title: CSR practices in luxury hotels of China: Perception, cultural and generation differences

authors: George, LIU

subjects: luxury hotel

abstract: This study investigates how corporate social responsibility (CSR) practices are perceived in Chinese luxury hotels, focusing on cultural influences, generational cohorts, and organisational roles. Grounded in Hofstede’s national cultural dimensions, self-determination theory (SDT), and the Confucian concept of self-cultivation, the research addresses a gap in understanding how cultural values and intergenerational differences shape staff CSR perceptions in the luxury hospitality context. 

Adopting a pragmatic research philosophy, an exploratory sequential mixed-methods design was employed. In the qualitative phase, 37 semi-structured interviews with hotel staff (including senior managers, middle managers, and line employees) revealed key themes by the use of narrative interpretation: (a) deep-rooted cultural norms (collectivism, harmony, respect for hierarchy) that reinforce CSR as a collective obligation led by top management, (b) generational value differences in CSR engagement, and (c) variations in perceived CSR benefits across job positions. 

Building on these insights, the quantitative phase surveyed staff across luxury hotels in China (N = 533). Structural equation modelling results confirmed and extended the qualitative patterns. For example, 72% of managers affirmed that CSR initiatives strengthened brand reputation, whereas only 48% of line staff shared this view, reflecting positional gaps in perceived strategic value. Generational contrasts were also evident: nearly two-thirds of Millennial and Gen Z staff reported strong personal commitment to CSR activities, compared to about half of older staff. Statistical analysis using partial least squares structural equation modelling (PLS-SEM) confirmed significant employee-level differences in perceptions of CSR practices within luxury hotels in China. Notably, perceived CSR was particularly pronounced among senior-level staff, reflecting hierarchical variations in CSR prioritisation and implementation. 

This research has provided a pioneering examination of CSR practices in China’s luxury hotel sector, demonstrating how cultural context and generational dynamics jointly shape staff perceptions of CSR. The study involved a nuanced synthesis of qualitative depth and quantitative rigour, yielding empirical evidence to describe similarities and differences between the perceptions and generations. These differences, rooted in China’s cultural heritage and the evolving values of its workforce, have significant implications for both theory and practice. The findings extend the CSR literature by confirming that why and how staff engage with CSR is contingent on a confluence of individual values, leadership influence, and socio-cultural background, aligning with global evidence of CSR’s positive impact while also highlighting unique intra-cultural variations.

Theoretically, this thesis extends CSR and organisational behaviour literature by integrating Western motivational theories with Chinese cultural frameworks, demonstrating how national culture and generational identity jointly shape CSR perceptions and engagement. Methodologically, the study validates the efficacy of a pragmatic, sequential mixed-methods approach, effectively capturing the nuanced, multi-dimensional CSR perceptions within a complex cultural setting. Practically, the findings offer actionable insights for human resource management (HRM), recommending differentiated CSR strategies and communication tailored to generational characteristics and cultural values, thus enhancing CSR integration, employee motivation, and organisational alignment.

Consequently, the main contributions of this thesis are: providing empirical evidence on how cultural and generational factors together influence perceptions of CSR in China’s luxury hospitality sector; demonstrating the effectiveness of a mixed-method approach to exploring complex cultural and organisational phenomena; and offering practical, culturally informed guidelines for improving CSR engagement among diverse staff groups in luxury hotels of China, highlighting the potential impact of staff perception on corporate ethical behaviour.

This study discusses its limitations, provides recommendations, and explores potential opportunities for future research.

text: CSR practices in luxury hotels of China: Perception, cultural and generation differences This study investigates how corporate social responsibility (CSR) practices are perceived in Chinese luxury hotels, focusing on cultural influences, generational cohorts, and organisational roles. Grounded in Hofstede’s national cultural dimensions, self-determination theory (SDT), and the Confucian concept of self-cultivation, the research addresses a gap in understanding how cultural values and intergenerational differences shape staff CSR perceptions in the luxury hospitality context. 

Adopting a pragmatic research philosophy, an exploratory sequential mixed-methods design was employed. In the qualitative phase, 37 semi-structured interviews with hotel staff (including senior managers, middle managers, and line employees) revealed key themes by the use of narrative interpretation: (a) deep-rooted cultural norms (collectivism, harmony, respect for hierarchy) that reinforce CSR as a collective obligation led by top management, (b) generational value differences in CSR engagement, and (c) variations in perceived CSR benefits across job positions. 

