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The purpose of the present study was to investigate the interrelationships among the dimensions of sport event experience, event satisfaction and event behavioral intentions.

The sample was comprised of 186 individuals who actively participated in a mass participation sport event. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the relationships among the latent constructs.

The results indicated that the dimensions of sport event experience predicted 55% of the variance of event satisfaction and 63% of the variance of event behavioral intentions was predicted by sport event experience dimensions and event satisfaction. Specifically, the sensory, affective and relational dimensions of experience sought to have a statistically significant and positive association with event satisfaction, while event satisfaction and the relational dimension of experience were found to have a statistically significant and positive correlation with event behavioral intentions. In addition, event satisfaction was found to mediate the relationships between sensory, affective and relational experiences and event behavioral intentions.

The present study is one of the first that explores the relationships among sport event experience’s dimensions, event satisfaction and positive behavioral intentions in the context of sport event participation.

Corporate Social Responsibility (CSR) reporting has been widely accepted as a vital tool for communicating with stakeholders on a range of social, environmental, and governance issues, but how companies define, interpret, apply, integrate, and communicate their CSR efforts and impacts in corporate reporting is anything but a straightforward task. The purpose of this chapter is to explore the concept of materiality in CSR reporting and demonstrate practical examples of good CSR and Sustainable Development Goals (SDGs) reporting practices. We chose the aviation industry because of its economic relevance, constant growth, and future expected changes in the aftermath of COVID-19. In addition, airlines affect many of the SDGs directly and indirectly with contending results. This chapter is timely because of the growing willingness by companies to integrate CSR and environmental, social, and governance (ESG) thinking into the corporate strategy and business operations using materiality assessment and enhancing their competitive advantage and ability to maintain long-term value and because ESG and ethical investing have become part of the mainstream investing. Thus, this chapter contributes to an understanding of the wide range of existing and new reporting frameworks and regulations and reinforces the importance of discussing how this diversity of approaches can affect the work toward worldwide comparability of CSR and sustainability reporting.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

Despite differing strategies towards environmental sustainability in developed and developing nations, the manufacturing sector in these regional domains faces substantial environmental issues. The purpose of this study is to examine the green-lean-six sigma (GLSS) enablers and outcomes for enhancing environmental sustainability of manufacturing firms in both, a developed and developing country context by using an environment-centric natural resource-based view (NRBV).

First, a framework of GLSS enablers and outcomes aligned with the NRBV strategic capabilities is proposed through a systematic literature review. Second, this framework is used to empirically investigate the GLSS enablers and outcomes of manufacturing firms through in-depth interviews with lean six sigma and environmental consultants from New Zealand (NZ) and Pakistan (PK) (developed and developing nations).

Analysis from both regional domains highlights the use of GLSS enablers and outcomes under different NRBV capabilities of pollution prevention, product stewardship and sustainable development. A comparison reveals that NZ firms practice GLSS to comply with environmental regulatory requirements, avoid penalties and maintain their clean-green image. Conversely, Pakistani firms execute GLSS to reduce energy use, satisfy international customers and create a green image.

This paper provides new insights on GLSS for environmental sustainability which can assist industrial experts and academia for future strategies and research.

This is one of the early comparative studies that has used the NRBV to investigate GLSS enablers and outcomes in manufacturing firms for enhancing environmental performance comparing developed and developing nations

This study explores match related factors and their impact on the batting strike rate in Twenty20 cricket – an aspect which can generate excitement and fan engagement in cricket matches.

Data was collected from www.cricinfo.com using a web scraping tool based on R programming from February 17, 2005, to October 25, 2022, numbering 4,221 men’s Twenty20 international innings featuring 41 national teams that had taken place in 85 venues across 11 countries of play. Hypothesis testing was conducted using one-way ANOVA.

The findings indicate that batters score faster in the first inning of a match, and mean strike rates also vary significantly based on the country of play. Further, the study analyses the top performing national sides, venues and country of play in terms of mean batting strike rate, thus providing insights to cricket boards, international regulating bodies of cricket, sponsors, media companies and coaching staff for better decision-making based on batting strike rate.

The originality of the study lies in its focus on using non-marketing strategies to increase fan engagement. Further, this study is the first one to examine different venues from the perspective of batting strike rate in men’s Twenty20 international matches.

The main objective of this paper is to analyse the relationship between innovation and traditional concepts to explain the phenomenon of traditional food with innovation from a market and consumer behaviour perspective in the Mexican context.

The research is carried out in two phases: (1) analysis of the offer in distribution and (2) consumer research. First, a mixed observation technique in the offer of traditional foods with innovation was carried out. The data were recollected from 24 companies' websites and was complemented with information from main distribution chains of the city of Hermosillo (Mexico). Second, a survey was carried out with 310 Mexican consumers. The data obtained were analysed using bi-variable and multivariable techniques.

The findings from the websites showed that there are 19 traditional products with innovation that are marketed through this medium, while 39 traditional products with innovation are offered in distribution chains. Of all foods, 61% showed innovations in ingredients and materials. Also, the consumer evaluations identified three segments: the consumers orientated towards innovations, convenience and health (42.2%), those orientated towards sensory innovations (39%), and those more inclined towards innovations in marketing and availability (18.7%).

The research considers a partial perspective of the agri-food chain and not an integral vision, it is limited to a specific area and to certain traditional foods.

The symbiosis between innovation and tradition is identified from the perspective of supply and demand. The trend that exists in the market regarding the types of innovations and the gaps that exist regarding environmental elements are recognized.

The data obtained in the research generate information for business decision-making and entrepreneurship; in addition indicates new dietary and consumption patterns. It also provides knowledge about innovation and tradition, and highlights the relevance of traditional food.

This study tries to fill a gap in the literature by focusing on the market and consumer behaviour perspective for traditional food with innovation.

Our goals were to explore the pedagogies applied by instructors that supported Black, Indigenous and People of Color (BIPOC) student learning in a leadership course and the leadership behaviors BIPOC students identified as being applicable after the course.

Through survey research and qualitative data analysis, three prominent themes emerged.

High-quality, purposeful pedagogy created opportunities for students to learn. Second, a supportive, interactive community engaged students with the instructor, each other and the course material to support participation in learning. As a result, students reported experiencing big shifts, new growth and increased confidence during their leadership courses.

We discuss our findings and offer specific recommendations for leadership educators to better support BIPOC students in their leadership courses and classrooms and for further research with BIPOC students.

This study aims to provide new insights into the relationship between individual characteristics, particularly personality traits and mature students' intention to use (ITU) mobile learning (m-learning).

The research model was constructed by integrating the Big Five personality traits into the unified theory of acceptance and use of technology (UTAUT) model. The data were collected from mature students at a university research center in Macau. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and test the proposed hypotheses.

The results reveal that personality traits play a significant role in determining mature students' ITU m-learning technology. In particular, social influence (SI) mediates the relationship between agreeableness (AGB) and ITU.

This study examines how personality traits collectively influence mature students' receptiveness and intentions toward m-learning. As mature learners' motivations and preferences remain underexplored, insights into trait-technology links could address current gaps and optimize mobile educational support tailored to their distinct characteristics and needs.