coping patterns

Coping Patterns
  • Used in emergency situations such as a flood or a
  • Can be extended to situations where there exist
    serious threats
  • Four Questions that determine the typical coping

Coping Patterns - Questions
  • Q1 Are the risks serious in the absence of
  • Q2 Are the risks serious if change is made?
  • Q3 Is it realistic to hope for a better
  • Q4 Is there sufficient time to search and

Coping Patterns - 3
  • If answer to Q. 1 is yes, then next is relevant
  • If answer to Q. 2 is yes, then go to Q. 3
  • If answer to Q. 3 is no, then the coping pattern
    may be defensive avoidance
  • If answer to Q. 3 and Q. 4 is yes, then the
    coping strategy can be a vigilant process of
    search, appraisal, and contingency planning.
  • If answer to Q. 4 is no, (e.g. a fire) then the
    coping pattern may be hypervigilance

  • Typical response to disasters
  • The decision maker focuses on the expected
    unfavorable consequences and fails to process
    information indicating that they may not happen.
  • Pressure is felt to take immediate action.
  • Hastily choose without considering the overall
    result or other possible actions.

Defensive Avoidance
  • This coping pattern is most appropriate for the
    design of information systems and decision
    support systems.
  • Marked by decision maker avoiding exposure to
    disturbing information, wishful thinking,
    distortion of information received and selective
  • If risk of postponing decision is low,
    procrastination is chosen.
  • If not, buck passing is tried.
  • Bolstering is used beforehand in the lack of
    complete information
  • After decision, bolstering is used to reduce
    cognitive dissonance.

Defensive Avoidance - 2
  • Some example bolstering tactics
  • Exaggeration of favorable consequences
  • minimizing unfavorable consequences
  • Denial of adverse feelings
  • Exaggeration of remoteness of action that will be
    required following decision
  • Assuming lack of concern by society (iit is a
    private decision).
  • Minimizing of personal responsibility.

Defensive Avoidance - 3
  • This pattern can also be observed in a group
  • Janis coined the term groupthink for collective
    defensive avoidance
  • E.g. industry that fails to react to vigorous
    price, quality and design competition by foreign
  • Symptoms of groupthink - see next slide

Groupthink Symptoms
  • Illusion of invulnerability - The company is
    large and powerful and has customer loyalty.
  • Collective Rationalization - No one can match our
  • Belief in the inherent morality of the group -
    The managers are the best trained and preserve
    traditional values.
  • Stereotypes of outgroups - The competitors
    products are inferior. They can not provide

Groupthink Symptoms - contd.
  • Direct pressure on dissenters -demotion or firing
    of managers who disagree on a subject.
  • Self-censorship - The subject of foreign
    competition is never put on the table by anyone
    in the group.
  • Illusion of unanimity - No one is objecting, so
    everyone must agree that foreign competition is
    not serious.
  • Self-appointed mind guards - evidence that
    contradicts the thinking of the group is removed
    as it moves up the organization.

Groupthink Example PATCO Strike of 1981
  • Illusion of Vulnerability - The air system can
    not survive long without air traffic controllers
    (ATCs). Plans to replace them will not work.
  • Collective Rationalization - The oath not to
    strike wasnt binding in this case, even though a
    strike was illegal.
  • Belief in Inherent morality of the group - The
    strike for higher pay is morally justified
    because ATCs are responsible for more lives now.
  • Stereotypes of Outgroups - The government is a
    typical bureaucracy. Reagan is just bluffing on a
    threat to fire us. No one has listened to our

Groupthink Example PATCO Strike of 1981 - contd.
  • Direct Pressure of Dissenters - John Feydon,
    President of PATCO until 1980, forced to resign
    because he did not support strike.
  • Self-censorship - Quotes such as Doubts seemed
    in the minority...The union is tight, almost
    like a family. 20 of strike force returned to
  • Illusion of Unanimity - Other unions offered
    token support for PATCO. AFL/CIO privately was
    critical of PATCOs strike.
  • Self-Appointed Mind Guards - Negotiators claimed
    there was no alternative but to strike.

Deciding Among Alternatives
Introduction to Methods
  • Numerous method help one decide among
  • They generally assume that all alternatives are
    known or can be know, even though the search
    process often stops well before all feasible
    alternatives have been examined.

