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Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

Artificial intelligence and augmented reality are two key tools gaining importance in the digital era due to their wide range of applications in different fields and sectors. Industry 4.0 lays emphasis principally on the technology used to help the business remain competitive and sustainable. Sustainable development goals are another important objective of the UN which has laid responsibility for every business to support addressing the global challenges. Purpose: This chapter essentially aims to present the standpoint of artificial intelligence and augmented reality in meeting the sustainability perspective of organizations. Information about the study is gathered through secondary approaches, critically reviewing published literature, scientific reports, and statistical data accessible through business reports, and corporate websites. Further analyzed to present the perspectives of the authors in the study. Globally artificial intelligence market size is predicted to reach $190 billion by 2025, while the funding for startups doubled during the period 2011–2020 globally. The investment in artificial intelligence is going to reach $500 by 2024 resulting in substantial revenue returns. The augmented reality market size could reach $97 billion by 2028. Artificial intelligence today is increasingly used in many fields and is attracting multiple applications in many sectors such as manufacturing, retail, education, IT, and health care and has also contributed to sustainable development the same time by providing energy conservation options, optimization, and reduction of resources, minimizing wastage, offering timely assistance on maintenance schedules, practices which are enabling organizations to reach closer to sustainability and transformation.

Due to the COVID-19 pandemic, many people have experienced career shocks, especially employees in the hotel industry. To address how to retain talent in the industry, this study aims to examine the joint impacts of employee resilience, work social support and proactive personality on hotel employees’ career change intentions.

A survey questionnaire was developed to test the proposed framework. Data from 339 current hotel employees in the USA was analyzed using the PROCESS model.

Results show a significant three-way interaction, indicating that for less proactive employees, resilience is negatively associated with career change intentions. However, for highly proactive employees, an additional situation cue in the form of strong work social support is required to activate the expression of resilience. Highly proactive and resilient employees who receive strong supervisor or coworker support during the pandemic have lower career change intentions. However, highly proactive employees who receive weak supervisor or coworker support exhibit similar levels of career change intentions, regardless of resilience level.

Hotel managers should consider helping employees enhance their resilience and overcome career shocks by providing training and resources and establishing a learning culture. More importantly, it is essential to offer strong supervisor and coworker support to promote resilience among proactive employees. Hotel managers should actively promote strong work social support, and offer training and counseling opportunities to promote employee retention during the pandemic.

To the best of the authors’ knowledge, this study is the first to examine employee resilience in the hospitality field. This study contributes to the employee resilience literature as well as trait activation theory by examining situational cues that can activate employee resilience and by providing empirical evidence to reveal the boundary conditions of how employee resilience impacts career change intentions.

Space tourism is fairly neglected in academic research and requires further exploration. Public reaction on social media offers great insights to understand the patterns of behaviour but is often ignored as a potential data source. Thus, this study aims to fill the gap by add to the literature on space tourism, social media analytics and behaviour.

The study adopts a qualitative approach and uses Twitter data for drawing conclusions. An exploratory design is used by analysing 10,000 tweets through unsupervised machine learning and two sets of analysis were conducted. First, sentiment analysis is performed using NRC Emotion Lexicon, which classifies the data as per eight basic emotions and polarity as positive and negative. The findings are complemented with a comparison cloud. Second, LDA Topic modelling using Gibbs Method is used to find ten broad topics that are used for discussions in space tourism tweets. Data visualisation technique is used to depict results using R language on RStudio.

A total of 21,784 emotions have tapped using the NRC Emotion Lexicon. Results indicate the dominance of positive sentiments (25%) with it surpassing the negative sentiments by many folds. The top emotions include trust and anticipation. The LDA-based Topic modelling identified seven correlated topic models that have been grouped by the author as space tourism in media, aspirations, ethical issues, criticism, descriptive, symbolism and miscellaneous.

To the best of the author’s knowledge, no study has attempted to study the response of space tourism on social media by tapping discussions in the form of Tweets. Thus, this study adds extensively and acts as a preliminary investigation on the public sentiments of space tourism on social media.

This study aims to evaluate student motivation before and after the summer internship, in other words, to evaluate whether the summer internship affects male and female motivations differently.

