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In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues.

In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical. Specifically, the authors use cause-and-effect diagrams to create causal networks, and leverage elicitation methods to estimate the likelihood of risk scenarios by means of computer-based simulation.

This framework enables CI practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows CI practitioners to identify critical root causes of a given problem under investigation before generating solutions.

This is the first framework that translates qualitative insights from a cause-and-effect diagram into a closed-form relationship between inputs and outputs by means of BBN models, simulation and regression.

The evaluation of competing hypotheses is an essential aspect of the audit process. The method of evaluation and re-evaluation may have implications for both efficiency and effectiveness. This paper presents the results of a field experiment using a case study set in the context of a fraud investigation in which practicing auditors were required to engage in multiple hypothesis probability estimation and revision regarding the perpetrator of the fraud. The experiment examined the effect of two different methods of facilitating multiple hypothesis probability estimation and revision consistent with the completeness and complementarity norms of probability theory as it applies to the independence versus dependence of competing hypotheses and with the prescriptions of Bayes' Theorem. The first method was to have participants use linear probability elicitation scales and receive prior tutoring in probability theory emphasizing the axioms of completeness and complementarity. The second method was to provide a graphical decision aid, without prior tutoring, to aid the participants in expressing their responses. A third condition in which participants used linear probability elicitation scales but received no tutoring in probability theory, provided a benchmark against which to assess the effects of the two treatments.

Participants receiving prior tutoring in probability theory and using linear probability elicitation scales complied in their estimations and revisions with the probability axioms of completeness and complementarity. However, they engaged in frequent violations of the normative probability model and of Bayes' Theorem. They did not distribute changes in the probability of the target hypothesis to the nontarget hypotheses, and they engaged in “eliminations and resuscitations” whereby they eliminated a suspect by assigning a zero probability to that suspect at an intermediate iteration and resuscitated that suspect by reassigning him or her a positive probability at a later iteration. The participants using the graphical decision aids, by construction, did not violate the probability axioms of completeness and complementarity. However, with no imposed constraints, the patterns of their revisions were different. When they revised the probability of the target hypothesis, they revised the probabilities of the nontarget hypotheses. They did not engage in eliminations and resuscitations. These patterns are more consistent with the norms of probability theory and with Bayes' Theorem. Possible explanations of this phenomenon are proposed and discussed, including implications for audit practice and future research.

Little is known about perceptions of medical expenditure risks despite their presumed relevance to the demand for health insurance. This is the first study to examine households’ beliefs about their future spending on health care. The study made a unique elicitation of subjective probabilities of medical expenditures from rural Ethiopians participating in a panel survey and offered the opportunity to enrol in a health insurance programme. The vast majority of respondents give logically consistent responses to the subjective probability questions. The data indicate that the cross-sectional variance of realized expenditures, which is often used to proxy risk exposure, greatly overestimate the risk faced by any single household. Consistent with the serial correlation observed in realized expenditures, expectations are positively correlated with past expenses. They are revised upward in response to an increase in realized expenditure and, to some extent, they predict expenditure incurred in the year ahead. Despite containing information on future medical expenditures, there is no evidence that expectations influence the decision to take out health insurance, although plans to insure are positively related to the perceived volatility of expenses.

These results suggest that adverse selection may not threaten the viability of voluntary health insurance. A caveat is that measurement error in the reported probabilities may weaken the test for adverse selection. Notwithstanding this limitation, measurement of household-specific distributions of future medical expenses is feasible and avoids relying on the cross-sectional variance, which provides an upwardly biased estimate of medical expenditure risk.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.

This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making.

A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations.

Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data.

This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety.

By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector.

This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.

In this paper, the authors consider how qualitative research techniques that are used in applied psychology to understand a person’s feelings and needs provides a means to elicit their security needs.

Recognizing that the codes uncovered during a grounded theory analysis of semi-structured interview data can be interpreted as policy attributes, the paper develops a grounded theory-based methodology that can be extended to elicit attribute-based access control style policies. In this methodology, user-participants are interviewed and machine learning is used to build a Bayesian network-based policy from the subsequent (grounded theory) analysis of the interview data.

Using a running example – based on a social psychology research study centered around photograph sharing – the paper demonstrates that in principle, qualitative research techniques can be used in a systematic manner to elicit security policy requirements.

While in principle qualitative research techniques can be used to elicit user requirements, the originality of this paper is a systematic methodology and its mapping into what is actionable, that is, providing a means to generate a machine-interpretable security policy at the end of the elicitation process.

The psychological literature on subjective probability estimation is reviewed to determine the feasibility of designing probabilistic information retrieval systems using such estimates. Their use has been considered by some writers, but psychological issues have not been addressed. Research pertinent to probabilistic information retrieval is examined and implications for probabilistic information retrieval are drawn. It is shown that accurate human probability estimation is possible, both in the laboratory and in real world tasks, e.g., in meteorological forecasting; but that it is also a task subject to systematic bias, or inaccuracy. Proposed techniques for debiasing are considered. The highly task‐dependent nature of such estimates is also discussed; two implications are that results from laboratory studies may have limited relevance to real world tasks and that empirical studies specific to the context of information retrieval need to be made. Human probability estimation appears to be a difficult task, but one which can be done well with proper training and use of debiasing techniques. It is premature to say how useful such estimates would be in probabilistic information retrieval, but their use should not yet be ruled out.

The purpose of this paper was to build the MACTAK methodology, which aims to transform collective tacit knowledge into the explicit one using knowledge elicitation techniques, associated to quality tools structured by systemography, represent it in a symbolic language and production rules and model it in two expert systems which can assist the investigation of defect causes during the metal packaging production process.

The method applied in the research was classified as exploratory, because a preliminary study was conducted, to better suit the mapping methodology for eliciting collective tacit knowledge, to the reality which was intended to be known. Through studies and the application of the systemography technique, a methodology for the elicitation of collective tacit knowledge has been developed. It suggests a systematic sequence of activities, to map and transform collective tacit knowledge into the explicit one, on the production process which was studied.

The types of tacit knowledge were mapped and became explicit through the application of the methodology proposed. A knowledge management system was created, as such knowledge was validated by other mechanics during their training on the shop-floor, which resulted in a structure of unique and shared knowledge. They became explicit for being stored in a knowledge base and presented to its users through the expert system. It is concluded that the methodology of acquiring collective tacit knowledge helped on the reduction of rework index by standardizing the way used to investigate the cause of the defect, in the studied company.

The MACTAK methodology was developed for exclusive use in industrial processes where the following elements are presented: process, method, environment, raw materials, labor work, measurement and machine. In this method, the detection of the problem occurs from statistical data.

The methodology began in August 2010, and in October 2011, obtained as a result a reduction in rework cost equivalent to US$17,780.95.

The methodology is unique, as it refers to the systematic use of knowledge acquisition techniques and tools of quality, and the methodology has a characteristic of direct application in manufacturing processes. The beneficiaries, in this case, are mechanicals of production and quality inspectors that work at the operation level in the company. For the organizational and tactical level, the beneficiaries are engineers of production.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.