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The purpose of this paper is to put forth maximum difference scaling to more accurately identify importance scores for customer requirements, which will also allow need‐based segments to be recognized and utilized within the QFD process.

An overview of research methods to explore customer requirements are discussed, followed by survey data analysis of customer requirements which compares and contrasts stated importance ratings to maximum difference scaling results.

The results from this study suggest that maximum difference scaling offers some advantages compared to traditional stated importance ratings, as well as other traditional methods for determining importance ratings. Providing significantly more discriminating power among customer requirements, maximum difference scaling allows researchers to have a more accurate and valid view of the relative importance of customer requirements as well as the ability to form need‐based segments.

Limitations of all research methods are discussed, including those limitations of maximum difference scaling.

The method put forth in this article provides practitioners with an improved methodology for determining the importance of customer requirements.

While maximum difference scaling has been proposed and tested in other fields, this is the first article of maximum difference scaling being applied to a QFD project.

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.

Traditional methods of capturing and determining logistics attribute importance have serious research limitations. The purpose of this paper is to introduce maximum difference (MD) scaling as a new research methodology that will improve validity in measuring logistics attribute importance, overcoming many of the limitations associated with traditional methods. In addition, this new research method will allow logistics researchers to identify meaningful need‐based segments, an important goal of logistics research.

This paper provides an overview of MD scaling along with important research advantages, limitations, and practical applications. Additionally, a detailed research process is put forth so that this technique can be implemented by logistics researchers. Finally, an application of this technique is presented to illustrate the research method.

The importance of truck driver satisfaction attributes was analyzed using bivariate correlation analysis as well as MD scaling analysis. The two sets of results are compared and contrasted. The resulting rank order of attributes is very different and MD scaling results are shown to possess important advantages. As a result of this analysis, MD scaling analysis allows for meaningful, need‐based segmentation analysis, resulting in two unique need‐based driver segments.

From a practitioner viewpoint, knowing which attributes are most important will help in investing scarce resources to improve decision making and raise a firm's ROI. Although a number of relevant applications exist, the most important may include examining: the importance of customer service attributes; the importance of logistics service quality attributes; and the importance of customer satisfaction attributes.

MD scaling is a relatively new research technique, a technique that has yet to be utilized or even explored in existing logistics and supply chain literature. Yet, evidence is mounting in other fields that suggest this technique has many important and unique advantages. This paper is the first overview, discussion, and application of this technique for logistics and supply chain management and creates a strong foundation for implementing MD scaling in future logistics and supply chain management research.

The number of studies on the use of choice cues in the purchase decision of a smartphone does not appear to be extensive, given the size and rate of growth of the market. Surprisingly, it appears that no study of this type in the Chinese context has been undertaken. Therefore, the purpose of this paper is to fill the existing gap in the marketing literature in this area.

Best–Worst (BW) scaling method was used in the study. It is suggested that the method overcomes some of the biases commonly found in surveys where Likert-type scales are used, and it has superior discriminating power, because respondents are asked to rank the most and the least important factor from a group, and are thereby forced to make tradeoffs between factors.

Among the 13 choice cues, connectivity, price and memory capacity are found to be the most important, whereas recommendation from others, ease of handling and availability of apps are found to be the least important. Findings due to gender, income and age difference were also analyzed and discussed for orderly decision-making purposes.

The ranking of factors showing what choice cues consumers consider most or least important in a particular market helps practitioners to develop appropriate adaptation strategies for the market. The comparison of findings for gender, income and age difference can further help practitioners to devise various alternative marketing strategies for different market segments and identify underserved segments, if any.

The BW scaling method, however, appropriate in ranking order of importance, had never been used in ranking choice cues of smartphone purchase. Moreover, there seems to be a dearth of studies about ranking of choice cues on smartphone purchases in the Chinese context.

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.

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.

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 study aims to identify the most important attributes that the consumer uses in the process of choosing wine, which can then be used by wine‐producing companies in marketing strategies.

The methodology consisted of a survey of 421 wine consumers using the best‐worst scaling methodology. Various consumer segmentations were made by gender, income and age groups.

The two main attributes that condition consumers in choosing wine are previous tasting and region of origin. The latter attribute is valued mainly by women and in general by consumers over 34 years old who have a net monthly family income above €1,500. The previously tasted attribute, which on many occasions is associated with the price attribute, is valued basically by men and particularly by younger consumers and those with lower incomes.

It must be pointed out that in the short term the basic strategy of wine‐producing enterprises from any given region of origin is to compete on price. However, in the long term increasing their prestige is all that remains to compete actively with the various regions of origin.

This paper contributes to a greater knowledge of Spanish consumer habits by analysing the most important wine attributes in the process of purchasing wine.

The purpose of this paper is to provide insights for flower retailers, horticultural practitioners and marketing managers into the prioritisation of cut flower attributes by German residents.

Applying a best–worst scaling approach, this analysis identified the relative ranking of importance amongst product attributes relevant to German consumers when buying fresh cut flowers. A latent class analysis determined four flower consumer segments for further study. The study builds on a sample of 978 consumers and is consistent with the most recent German census in terms of age, gender, income and federal state.

The best-worst analysis showed that intrinsic flower attributes, in particular appearance, freshness and scent were found to be more important to German consumers than the extrinsic attributes studied, namely, price, country of origin and a certification indicating fair trade. The latent class analysis determined four consumer segments that desire either budget, luxury or ethical flowers or more information about flowers. For all identified consumer segments, appearance was the attribute of greatest importance. The segments that desired luxury or ethical flowers, as well as the segment that desires more information were interested in appearance, but also had relatively large shares of preferences dedicated to flower freshness guarantees. The preference for freshness guarantees in addition to appearance may be interpreted jointly as a desire for not only beautiful and aesthetically pleasing flowers, but for sustained beauty.

Internationally, the study fills a research gap by exploring consumer’s relative preference for cut flower attributes. In contrast to existing studies on consumer preferences for flowers in Germany, the present study builds on a sample that was targeted in terms of age, gender, net household income and federal state to the most recent German census.

Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data.

The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations.

First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division.

Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD.