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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.

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.

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 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 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.

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 explore differences in wine preferences between Generation Y and older cohorts in the USA.

A total of 260 US consumers participated in a web‐based survey that took place in April 2010. The best‐worst scaling method was applied measuring the level of importance given by participants to a list of most common attributes used in choice of wine. Independent sample t‐tests were applied to compare the best‐worst scores between Generation Y and older cohorts.

Differences were found in the level of importance that Generation Y gives to wine attributes in comparison to older cohorts. Generation Y was found to attach more importance to attributes such as “Someone recommended it”, “Attractive front label” and “Promotional display in‐store”, whereas older cohorts gave more importance to attributes such as “I read about it” and “Grape variety”. This suggests that Generation Y preferences for wine are driven by marketing added‐value activities such as promotions and labelling, whereas limited importance is given to information about wine, reflecting lack of subjective knowledge, experience and involvement about wine.

This research adds to generation‐based research in wine marketing and contributes towards a better understanding of the differences between generation cohorts in relation to their preferences towards wines.

This study is among the first to compare wine preferences of Generation Y with older cohorts using the best‐worst scaling method.

The production process of processed food products may involve several countries. This multi-dimensionality of the country of origin (COO) may affect consumer preferences for the products. We apply Case 2 best–worst scaling to measure Japanese consumers’ preferences for three dimensions of the COO of a vegetable juice product.

The three dimensions of the COO include these: the country where the raw materials of the product were grown (the country of growing), the country where the raw materials were processed (the country of processing) and the country where the food company producing the product is headquartered (the country of the company). Japan, Australia, Thailand and China are the countries considered for the three COO-related attributes. Sixteen juice products (profiles) were created from the three four-level attributes. A survey queried 416 consumers to select the best and worst ones from among the three attribute levels shown in each profile.

The average utility of the country of growing is the highest among those of the three COO-related attributes. However, consumers evaluate the country of growing as the least preferred among the three attributes with respect to a country with a negative food quality reputation.

This is the first Case 2 best–worst scaling study to measure consumer preferences for the three dimensions of the COO of processed food products. It suggests marketing strategies for domestic and international juice companies.

The purpose of this paper is to review and extend previous work involving branding and website performance and aesthetics by applying a best-worst scaling (BWS) approach. Respondents were tasked with choosing the best and worst from 16-brand attribute statements representing website communications and aesthetics, a more effective understanding of consumer segmentation is revealed between the offline facility to the online retail experience.

A research design utilising BWS was deployed to provide an alternate perspective of consumer choice in an omni-channel retail environment.

Findings indicate a bifurcated audience with one-half reliant on communication and the other half on aesthetics is most important in the online environment when generating a positive relationship towards the retail brand.

This study was conducted within a single retail format category (sportswear and apparel) so future studies need to estimate cross-category implications. The authors also suggest online experimental designs for consumers to compare traditional retail brand images. Finally as this study utilised Australian responses only the authors suggest cross-cultural comparisons to better understand the universal nature of website functionality and its link with traditional retail formats.

Retail executives will find the results from this study useful in two ways. To ensure consistency of brand image across channels managers must communicate efficiently and effectively. Brand managers must mediate between customer segments with some preferring clear concise website communications and others well established aesthetics.

This study adopts a BWS approach to better understand consumer choices in an online/offline branding environment.

This study aims to use product attributes and retail display information to develop cross‐national segments.

The study uses consumers' choice in wine stores to develop segments of consumers, based on the relative importance of 13 factors consumers use for the retail purchase of wine. Data are collected from 11 countries, using seven languages on five continents. Best worst scaling (BWS) and latent class analysis were used to develop the clusters.

A three‐cluster structure provides a straightforward and robust segmentation across the 11 countries. This model fits better than one based on 11 single country segments. The three segments reveal different ways in which consumers choose wines: cognitive‐based, assurance‐based, and in‐store promotion‐based.

The samples are convenience‐based and do not represent the population of wine drinkers in each country. Choice criteria, including retail communications, can be used to develop useful and robust segments cross‐nationally.

The three segments found in this study provide clear guidelines for wine marketers depending on whether they work for small or large wine companies. The use of choice attributes and BWS show the utility of this method in cross‐national research.

This research demonstrates that product attributes and retail communication devices like labels and displays can be used for cross‐national segmentation. Applying BWS and Latent Class Clustering to choice criteria leads to clear, usable, and robust segmentation across a wide range of cultures and product use histories.