The Effects of Process and Outcome Similarity on Users' Evaluations of Decision Aids*

Decision aids (DA) used in online shopping contexts have been shown to improve users' product choices. Given that previous research (e.g., Byrne & Griffitt, 1973 ) has demonstrated the positive effects of perceived similarity on an individual's evaluation of others, this study investigates the effects of users' perceived similarity with a DA on their evaluations of that DA. More specifically, we investigate the effect of users' perceptions of the similarity between their own decision process and that followed by the DA to arrive at a recommendation (decision process similarity), as well as the similarity between the recommendations made by the DA and users' initial choices (outcome similarity), on their evaluations of the DA's usefulness and trustworthiness. The results of this study show that perceived process similarity exerts positive and significant effects on users' perceptions of the DA's usefulness and trustworthiness. However, the effects of perceived outcome similarity on trust are completely mediated by perceived process similarity. It is also observed that the level of the user's domain knowledge moderates the effects of perceived decision process similarity on both perceived usefulness and trustworthiness. These results have implications for DA design. It is important that designers consider the process by which users make decisions for themselves and align the DA's decision process with those of the user's, especially for the novice user. The full mediation of the effect of outcome similarity on trust by process similarity highlights how a similar decision process can mitigate some of the negative effects of outcome dissimilarity.     

Perceiving freedom givers: Effects of granting decision latitude on personality and leadership perceptions

A perennial question facing managers is how much decision latitude to give their employees at work. The current research investigates how decision latitude affects employees' perceptions of managers' personalities and, in turn, their leadership effectiveness. Results from three studies using different methods (two experiments and a survey) indicate an inverted-U shaped relationship between degree of decision latitude and leadership effectiveness perceptions. The increase in leadership effectiveness perception between low and moderate decision latitude was explained by an increase in perceived agreeableness; the decrease in leadership effectiveness perception between moderate and high decision latitude was explained by a decrease in perceived conscientiousness. Theoretical and practical implications of these findings are discussed.     

How hot cognition can lead us astray: The effect of anger on strategic decision making

Anger is one of the most frequently experienced emotions. However, the extant research on the impact of anger has predominantly focused on its effect on decision making in simple decision tasks. Strategic decision making differs significantly from such tasks, as it is characterized by complexity, ambiguity, and a high information load. To better understand the impact of emotions on strategic decision making, we investigate the effect of anger on decision quality and decision speed. To do so, we carry out a strategy field experiment with 52 executives in which we use participants’ psychophysiological skin conductance response. In line with psychological research on non-strategic decisions, we find that anger negatively influences decision quality in strategic decision making. However, in contrast to predictions made by research on non-strategic decisions, we find no increase in decision speed among angry participants. We thus extend extant theory by suggesting that anger impacts the quality of strategic decisions but does not affect important process characteristics such as decision speed.     

Industry-Dominated Science Advisory Boards Are Perceived To Be Legitimate…But Only When They Recommend More Stringent Risk Management Policies

In 2017, the US Environmental Protection Agency (EPA) was criticized for two controversial directives that restricted the eligibility of academic scientists to serve on the agency's key science advisory boards (SABs). The EPA portrayed these directives as necessary to ensure the integrity of the SAB. Critics portrayed them as a tactic by the agency to advance a more industry-friendly deregulatory agenda. With this backdrop, this research examined board composition and its effect on the perceived legitimacy of risk management recommendations by the SAB. In an experiment, we presented participants with hypothetical EPA SABs composed of different proportions of academic and industry scientists. We then asked participants to rate their satisfaction with, and the legitimacy of, these boards in light of their decisions in scenarios based on actual EPA SAB deliberations. Participants perceived higher levels of satisfaction and legitimacy when SABs made more stringent risk management recommendations. While SABs dominated by industry scientists were perceived to be more strongly motivated to protect business interests, we found no effect of board composition on perceptions of satisfaction and legitimacy. These results are consistent with prior research on decision quality that suggests people use normative outcomes as a heuristic for assessing the quality of deliberations. Moreover, these results suggest that members of the public are supportive of federal SABs regardless of their composition, but only if they take actions that are consistent with normative expectations.     

When do Public Managers Imitate Each Other? Mimetic Decision Making in Contracting Decisions of Danish Municipalities

Using organizational new institutional theory, this paper explores a core mechanism underlying contracting decisions in public organizations. A central proposition of this branch of institutional theory is that uncertainty leads to organizational isomorphism. The present study investigates this proposition by asking: When does perceived uncertainty lead public managers to imitative behavior in contracting out decisions? Contrary to most previous studies, we apply an individual level approach and relate different types of perceived uncertainty of decision makers to mimetic decision making. We define mimetic decision making as when decision makers deliberately obtain information about other organizations in order to possibly imitate them. In a survey of Danish municipal managers facing important and complex contracting decisions, we test our hypotheses about three types of perceived uncertainty and mimetic decision making. The results show that technological uncertainty is strongly related to mimetic decision making among public managers. However, we do not find significant results for either volume uncertainty or performance uncertainty. The paper illustrates how uncertainty, through mimetic decision making, is connected to organizational isomorphism. It further highlights that future studies should pay attention to the multidimensionality of uncertainty and its consequences.     

