Formal Infrastructure and Ethical Decision Making: An Empirical Investigation and Implications for Supply Management

Building on Rest's (1986) conceptual model of ethical decision making, we derive and empirically test a model that links an organization's formal ethical infrastructure to individuals’ moral awareness of ethical situations, moral judgment, and moral intention. We contribute to the literature by shedding light on the importance of a multifaceted formal ethical infrastructure—consisting of formal communication, recurrent communication, formal surveillance, and formal sanctions—as a crucial antecedent of moral awareness. In so doing, we discern how these four elements of a formal ethical infrastructure combine to collectively influence moral awareness based on a second‐order factor structure using structural equation modeling. We test our model based on survey data from 805 respondents with significant work experience across three separate ethical scenarios. Our results across the three scenarios provide overall support for our model. We found that a second‐order factor structure for the formal ethical infrastructure explains the variance among the four infrastructure elements and that a multifaceted formal ethical infrastructure significantly increases moral awareness. Our results further suggest a strong positive effect of moral awareness on moral judgment, which in turn was found to have a positive impact on moral intention. These results were substantiated when taking several individual and contextual control variables into account, such as gender, age, religiosity, work satisfaction, and a de facto ethical climate. Implications for theory, practice, and supply management are discussed.     

The Impact of Ethical Tools on Aggressiveness in Financial Reporting

The proposed adoption of International Financial Reporting Standards (IFRS) in the United States has ignited a debate as to whether the principles‐based nature of these standards better serves the interests of investors. While it is argued that these principled‐based standards will encourage more transparent financial reporting than the current rules‐based U.S. standards, critics argue that IFRS will invite more aggressive financial reporting through the liberal exercising of professional judgment. This empirical study aims to understand what individual and organizational factors may affect aggressiveness when making accounting judgments. In particular, we examine the influence that prior ethics training, codes of ethics and an individual's predominant moral reasoning schema have on adherence to company policy in an accounting‐related (depreciation) judgment. Results of the study show that respondents with prior ethics training are more likely to adhere to company accounting policy than those who have not had formal ethics education. Respondents presented with a company ethics code also were less aggressive in their accounting judgments than those who were not presented with a code prior to reading the scenario. Finally, decision aggressiveness was moderated by individuals who used conventional moral reasoning schemas.     

Equity in Social Risk: Some Empirical Observations

In societal risk analysis the equity of the distribution of risks is often an important consideration owing to the special nature of health risks. We empirically validate some assumptions about equity that have been discussed in the decision analytic literature. Our results show that the way fatalities are distributed throughout a society is considered along with the number of fatalities in evaluating alternative policies involving mortality risks. The concepts of ex ante equity and ex post equity are both shown to be important in judgments of fairness. We next present a decision model based on multiattribute preference theory incorporating the number of fatalities, as well as ex ante equity and ex post equity. When ex ante equity and ex post equity are positively weighted in this fair-risk model , options with more equal risk distributions are ranked higher. Next we empirically show that the distribution of benefits has an impact on judgments of fairness. The fair-risk model does not include information on the benefits distribution, so it would apply when benefits are distributed equally or when the decision maker wishes to not include benefits in the model. We briefly discuss how the notion of proportional equity can incorporate benefits into judgments of the fairness of risk distributions. We then include benefits in a more general model in which fair risk-benefit combinations are those that are exchange equitable. A key implication of this envy-free risk–benefit model is that an unequal distribution of risks may be preferred if it is accompanied by a compensatory differential in benefits consistent with peoples' preference tradeoffs between received benefits and assumed risks. Finally, we discuss how perceived deservedness may influence judgments about equity. We conclude with a discussion of the implications of research on alternative notions of equity for policy makers dealing with social risks.     

Ethically adrift: How others pull our moral compass from true North,and how we can fix it

This chapter focuses on the social nature of morality. Using the metaphor of the moral compass to describe individuals’ inner sense of right and wrong, we offer a framework that identifies social reasons why our moral compasses can come under others’ control, leading even good people to cross ethical boundaries. Departing from prior work on how individuals’ cognitive limitations explain unethical behavior, we focus on socio-psychological processes that facilitate moral neglect, moral justification, and immoral action, all of which undermine moral behavior. In addition, we describe organizational factors that exacerbate the detrimental effects of each facilitator. We conclude by advising organizational scholars to take a more integrative approach to developing and evaluating theory about unethical behavior and by suggesting further study of interventions that might disempower these social triggers of unethical behavior, allowing us to regain control of our moral compasses.

