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Article
Publication date: 11 March 2021

Abroon Qazi and Mecit Can Emre Simsekler

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss…

Abstract

Purpose

The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting.

Design/methodology/approach

The proposed “Worst Expected Bestmethod is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model.

Findings

Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation.

Originality/value

This paper introduces a new “Worst Expected Bestmethod to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Book part
Publication date: 23 October 2023

Glenn W. Harrison and J. Todd Swarthout

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset…

Abstract

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

Details

Models of Risk Preferences: Descriptive and Normative Challenges
Type: Book
ISBN: 978-1-83797-269-2

Keywords

Book part
Publication date: 11 September 2020

Bartosz Sawik

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and…

Abstract

Supply chain is an important aspect for all the companies and can affect many aspects of companies. Especially the disruption in supply chain is causing huge impacts and consequences that are difficult to deal with. This chapter presents a review of selected multiple criteria problems used in supply chain optimization. Research analyzed the multiple criteria decision-making methods to tackle the problem of supplier evaluation and selection. It also focuses on the problem of supply chain when a disruption happens and presents strategies to deal with the issue of disruptions in supply chain and how to mitigate the impact of disruptions. Prevention, response, protection, and recovery strategies are explained. Practical part is focused in the risk-averse models to minimize expected worst-case scenario by single sourcing. Computational experiments for practical examples have been solved using CPLEX solver.

Article
Publication date: 1 February 1990

Gordon Wills, Sherril H. Kennedy, John Cheese and Angela Rushton

To achieve a full understanding of the role ofmarketing from plan to profit requires a knowledgeof the basic building blocks. This textbookintroduces the key concepts in the art…

16056

Abstract

To achieve a full understanding of the role of marketing from plan to profit requires a knowledge of the basic building blocks. This textbook introduces the key concepts in the art or science of marketing to practising managers. Understanding your customers and consumers, the 4 Ps (Product, Place, Price and Promotion) provides the basic tools for effective marketing. Deploying your resources and informing your managerial decision making is dealt with in Unit VII introducing marketing intelligence, competition, budgeting and organisational issues. The logical conclusion of this effort is achieving sales and the particular techniques involved are explored in the final section.

Details

Management Decision, vol. 28 no. 2
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 6 July 2010

Timothy M. Daly, Julie Anne Lee, Geoffrey N. Soutar and Sarah Rasmi

This study aims to develop and validate a bestworst scaling (BWS) measure of preferred conflict‐handling styles, named the Conflict‐handling BWS (CHBWS).

4612

Abstract

Purpose

This study aims to develop and validate a bestworst scaling (BWS) measure of preferred conflict‐handling styles, named the Conflict‐handling BWS (CHBWS).

Design/methodology/approach

The authors conducted three studies. Study 1 consisted of a sample of psychology students (n=136) from a Canadian university and was designed to assess the convergent validity of the CHBWS by comparing it with the ROCI‐II and DUTCH instruments. Study 2 consisted of a sample of psychology students (n=154) from a US university and was designed to assess the predictive validity of the CHBWS by relating conflict‐handling styles to consumer complaint behavior. Study 3 consisted of a random sample of adults registered with an online survey company in Australia (n=204) and Germany (n=214). This study was designed to assess the antecedent relationship of Schwartz's personal values to conflict‐handling styles.

Findings

The study shows that bestworst scaling is a valid and advantageous way of measuring conflict‐handling styles. The CHBWS demonstrated both convergent and predictive validity, and was able to reproduce the structure of the dual‐concerns model. The study also showed that preferred conflict‐handling style influences the choice of complaint behavior in a retail service failure situation. Furthermore, the study demonstrated that Schwartz's personal values can influence the preferred conflict‐handling style in two individualistic cultures.

Originality/value

This is the first study to measure conflict‐handling style preferences using a BWS approach. Furthermore, it is the first study to relate consumer complaint behavior to preferred conflict‐handling style.

Details

International Journal of Conflict Management, vol. 21 no. 3
Type: Research Article
ISSN: 1044-4068

Keywords

Article
Publication date: 17 February 2021

David C. Hackman

This article introduces the best-worst scaling object case, a quantitative method of producing individual level models of heterogeneous perceptions, for use in behavioural…

Abstract

Purpose

This article introduces the best-worst scaling object case, a quantitative method of producing individual level models of heterogeneous perceptions, for use in behavioural decision making research in projects. Heterogeneous individual perceptions refer to observed or unobserved differences between individual perceptions that impact the outcome being studied. Individual level models of perceptions are important to account for the impact of heterogeneous perceptions on measurement tasks, so they do not become an unobserved source of variance that potentially biases research inferences.

Design/methodology/approach

An overview of individual heterogeneity is provided highlighting the requirement for individual level models in quantitative perception measurements. A literature review is then conducted of the quantitative methods and tasks used to measure perceptions in behavioural decision making research in projects and their potential to produce individual level models.

