Knowledge that Transforms

This research investigates preference uncertainty generated as a function of specific alternative characteristics during multiattribute evaluative judgments. We propose that preference uncertainty has at least two behavioral manifestations: longer judgment times and greater response error in expressed preferences. We investigate two hypotheses regarding stimulus-based causes of preference uncertainty. As predicted by our attribute conflict hypothesis, greater within-alternative conflict (discrepancy among the attributes of an evaluative alternative) led to longer judgment times and greater response error. As predicted by our attribute extremity hypothesis, greater attribute extremity (very high or low attribute values) resulted in shorter judgment times and less response error. We also found that judgment times and response errors were strongly positively correlated at the item level, consistent with our assumption that preference uncertainty generated by stimulus characteristics is manifested in judgment time and error. Finally, we found that the item-level preference uncertainty effects proposed here operate in parallel with strategy-level, effort-accuracy tradeoffs observable across participants. These findings are consistent with the RandMAU random multiattribute utility model developed in a companion article by Fischer et al. (2000).

The present research develops and tests a theoretical extension of the Technology Acceptance Model TAM that explains perceived usefulness and usage intentions in terms of social influence and cogni...

Aggregation is a pervasive theme in accounting. The preparation of financial statements involves extensive aggregation—information regarding several transactions is summarized using a few account balances. In this article, we study linear, double-entry aggregation rules. The level of aggregation (transactions versus account balance information) affects a decision maker's ability to discriminate between two entities. We show that the orientation of the discriminant function relative to the row space and the nullspace (two fundamental subspaces) of the double-entry matrix determines the information loss due to aggregation. In addition, we observe that an interdependency in account balances is introduced by the double-entry process. The cause and effect property (debit and credit) translates into a negative covariance being introduced among account balances; this, in turn, affects the decision maker's optimal use of information. Finally, in discussing benefits to aggregation, we present an example in which adopting a double-entry aggregation rule serves as a commitment device for the owner.

This study investigates the effect of knowledge distribution and group structure on performance in MBA game teams. We found that group performance was contingent on the distribution of knowledge within the group and networks of social relationships among group members. Studying 39 teams of MBA students in two management simulation games, we found that, in general, groups that had broadly distributed knowledge, i.e., groups made up of members who had general knowledge, outperformed groups that had knowledge concentrated in different members, i.e., groups made up of members who had specialized or both specialized and general knowledge. However, the advantage that the former enjoyed over the latter disappeared when groups of specialists or mixed groups had decentralized network structures.

We consider the resource-constrained project scheduling problem. The purpose of this paper is to direct the focus to a branch-and-bound concept that can, by simple adaptations, operate on a wide range of problem settings. The general approach can, e.g., deal with multimode problems, resource availability varying with time, and a wide range of objectives. Even the simple assembly line balancing problem of type-1 can be competitively approached with some modifications. Although the algorithm is the most general and simple one currently available for resource-constrained project scheduling, the computational performance can compete with the best approaches available for the single-mode problem. The algorithm uses far less memory than the state-of-the-art procedure, i.e., 256 KB versus 24 MB, for solving the standard benchmark set with projects consisting of 32 activities within comparable time. If both approaches are allowed to make limited use of memory, i.e., 256 KB, then more than 97% of the benchmark instances can be solved within fractions of the time required by the current state-of-the-art procedure. The truncated version of our algorithm achieves at 256 KB approximately the results of the truncated version of the state-of-the-art approach at 24 MB. Since in general the memory requirements exponentially grow with the number of activities the project consists of, memory will become a critical resource, and the strategy to access previously stored information will gain fundamental importance when solving larger projects.

Process industries share many characteristics because their transformation systems are designed for nondiscrete materials. Hence, the process industries typically are lumped together in a general group and contrasted from the discrete industries as a whole. The result is a poor understanding of the differences between distinct types of process industries. In this article, 19 different process industry sites are analyzed for the purpose of identifying the key differences between their transformation systems. Using cluster analysis, seven major subtypes of process industries are identified within the sample: (1) process job shop, (2) fast batch, (3) custom blending, (4) stock hybrid, (5) custom hybrid, (6) multistage continuous, and (7) rigid continuous. It is shown how these seven subtypes differ on the composite dimensions of (1) materials diversity, (2) equipment, (3) materials movement, and (4) run time. The research and managerial implications of these results are discussed.

This paper proposes a two-step method to successively elicit utility functions and decision weights under rank-dependent expected utility theory and its “more descriptive” version: cumulative prospect theory. The novelty of the method is that it is parameter-free, and thus elicits the whole individual preference functional without imposing any prior restriction. This method is used in an experimental study to elicit individual utility and probability weighting functions for monetary outcomes in the gain and loss domains. Concave utility functions are obtained for gains and convex utility functions for losses. The elicited weighting functions satisfy upper and lower subadditivity and are consistent with previous parametric estimations. The data also show that the probability weighting function for losses is more “elevated” than for gains.

This note presents two counterexamples to illustrate that neither implication of Theorem 4 in Levy (1992) is correct.

The paper explores the uniqueness properties of the subjective probabilities in two axiomatizations of state-dependent preferences. Karni, Schmeidler, and Vind's (KSV 1983) system depends on selecting an arbitrary auxiliary probability, and as such, does not guarantee the uniqueness of the derived subjective probability. However, an axiom system initially designed by Karni and Schmeidler (KS 1981) and further elaborated upon here does guarantee the desired uniqueness as well as a useful property of “stability” of the derived solution. When the preference relation displays state-independence, even the KS probabilities may not agree with those derived from the classic Anscombe-Aumann (AA 1963) theorem. However, we claim that, in this case, the KS rather than the AA probabilities are the appropriate representation of the agent's beliefs.

Tackling the steep increase in drug costs is an especially important issue among many health care providers and insurers. To entice the clinics to become more cost efficient, the U.S. federal government, as well as many HMOs, have developed various cost containment initiatives recently. However, the impact of these initiatives on the patients' well-being, the clinic's profitability, and the pharmaceutical firm's profitability has not been formally analyzed. In this paper we develop a mathematical model that is intended to examine the impact of a reimbursement policy for drug usage. Despite the simplistic structure of our model, the analysis enhances our understanding of the joint impact of the reimbursement policy on the patients, the clinic, and the pharmaceutical firm. Thus, our analysis can provide valuable information for evaluating the effectiveness of implementing such a reimbursement policy. In addition, we utilize the data gathered from a clinic to help support the assumptions and results of the underlying model.