Optimization Techniques Under Certainty
  • All alternatives and their outcomes are known.
    The computational problem is to choose which one
    is optimal.
  • Use optimization techniques
  • systems of equations
  • linear programming
  • integer programming
  • dynamic programming
  • queuing models
  • inventory models, etc.
  • Capital budgeting analysis
  • Break-even Analysis

Mathematical Programming
  • Mathematical Programming is the name for a family
    of tools designed to solve managerial problems in
    which the decision maker must allocate scarce (or
    limited) resources among various activities to
    optimize a measurable goal.
  • Example Distribution of machine time (the
    resource) among various products (the activities)
    is a typical allocation problem

Sample Linear Programming
  • XYZ corporation makes servers. A decision must
    be made. How many servers should be produced
    next month in the Boston plant? Two types of
    servers are considered S-7 requires 300 days of
    labor and 10,000 in materials S-8 requires 500
    days of labor 15,000 in materials. The profit
    contribution of S-7 is 8,000 whereas that of S-8
    is 12,000. The plant has a capacity of 200,000
    days per month while the material budget is
    8,000,000 per month. Marketing requires that at
    least 100 units of S-8 be produced.
  • Problem How many units of S-7 and S-8 should
    be produced?

The Model
  • Decision Variables X units of S-7 to be
    produced Y units of S-8.
  • Result Variable The total profit. The
    objective is to maximize total profit.
  • Objective function
  • Total Profit 8,000X 12,000Y
  • Constraints
  • Labor Constraint 300X 500Y lt 20,000 (in
  • Budget Constraint 10,000X 15,000Y lt 8,000,000
    (in dollars)
  • Marketing Requirement X gt 100 (in units).

Optimization Techniques
  • Computer algorithms and programs are readily
    available to handle many problems of this class.
  • The major problem is to construct the model
  • Reference other books on Optimization,
    Mathematical Programming, or Operations Research,
    or Management Science for a further discussion of
    these models and their application.

Statistical Decision Theory
  • Decision Theory provides a rational framework
    for choosing between alternative courses of
    action when the consequences resulting from
    choice are imperfectly known.
  • The necessity of making decisions in the face of
    uncertainty is an integral part of our lives.
  • The theory provides techniques for mathematically
    evaluating potential outcomes of alternative
    actions in a given decision situation.
  • In all cases, the decision-Maker has an objective
    (e.g. maximize profit).
  • Two methods Payoff Matrix and Decision Tree.

Statistical Decision TheoryPayoff Matrix
  • The payoff matrix consists of rows for the
    alternatives or strategies available and columns
    for the conditions that affect the outcomes
  • Each cell contains the payoff (the consequences)
    in dollars if that strategy is chosen and that
    state occurs
  • If it is known with certainty which state will
    prevail, then choose the strategy that has the
    highest payoff for that state
  • This is simply the strategy of maximizing
    expected utility.

General Payoff Matrix
States of Nature
Example 1 The Anniversary Problem
You are suddenly driving home from work in the
evening when you suddenly recall that your
wedding anniversary comes about this time of
year. In fact, it seems quite probable, (but
not certain), that it is today. You can still
stop at the local florist and buy a dozen roses,
or you may go home empty-handed and hope the
anniversary lies in the future. What do you do?
Possible Outcomes (States of Nature)
Decision Alternatives (Strategies)
It IS NOT Your Anniversary
It IS Your Anniversary
Buy Flowers
Do Not Buy Flowers
Anniversary Problem Payoff Matrix
Decision Tree for Anniversary Problem
NOT Anniversary
Buy Flowers
Do Not Buy Flowers
NOT Anniversary
Decision Point
Resolution of Uncertainty
Example 2 Fast Service Restaurant
An entrepreneur is deciding among three
alternatives for a fast-service restaurant that
she owns (1) leave as is (2) refurbish t to
improve layout (3) or re-build completely to add
capacity and improve layout.
There are three significant, independent
conditions (assume only one can occur) that
affect the possible profit (payoff) from each
alternative strategies. These conditions are
(1) a competitor may open on a nearby property
(2) a proposed highway re-routing will change
the traffic passing by (3) conditions will stay
approximately the same as they are. What should
the entrepreneur do?
Payoff Matrix (in Thousands of )
New Competitor 0.20
Highway Rerouting 0.30
Same - 0.5
Do Nothing
Analysis with Knowledge
  • If we assume conditions remain the same, Rebuild
    is the best strategy. (Payoff 7,000).
  • If probabilities are assigned, using a criteria
    of maximizing expected value
  • Do Nothing (0.5)(2) (.2)(0) (0.3)(-1) 0.7
    or 700
  • Refurbish (0.5)(4) (0.2)(3) (0.3)(-3) 1.70
    or 1,700
  • Rebuild (0.5)(7) (0.2)(2) (0.3)(-10) 0.90
    0r 900
  • Therefore, refurbishment is the best choice.

