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This study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.

324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.

LPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.

This study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

Following counterarguments from previous studies, overwhelming evidence from meta-analyses shows that supply-chain integration (SCI) is not universally associated with improved performance. This study aims to clarify all the mechanisms purported to reach the combined effects of SCI dimensions.

In this study, the authors first conducted a literature review and identified the effects of different individual SCI dimensions and their interactions and mediation, or arcs of integration, as reported by past studies. Data from a survey of 348 firms were analyzed using multiple regressions, structural equation modeling and latent class analysis.

The results showed that the positive independent associations between SCI dimensions and operational performance change as they interact. Larger combinational effects were found in a second-order SCI construct and a mediation model. Smaller effects and fewer significant paths (and even negative signs) were also found. This shows that more SCI is not always better. Finally, configurational effects varied: the latent class analysis showed that a more inward (internal integration) gestalt performed better for some firms.

Some variables that could affect operations performance were missing from the models, and this paper did not specify the directions of relationships among the five performance dimensions. The authors are aware of cumulative models, but they are not within the scope of this paper. In addition, this study did not consider other sociocultural factors that may influence different dimensions of SCI.

This study shows SCI dimensions play different roles. Therefore, it is needed to theorize the distinct roles of internal and external integration and how they somehow compensate or substitute for each other. The results suggest that future studies need to reexamine the universal predictions especially from resource-based, resource-advantage and relational views. SCIs are not inimitable and rare resources for all firms. Furthermore, implementing SCI does not guarantee resource and relational advantages for all firms.

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.

Employee engagement (EE) continues to be one of the most difficult challenges for organizations today. Numerous factors have been linked to EE, according to studies. However, the necessary human resource management (HRM) strategies and systems for enhancing EE have not yet been developed. It is questionable if all employees inside the company require the same HRM strategies, to boost engagement as one size does not fit all. Therefore, it is necessary to create employee profiles based on factors associated with EE. This study aims to develop employee profiles based on engagement dimensions and outcomes. It seeks to comprehend the relationship between engagement level and factors such as age, years of service and employment grade.

Using latent profile analysis (LPA), we identified five EE profiles (highly engaged, engaged, moderately engaged, disengaged and highly disengaged). These five profiles were characterized by five EE dimensions (Culture Dimensions, Leadership Dimensions, People Process, Business alignment Dimension and Job Dimension) and EE outcomes (Say, Stay and Strive).

The study revealed that Engaged profiles exhibited low stay outcomes. The highest percentage of disengaged employees fall under 25 years of age with less than 5 years of experience and are at the entry level.

The study highlights the significance of the people processes dimensions in enhancing engagement. Profiles with low people process dimensions showed high disengagement. Person-centered LPA adds and complements variable-centered approach to develop a better understanding of EE and help organizations devise more personalized strategies. The study would be of interest to both academics and practitioners.

The novelty of this study lies in its attempt to model the employee profiles to comprehend the relationship between engagement levels using LPA.

The purpose of this study is to explore how consumers apply clean-eating criteria to a range of food characteristics, and the extent to which individuals are consistent in how they apply clean-eating criteria across products. Further, this study investigates how the clean-eating approach relates to underlying food choice motives.

Data were collected in a consumer survey (n = 666) in Sweden, where participants were prompted about the importance of a set of intrinsic food attributes of the “free-from” and “added” types, for three different food product types (bread, processed meat, ready meals). Data were analyzed using latent class cluster analysis, to explore segments of consumers that place similar importance to the food characteristics and hold similar food choice motives.

Clean eating can be described by two distinctly different attainment strategies: avoiding undesirable characteristics or by simultaneously approaching desirable characteristics. Notably, individuals who apply clean-eating criteria in their food choices strive for healthy, natural and environmentally friendly food, but the clean-by-approach strategy implies a stronger focus on personal health in the form of weight control.

While claims and labels on food packages concerning clean eating are implemented by food manufacturers, it remains unregulated. This study provides information for future regulations on how consumers apply clean-eating criteria, and their motives thereof. Further, the results provide insights food manufacturers regarding motives for clean eating in different consumer segments.

This study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.

Using latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO₂ emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.

The analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO₂ emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.

This study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

This study aims to advance the understanding on individual variations in PhD candidates’ personal interest in their doctorate and supervisory and research community support, and several individual and structural attributes potentially having an impact on the profiles.