Findings

The existing quantitative methods cannot produce the necessary individual level models primarily due to the inability to address individual level scale effects, responses styles and biases. Therefore, individual heterogeneity in perceptions can become an unobserved source of variance that potentially biases research inferences.

Practical implications

A method new to project management research, the best-worst scaling object case, is proposed to produce individual level models of heterogeneous perceptions. Guidance on how to implement this method at the individual level is provided along with a discussion of possible future behavioural decision making research in projects.

Originality/value

This article identifies a largely unacknowledged measurement limitation of quantitative behavioural decision making research in projects and provides a practical solution: implementing the best-worst scaling object case at the individual level.

Details

International Journal of Managing Projects in Business, vol. 14 no. 5
Type: Research Article
ISSN: 1753-8378

Keywords

Article
Publication date: 20 March 2009

Simone Mueller and Cam Rungie

The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual bestworst scores to detect which attributes are…

1838

Abstract

Purpose

The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual bestworst scores to detect which attributes are determining utility components and drive distinct consumer segments.

Design/methodology/approach

First an analysis of variance and covariance is used to find attributes which are perceived to have different importance by different consumers and which jointly drive consumer segments. Then we model consumer heterogeneity with Latent Clustering and identify utility dimensions of on‐premise wine purchase behaviour with a principal component analysis.

Findings

Four consumer segments were found on the UK on‐premise market, which differ in the relative strength of five wine choice utility dimensions: ease of trial, new experience, restaurant advice, low risk food matching and cognitive choice. These segments are characterised by sociodemographics as well as wine and dine behaviour variables.

Research limitations/implications

Attributes with high variance signal respondents’ disagreement on their importance and indicate the existence of distinctive consumer segments. Attributes jointly driving those segments can be identified by a high covariance. Principal component analysis condenses a small number of behavioural drivers which allow an effective interpretation and targeting of different consumer segments.

Practical implications

This paper's analysis opens new doors for marketing research to a more insightful interpretation of bestworst data and attitude scales. This information gives marketing managers powerful advice on which attributes they have to focus in order to target different consumer segments.

Originality/value

This is the first study considering individual differences in BW scores to find post hoc segments based on revealed differences in attribute importance.

Details

International Journal of Wine Business Research, vol. 21 no. 1
Type: Research Article
ISSN: 1751-1062

Keywords

Article
Publication date: 8 June 2021

Kavitha Ranganathan

The role of personal value systems as antecedents to risk has been largely ignored. Following Gigerenzer's view of ecological rationality, the authors argue an individual's…

Abstract

Purpose

The role of personal value systems as antecedents to risk has been largely ignored. Following Gigerenzer's view of ecological rationality, the authors argue an individual's personal value system serves as concrete motivations that guide risky choices and facilitate adaptation to one's environment.

Design/methodology/approach

The authors elicit risk attitudes using a satisficing-based risk elicitation method that exploits the idea of worst-case aspiration or minimum portfolio returns given a portfolio comprising a safe and risky prospect. The elicited worst-case aspiration allows for more descriptive and natural ways of characterizing attitudes to risk (i.e. satisficing measures of risk). Using the Schwartz Value Survey, the authors assess the relative importance individuals place on value systems, such as personal focus versus social focus. The authors argue that preference to value systems has linkages with the worst-case aspiration setting emphasized in the satisficing task.

Findings

This study’s findings suggest that individuals who are willing to give up higher potential returns to protect their downside risk (by setting higher worst-case aspiration) are positively associated with personal focus—concern about own outcomes than social focus—concern about the outcomes for others or established institutions.

Research limitations/implications

Currently, the study’s setting is in the domain of financial decision-making. Going forward, milestones could be set for studying risky real-world choices by simply changing the risk measure in different contexts, such as job choices, education, health and social interactions.

Originality/value

This study contributes to the discussion on the psychometric structure of risk. Prescriptive benefits of satisficing as a positive heuristic, which is interpreted as setting achievable goals or aspiration levels, are extensive and recognized in various industries ranging from agriculture, airlines, insurance to financial advising. More recently, cognitive processes, such as emotions and personal value systems, are recognized as a type of social cognition that subserve heuristic functions that can guide behavior quickly and accurately.

Details

Management Decision, vol. 59 no. 7
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 26 March 2024

Çağla Cergibozan and İlker Gölcük

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it…

Abstract

Purpose

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

Design/methodology/approach

The paper proposes a fuzzy bestworst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

Findings

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

Originality/value

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 3 September 2019

Abhinav Kumar Sharma and Indrajit Mukherjee

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and…

Abstract

Purpose

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

Design/methodology/approach

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Findings

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

Research limitations/implications

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

Practical implications

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

Originality/value

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

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