Statistical Decision Theory Imperfect Knowledge
of Consequences
  • If there is uncertainty about the probabilities
    of the various conditions, then you can use one
    of several rules for deciding
  • minimize regret -select strategy which minimize
    the sum of regrets for the strategy
  • maximin - Select strategy which has highest
    payoff if the worst state of nature occurs
  • maximax - Select strategy which has highest
    payoff if most favorable state of nature occurs
  • Each one of these rules has been criticized in
    the literature. They have disadvantages if
    applied as a general decision rule. You must
    decide if the rule is appropriate for the

Regret Definition
  • The regrets are the differences between the best
    payoff for a state of nature and the other
  • To compute a matrix of regret, subtract the value
    in each entry in a column from the highest value
    in the column.
  • Sum the rows to compute the regrets for each
    action (assuming the payoff matrix has columns
    for states and rows to show actions)

Analysis with Imperfect Knowledge
  • Minimize Regret
  • Do Nothing 5 3 0 8
  • Refurbish 3 0 2 5
  • Rebuild 0 1 9 10
  • Therefore, action which minimizes regrets is to
  • Maximin Rule Identify state with worst payoff,
    and choose strategy with least unfavorable
    payoff. Therefore, choose to Do Nothing.
  • Maximax Rule Rebuild

Statistical Decision Theory
  • When decisions must be made under uncertainty,
    the emphasis is on Bayesian decision theory which
    recommends maximizing subjective expected
  • Bayesian decision theory provides a framework in
    which all available information is used to deduce
    which of the decision alternatives is best
    according to the decision makers preferences.
  • Distinguish between a good decision and a good

The Concept of Utility
  • Not all outcomes can be compared in terms of
    dollars. Dollars and other measures work well in
    a narrow range of values, but not at extremes
    (e.g. overtime pay).
  • Here money is used as a substitute measure of the
    outcomes utility.
  • Whereas utility may be linear in a certain range
    in comparison to money, it generally is not under
    all ranges.

Utility vs. Money
The Linear Assumption of Money for Utility in
this Narrow Range
Indifference Curves
  • Any two possible outcomes can be compared, and
    generally one can say which one is preferred.
  • In some cases they may be equally desirable, in
    which case you are indifferent.
  • Example You may prefer a weeks vacation in
    Florida rather than paid double time to work a
    week extra.
  • There is a tradeoff here between two
    value-properties. (e.g. leisure time vs. money).

Indifference Curves
Leisure Time
Other Alternative Selection Techniques
  • Ranking, Weighting, or Elimination by aspects -
    often used to evaluate competitive bids.
  • Game Theory (for conflict bargaining) - when one
    decision unit (player) gains, the other loses.
  • Classical Statistical Inference
  • sampling
  • probability distributions
  • regression and correlation analysis
  • testing of hypotheses

Rational Choice and the Framing of Problems
  • Alternative descriptions of a problem often give
    rise to different preferences.
  • Example Consider the following statistical
    information provided on two alternative
    treatments of lung cancer. The same statistics
    are presented in terms of survival rates and in
    terms of mortality rates to two groups of

Rational Choice and the Framing of Problems -
  • Example Survival Frame
  • Surgery Of 100 people having surgery, 90 live
    through the post-operative period, 68 are alive
    at the end of the first year and 34 are alive at
    the end of five years.
  • Radiation Therapy Of 100 people having
    radiation therapy all live through the treatment,
    77 are alive at the end of one year, and 22 are
    alive at the end of five years.

Rational Choice and the Framing of Problems -
  • Example Mortality Frame
  • Surgery Of 100 people having surgery 10 die
    during surgery or the post-operative period, 32
    dies by the end of the first year, and 66 die by
    the end of five years.
  • Radiation Therapy Of the 100 people having
    radiation therapy, none die during the treatment,
    23 die by the end of year one, and 78 die by the
    end of five years.

Framing Example Results
  • The inconsequential difference in framing
    produced a marked effect. The overall percentage
    of respondents who favored radiation therapy rose
    from 18 in the survival frame to 44 in the
    mortality frame.
  • Radiation Therapy appears better than surgery
    when stated as a reduction of the risk of
    immediate death from 10 to 0, rather than as an
    increase from 90 to 100 in rate of survival.
  • Framing effect was similar for physicians,
    business students, and a group of clinic patients.

What do We learn from Framing?
  • Normative models of choice, which assume
    invariance of preferences, can not provide an
    adequate descriptive account of choice behavior.

