An Exploratory Study of Choice Rules Favored for High-Stakes Decisions

As information technology becomes more sophisticated, consumers will be able to access more information to help them make difficult high-stakes choices, such as medical and financial investments or career decision making. The purpose of this article is to examine how consumers think such information should be used in making decisions for which there are high stakes. Results, based on five exploratory studies, indicate that subjects do not spontaneously favor the use of compensatory decision procedures, such as multiattribute utility theory (MAUT). Explanation and structured pedagogical procedures significantly increase the subjects’ endorsement of decision rules over no decision rules, but they do not increase the endorsement of MAUT. Further, subjects believe that they would be more likely to use compensatory models when they have more options and more information about the options, more time, less certainty about their goals, and more accountability. Paradoxically, although subjects generally do not want to use compensatory rules themselves, they are more likely to want their agents (e.g., physicians or financial or career advisors) to use these rules in making decisions.

[1]  Wayne A. Wickelgran.  Context-sensitive coding, associative memory, and serial order in (speech) behavior. , 1969 .

[2]  A. Tversky.  Intransitivity of preferences. , 1969 .

[3]  D. Kahneman,et al.  Attention and Effort , 1973 .

[4]  Peter Wright.  The harassed decision maker: Time pressures, distractions, and the use of evidence. , 1974 .

[5]  John W. Payne,et al.  Task complexity and contingent processing in decision making: An information search and protocol analysis☆ , 1976 .

[6]  I. Janis,et al.  Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment , 1977 .

[7]  John R. Hauser,et al.  A Normative Methodology for Modeling Consumer Response to Innovation , 1977, Oper. Res..

[8]  Daniel W. McAllister,et al.  The Contingency Model for the Selection of Decision Strategies: An Empirical Test. , 1978 .

[9]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  R. Olshavsky,et al.  Task Complexity and Contingent Processing in Brand Choice , 1979 .

[11]  Richard W. Olshavsky,et al.  Task complexity and contingent processing in decision making: A replication and extension , 1979 .

[12]  Gregory W. Fischer,et al.  UTILITY MODELS FOR MULTIPLE OBJECTIVE DECISIONS: DO THEY ACCURATELY REPRESENT HUMAN PREFERENCES?* , 1979 .

[13]  John R. Hauser,et al.  Assessment of Attribute Importances and Consumer Utility Functions: von Neumann-Morgenstern Theory Applied to Consumer Behavior , 1979 .

[14]  J. Bettman,et al.  Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis , 1980 .

[15]  Henry Montgomery,et al.  Attractiveness of decision rules , 1980 .

[16]  S G Pauker,et al.  Impact of patient preferences on the selection of therapy. , 1981, Journal of chronic diseases.

[17]  P. Schoemaker.  The Expected Utility Model: Its Variants, Purposes, Evidence and Limitations , 1982 .

[18]  Shelley R. Tapp,et al.  Choice Strategy in a Difficult Task Environment , 1982 .

[19]  John W. Payne,et al.  Contingent decision behavior. , 1982 .

[20]  D Zakay,et al.  Time pressure, training and decision effectiveness. , 1984, Ergonomics.

[21]  B. Lo,et al.  Do patients want to participate in medical decision making? , 1984, JAMA.

[22]  Dan Zakay,et al.  Post-decisional confidence and conflict experienced in a choice process , 1985 .

[23]  Jean C. Bedard,et al.  The Effects of Task Size and Similarity on the Decision Behavior of Bank Loan Officers , 1985 .

[24]  Don N. Kleinmuntz,et al.  Cognitive Heuristics and Feedback in a Dynamic Decision Environment , 1985 .

[25]  William Revelle,et al.  Individual differences in the use of simplification strategies in a complex decision-making task. , 1985 .

[26]  A. Tversky,et al.  Rational choice and the framing of decisions , 1990 .

[27]  G. Sundström.  Information search and decision making: The effects of information displays , 1987 .

[28]  G. Keinan.  Decision making under stress: scanning of alternatives under controllable and uncontrollable threats. , 1987, Journal of personality and social psychology.

[29]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[30]  Robert C. Blattberg,et al.  Database Models And Managerial Intuition: 50% Model + 50% Manager , 1990 .

[31]  B Kleinmuntz,et al.  Why we still use our heads instead of formulas: toward an integrative approach. , 1990, Psychological bulletin.

[32]  Philip E. Tetlock,et al.  An Alternative Metaphor in the Study of Judgment and Choice: People as Politicians , 1991 .

[33]  H. Mano.  Judgments under distress: Assessing the role of unpleasantness and arousal in judgment formation , 1992 .

[34]  C. Vlek,et al.  Multi-attribute decision support and complexity: An evaluation and process analysis of aided versus unaided decision making , 1992 .

[35]  Jonathan Baron,et al.  Decision-making biases in children and early adolescents: exploratory studies , 1993 .

[36]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[37]  Hardest medical choices shift to patients. , 1994, The New York times on the Web.

[38]  J B Tomblin,et al.  Clinical decision making: describing the decision rules of practicing speech-language pathologists. , 1994, Journal of speech and hearing research.

[39]  J. Mazanec,et al.  Consumer decision making. , 1994 .