Chapter 14 – Decision Making Vocabulary
Learning Outcomes
Upon mastering the chapter, you should be able to:
Distinguish between the rational (economic) approach and the bounded-rationality approach to decision making.
Recognize the limits of intuitive, heuristic, and bias-laden decision processes.
Explain how framing heuristics can trigger \text{escalation of commitment}.
Identify and apply the four basic decision-making styles (directive, analytic, conceptual, behavioral).
Apply the Vroom-Yetton-Jago contingency model, including its five decision processes and seven diagnostic questions.
Overview of Managerial Decision Problems
Managers face two broad problem types:
Well-structured problems: straightforward, repetitive, familiar, easily defined.
Solved routinely via policies/procedures (e.g., vacation requests decided by seniority).
Poorly structured problems: novel, complex, information-poor.
Common at middle and senior levels, especially in rapidly changing health-care environments.
Choice of model depends on problem structure and resource constraints (time, data, cognitive bandwidth).
Rational (Economic) Approach
Idealized, sequential, data-rich method (Figure 14-1).
Nine explicit steps:
Monitor internal & external environments – scan regulations, \text{financial statements}, competitors.
Identify the problem – define who, what, where, when, why, how.
Determine desired outcomes – specify performance targets.
Analyze the problem – fact-finding & root-cause analysis.
Generate alternatives – solicit stakeholder input, consult evidence-based research.
Evaluate alternatives – estimate probability each option meets outcomes.
Choose best alternative – select single highest-value path.
Implement – allocate resources, issue directives.
Evaluate decision – compare results to desired outcomes; loop back to Step 1.
Limitations: rarely possess perfect information, unlimited time, or bias-free cognition.
Bounded Rationality Model
Coined by Herbert A. Simon (1957).
Recognizes cognitive limits vs. environmental complexity.
Managers satisfice (seek "good-enough" solutions) rather than optimize.
Three core realities (Dequech, 2001):
Multiple, sometimes conflicting objectives; managers must generate options.
Limited mental capacity → use heuristics to narrow alternatives & forecast consequences.
Accept solutions that meet realistic aspiration levels instead of perfect optima.
Intuition in Decision Making
Defined as a rapid, non-conscious "pattern recognition" or "cognitive short circuit." (Hall, 2002)
When to rely on intuition (Agor, 1985; 1986):
High uncertainty, little precedent, low data predictability.
Limited facts, multiple plausible alternatives, severe time pressure.
Development factors:
Personality type (Myers-Briggs "N" dimension) vs. situational learning.
Senior leaders show higher intuitive-use scores; 85 \% of 36 CEO decisions driven largely by intuition (Maidique, 2011).
Top 10 U.S. companies (Peters & Waterman, 1984) actively cultivate intuitive skills; MBA curricula now embed intuition modules.
Heuristics & Biases
Heuristics = cognitive "rules of thumb" (Tversky & Kahneman, 1974).
Three common forms:
Availability
Probability judged by ease of recall.
Recency, frequency, emotional salience inflate perceived likelihood (e.g., recent poor diabetic control cases → misdiagnosis in Case 14-1).
Representativeness
Judge likelihood by similarity to a prototype; neglect base rates.
Leads to stereotyping & discrimination (also in Case 14-1).
Anchoring & Adjustment
Start from initial value (anchor) → insufficiently adjust (e.g., salary offer in Case 14-2).
Clinical parallels (Case 14-3):
Availability error: over- or under-estimating disease probability based on memorable cases.
Representation error: ignoring prevalence; focusing only on prototype match.
Anchoring error: clinging to early diagnostic label despite contradictory data.
Escalation of Commitment & Framing Heuristics
Escalation of commitment (Staw, 1981): continuing to invest in a failing course due to sunk cost, ego, or political factors.
Allegheny Health System bankruptcy; Expo 86 cost overrun \approx\$300 million.
Framing effects: identical data framed as gains vs. losses shapes risk preference (Levin et al., 1988).
40 \% success vs. 60 \% failure description altered support for cancer treatment.
Avoidance tactics (Staw & Ross, 1987):
Consciously recognize self-bias.
Define failure explicitly; welcome dissenting data.
Adopt outsider/"premortem" perspective to reassess.
Decision-Style Model (Rowe & Boulgarides)
Two underlying dimensions:
Value orientation: task/technical vs. people/social focus.
Tolerance for ambiguity (cognitive complexity).
Four styles (Fig. 14-2):
Directive (Low ambiguity, task-oriented)
Fast, autocratic, power-centric, prefers verbal data, short-term, tight control.
Analytic (High ambiguity tolerance, task-oriented)
Info-hungry, systematic, slower, enjoys written reports, thrives on complexity, status-driven.
Conceptual (High ambiguity, people-oriented)
Creative, long-range, participative, ethical ideals; "thinkers."
Behavioral (Low ambiguity, people-oriented)
Supportive, communicative, conflict-averse, uses meetings for cohesion.
Managers often default to one style yet can train to deploy all four contextually.
Vroom-Yetton-Jago Contingency Model
Matches decision process to situational demands.
Five processes: \text{AI}, \text{AII}, \text{CI}, \text{CII}, \text{GII}.
AI: Solo decision using existing data.
AII: Collect information individually; leader decides.
CI: One-on-one consultations; leader decides.
CII: Group meeting for ideas; leader decides.
GII: Group consensus; leader as facilitator, accepts any solution with full support.
Seven diagnostic yes/no questions (Figure 14-3):
1. Is decision quality critical?
2. Do I already have sufficient information?
3. Is the problem well structured?
4. Is subordinate commitment essential?
5. Will subordinates accept my solo decision?
6. Do subordinates share organizational goals?
7. Is subordinate conflict likely?Decision rules (sample):
Low quality & low commitment need → choose AI.
High quality need + unstructured problem + low leader info → choose GII.
Commitment vital yet employees unlikely to accept autocracy → avoid AI, AII.
Practical caveat: works best when leader can accurately gauge quality vs. acceptance factors.
Healthcare & Ethical Implications
Clinical decisions heavily influenced by intuition/heuristics; organizational resources & outcomes hinge on them.
Misdiagnosis (Case 14-1) → malpractice, patient harm, financial/legal risk.
Salary anchoring (Case 14-2) raises equity and fairness concerns; affects morale & turnover.
Vaccination triage thought experiment (Exercise 14-2): highlights ethical frameworks (utilitarianism, life-years, vulnerability) and decision steps.
Implicit bias testing (Harvard Project Implicit) encourages self-awareness to improve clinical & managerial judgments.
Comparative Summary & Practical Takeaways
Rational model = gold standard but rare in real time; aim for it when stakes high & data ample.
Bounded rationality acknowledges human limits; use structured processes to guard against error.
Develop meta-cognition: know when to trust gut vs. when to slow down & analyze.
Employ debiasing tools: premortems, red teams, checklists, data transparency.
Match decision style/process (Rowe-Boulgarides, Vroom-Yetton-Jago) to situational variables: urgency, complexity, stakeholder alignment.
Continuous evaluation completes the loop, enabling organizational learning and improved future choices.