A comparison of simplified value function approaches for treating uncertainty in multi-criteria decision analysis

Uncertainty is present in many decisions where an action's consequences are unknown because they depend on future events. Multi-attribute utility theory (MAUT) offers an axiomatic basis for choice, but practitioners may prefer to use simpler decision models for transparency, ease of use, or other practical reasons. We identify some ‘simplified’ models currently in use and use a simulation experiment to evaluate their ability to approximate results obtained using MAUT. Our basic message is that avoiding assessment errors in the application of a simplified model is more important than the choice of a particular type of model, but that the best performance over a range of decision problems is from a model using a small number of quantiles.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

A numerical analysis of supply chain performance under split decision rights

We examine periodic review supply chain models where order quantity and shipping frequency are both decision variables and decision-making rights are split between supply chain agents. We address two general questions: (1) when does decentralized decision making result in the greatest loss in supply chain performance; and (2) what effect does the distribution of channel power have on performance loss. We characterize optimal policies where possible in each scenario and we use numerical analysis to generate insights. We find that performance losses from decentralized control are somewhat limited in our results due to risk pooling and that the magnitude of performance loss is strongly influenced by the relative holding and penalty costs, but somewhat invariant to demand uncertainty. Furthermore, we find that concentrating channel power with the supplier can lead to supply chain profits that are very close to a centralized scenario, but also results in lower customer service levels.     

Integrating performance and risk aspects of supply chain design processes

Abstract

Supply chain design is a complex and relatively poorly structured process, involving choosing many decisional parameters and it usually requires consideration of numerous sources of uncertainty. Many conventional processes of supply chain design involve taking a deterministic approach, using point estimates, on important measures of supply chain effectiveness such as cost, quality, delivery reliability and service levels. Supply chain disruptions are often separately considered as risks, both in the research literature and in practice, meaning that a purely traditional risk management and risk minimization approach is taken. We have developed and applied an approach that combines the intellect and experience of the supply chain designer with the power of evaluation provided by a Monte Carlo simulation model, which uses decision analysis techniques to explicitly incorporate the full spectrum of uncertain quantities across the set of alternative supply chain designs being considered. After defining and setting out the general decision variables and uncertainty factors for 16 distinct supply chain design decision categories, we then apply that approach to combine the decision-makers’ heuristics with the probabilistic modeling approach, iteratively, to achieve the best of both elements of such an approach. This novel approach to fully integrating performance and risk elements of supply chain designs is then illustrated with a case study. Finally, we call for further developmental research and field work to refine this approach.     

Organizational Form,Ownership Structure and Corporate Performance: A Contextual Empirical Analysis of UK Companies1

This paper contributes to the literature on organizational form and performance (the performance-conduct paradigm) by examining the impact of two sets of variables that have not been examined previously: measures of environmental uncertainty and alternative forms of controlling separation of ownership from control. The paper first examines the extent to which contextual variables such as perceived environmental uncertainty and age of the organizational form moderate the relationship between organizational form and corporate performance. Next, the paper extends the analysis by examining the possible effects of ownership and control structures on performance. Measures suggested by agency theory, such as conventional ownership proxies (i.e. proportion of shares held by identifiable, homogenous groups), the proportion of non-executive board members and Fama and Jensen's concept of separation of decision management from decision control are used in the analysis. The results indicate that these measures have direct impact on corporate performance in their own right, though the main impact is indirect through interactions with organizational form variables.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

Two ways to handle dependent uncertainties in multi-criteria decision problems

We consider multi-criteria group decision-making problems, where the decision makers (DMs) want to identify their most preferred alternative(s) based on uncertain or inaccurate criteria measurements. In many real-life problems the uncertainties may be dependent. In this paper, we focus on multicriteria decision-making (MCDM) problems where the criteria and their uncertainties are computed using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. The simulation model determines for the criteria a joint probability distribution, which quantifies the uncertainties and their dependencies. We present and compare two methods for treating the uncertainty and dependency information within the SMAA-2 multi-criteria decision aid method. The first method applies directly the discrete sample generated by the simulation model. The second method is based on using a multivariate Gaussian distribution. We demonstrate the methods using a decision support model for a retailer operating in the deregulated European electricity market.     

