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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 the paper is the analysis of the evolution of students’ satisfaction over time in a large Italian university and the effects on it because of some characteristics of the teachers: didactic practices, beliefs and needs with regard to teaching and learning.

The first step of the analysis identifies a latent construct, measured with items composing the questionnaire, and proposes a reduced set of indicators to measure satisfaction and to model its evolution over time (information collected in three consecutive academic years is available). A second step clusters teachers in homogenous groups with reference to their opinions, beliefs and needs, collected with a new survey conducted at the University of Padova, with the aim of developing strategies to support academic teachers. Then, a mixture conditional latent growth model is estimated with covariates affecting the latent parameters and class membership.

Model estimation identifies a large group of university courses with a high level of satisfaction, which stays constant over time, and a small group of problematic courses with low satisfaction, moreover, that decreases over the three considered academic years. Interesting significant effects of covariates related to both the teacher and the didactic activity are estimated.

Statistical analyses show that the implementation of innovative didactic practices and commitment to quality of teaching are important factors to be encouraged by the university management. On the contrary, the traditionalist way of teaching and a low passion for teaching do not improve students’ satisfaction.

The effects of the COVID-19 pandemic on cities have transformed the lives of urban societies across the globe. One of such effects has been the redefinition of access and urban mobility patterns, exposing divides and inequalities along the lines of class, gender and social positions. In Latin America, long-term lockdowns and widespread containment-oriented restrictions have deepened already acute conditions of poverty and deprivation. Low-income and socially vulnerable households and individuals in countries such as Colombia find themselves unable, or in a disadvantaged position, to work from home, access goods and services securely and avoid transport modes that increase exposure to contagion. This chapter examines inequalities in urban mobility and access to essential opportunities in urban settings in Colombia, through data collected from 3,900 respondents to a web survey organised during the national lockdown in the country in April 2020. The chapter presents a Latent Class Analysis model exploring how intersecting differences in class, gender, ethnicity, age and other relevant socioeconomic characteristics, influence the degree of adaptability and capacity to adapt to the challenging conditions posed by COVID-19 for physical travel and carrying out everyday activities. Building on three distinct classes of mobility and access-related conditions, the chapter reflects on structural inequalities associated with Colombian cities’ urban form, functional and productive structures and its wide social gaps. The chapter builds on empirical findings to reflect on urban policy and discuss avenues for addressing social and spatial inequalities worsened by the pandemic.

This paper analyses how the purchase channel and customer complaint goals affect the sequential choice of post–purchase complaint channels when customers experience a service failure followed by a service recovery failure (double deviation).

An online survey involving a scenario manipulation was conducted with 577 apparel shoppers. The study employs multi-group latent class analysis to estimate latent customer segments within both online and offline groups of shoppers and compare latent classes between the two groups.

The results show that the purchase channel has a lock-in effect on the complaint channel, which is stronger for offline buyers. Moreover, there is evidence of channel synergy effects in the case of having to complain twice: shoppers who complain in store in the first attempt turn to online channels in the second complaint attempt, and vice versa. Complaint goals shape the choice of complaint channels and define different shopper segments.

The present study is the first to adopt a cross-stage approach that analyses the dependencies between the purchase channel and the complaint channel used on two subsequent occasions: the first complaint after a service failure and the second following a service recovery failure.

This paper aims to classify tourist accommodation using data from Booking.com and TripAdvisor and analyse the extent to which the different segments identified differ in terms of being adults-only.

In total, 1,535 properties located in nine Spanish sun and beach destinations were examined using a latent class cluster analysis (LCCA). The bias-adjusted three-step approach was used to investigate the differences between belonging to adults-only accommodation or not among the identified clusters.

Results show that adults-only accommodation tends to belong to the cluster with higher online ratings. In small Spanish islands, adults-only hotels account for a large share (more than 25%) of hotels.

It was not possible to analyse whether the higher rating was due to the accommodation being better or due to the tourists being more satisfied with their stay.

In urban destinations, the model is not widely used. However, in coastal destinations, it is becoming more than a novelty or a new trend.

In small Spanish islands, people traveling with children are becoming a minority. Families may feel discriminated against and express dissatisfaction with this situation in the future.

This study covers the gap in the academic literature on this growing hotel segment.

The purpose of this paper is to identify the patterns of customer exposure to touchpoints by segmenting consumers based on the frequency of their exposure, and to understand the relationship of patterns of exposure with customer loyalty intentions (relationship commitment, self-disclosure and positive word-of-mouth) and demographic characteristics.

An online survey of almost 4,000 customers was employed in a supermarket retail setting. Customers were segmented based on their frequency of recalled exposure to multiple touchpoints, by means of a latent class cluster analysis, while considering the role of demographic characteristics. Afterwards, loyalty intentions variables were regressed on the resulting customer segments.

Based on the touchpoint exposure, six customer segments emerge. The main differences across segments relate to the intensity of frequency of exposure and the types of touchpoints customers have been exposed to. Sex, age, shopping role and geographic area of residence are related to segment membership. The identified patterns of exposure explain relationship commitment, self-disclosure and positive word-of-mouth: clusters displaying higher exposure to touchpoints display higher loyalty intentions than clusters displaying lower exposure.

The study offers actionable implications for brands and retailers on how to manage touchpoints for implementing omnichannel strategies.

As far as the authors know, this study is the first to identify exposure to multiple touchpoints and understand the role of demographics as far as touchpoint exposure is concerned. It also provides interesting findings on the relationship of different combinations of touchpoints with customer loyalty.

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.

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.