Sample Planning Models
Planning Models
  • A planning model is a method for structuring,
    manipulating, and communicating future plans.
  • Simple Profit Model
  • Sales input variable
  • Cost of Sales 0.4 x sales
  • Gross Margin sales - cost of sales
  • Operating expenses input variable
  • Profit before taxes gross margin - operating
  • Taxes 0.48 x profit before taxes
  • Net Profit profit before taxes - taxes

Sample Profit Plan
Sales 100,000 Less cost of Sales (
40,000) Gross Profit 60,000 Less
Operating Expenses ( 52,000) Profit Before
Taxes 8,000 Less Taxes (
3,840) Net Profit 4,160
Use of Planning Models
  • Model building can begin with simple models
    calling for inputs of major, high-level items.
  • Subsequent model development can expand the
    details f the model to calculate the high-level
    items from more basic input. Example
  • Selling Expense 0.10 x sales
  • Advertising expenses 0.05 x sales
  • Interest expense 0.10 x average long-term debt
    0.12 x average short term loans
  • bad debt expense 0.01 x accounts receivable
    balance at beginning of period
  • administrative expense input variable
  • operating expense selling advertising
    interest bad debt administrative expense.

Use of Planning Models - contd.
  • These models are are characteristic of Managerial
  • Individual terms can be estimated using
    techniques of statistics based on past history.
  • Planning Models provide opportunities for what
    if scenerios.

Summary and Relevance of Decision-Making Concepts
for Information Systems Design
How Information Systems Might Help Counteract
Common Flaws in Decision Making
POOR FRAMING Description Allowing a decision to
be influenced excessively by the language used
for describing the decision How an information
system might help Provide information
encouraging different ways to think about the
definition of the issue RECENCY
EFFECTS Description Giving undue weight to the
most recent information How an information system
might help Provide information showing how the
most recent information might not be
How Information Systems Might Help Counteract
Common Flaws in Decision Making
  • Description Giving undue weight to the first
    information received
  • How an information system might help Show how
    some information is inconsistent with the first
    information received
  • Description Overestimating the probability of
    familiar or dramatic events underestimating the
    probability of negative events
  • How an information system might help Make it
    easier to estimate probabilities based on
    pertinent data

How Information Systems Might Help Counteract
Common Flaws in Decision Making
OVERCONFIDENCE Description Believing too
strongly in ones own knowledge How an
information system might help Provide
counterexamples or models showing that other
conclusions might also make sense ESCALATION
PHENOMENA Description Unwillingness to abandon
courses of action decided upon previously How an
information system might help Provide
information or models showing how the current
approach might give poor results
How Information Systems Might Help Counteract
Common Flaws in Decision Making
  • Description Reusing strategies that were
    successful in the past, regardless of whether
    they fit the current situation
  • How an information system might help Provide
    information showing how the current situation
    differs from past situatioins
  • Description Bowing to group consensus and
    cohesiveness instead of bringing out unpopular
  • How an information system might help Provide
    information inconsistent with the current
    consensus and prove its relevance

Support for the Intelligence Phase
  • The search process involves an examination of
    data both in predefined and in ad hoc ways.
    Information systems support should provide both
  • Scanning of internal and external databases for
    opportunities and problems.
  • Filtering should be used to avoid information
  • Information system should scan all data and
    trigger a request for human examination of
    situations apparently calling for attention (e.g.
    examination of key indicators and critical
    success factors).

Support for the Intelligence Phase - contd.
  • Routine and ad-hoc reports can aid in the
    intelligence phase (e.g. summarization,
    comparison, prediction, confirmation).
  • Various models should be included in the scanning
    and report layouts (e.g. historical, planning,
  • Either the system of the organization should
    provide communication channels for perceived
    problems to be moved up the organization until
    they can be acted upon.

Support for the Design Phase
  • The information system should contain decision
    models to process data and generate alternative
  • It should assist with checklists, templates of
    decision processes, scenarios, etc.
  • The models should assist in analyzing the

Support for the Choice Phase
  • An information system is most effective if the
    results of design are presented in a
    decision-impelling format.
  • Presentation of alternatives. Use of appropriate
    methods depending on presence of certainty, risk,
  • When the choice is made, the role of the system
    changes to the collection of data for further
    feedback and assessment.

Relevance for Information System Design
  • Provide support for decision-makers in
    semi-structured and unstructured situations by
    bringing together human judgment and computerized
    information. Structured problems are easily
    handled by methods of management science and
    operations research.
  • Provide support for model development, whether
    formal models or mental models.
  • Provide tailorability to style of the
  • Recognize that organizations place constraints on
    the decision-maker. Rationality is not always an

Relevance for Information System Design - (contd.)
  • Recognize that uncertainty is a part of life.
  • Recognize that stress is a part of life.
  • Looks at methods for sensitivity analysis
    what-if analysis and goal-seeking analysis,
    and other methods for deciding among
  • Promote organizational learning.
  • Provide knowledge components for very difficult
  • Specific examples will be discussed when we look
    at specific examples for compute-based support of

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