Search results

This chapter explores a relatively underresearched assumption in the diversity literature, namely, that more variety in demographic characteristics, educational or functional backgrounds, or hierarchical status in the workforce represents a wider repertoire of perspectives, approaches, and ways of thinking. Using data from members of innovation teams across 27 organizations in 11 industries (for which variation in thinking should be highly valued), we explore at the individual level whether people with different demographic and informational backgrounds evidence differences in ways of thinking which we define in terms of cognitive styles, learning styles, cultural orientations, and communication preferences. We find large differences in ways of thinking due to culture and communication preferences but modest and limited differences in ways of thinking by level and type of education, occupational function, and hierarchical status. We find few differences by gender. The findings raise questions about the frequently repeated claims that categorical and informational diversity among organizational members reflects differences in ways of thinking.

Wall-modeled large eddy simulation (LES) is a practical tool for solving wall-bounded flows with less computational cost by avoiding the explicit resolution of the near-wall region. However, its use is limited in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects.

A precursor to this methodology has already been tested in Radhakrishnan et al. (2021) for equilibrium flows using LES of channel flow data. In the current methodology, the high-fidelity data chosen for training includes direct numerical simulation of a double diffuser that has strong non-equilibrium flow regions, and LES of a channel flow. The ultimate purpose of the model is to distinguish between equilibrium and non-equilibrium regions, and to provide the appropriate wall shear stress. The ML system used for this study is gradient-boosted regression trees.

The authors show that the model can be trained to make accurate predictions for both equilibrium and non-equilibrium boundary layers. In example, the authors find that the model is very effective for corner flows and flows that involve relaminarization, while performing rather ineffectively at recirculation regions.

Data from relaminarization regions help the model to better understand such phenomenon and to provide an appropriate boundary condition based on that. This motivates the authors to continue the research in this direction by adding more non-equilibrium phenomena to the training data to capture recirculation as well.

The cruise sector's workforce is highly diverse in terms of nationalities, age, and gender. However, diversity in the workforce does not guarantee business success.

Decades of research on the effects of diversity indicate that it can negatively or positively affect an organization's performance. A more diverse workforce does not automatically perform better financially, feels more committed to their companies, nor experiences higher levels of satisfaction. Indeed, data suggest diversity may produce more conflict, employee turnover, but if well managed can lead to greater creativity and innovation.

This chapter explores the cruise industry's diversity and inclusion challenges and management practices. To examine what cruise companies are doing in this field, we reviewed the public data of the four largest cruise companies. We analyzed how these companies define diversity, their commitment to inclusion, their practices, their metrics, and their primary objectives.

Firstly, there is no theoretical model that includes all variables that affect the management of diversity in the cruise sector. Secondly, companies communicate a commitment to inclusion in their corporate social responsibility (CSR) reports and refer to similar policies implemented by the hospitality industry. Thirdly, the main challenges are the multicultural environment and the limited female representation.

The major limitation of this study is the data source. We recommend further studies supported by nonpublic company data. We encourage cruise industry leaders to support the research to develop an empirically tested model that captures the specific variables that affect diversity management in the industry.

The paper has a twofold purpose: (1) to test the application of means-end theory to providers of hospitality goods and services, and (2) to explore this question in the context of winery tasting rooms when they had a unique opportunity to restructure their hospitality experience due to government restrictions in response to COVID.

A qualitative approach was adopted, and a convenience sample was used to conduct semi-structured laddering interviews. Forty interview transcripts were coded as means-end ladders, which were analyzed using a custom computer program to develop the implication matrix and the hierarchical value map.

This paper demonstrates that means-end is a useful approach to investigate the values and behaviors of the producer, specifically hospitality hosts. It finds that the principal goal of tasting rooms is to generate sales, and offering a compelling guest experience is the characteristic that contributes the most to achieving that goal. The staff and the atmosphere created for the guests are the two factors with the greatest influence on the guest experience.

This is the first paper to use means-end theory to study the hospitality host, or the producer of goods and services in general, and the first to study winery hospitality primarily through the lens of means-end theory. The study also helps fill a gap in research on tasting room sales focused on the winery’s goals.

First, to analyze the relationships between school effectiveness, as expressed in the improvement gained in academic achievements, and school structure; and second, to analyze the relationships between school effectiveness and school features (e.g. ethnicity, SES, rural vs. central location, class size, prior achievements, and per-student allocation) and to measure their contribution to the explained variation in school effectiveness.

Research on school effectiveness emphasizes the examination of school characteristics that have been found to contribute more to promoting school outcomes. Therefore, by using regression models the purpose of the research is twofold.

The research findings reveal positive significant relationships between school effectiveness, and students' prior achievements, and SES. However, school effectiveness is less related to school structure.

Future research in the field of school effectiveness should extend beyond solely focusing on academic achievements and include the evaluation of broader aspects of students' development, such as values and self-esteem. Recognizing the multifaceted nature of schools, it is crucial to incorporate additional variables into ongoing research efforts to provide a more comprehensive understanding of overall school effectiveness. Moreover, considering the diverse stakeholders involved in the educational process, including parents, teachers, principals, and the community, future studies should strive to examine the collective impact of these factors on school effectiveness. However, it is important to acknowledge the limitations of our study. Our research is constrained by a specific sample size, which may limit the generalizability of our findings. We assert that future studies with larger and more diverse sample sizes would strengthen the validity and significance of our results, allowing for broader implications and insights into enhancing school effectiveness.

This research holds significant importance in understanding the relationships between school effectiveness, school structure, and various school features in the context of Israeli primary schools. By examining the impact of school structure on academic achievements and exploring the contributions of other school-related factors, this study provides insights into the dynamics of educational effectiveness for educational stakeholders.

This paper explores sustainability within supply chain management and its link towards resilience through the lens of ethics. The wicked problem of sustainability impacts supply chains and society at large, and the ability of supply chains to remain viable in the future is based on the sustainability of business practices. The paper argues that a substantive proportion of sustainability mechanisms (such as the Triple Bottom Line approach and codes of conducts) can be viewed within the ethical paradigm of deontology, whereby the morality of an action is based on adherence to rules. However, there are numerous critiques of such approaches and their success in creating more sustainable practices. Therefore, the paper proposes there is a need for systems thinking approaches to be incorporated into the exploration of the link between ethics, sustainability and supply chain resilience. Using two examples of social sustainability issues in Australia – modern slavery legislation and horticultural ¹ worker exploitation – the paper demonstrates the utility of various systems thinking methodologies to explore the complexity of these issues. The paper sets out a conceptual call to arms for researchers and practitioners to apply a holistic lens towards how the morality of actions is shaped and influences supply chain sustainability practices.

This chapter reviews research on age-related differences in how consumers process information. Specifically, it discusses many of the effects of aging on the quality and quantity of consumers' sensory, cognitive, and emotional functioning. Some studies suggest that the manner in which elderly (age 65 and over) consumers process information may render them more vulnerable than young and middle-aged consumers to malign persuasion attempts. This chapter reveals that age has selective effects on information processing such that elderly consumers are sometimes more susceptible to marketing influence and sometimes they are less susceptible.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.