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The purpose of this paper is to present a new method about dynamic decision problems with three-parameter grey numbers from other angle of view which not only aggregates the attribute values of alternatives of all the periods, but also excavates changes of attribute values about alternatives between the adjacent periods.

The authors adopt grey target method to calculate the distance between every alternative and the best, worst bull’s eye, the distance between change series and the best, worst change bull’s eye, then both distances can be aggregated to reflect information about two aspects.

This dynamic decision-making method not only aggregates the existing state of alternatives all of the stages, but also excavates the change information from vertical and horizontal direction, the decision result conforming to decision maker’s psychological behavior is obtained though adjusting the priority parameter.

The paper considers on change of alternative’s attribute values from one period to the next period, and the dynamic characteristic has been reflected adequately. The grey target decision-making method reflects the distance between alternative and bull’s eye, the comprehensive target distance between alternative and positive, worst bull’s eye about change series are separately provided. And the final target distance reflecting both existing state and change trend is constructed.

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

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.

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.

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.

This paper's analysis opens new doors for marketing research to a more insightful interpretation of best‐worst 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.

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

This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage.

This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model.

A total of 20 challenges (CH's) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area.

Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time.

To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.

Most marketing researchers use rating scales to understand consumer preferences. These have a range of problems, which can be greatly ameliorated by the use of a new technique, best‐worst scaling (BWS). The purpose of this paper is to demonstrate the BWS method by an empirical example, which demonstrates the steps to design and analyze a BW study.

A brief critique of ratings and rankings is presented. Then the basic concept of BWS is described, followed by how to use the BW method to explore how Australian and Israeli consumers choose wine in a retail store. The paper demonstrates the design of the questionnaire as well as the steps to analyze and present the results.

The BWS approach can be easily implemented for research in wine business especially for multicultural comparisons as it avoids scale confounds. After transformation of the best and worst scores of each respondent for each attribute, the data can be analyzed directly using various statistical methods and can be expressed as choice probabilities.

The advantage of BWS is its ability to compare attributes using B−W and B/W scores. The BW method provides a better discrimination of the attributes analyzed.

The simplicity of the analysis and graphical presentation makes a significant contribution to practitioners as the B−W counts and probabilities of attributes are easy to obtain and understand.

This paper presents BWS method in a form that researchers and practitioners can use and adopt for research and market surveys. The paper presents an empirical example using BWS method to determine the importance of wine cues while consumers are choosing wine in a retail store.

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.

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.

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.

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.

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.

The purpose of this paper is to investigate Spanish consumer preferences for several food-labelling schemes on semi-cured, pasteurised sheep milk cheese. In particular, the authors used three labels regulated by the European Union regulation (European organic logo, protected denomination of origin (PDO) and nutritional fat content), and the remaining four have been introduced to the European food market by private initiatives (local, carbon footprint, food miles and animal welfare).

A Best-Worst Discrete Choice approach was applied in Spain during Fall 2011 by administrating a survey to 549 consumers.

The results suggest that the most valued labels are the PDO, followed by the organic logo and the nutritional panel. The least valued are food-miles labelling and carbon foodprint labels, while local-origin labels and animal welfare are in the middle position.

This study is the first to value consumer preferences for cheese products bearing several public and private European food-labelling schemes since literature on consumer preferences for food labels has only dealt with a comparison of a few (two or at most three) food-labelling schemes. In addition, the added value of this paper is also the use of the BWC approach that has the advantage of providing the best way to discriminate the degree of importance given by respondents to each food labels by overcoming the problem of bias caused by differences in the use of rating scales.

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

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.

The study shows that best‐worst 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.

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.

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.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

The purpose of this paper is to investigate whether mutual fund investors can make effective cash flow timing decisions and examine the sensitivity of these decisions to past fund performance using cash flow data at the individual fund level.

This study examines performance persistence and investor timing ability of 200 domestic equity mutual funds in Taiwan between 1996 and 2009. In particular, a performance gap measuring the difference between dollar‐weighted average monthly returns and geometric average monthly returns is used to evaluate investors' timing ability.

The empirical results show that funds that have performed well (poorly) in the previous year tend to continue performing well (poorly) in the following year, and investors' timing performance is negatively related to fund performance. The results also show that investors' timing performance is significantly and negatively related to fund size, length of fund history, and momentum‐style of funds, but positively related to value‐style funds. These results suggest that mutual fund investors are loss‐averse and demonstrate return‐chasing behavior in well‐performing funds.

The paper contributes to the mutual fund performance literature by proposing an integrated framework that jointly tests fund performance and how it affects investors' cash flow timing decisions. Furthermore, the paper individually measures investors' timing sensitivity for the current best (worst) performance funds and consecutive two‐year best (worst) performance funds, and contributes to a growing body of research on the behavior of mutual fund investors.