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The purpose of this paper is to develop an integrated model assessing the frequency and timing between reports on the Association for the Advancement of Sustainability in Higher Education (AASHE) Sustainability Tracking, Assessment and Rating System (STARS) reporting the framework by higher education institutions (HEIs) and the relationship between the STARS score and reputation (enrollment), finances (endowment) and performance (emissions).

The development of the theoretical model is based on learning, signaling and legitimacy theories. This study collects data from the AASHE STARS to indicate the rating level of 202 HEIs, control variables, enrollment, endowments and emissions. The hypotheses were tested using generalized linear models.

Findings suggest that as HEIs report on their sustainability activity, they learn to report better but that there is also an “un-learning” aspect if the HEI skips reporting in a period. The results support the main hypothesis that there is a relationship between reporting and engagement with the HEIs in the form of enrollment and endowments. Finally, the findings provide evidence that the HEIs’ reporting is associated with a reduction in emissions.

The findings suggest that HEIs should develop a reporting strategy on a standardized framework such as AASHE STARs and they ought to codify the approach to learn from prior reporting. Students and alumni are increasingly seeking to engage the HEI in the sustainability process and the report is a mechanism for signaling activities.

The findings suggest that AASHE STARS scores may be used by HEIs as a signaling mechanism to stakeholders of their commitment to sustainability. The signal is a mechanism to reduce information asymmetry between the HEI and stakeholders who may want more information on the institution’s attempts toward sustainability but lack access to information. Further, HEI partners have a mechanism to assess the overall level of commitment of the HEI toward sustainability and can, therefore, engage accordingly.

There has been significant work on signaling theory and sustainability. However, the relationship between STARs reporting as a signal that legitimates the HEI, learning how to report well and HEI performance has received less attention. The current study demonstrates that the STARS framework as a reporting mechanism signals the HEIs’ level of commitment to sustainability thereby legitimating it resulting in improved performance.

The Nanoscience Project at Hampton University (NanoHU) responds to the international call for more workers in the field of science, technology, engineering, and mathematics (STEM) who are nano-savvy and prepared for engagement in the fourth industrial revolution. The project’s initial intent to answer statewide and national initiatives was congruent with Hampton University’s (HU) desire for increased diversification of research interests across HU and enhanced the preparation of its students for doctoral degrees. Funded by the National Science Foundation, the five-year project (2012–2017) purposed to develop and systematically implement an integrated, multidisciplinary STEM research and education program in nanoscience at HU. Evidence of NanoHU’s success is demonstrated in the following accomplishments at the University: (1) a new Nanoscience Minor, (2) a new “Introduction to Nanoscience” course that has had a total enrollment of 82 students from STEM and non-STEM fields, (3) the NanoHU Scholars Program that has prepared 23 Scholars for entry into graduate programs and 12 NanoHU Fellows for similar pursuits, (4) a Faculty Development Program that has supported a total of 20 STEM and non-STEM faculty members, (5) a NanoHU Seminar Series that has informed the HU community about the science, business, legal, and ethical topics pertaining to nanoscience and nanotechnology, and (6) a viable outreach program that has prepared high school students (NanoHU Pioneers) for successful matriculation as STEM majors at the college level and stimulated STEM interest in the surrounding community. It is worth emphasizing that execution of the project also resulted in engagement between STEM and non-STEM constituents of the University, establishing a platform for a formal science, technology, engineering, arts, and mathematics (STEAM) institutional initiative. Efforts to communicate the importance of nanoscience to the HU community through seminars resulted in an infusion of nanoscience modules in STEM and non-STEM courses including courses in English, Journalism, Ethics, and other pre-law courses. Although NanoHU is specific to the needs of HU, its collaborative construct promises to be an innovative model for STEM and STEAM programs at other institutions with a similar construct.

This paper provides an overview of the Post 9/11 GI Bill and outlines the steps that the Obama administration has taken to provide for the educational and training needs of veterans and other eligible dependents.

This paper offers a policy analytic review of the extant literature on veterans’ education and regulations pertaining to the Post 9/11 GI Bill.

It is found that although the Post 9/11 GI Bill was enacted with the right intentions, several changes had to be made to protect both veterans and tax payers as well as the integrity of the GI Bill.

To help educators understand the role that the Obama administration has played in advancing veteran education and training.

