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The purpose of this paper is to measure students’ satisfaction with the didactics in a large Italian university, that of Padua, giving special attention to its evolution over time in consecutive academic years. The overall level of the quality of the didactics is examined and its change over time is modeled. Moreover, the effect of courses’ and teachers’ variables on it is estimated.

Latent cluster class models and mixture latent class Markov models are estimated in order to identify groups of courses that are homogeneous for the level of the quality of the didactics. Evolution over the three academic years of satisfaction is monitored. The effect on the clustering and its dynamics of potential covariates is also examined.

Results of model estimation reveal some interesting evidences that are important indications for the university management to define targeted strategies to elevate teaching quality.

The paper gives its original contribution both on the side of methods applied to analyze data collected with students evaluation of teaching and on the evidences obtained for a large university.

The purpose of this paper is to explore key attributes of organic rice that consumers use in the process of choosing organic rice, and to segment organic rice market in Bangkok. Moreover, the study tends to identify the best clustering techniques, between latent class cluster analysis (LCCA) and traditional cluster analysis (CA), for precise segmentation.

Best–worst scaling (BWS) method was applied to measure the level of relative importance of organic rice attributes. Then, LCCA and CA techniques were applied to recognize market segmentation. Finally, homogeneity and heterogeneity of the resulting clusters were determined to compare performance of the two clustering techniques.

The LCCA technique was identified better than the CA in classification of consumers. According to LCCA solution, the organic rice market in Bangkok (Thailand) consisted of six distinct clusters, which can be grouped into three categories based on consumers’ profile. Organic rice consumer categories were identified as “Art of eating” and “Superior quality seeker” clusters focusing on special features and quality of the organic rice; consumer category “Basic concern” cluster heavily relied on organic certification logo and manufacturing information; and other consumer categories were “Price driven,” “Eyes on price” and “Thorough explorer” clusters.

This study first applies BWS score to examine consumers’ preference for organic rice attributes and segments market, providing results for practical use for retailers, producers and marketers.

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.

The purpose of this paper is to identify the internationalisation patterns of small‐ and medium‐sized enterprises (SMEs) quantitatively, to describe SMEs as they follow different patterns over time and to discuss the determinants of these patterns through empirical study.

The authors conducted a questionnaire survey among mature German SMEs (n=674). To identify internationalisation patterns, a latent class clustering approach was applied. Because of the large sample, a multinomial logistic regression analysis could be used to analyse the factors influencing these patterns.

The authors empirically find three internationalisation patterns: traditionals, born globals and born‐again globals. Comparing modern SMEs with the same SMEs from ten years ago, it was found that firms may change their patterns. Moreover, the patterns are determined by international orientation, growth orientation, communication capability, intelligence generation capability and marketing‐mix standardisation.

Combining elements of the Uppsala model (countries and operation modes) and born global research (time lag and foreign sales ratio), three internationalisation patterns of established international SMEs from traditional sectors were identified empirically. Because of the multidimensional nature of internationalisation, the patterns may change over time. Different firm‐level factors determine the internationalisation patterns.

Instead of applying “arbitrary” thresholds, the paper provides a quantitative approach to identifying internationalisation patterns. These patterns confirm the three main internationalisation pathways discussed in the international marketing literature. The paper further advances the field by describing the patterns, showing evidence that the patterns may cross over time and providing information on the factors that influence the patterns.

In Italy, the craft beer (CB) market has undergone a trend of exponential growth in recent years, showing, at the same time, differences among different geographical areas. This research aimed to define the consumer preferences towards different CB attributes by involving a sample of individuals from Piedmont (from North-West Italy). Furthermore, the experimentation was designed to distinguish heterogeneous individuals' consumption profiles each characterised by different CB preferences, drinking habits and socio-demographic characteristics.

The exploration of individuals' preferences towards 12 CB quality attributes was made throughout a choice experiment based on the Best-Worst Scaling (BWS) methodology approach. In addition, the BWS results were employed in the latent class analysis to identify the best sample segmentation in relation to attributes preferences.

The “Brand knowledge”, “I have already tried it” were the most important attributes for CB choice. On the contrary, the “Type of packaging” and “Price” were the least important for CB choice. The “Loyal”, “Attentive to quality composition” and “Territorial brand” clusters were defined in function of CB consumers preferences and described in terms of individuals consumption habits and socio-demographic characteristics.

