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Posting and sharing about food on social media has surged in popularity amongst younger generations such as Millennials and Generation Z. This study aims to analyse and compare food-tourism sharing behaviour on social media across generations. First, this study specifically investigates the factors influencing the intention to share food experiences on social media; second, it examines the impact of sharing intention on actual behaviour and loyalty; and third, it determines whether Millennials and Generation Z differ in these relationships.

A survey was carried out of Millennial and Generation Z travellers who shared food experiences on social media. Structural equation modelling (SEM) and multi-group analysis were performed to examine the cause-and-effect relationship in both generations.

The findings reveal differences in motivation, satisfaction, sharing intention, sharing behaviour and loyalty between generations (Millennials and Generation Z).

This study contributes to the literature on the antecedents of food-sharing behaviour in online communities by indicating factors that influence the sharing of culinary experiences and brand or destination loyalty across generations. Suggestions for future research include exploring online food-sharing behaviour through cross-cultural comparisons in various regions.

As Millennials and Generation Z will expand their market share in the coming years, the findings of this study can help improve marketing strategies for culinary tourism and generate more intense food experiences for both generations.

The outcome of the research provides new insights to develop a conceptual model of food-sharing behaviour and tourism on social media by drawing comparisons across generations.

There is a dearth of research in the field of social marketing that attempts to understand why consumers prefer to use plastic packages when using online food delivery services. In addressing this issue, this study aims to investigate the role of moral disengagement, myopia and environmental apathy in the young generations' intentions to use plastic bags while ordering food online. It also examines the mediating role of moral disengagement and the moderating role of guilt in the context of the online food delivery service industry in China.

An online survey was designed to collect data, which yielded 256 usable responses. The partial least squares structural equation modelling (PLS-SEM) technique (SmartPLS 4.0) was used to test the study hypotheses.

The results indicate that environmental apathy, myopia and moral disengagement exert significant negative effects on consumer intention to use plastic. In addition, moral disengagement was able to mediate the links between “environmental apathy”, “myopia” and “plastic usage intention”. Lastly, consumers’ guilt was found to be a significant moderator in the link between moral disengagement and plastic usage intention.

This research holds significant importance for social marketers in the online food delivery service industry. Particularly, by understanding consumers' negative behavioural aspects, social marketers can implement marketing strategies that emphasise green practices for environmental well-being.

This is a pioneer study that focuses on the negative aspects of consumer behaviour, such as myopia, environmental apathy and moral disengagement, to understand what drives young consumers to use plastic. Additionally, this study investigates several new relationships in the social marketing field, such as the mediating effect of moral disengagement between myopia, environmental apathy and plastic usage intention. It also tests the moderating effect of guilt on the link between moral disengagement and use intention.

Guided by the conservation of resources theory, this study aims to present a comprehensive framework examining leadership, Guanxi, work-family conflict and work engagement. It specifically explores how group-level transformational leadership influences individual-level Guanxi and work-family conflict and how these factors, in turn, impact work engagement.

The study surveyed 473 teachers in the Klang Valley, Malaysia, and used hierarchical linear modelling.

The results reveal that transformational leadership directly enhances non-work relationships, reduces work-family conflict and indirectly predicts increased work engagement. This indirect influence occurs through the mediation of Guanxi and the work-family conflict. Notably, while stronger Guanxi is associated with greater work engagement in the professional sphere, it does not necessarily mitigate the work-family conflict in the personal domain.

These findings provide valuable insights into maintaining and enhancing work engagement by implementing transformational leadership through more effective channels, such as Guanxi and work-family conflict management.

This study aims to explore the impact of chief executive officers’ (CEOs) values on the socially responsible behaviors (SRBs) of top management teams. Drawing from the social learning framework, it examines the mechanisms through which CEOs’ values shape SRBs within organizational leadership.

Using the hierarchical regression model, this study assesses direct effects, while the Monte Carlo method is used to evaluate indirect effects. The analysis is based on time-lagged data collected from 122 CEOs and 287 corresponding top managers from small- and medium-sized enterprises in China.

The results indicate a positive correlation between CEOs’ self-transcendent values and their own SRBs (i.e. doing-good and avoiding harm behavior). This, in turn, promotes top managers’ SRBs.

By highlighting the micro-foundations of corporate social responsibility, this study enriches the understanding of SRBs enhancement in management teams. It reveals the significance of CEO self-transcendent values as a precursor to SRBs and elucidates the learning processes involved.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

The purpose of this study is to investigate the effects of different bullet screen types (functional vs social) on the continuous watching intention of consumers, as well as the influence mechanism. In addition, this study analyses the moderating role of consumer motivational orientation on the above effects.

First, objective data were obtained through the crawler to test the proposed hypotheses. An objective data analysis (417 group sample) was conducted to analyse the relationship between the percentage of social bullet screens and consumers sustained growth ratio to indirectly test the primary effect. Second, a questionnaire survey was conducted to test (176 questionnaires) the mediating role of perceived social crowding. Finally, a simulated online contextual experiment (340 participants) is conducted to explore the moderating role of consumer motivational orientation.

First, functional bullet screens produce higher continuous watching intention and lower perceived social crowding than social bullet screens. Second, perceived social crowding mediates the relationship between bullet screen type and continuous watching intention. Third, consumers' motivational orientation type (task-motivated vs recreation-motivated) moderates the relationship among bullet screen type, perceived social crowding and continuous watching intention.

The results of this study shed light on the differential impact of different types of bullet screens (functional and social) on consumers' continuous watching intentions, which makes up for the lack of research on the content of bullet screens in the field of livestreaming. Meanwhile, compared with the previous positive psychological research perspective, this study explores the intermediate mechanism of bullet screen type on consumers' continuous watching intention through a negative psychological perspective, which helps e-commerce companies and streamers better understand the differential impact of different bullet screens. Finally, this study explores the joint influence effect of bullet screen and consumer motivation type, which fills the theoretical research gap of consumer motivation orientation type in the category of live-streaming.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

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.