Building on these insights, the quantitative phase surveyed staff across luxury hotels in China (N = 533). Structural equation modelling results confirmed and extended the qualitative patterns. For example, 72% of managers affirmed that CSR initiatives strengthened brand reputation, whereas only 48% of line staff shared this view, reflecting positional gaps in perceived strategic value. Generational contrasts were also evident: nearly two-thirds of Millennial and Gen Z staff reported strong personal commitment to CSR activities, compared to about half of older staff. Statistical analysis using partial least squares structural equation modelling (PLS-SEM) confirmed significant employee-level differences in perceptions of CSR practices within luxury hotels in China. Notably, perceived CSR was particularly pronounced among senior-level staff, reflecting hierarchical variations in CSR prioritisation and implementation. 

This research has provided a pioneering examination of CSR practices in China’s luxury hotel sector, demonstrating how cultural context and generational dynamics jointly shape staff perceptions of CSR. The study involved a nuanced synthesis of qualitative depth and quantitative rigour, yielding empirical evidence to describe similarities and differences between the perceptions and generations. These differences, rooted in China’s cultural heritage and the evolving values of its workforce, have significant implications for both theory and practice. The findings extend the CSR literature by confirming that why and how staff engage with CSR is contingent on a confluence of individual values, leadership influence, and socio-cultural background, aligning with global evidence of CSR’s positive impact while also highlighting unique intra-cultural variations.

Theoretically, this thesis extends CSR and organisational behaviour literature by integrating Western motivational theories with Chinese cultural frameworks, demonstrating how national culture and generational identity jointly shape CSR perceptions and engagement. Methodologically, the study validates the efficacy of a pragmatic, sequential mixed-methods approach, effectively capturing the nuanced, multi-dimensional CSR perceptions within a complex cultural setting. Practically, the findings offer actionable insights for human resource management (HRM), recommending differentiated CSR strategies and communication tailored to generational characteristics and cultural values, thus enhancing CSR integration, employee motivation, and organisational alignment.

Consequently, the main contributions of this thesis are: providing empirical evidence on how cultural and generational factors together influence perceptions of CSR in China’s luxury hospitality sector; demonstrating the effectiveness of a mixed-method approach to exploring complex cultural and organisational phenomena; and offering practical, culturally informed guidelines for improving CSR engagement among diverse staff groups in luxury hotels of China, highlighting the potential impact of staff perception on corporate ethical behaviour.

This study discusses its limitations, provides recommendations, and explores potential opportunities for future research.

year: 2026-01-23

title: Tree-based Density Estimation: Algorithms and Applications

authors: Schmidberger, Gabi

subjects: machine learning

abstract: Data Mining can be seen as an extension to statistics. It comprises the preparation
of data and the process of gathering new knowledge from it. The extraction of
new knowledge is supported by various machine learning methods. Many of the
algorithms are based on probabilistic principles or use density estimations for their
computations. Density estimation has been practised in the field of statistics for
several centuries. In the simplest case, a histogram estimator, like the simple equalwidth
histogram, can be used for this task and has been shown to be a practical
tool to represent the distribution of data visually and for computation. Like other
nonparametric approaches, it can provide a flexible solution. However, flexibility
in existing approaches is generally restricted because the size of the bins is fixed 
either the width of the bins or the number of values in them. Attempts have been
made to generate histograms with a variable bin width and a variable number of
values per interval, but the computational approaches in these methods have proven
too difficult and too slow even with modern computer technology.
In this thesis new flexible histogram estimation methods are developed and tested
as part of various machine learning tasks, namely discretization, naive Bayes classification,
clustering and multiple-instance learning. Not only are the new density
estimation methods applied to machine learning tasks, they also borrow design
principles from algorithms that are ubiquitous in artificial intelligence: divide-andconquer
methods are a well known way to tackle large problems by dividing them
into small subproblems. Decision trees, used for machine learning classification,
successfully apply this approach. This thesis presents algorithms that build density
estimators using a binary split tree to cut a range of values into subranges of
varying length. No class values are required for this splitting process, making it an
unsupervised method. The result is a histogram estimator that adapts well even to
complex density functions a novel density estimation method with flexible density
estimation ability and good computational behaviour.
Algorithms are presented for both univariate and multivariate data. The univariate
histogram estimator is applied to discretization for density estimation and
also used as density estimator inside a naive Bayes classifier. The multivariate histogram,
used as the basis for a clustering method, is applied to improve the runtime
behaviour of a well-known algorithm for multiple-instance classification. Performance
in these applications is evaluated by comparing the new approaches with
existing methods.