The authors explored the interrelationship between personal interest – social support profiles, and nationality, gender, research group and study status and the risk of dropping out. A total of 768 PhD candidates from a research-intensive university in Finland responded to a modified version of the cross-cultural doctoral experience survey. Latent profile analysis was used to explore the individual variations in PhD candidates’ interest and support from the supervisor and research community.

Three distinctive PhD interest-social support profiles were detected; the high interest–high support profile (74.4%, n = 570), the high interest–moderate support profile (18.2%, n = 140) and the moderate interest–moderate support profile (7.4%, n = 56). The profiles exhibited high to moderate levels of research, development and instrumental interest. Individuals in the high interest–moderate support and in the moderate interest–moderate support profiles were more prone to consider dropping out from their PhD than in the high interest–high support profile.

The results indicate that by cultivating PhD candidates’ interest and providing sufficient supervisory and the research community offers a means for preventing candidates from discontinuing their doctorate. Hence, building a supportive learning environment that cultivates a PhD candidate’s personal interest is likely to reduce high dropout rates among the candidates.

Local food impacts tourist satisfaction and drives the choice of a tourist destination. However, it is not clear which attributes of local food products drive tourists’ preferences. More specifically, little is known about potential segments in the tourist market. Acknowledging consumers’ divergent inclinations, we explore tourist preferences toward local food attributes through a market segmentation approach.

This study uses the Best-Worst Scaling method to examine the preferences of 311 tourists for attributes of local food products that are known to affect their choices. We employ a Latent Class Analysis to identify market segments with distinct preferences.

Results indicate that tourists prioritize taste, quality, authenticity, healthiness, connection to local culture and tradition and environmental friendliness over price, appearance, enhancement of local economy, availability and packaging. We further identify three segments: Sensory Seekers, Cultural Experiencers, and Price Conscious. The largest segment, Sensory Seekers, is driven by taste and quality attributes, while Cultural Experiencers prioritize connection to local culture and authenticity. The Price Conscious segment places a greater emphasis on price than the other segments. Cultural Experiencers demonstrate the highest willingness to pay for local food products.

The positioning of local products should target Cultural Experiencers. Agri-firms and state agencies promoting a tourist destination internationally can also use the connection to local culture and authenticity in their marketing campaigns.

Our research contributes to food marketing literature by showing which local food attributes drive tourist preferences. Moreover, we uncover unobservable heterogeneous preferences among tourists.

This study aims to contribute to strategic crisis communication research by exploring international media representations of third sector crises and crisis response; expanding the range of crisis types beyond transgressions; and developing a framework that integrates framing and crisis communication theory.

Quantitative content analysis was applied to identify patterns in crisis reporting of 18 news media outlets in Canada, Germany, India, Switzerland, UK and US. Using an inductive framing approach, crisis coverage of nonprofit organizations (NPOs) and intergovernmental organizations (IGOs) between 2015 and 2018 was analyzed across a wide range of crises, including but not limited to prominent cases such as Oxfam, Kids Company, or the Islamic Research Foundation.

The news media in six countries report more internal crises in the third sector than external crises. The most frequent crisis types were fraud and corruption, sexual violence/personal exploitation and attacks on organizations. Exploratory factor analysis revealed three components of crisis response strategies quoted in the media, conditional rebuild, defensive and justified denial strategies. Causal attributions and conditional rebuild strategies significantly influenced media evaluations of organizational crisis response. Three frames of third sector crises were detected; the critique, the damage and the victim frame. These frames emphasize different crisis types, causes, crisis response strategies and evaluations of crisis response.

The study reveals the particularities of crises and crisis communication in the third sector and identifies factors that influence mediated portrayals of crises and crisis response strategies of nonprofit organizations (NPOs) from an international comparative perspective. The findings have relevant implications for crisis communication theory and practice.

The main objective of this study is to determine the factors that have the greatest impact on travelers' opinions of airports.

11,656 customer reviews for 649 airports around the world were gathered following the COVID-19 outbreak from the website that rates airport quality. The dataset was examined using hierarchical regression, PLS-SEM, and the unsupervised Bayesian algorithm-based PSEM in order to verify the hypothesis.

The results showed that people’s intentions to recommend airports are significantly influenced by their opinions of how well the servicescape, staff, and services are.

By encouraging air travelers to have positive intentions toward recommending the airports, this research offers airport managers decision-support implications for how to improve airport service quality. This will increase the likelihood of retaining more passengers.

This study also suggests a quick-to-implement visual decision-making mechanism based on PSEM that is simple to understand.