Investigating whether the motivation score predicts grade point average was included by adopting a quantitative methodology. The defense engineering summer internship project provides candidates who learned defense engineering knowledge after the project an opportunity to generate motivation of involving in defense engineering discipline and industry.

This study found a marginal significant main effect on extrinsic motivation – introjected (EMIN) and intrinsic motivation to experience stimulation (IMS) in a summer internship. Furthermore, the study reveals that amotivation construct significantly predicted gender and previous academic performance (GPA) pre-summer internship, and amotivation, Extrinsic motivation – external regulation (EME) and extrinsic motivation – introjection (EMIN) significantly predicted GPA post-summer internship.

The project was done with support from the U.S. Department of defense grant N00014-19-1-2728. These findings are investigated from the project.

This paper aims to explore various cultural and behavioral issues associated with the problem of investment fraud in Indonesia.

By examining multiple cases of investment fraud in Indonesia as well as reviewing publicly available government reports, this study highlights several important cultural and behavioral issues associated with the susceptibility of Indonesian financial services consumers to investment fraud to understand better the dynamics of the victimization process. By using multiple cultural and behavioral theories, this study demonstrates how such issues shape the interactions between investment fraudsters and investment fraud victims.

This study demonstrates that multiple cultural and behavioral factors have created and shaped an environment where fraudsters can exploit people’s behavioral loopholes for their fraudulent schemes. In particular, the high power distance and high collectivism have been identified by this study as contributing to the high level of materialism in the country, which in turn makes people more susceptible to the temptation of get-rich-quick schemes. Investment fraudsters, being students of human behavior, use their behavioral knowledge to devise various means to deceive their victims. They use multiple psychological principles to stimulate target victims “gullibility to make them more vulnerable to fraudulent persuasion. In many cases, even financially literate people are not immune to fraudsters” deceitful messages. This study highlights gullibility production as a foundation for investment fraudsters to devise their means by which victims are manipulated to accept certain beliefs that depart from facts and evidence.

This paper contributes to the innovation in anti-fraud practice by building a better understanding of multiple cultural and behavioral issues associated with investment fraud victimization.

This paper brings a new perspective into the field of anti-fraud to stimulate innovation, in particular in investment fraud prevention.

The purpose of this study is to discover the components of virtual leadership in universities and higher education institutions during Covid-19 pandemic.

This research uses the qualitative research design, and semi-structured interviews with senior leaders were conducted with non-random purposeful and snowball sampling methods. Also, grounded theory was used for analyzing, coding and classifying the data.

After analyzing data, five main components influencing virtual leadership including leader's personality traits, followers' characteristics, technological, environmental and organizational components were identified.

As leadership, particularly virtual leadership, highly depends on context such as the level of technology in that certain region, the results cannot be generalized to other sectors or countries.

The research advances leadership development theory by highlighting the main components of virtual leadership in educational sector as a comprehensive perspective and complement the other aspects of virtual leadership such as consequences and challenges investigated by other studies.

Prior studies have shown that physician-led hospitals have several advantages over non-physician-led hospitals. This study seeks to test whether these advantages also extend to periods of extreme disruptions such as the COVID-19 pandemic, which affect bed availability and hospital utilization.

The authors utilize a bounded Tobit estimation to identify differences in patient satisfaction rates and in-hospital utilization rates of top-rated hospitals in the United States.

Among top-rated US hospitals, those that are physician-led achieve higher patient satisfaction ratings and are more likely to have higher utilization rates.

While the COVID-19 pandemic generated greater demand for inpatient beds, physician-led hospitals improved their hospitals’ capacity utilization as compared with those led by non-physician leaders. A longitudinal study to show the change over the years and whether physician Chief Executive Officers (CEOs) are more likely to improve their hospitals’ ratings than non-physician CEOs is highly recommended.

Recruiting and retaining physicians to lead hospitals, especially during disruptions, improve hospital’s operating efficiency and enhance patient satisfaction.

The paper reviews prior research on physician leadership and adds further insights into the crisis leadership literature. The authors provide evidence based on quantitative data analysis that during the COVID-19 pandemic, physician-led top-rated US hospitals experienced an improvement in operating efficiency.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”