text: Tree-based Density Estimation: Algorithms and Applications Data Mining can be seen as an extension to statistics. It comprises the preparation
of data and the process of gathering new knowledge from it. The extraction of
new knowledge is supported by various machine learning methods. Many of the
algorithms are based on probabilistic principles or use density estimations for their
computations. Density estimation has been practised in the field of statistics for
several centuries. In the simplest case, a histogram estimator, like the simple equalwidth
histogram, can be used for this task and has been shown to be a practical
tool to represent the distribution of data visually and for computation. Like other
nonparametric approaches, it can provide a flexible solution. However, flexibility
in existing approaches is generally restricted because the size of the bins is fixed 
either the width of the bins or the number of values in them. Attempts have been
made to generate histograms with a variable bin width and a variable number of
values per interval, but the computational approaches in these methods have proven
too difficult and too slow even with modern computer technology.
In this thesis new flexible histogram estimation methods are developed and tested
as part of various machine learning tasks, namely discretization, naive Bayes classification,
clustering and multiple-instance learning. Not only are the new density
estimation methods applied to machine learning tasks, they also borrow design
principles from algorithms that are ubiquitous in artificial intelligence: divide-andconquer
methods are a well known way to tackle large problems by dividing them
into small subproblems. Decision trees, used for machine learning classification,
successfully apply this approach. This thesis presents algorithms that build density
estimators using a binary split tree to cut a range of values into subranges of
varying length. No class values are required for this splitting process, making it an
unsupervised method. The result is a histogram estimator that adapts well even to
complex density functions a novel density estimation method with flexible density
estimation ability and good computational behaviour.
Algorithms are presented for both univariate and multivariate data. The univariate
histogram estimator is applied to discretization for density estimation and
also used as density estimator inside a naive Bayes classifier. The multivariate histogram,
used as the basis for a clustering method, is applied to improve the runtime
behaviour of a well-known algorithm for multiple-instance classification. Performance
in these applications is evaluated by comparing the new approaches with
existing methods.

year: 2009

title: Oranga whānau, oranga niho: The oral health status of 5-year-old Māori children. A case study

authors: Te Amo, Kirstin Mei

subjects: Maori health

abstract: Research has shown that the oral health of Māori is far worse than non-Māori across all
age groups. The objective of this research study was to assess the dental wellbeing of
5-year olds with a specific focus on Māori children currently residing in the Hamilton City
region. In addition, this research focused on the impact that social, economic, cultural
and environmental factors have on oral health. A total of 32 participants were invited to
take part in this research: 15 5-year-old children from three selected schools, 15 (of the
children's) caregivers and 2 dental therapists who work in the Hamilton City region.
The findings indicated that overall Māori children and children of lower socio-economic
status had a much higher prevalence of dental caries (tooth decay) than non-Māori
children and children of higher socio-economic status. A number of contributing factors
were shown to be responsible for this disparity including the cultural inappropriateness
of oral health services and resources, affordability, role-modelling, parental awareness
and education, and the transient nature of families.
It was found that no one strategy or intervention will achieve dramatic improvements in
Māori oral health as a concerted effort is required by Local and Central Government, the
Health Sector and Māori communities.

text: Oranga whānau, oranga niho: The oral health status of 5-year-old Māori children. A case study Research has shown that the oral health of Māori is far worse than non-Māori across all
age groups. The objective of this research study was to assess the dental wellbeing of
5-year olds with a specific focus on Māori children currently residing in the Hamilton City
region. In addition, this research focused on the impact that social, economic, cultural
and environmental factors have on oral health. A total of 32 participants were invited to
take part in this research: 15 5-year-old children from three selected schools, 15 (of the
children's) caregivers and 2 dental therapists who work in the Hamilton City region.
The findings indicated that overall Māori children and children of lower socio-economic
status had a much higher prevalence of dental caries (tooth decay) than non-Māori
children and children of higher socio-economic status. A number of contributing factors
were shown to be responsible for this disparity including the cultural inappropriateness
of oral health services and resources, affordability, role-modelling, parental awareness
and education, and the transient nature of families.
It was found that no one strategy or intervention will achieve dramatic improvements in
Māori oral health as a concerted effort is required by Local and Central Government, the
Health Sector and Māori communities.

year: 2007