A solitary organism has no need for moral rules, nor does a creature living among others without mutual dependency. (Høgh-Oleson, 2010, p. 3).

     

Dependence Assessment in Human Reliability Analysis Using Evidence Theory and AHP

Dependence assessment among human errors in human reliability analysis (HRA) is an important issue. Many of the dependence assessment methods in HRA rely heavily on the expert's opinion, thus are subjective and may sometimes cause inconsistency. In this article, we propose a computational model based on the Dempster‐Shafer evidence theory (DSET) and the analytic hierarchy process (AHP) method to handle dependence in HRA. First, dependence influencing factors among human tasks are identified and the weights of the factors are determined by experts using the AHP method. Second, judgment on each factor is given by the analyst referring to anchors and linguistic labels. Third, the judgments are represented as basic belief assignments (BBAs) and are integrated into a fused BBA by weighted average combination in DSET. Finally, the CHEP is calculated based on the fused BBA. The proposed model can deal with ambiguity and the degree of confidence in the judgments, and is able to reduce the subjectivity and improve the consistency in the evaluation process.     

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.     

Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments

Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan‐Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January–March 2009.     

A fuzzy AHP application in government-sponsored R&D project selection

Due to the funding scale and complexity of technology, the selection of government sponsored technology development projects can be viewed as a multiple-attribute decision that is normally made by a review committee with experts from academia, industry, and the government. In this paper, we present a fuzzy analytic hierarchy process method and utilize crisp judgment matrix to evaluate subjective expert judgments made by the technical committee of the Industrial Technology Development Program in Taiwan. Our results indicate that the scientific and technological merit is the most important evaluation criterion considered in overall technical committees. We demonstrate how the relative importance of the evaluation criteria changes under various risk environments via simulation.     

Sizing Up a Superstorm: Exploring the Role of Recalled Experience and Attribution of Responsibility in Judgments of Future Hurricane Risk

Research suggests that hurricane‐related risk perception is a critical predictor of behavioral response, such as evacuation. Less is known, however, about the precursors of these subjective risk judgments, especially when time has elapsed from a focal event. Drawing broadly from the risk communication, social psychology, and natural hazards literature, and specifically from concepts adapted from the risk information seeking and processing model and the protective action decision model, we examine how individuals’ distant recollections, including attribution of responsibility for the effects of a storm, attitude toward relevant information, and past hurricane experience, relate to risk judgment for a future, similar event. The present study reports on a survey involving U.S. residents in Connecticut, New Jersey, and New York (n = 619) impacted by Hurricane Sandy. While some results confirm past findings, such as that hurricane experience increases risk judgment, others suggest additional complexity, such as how various types of experience (e.g., having evacuated vs. having experienced losses) may heighten or attenuate individual‐level judgments of responsibility. We suggest avenues for future research, as well as implications for federal agencies involved in severe weather/natural hazard forecasting and communication with public audiences.     

Exemplifying “Us”: Integrating social identity theory of leadership with cognitive models of categorization

Identity leadership theorizing suggests that leadership effectiveness derives from a potential leader’s perceived ability to create, embody, promote, and embed a shared group identity. However, little is known about how people integrate this information to form a judgment of a leader. We use cognitive modeling to operationalize leadership judgments as exemplar-and prototype-based categorization processes. Analysis of attribute rating data for 80 highly recognizable Americans revealed that leadership judgments were well-characterized by an exemplar-based model. Judgments were based overwhelmingly on promoting shared collective interests and embedding group identity. The pattern of attribute weightings was consistent for judgments of a general leadership role (i.e., as a competent leader) as well as judgments for a specific leadership role (i.e., as an effective US president). We discuss the implications of these findings for our understanding of identity leadership as well as for integrated social-cognitive models of individuals’ judgements of and responses to leaders.     

Approach,Ability, Aftermath: A Psychological Process Framework of Unethical Behavior at Work

Abstract

Many of the scandalous organizational practices to have come to light in the last decade—rigging LIBOR, misselling payment protection insurance, rampant Wall Street insider trading, large-scale bribery of foreign officials, and the packaging and sale of toxic securities to naïve investors—require ethically problematic judgments and behaviors. However, dominant models of workplace unethical behavior fail to account for what we have learned from moral psychology and cognitive neuroscience in the past two decades about how and why people make the moral decisions they do. In this review, we explain how intuition, affect, physiology, and identity support and inform more deliberative reasoning process in the construction and enactment of moral behavior. We then describe how these processes play into how individuals approach a potential moral choice, whether they have the ability in the moment to enact it, and how it is encoded in the action' aftermath, feeding back into future approaches. Throughout, we attend to the role of organizational context in influencing these processes. By reviewing this large body of research and presenting a new framework that attempts to integrate these new findings, our hope is to motivate new research about how to support more moral workplace behavior that starts from what we know now.     