Structure of Uncertainty and Decision Making: An Experimental Investigation*

Decisions in the real world usually involve imprecise information or uncertainty about the precesses by which outcomes may be determined. This research reports the results of a laboratory experiment which examined whether the structure of uncertainty, namely, both the center and the range of the probability distribution describing the uncertainty, is an important determinant of choice. Specifically, it examines how the uncertainty of audit by the Internal Revenue Service of income tax returns affects taxpayers' decisions about intentional noncompliance. The context is relevant as almost nothing is known about how taxpayers assess detection risks using the probability information they have. The study focuses on intentional noncompliance. The factors affecting it are distinct and separate from those affecting unintentional noncompliance. Other factors that affect intentional tax noncompliance, such as risk, tax rates, and penalty rates, were controlled in the experiment. It was hypothesized that the lower the mean and the lesser the range (ambiguity) of the perceived audit probability, the greater the international noncompliance. As hypothesized, the analysis indicates that both the mean and the range of the perceived audit probability rate affect intentional noncompliance, though the effect of ambiguity is greater at a relatively higher level of mean. This result suggests that the strength of the information describing an uncertain event is captured better by both the mean and the range of the uncertainty than either of those components singly.     

An Experimental Study of the Effectiveness of Three Debiasing Techniques*

Subjective probability distributions constitute an important part of the input to decision analysis and other decision aids. The long list of persistent biases associated with human judgments under uncertainy [16] suggests, however, that these biases can be translated into the elicited probabilities which, in turn, may be reflected in the output of the decision aids, potentially leading to biased decisions. This experiment studies the effectiveness of three debiasing techniques in elicitation of subjective probability distributions. It is hypothesized that the Socratic procedure [18] and the devil's advocate approach [6] [7] [31] [32] [33] [34] will increase subjective uncertainty and thus help assessors overcome a persistent bias called “overconfidence.” Mental encoding of the frequency of the observed instances into prespecified intervals, however, is expected to decrease subjective uncertainty and to help assessors better capture, mentally, the location and skewness of the observed distribution. The assessors' ratings of uncertainty confirm these hypotheses related to subjective uncertainty but three other measures based on the dispersion of the elicited subjective probability distributions do not. Possible explanations are discussed. An intriguing explanation is that debiasing may affect what some have called “second order” uncertainty. While uncertainty ratings may include this second component, the measures based on the elicited distributions relate only to “first order” uncertainty.     

基于Agent的情感劝说的决策过程及模型研究

Agent具有情感后进行的劝说决策会更加理性,而现有研究还不够全面深入。针对此问题,首先结合形式逻辑理论,定义了基于Agent的情感劝说及其决策过程,并将基于Agent的情感劝说的决策过程划分为评价情感劝说行为、更新情感劝说状态、调整情感劝说目标、产生情感劝说行为四个阶段;其次针对这四个阶段,结合OCC情感模型和PAD心情模型,运用多属性效用理论,引入情感淡化因子和情感评价因子,定义了Agent情感触发函数,建立了八种Agent基本情感与劝说目标的映射关系,将Agent的情感劝说行为分为奖励型、申辩型、威胁型和反辩型四类,分别构建了相应的决策模型,从而构成了更加完整和合理的基于Agent的情感劝说的决策过程模型;最后通过算例证明了模型的合理性和有效性。     

DECISION SUPPORT SYSTEMS FOR ILL-STRUCTURED PROBLEMS: AN EMPIRICAL STUDY

Decision support systems (DSSs) are more complex than most other traditional decision-aid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable. The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems.     

A Decision-Theoretic Valuation of Information in Sealed-Bid Auctions for Items of Known Value

This paper considers the question of how much time and effort should be spent in preparing a bid for a single item of known value sold at a first-price sealed-bid auction. A decision-theoretic approach to this bid decision summarizes the decision maker's knowledge of the competitive environment through his or her subjective probability distribution of the highest competing bid. Research activities such as collecting and analyzing bid histories are efforts to obtain additional information that reduces the uncertainty in the highest competing bid. The decision-theoretic concepts of expected value of perfect and imperfect information are used to place an economic value on such research activities. The results presented allow the decision maker to quantify the expected value of imperfect information when the uncertainty is normally distributed. The results show that additional research is most valuable prior to auctions the bidder expects to win.     

Multicriteria decision making applied to waste recycling in Brazil

Waste management, like other environmental issues, tends to be a suitable topic for problem solving using multicriteria decision-making techniques when uncertainty is involved. This paper presents two cases where the decision makers had different preferences. In the first case, social agents required an evaluation of different disposal alternatives for plastic waste. In the second case, existing construction and demolition waste recycling facilities required a performance evaluation.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.