The BWS methodology allowed the definition of a preference index for each selected CB attributes. These indications could have concrete importance on production and marketing choices in an increasingly extended and globalised market, also at large-scale distribution level. Furthermore, the definition of different consumption profiles allowed to highlight the heterogeneity of consumption (preferences and habits) towards CB.

Although trade shows are a significant part of the B2B communications mix, academic research in the area is sparse. To successfully manage this medium, a careful understanding of attendee behavior on the trade show floor is necessary. Drawing from the rich literature on shopper typologies in retailing (which parallels the trade show atmosphere), this paper sets out to develop a set of attendee metrics that show organizers can track regularly.

Through latent class clustering on unique attendee‐level data from a popular computer trade show, five segments of attendee activity are uncovered that differ along dimensions such as the attendee's involvement and focus and the exhibitor's booth size, booth accessibility, and product display.

Significant heterogeneity is found in attendee activities on the show floor. There are interesting similarities and differences between the retail and B2B shopper. Implications for trade show organizers and exhibitors are discussed and directions for future research suggested.

Since the data employed are becoming more readily available, the hope is that managers and academic researchers might find the suggested metrics and segmentation approach useful in advancing a deeper understanding of the trade show attendee.

Consumer values increasingly favor sustainable development in products and services, thereby fostering the need to develop new operational and managerial practices that support sustainability in supply chain management. The purpose of this study is to identify relevant product features related to sustainable development in this context, and use the choice of mobile phone as an example in measuring their importance.

The study used two different methods (qualitative and quantitative) in two phases. First it organized focus‐group discussions in order to identify the features of sustainability that affect the choice of a mobile phone. The most significant features served as a starting point for the choice of attributes to be included in the final step, choice‐based conjoint analysis (CBC), which assesses respondents' value functions by means of latent class clustering. Between the two major phases it carried out two additional pre‐tests in order to reduce the number of attributes.

The results provide fundamental information concerning the relative importance of sustainability features in the selection of a mobile phone. The study identified four different clusters of purchasers: updaters, budgeters, environmentalists, and long‐life users. According to the findings, some consumers are willing to pay a premium for sustainability features. The authors discuss the potential implications of the results in the context of supply chain design.

The literature on supply chain management tends to see the consumers as a “black box”. This paper reports the first results of opening this box by linking the supply chain perspective to consumer choice behavior.

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.

The purpose of this study is to create the taxonomy of firms based on the nature of the relationship between market-based resources and marketing capabilities. Anchored in the configuration theory, the present study aims to explore simultaneous roles of market-based resources, i.e. customer orientation and competitor orientation, and marketing capabilities, i.e. the execution of marketing practices and activities within a firm, on firm performance.

A self-administrated questionnaire was used to collect data from chief executive officers or top managers of 220 firms in Bosnia and Herzegovina, a transitional economy in South Eastern Europe.

Drawing on a configuration approach via the latent class analysis, the authors empirically derive four distinct strategic marketing patterns, namely, marketing super achievers, marketing-focused virtuosi, marketing drifters and marketing mass pushers. The findings also highlight how business performance outcomes differ as a function of a class membership.

Cross-sectional research design and focus on a single country are main limitations of the present study. Thus, longitudinal studies in the context of developed and fast-reforming transition economies are advisable for future work.

This study enhances the knowledge on how a firm can configure or bundle its market-based resources and marketing capabilities to produce desired outcomes. Findings suggest that joint attention to building market-oriented culture and developing marketing capabilities seems to pay off. However, the authors found that a lack of market knowledge can be substituted by the firms’ ability to build effective promotional and branding capabilities. Thus, the present study adds to the emerging dialog on the relative importance of alternative strategic orientations in achieving superior business performance.

This study contributes to the strategic marketing literature by examining the synergistic effect of market-based resources and marketing capabilities on firm performance using a configurational approach. It also provides support for the equifinality proposition, suggesting that different “bundles” of market-based resources and marketing capabilities can lead to similar level of performance outcomes.

Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation.

The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments.

The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market.

The proposed statistical models are new in the field of financial markets.