A reconceptualization of authentic leadership: Leader legitimation via follower-centered assessment of the moral dimension

We explore some challenges that face authentic leadership scholarship including problems related to how the construct is understood and measured. We present a model of authentic leadership that looks at it, not as a leadership style, but as an outcome of a legitimation process. Authentic leadership represents legitimated follower perceptions of a leader's authenticity which are activated by moral judgments. We explain how a follower-centered assessment of the moral component helps explain leadership dynamics in situations involving ethical relativism, thus alleviating concerns regarding the presumed moral component of the construct. The overlap between leaders' and followers' value systems leads to impressions of authenticity, even in cases in which there are no clear universal moral standards. An authentic person's behavior cannot be labeled as “leadership” unless it is embraced by a follower who grants moral legitimacy to the leader. We then discuss the implications of our study for scholars and practitioners.     

The many (distinctive) faces of leadership: Inferring leadership domain from facial appearance

Previous research has shown that people form impressions of potential leaders from their faces and that certain facial features predict success in reaching prestigious leadership positions. However, much less is known about the accuracy or meta-accuracy of face-based leadership inferences. Here we examine a simple, but important, question: Can leadership domain be inferred from faces? We find that human judges can identify business, military, and sports leaders (but not political leaders) from their faces with above-chance accuracy. However, people are surprisingly bad at evaluating their own performance on this judgment task: We find no relationship between how well judges think they performed and their actual accuracy levels. In a follow-up study, we identify several basic dimensions of evaluation that correlate with face-based judgments of leadership domain, as well as those that predict actual leadership domain. We discuss the implications of our results for leadership perception and selection.     

THIRD-PARTY REACTIONS TO EMPLOYEE (MIS)TREATMENT: A JUSTICE PERSPECTIVE

To date, theory and research on organizational justice has tended to focus on the victim’s (i.e. the employee’s) perspective; the third party’s perspective has received relatively little systematic attention. In this chapter we develop a model describing how third parties make fairness judgments about an employee’s (mis)treatment by an organization or its agents (including supervisors and peers). Our model also identifies factors that can predict whether third parties will act on their unfairness perceptions. We identify several distinctions between the victim’s and third party’s perspectives. We conclude by explaining how the third party’s perspective offers numerous opportunities and challenges for research.     

Employee Misuse of Information Technology Resources: Testing a Contemporary Deterrence Model

Recent research in information systems and operations management has considered the positive impacts of information technology (IT). However, an undesirable side effect of firms’ increasing reliance on IT to support the distribution and delivery of goods and services to customers is a greater exposure to a diverse set of IT security risks. One such risk is intentional employee misuse of technology resources. In this article, we draw upon modern deterrence frameworks to develop a predictive model of technology misuse intention that incorporates formal and informal sanctions as well as employment context factors. The model specifies previously untested relationships between formal and informal sanctions, thereby providing fresh insight into the role of sanctions in deterring technology misuse in organizations. Our results suggest that a predisposition toward the need for social approval and moral beliefs regarding the behavior are key determinants of technology misuse. Contrary to criminological research that has questioned the relative importance of formal sanctions in the deterrence process, we also found that the threat of formal sanctions has both direct and indirect influences on technology misuse intention. Further, from an employment context standpoint, employees who spend more working days away from the office (i.e., “virtual” mode) appear more inclined to misuse their organization's technology resources. The findings have implications for the research and practice of technology management.     

The Aggregation of Expert Judgment: Do Good Things Come to Those Who Weight?

Good policy making should be based on available scientific knowledge. Sometimes this knowledge is well established through research, but often scientists must simply express their judgment, and this is particularly so in risk scenarios that are characterized by high levels of uncertainty. Usually in such cases, the opinions of several experts will be sought in order to pool knowledge and reduce error, raising the question of whether individual expert judgments should be given different weights. We argue—against the commonly advocated “classical method”—that no significant benefits are likely to accrue from unequal weighting in mathematical aggregation. Our argument hinges on the difficulty of constructing reliable and valid measures of substantive expertise upon which to base weights. Practical problems associated with attempts to evaluate experts are also addressed. While our discussion focuses on one specific weighting scheme that is currently gaining in popularity for expert knowledge elicitation, our general thesis applies to externally imposed unequal weighting schemes more generally.     

