Consumer Willingness to Pay for Livestock Credence Attribute Claim Verification

A choice experiment was used to determine consumer value for verification of livestock production process attributes. Willingness to pay for verification of production process attributes varied for both milk and pork chops across attributes and verifying entity. Statistically significant evidence of social desirability bias was found by comparing estimates of consumer preferences solicited using direct and indirect questioning. Indirect questioning may yield more accurate representations of consumer value than direct questioning, and therefore more accurate estimates for agribusiness decision making.animal welfare, certification, consumer demand, credence attribute, social desirability bias, verification, willingness to pay, Food Consumption/Nutrition/Food Safety, Livestock Production/Industries,

Multi-criteria decision making support tools for maintenance of marine machinery systems

PhD ThesisFor ship systems to remain reliable and safe they must be effectively maintained through a sound maintenance management system. The three major elements of maintenance management systems are; risk assessment, maintenance strategy selection and maintenance task interval determination. The implementation of these elements will generally determine the level of ship system safety and reliability. Reliability Centred Maintenance (RCM) is one method that can be used to optimise maintenance management systems. However the tools used within the framework of the RCM methodology have limitations which may compromise the efficiency of RCM in achieving the desired results. This research presents the development of tools to support the RCM methodology and improve its effectiveness in marine maintenance system applications. Each of the three elements of the maintenance management system has been considered in turn. With regard to risk assessment, two Multi-Criteria Decision Making techniques (MCDM); Vlsekriterijumska Optimizacija Ikompromisno Resenje, meaning: Multi-criteria Optimization and Compromise Solution (VIKOR) and Compromise Programming (CP) have been integrated into Failure Mode and Effects Analysis (FMEA) along with a novel averaging technique which allows the use of incomplete or imprecise failure data. Three hybrid MCDM techniques have then been compared for maintenance strategy selection; an integrated Delphi-Analytical Hierarchy Process (AHP) methodology, an integrated Delphi-AHP-PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluation) methodology and an integrated Delphi-AHP-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology. Maintenance task interval determination has been implemented using a MCDM framework integrating a delay time model to determine the optimum inspection interval and using the age replacement model for the scheduled replacement tasks. A case study based on a marine Diesel engine has been developed with input from experts in the field to demonstrate the effectiveness of the proposed methodologies.Tertiary Education Trust Fund (TETFUND), a scholarship body of the Federal Republic of Nigeria for providing the fund for this research. My gratitude also goes to Federal University of Petroleum Resource, Effurun, Nigeria for giving me the opportunity to be a beneficiary of the scholarship

Analysis of Cardinal and Ordinal Assumptions in Conjoint Analysis

Of twenty-three agricultural economics conjoint analyses conducted between 1990 and 2001, seventeen used interval-rating scales, with estimation procedures varying widely. This study tests cardinality assumptions in conjoint analysis when interval-rating scales are used, and tests whether the ordered probit or two-limit tobit model is the most valid. Results indicate that cardinality assumptions are invalid, but estimates of the underlying utility scale for the two models do not differ. Thus, while the ordered probit model is theoretically more appealing, the two-limit tobit model may be more useful in practice, especially in cases with limited degrees of freedom, such as with individual-level conjoint models.ordered probit, two-limit probit, conjoint analysis, cardinality, Research Methods/ Statistical Methods,

THEORETICAL AND EMPIRICAL CONSIDERATIONS OF ELICITING PREFERENCES AND MODEL ESTIMATION IN CONJOINT ANALYSIS

The theoretical underpinnings associated with eliciting consumer preferences and statistical properties of alternative models in conjoint analysis are examined. Results show that model selection makes little difference in the context of sign and significance of coefficients. However, results show that tobit is a better predictor of ordinal ranking relative to the probit model.Demand and Price Analysis,

Prioritising Alternative Solutions to Power Generation Problems Using MCDM Techniques: Nigeria as Case Study

The engine that energizes industrialization and which invariably result to improved standard of living of nations’ citizens is electric power. Hence a steady power supply is crucial for Nigeria to achieve her aim of becoming one of the most industrialised nation in the world. However the biggest challenge in Nigeria is electricity crisis, a crisis that had been without any visible end in sight. From the literature the problems of power generation in Nigeria ranges from improper maintenance of power generation infrastructure to militant activities. Although alternative solutions are available for addressing these problems but there is difficulty in selecting the optimal solution that will yield greater power output. This paper present a Multi-Criteria Decision Making (MCDM) tool for prioritising alternatives solutions to power generation problems. The tool uses a combination of entropy technique and Multi-Attribute Utility Theory (MAUT) method. To illustrate the suitability of the technique, two examples were utilised. Results of the analysis revealed that Reliability Centered Maintenance (RCM) and diplomatic approach are the optimal solutions for resolving problem of improper maintenance and militant activities respectively. The proposed tool will assist Government or electric power managers to use optimal solutions in solving power generation problems in order to maximise power plant output and consistently ameliorate power crisis.Â

Dominance measuring methods for the selection of cleaning services in a European underground transportation company

Dominance measuring methods are a recent approach for dealing with complex decisionmaking problems with imprecise, incomplete or partial information within multi-attribute value/utility theory. These methods compute pairwise dominance values and exploit the information included in the dominance matrix in different ways to derive measures of dominance intensity to rank the alternatives under consideration. We review dominance measuring methods proposed in the literature, describing how their possible drawbacks have been progressively overcome, and comparing their performance with other existing approaches, like surrogate weighting methods, the adaptation of classical decision rules to encompass an imprecise decision context, SMAA or Sarabando and Dias’ method. An example of the selection of cleaning services in a European underground transportation company is used to illustrate dominance measuring methods in a real complex decision-making problem

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl