Using Demographic Features for the Prediction of Basic Human Values Underlying Stakeholder Motivation

2020

IEEE Access

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This study aimed at analyzing the extent to which publicly observable pieces of information representing stakeholders' past and current choices can be utilized for the construction of motivational profiles. Motivation is operationalized by the theory of Basic Human Values, which organizes 10 values capturing distinct aspects of human motivation into a hierarchical order. The construction of motivational profiles for individual stakeholders is motivated by the need to enhance the existing decision-maker model in the Conflicting Incentives Risk Analysis (CIRA) method. This study utilized an online questionnaire to collect responses from participants (n = 331) about a wide range of habits and personal items that are easily observable in various contexts by an analyst. The validity of the set of observables as surrogate predictors of the motivational profiles is evaluated by various methods (i.e. comparison to previous results, cross-validation of models, comparison to test-retest reliability of the psychometric instrument) and techniques (calculation of prediction interval for individual profile scores). The assessment of the uncertainties associated with predicting motivational profiles is explored in detail. Additionally, an example illustrates how the profiles can be utilized for the assessment of action desirability (i.e. prediction of behavior) based on the utility calculations established in CIRA. The results contribute to an improved understanding about the accuracy with which human stakeholder motivation can be inferred from public observables and utilized within the context of information security risk analysis. INDEX TERMS Conflicting Incentives Risk Analysis, human motivation, public observables, profiling, risk analysis, stakeholder behavior.

Section: Discussionmentioning

confidence: 88%

Section: A Feature Selection and Comparison With Previous Resultsmentioning

confidence: 99%

“…[24]. Previous work has utilized the theory of Basic Human Values for operationalizing a Strategy Owner's motivation to enhance CIRA's real-world applicability [25].…”

Section: A Capturing Motivation: Theory Of Basic Human Valuesmentioning

confidence: 99%

mentioning

confidence: 99%

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Construction of Human Motivational Profiles by Observation for Risk Analysis

2020

IEEE Access

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“…For real-world applications which aim at simulating the Strategy Owner's mental model of the situation, it would be crucial to understand which framing is more active from the set of potential mental representations. The decisionmaker's value hierarchy obtained through unobtrusive measures [53], [54] may enable inferences about which cognitive representation is more active (i.e. how does the Strategy Owner actually perceive the situation?).…”

Section: A Limitations and Further Workmentioning

confidence: 99%

A Taxonomy of Situations within the Context of Risk Analysis

2019 25th Conference of Open Innovations Association (FRUCT)

2019

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Prediction of deliberate human decisions with potential negative impact on others would have great practical and scientific utility. The Conflicting Incentives Risk Analysis (CIRA) method defines risk as a result of misaligned incentives between various stakeholders. The method makes predictions based on action desirability from the perspective of the individual in the position to implement the action. Therefore, in order to assess action desirability it is necessary to characterize stakeholders and their perceptions about the situation as well. While classification systems and taxonomies related to stakeholder attributes are wellestablished, systematic classifications of situational aspects are underdeveloped in the literature. Therefore, the main objective of this paper is to present a classification of situational variables in the form of a taxonomy capturing key situational features that exert influence on decision-makers. The development of the taxonomy begins with mapping two major types of risks distinguished in the CIRA method to relevant psychological constructs. The principled, systematic development of dilemmas enabled by the taxonomy allows researchers to investigate the predictability of stakeholder behavior which may result in various types of risks. The taxonomy is extensible, thus additional concepts and variables can be included depending on the needs of the analysis and according to future developments within the fields of psychology and information security.

Predicting CEO Misbehavior from Observables: Comparative Evaluation of Two Major Personality Models

E-Business and Telecommunications

2019

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The primary purpose of this study is to demonstrate how publicly observable pieces of information can be used to build various psychological profiles that can be utilized for the prediction of behavior within a risk analysis framework 1 . In order to evaluate the feasibility of the proposed method, publicly available interview data is processed from a sample of chief executive officers (CEOs) using the IBM Watson Personality Insights service. The hypothesis-that group membership gives rise to a specific selection bias-is investigated by analyzing the IBM Watson-derived personality profiles at the aggregate level. The profiles are represented by two major theories of motivation and personality: the Basic Human Values and the Big Five models. Both theories are evaluated in terms of their utility for predicting adverse behavioral outcomes. The results show that both models are useful for identifying group-level differences between (1) the sample of CEOs and the general population, and (2) between two groups of CEOs, when a history of rule-breaking behavior is considered. The predictive performance evaluation conducted on the current sample shows that the binary logistic regression model built from the Basic Human Values outperforms the Big Five model, and that it provides a practically more useful measurement of individual differences. These results contribute to the development of a risk analysis method within the domain of information security, which addresses human-related risks.