Charismatic leadership: Eliciting and channeling follower emotions

Research on charismatic leadership has been criticized for the ambiguity of its central construct. Attempts to define and measure charisma have frequently treated it as a complex construct consisting of multiple components. However, little work has been done to develop a theoretical model that offers a parsimonious rationale explaining why certain leadership attributes are considered “charismatic” while others are not, or how these attributes combine to produce charismatic effects. Addressing these issues, we present a model that situates emotion as the primary variable in the charismatic process. We use recent research on the moral emotions to frame a theory of followership-relevant emotions (FREs) that describes how leaders use emotions such as compassion, admiration, and anger to compel their followers to act. We then discuss the Elicit-Channel (EC) model of charismatic leadership, positing that the charismatic relationship is a five-step, cyclical process. In the EC model, leaders elicit highly motivating emotions from their followers and then channel those emotions to produce action that, if successful, results in outcomes such as positive affect and trust. These outcomes then enable the leader to continue the cycle, eliciting emotion once more. We conclude by offering a research agenda, addressing potential methodological concerns, and discussing future directions.     

Representations as Decision Aids: The Asymmetric Effects of Words and Numbers on Auditors' Inherent Risk Judgments*

Decision aids often change the representations used to express risk cue information and communicate risk judgments. In this study, we hypothesize that numbers and words may have asymmetric effects when used to represent risk cue information as opposed to when used to communicate risk judgments. Specifically, decision makers' agreement on cue weights and judgment consensus may be higher with cue information stated in words rather than numbers, since appropriately chosen words can more directly convey the risk implications of information cues. In contrast, decision makers' consistency in applying their judgment policies and judgment consensus may be higher with risk judgments communicated in numbers instead of words, because the greater precision available with numbers allows finer discriminations of risk. To test our hypotheses, we conducted an experiment in which experienced financial auditors made inherent risk judgments for a hypothetical manufacturing client. Cue and response representations were manipulated between numbers and words. The results generally support our arguments. In addition, representing cue information in words increased the time required to acquire cue information relative to numeric representation. In contrast, requiring auditors to express their risk judgments in numbers increased the time required to express risk assessments relative to     

Combining Experts’ Judgments: Comparison of Algorithmic Methods Using Synthetic Data

Expert judgment (or expert elicitation) is a formal process for eliciting judgments from subject‐matter experts about the value of a decision‐relevant quantity. Judgments in the form of subjective probability distributions are obtained from several experts, raising the question how best to combine information from multiple experts. A number of algorithmic approaches have been proposed, of which the most commonly employed is the equal‐weight combination (the average of the experts’ distributions). We evaluate the properties of five combination methods (equal‐weight, best‐expert, performance, frequentist, and copula) using simulated expert‐judgment data for which we know the process generating the experts’ distributions. We examine cases in which two well‐calibrated experts are of equal or unequal quality and their judgments are independent, positively or negatively dependent. In this setting, the copula, frequentist, and best‐expert approaches perform better and the equal‐weight combination method performs worse than the alternative approaches.     

Stochastic Judgments in the AHP: The Measurement of Rank Reversal Probabilities

This paper presents a methodology for analyzing Analytic Hierarchy Process (AHP) rankings if the pairwise preference judgments are uncertain (stochastic). If the relative preference statements are represented by judgment intervals, rather than single values, then the rankings resulting from a traditional (deterministic) AHP analysis based on single judgment values may be reversed, and therefore incorrect. In the presence of stochastic judgments, the traditional AHP rankings may be stable or unstable, depending on the nature of the uncertainty. We develop multivariate statistical techniques to obtain both point estimates and confidence intervals of the rank reversal probabilities, and show how simulation experiments can be used as an effective and accurate tool for analyzing the stability of the preference rankings under uncertainty. If the rank reversal probability is low, then the rankings are stable and the decision maker can be confident that the AHP ranking is correct. However, if the likelihood of rank reversal is high, then the decision maker should interpret the AHP rankings cautiously, as there is a subtantial probability that these rankings are incorrect. High rank reversal probabilities indicate a need for exploring alternative problem formulations and methods of analysis. The information about the extent to which the ranking of the alternatives is sensitive to the stochastic nature of the pairwise judgments should be valuable information into the decision-making process, much like variability and confidence intervals are crucial tools for statistical inference. We provide simulation experiments and numerical examples to evaluate our method. Our analysis of rank reversal due to stochastic judgments is not related to previous research on rank reversal that focuses on mathematical properties inherent to the AHP methodology, for instance, the occurrence of rank reversal if a new alternative is added or an existing one is deleted.