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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 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.

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.

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.

The study aims to identify different consumer groups to better understand changes in urban Fijian food shopping behaviour and the implications for the local food industry.

The authors used a Latent class (LC) cluster analysis of survey data from 1,000 urban Fijian households to identify unique consumer segments based on household food shopping behaviour.

Five distinct urban household clusters were identified based on food shopping behaviour. The cluster with the highest income level spent significantly lower amounts on fresh fruit and vegetables (FFV) at the main traditional market, preferring to buy their FFV from modern supermarket outlets. Considering the vast majority of local smallholder farmers rely on traditional market channels to sell their produce, the growth and dominance of Fijian supermarkets are of some concern.

Future research should consider repeating these types of detailed consumer surveys to better understand the implications of changes in shopping behaviour over time, and the role that key stakeholders can play in ensuring smallholder farmers is not excluded from the market.

Smallholder-driven agriculture accounts for a significant share of Fiji's gross domestic product (GDP), so understanding how the retail food industry is transforming and how this is affecting smallholder farmers is critical to Fiji's social structure.

Research on food retailing and the role of the consumer is rare in small island developing states (SIDS), such as Fiji. Fiji has a somewhat unique set of circumstances. In the absence of significant foreign investment in food retailing, key factors such as urbanisation and rising urban income mean consumer preferences are important drivers of changes in shopping behaviour. The study provides insights into Fiji's changing food industry with implications for other SIDS, while contributing to the global literature in this field.

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.

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.

The environment surrounding U.S. higher education has changed substantially over the past 40 years. However, we have a limited understanding of what these changes mean for the higher education organizations (HEOs) that occupy this organizational field. In this paper, I use descriptive statistics and multilevel latent class analysis (MLCA) to analyze the financial behaviors of public four-year HEOs from 1986 to 2010 to evaluate how HEOs adapt financially to their changing environments. I advance the current conceptual and empirical understanding of public HEO behaviors by evaluating how public HEOs utilize combinations of revenue and spending streams to accomplish their mission and the extent to which the revenues and spending patterns of these institutions are related. Descriptive results confirm the shift away from state funding toward tuition revenues and the relative stability in spending patterns. MLCA results, which allow for the investigation of how combinations of revenue and spending streams work together, indicate that public HEOs are changing the combinations of revenues they rely on in different ways, revealing multiple specific pathways for how public HEOs adapt to their changing environments. The spending profiles, in contrast, remain stable with only a few HEOs changing their profile over time. I argue that the loose coupling between revenues and spending and discontinuity in their patterns of change over time suggests that public HEOs are able to establish a buffer between their environment and spending or activities that allows them to continue engaging in the same broad set of activities despite environmental changes.