This chapter clarifies the “dark tourism” in Vietnam; a case study of Hoa Lo Prison Relic. Development orientation of “dark tourism” in Vietnam contributes to healing the wounds of war. The author mainly uses a system approach, method of analysis and synthesis, and method of comparison. “Dark tourism” in Vietnam has not really developed as a specific type of tourism like ecotourism or meetings, incentives, conferences, and exhibitions (MICE) tourism. At Hoa Lo Prison historical site, “dark tourism” has been effectively exploited: (1) in the first 6 months of 2023, Hoa Lo Prison welcomes nearly 295 thousand visitors, charging fees of 7 billion VND, the situation of “sold out” takes place regularly; (2) many unique tourism products, one of the successful and widespread tourism products must be mentioned the “night tour” experience; (3) many creative forms of sightseeing with automatic explanation system so that visitors can experience 35 historical stories in their own way; and (4) always create the best conditions for visitors’ sightseeing experience. To promote dark tourism in Vietnam, it is necessary to execute some schemes: (i) enhance the sense of responsibility for peaceful tourism in the operation of monuments; (ii) stakeholders coordinate the organization of the tour program and tourist routes with appropriate contents and forms for each target group, especially foreign tourists; (iii) there is cooperation between national and international experts in organizing the “dark tourism” program aimed at peace, preventing the risk of war and conflict, and overcoming the consequences of war; and (iv) application of scientific and technological achievements in “dark tourism” activities.

By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities.

Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware.

The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities.

The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait.

The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023).

The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues.

The drive to improve learning and safety in our nation’s public schools has resulted in the widespread adoption of zero-tolerance disciplinary policies. The practice of punishing any school infraction regardless of extenuating circumstances has been particularly detrimental to students of color. Black and Latino students are more likely to be suspended, expelled, and/or referred to law enforcement for nonviolent and/or minor infractions. Students who are removed from school fall behind academically and have an increased risk of being arrested and thrust into the criminal justice system. This reality has moved the Obama administration to urge school officials to abandon overly zealous disciplinary policies. However, the recommendations set forth by the Obama administration are nonbinding and fail to address the root causes of racially discriminatory school discipline practices.

Any meaningful effort to understand and/or disrupt the pattern of pushing students out of schools and funneling them into the criminal justice system must consider the adverse effects of the following three factors: (1) unchecked racial biases among school personnel, (2) inadequately resourced poor performing schools, and (3) the ever-expanding economic inequality in society. Omitting of any of these items from the guidelines and recommendations represents a glaring limitation of the Supportive School Discipline Initiative as a tool for addressing racial disparities in school discipline and the school to prison pipeline.

We aim to show that students of color would benefit from “need-based” educational reforms, a Presidential Administration that directly addresses racial inequality, and economic policies that target the most financially strapped communities.

Purpose – This chapter is about the author's experience working for Barack Obama's presidential campaign and the racial as well as race–gender dynamics of such work.

Methodology/approach – An autoethnographic analysis was conducted based on participant observation, primarily, door knocking in California and Texas and phone canvassing Americans across the country on a daily or weekly basis.

Findings – The fieldwork revealed the persistence of racially unequal discourse and stratification, not the workings of a post-racial society. While some Americans openly used the word “nig*er” or referenced lynching, more common were the coded forms of racism, such as the notion of “good” (Obama) versus “bad” Blacks. Also, terms like “Muslim” or “not-American” revealed a citizenship-based racism usually reserved for Asian Americans and Latinos now being levied against Black Americans. This more coded racialization hinged on a combination of subordinating Muslims as perpetual racial foreigners and fearing the “browning of America” brought on by immigration.

Research limitations/implications – As this study of a campaign assessed one point in time from an autoethnographic perspective, it is not generalizable to the United States. It, however, is an important window into the social processes involving race (and gender) when historic candidates and elections move the country in a direction it has never been to before and perhaps will never be to again.

Originality/value of paper – Although many scholars have done racial analyses of Obama's campaign and of our society's negotiation of race in relation to the man, few have conducted in-depth analyses from the vantage of a full-scale Obama campaign volunteer.

Celebrity endorsement has been profusely used to promote the consumption and purchase intention of diverse product categories; however, there is scarce research about whether celebrity endorsement effectiveness differs between brands and product categories. In this context, this study aims to answer this question: “Does consumer purchase behavior influenced by celebrity endorsement differ between brands and products?”.

To this end, a conceptual model of the influence of celebrity endorsement on consumer purchase behavior is examined through a multiple group structural equation modeling on two consumer samples (315 = brands; 308 = product categories). Samples were categorized as a celebrity promoting either a product category or a brand, and food products were selected as the product category for the investigation.

Findings indicate differences in the effectiveness of celebrity endorsement between brands and product categories: credibility is the most relevant variable influencing consumer purchase intention for product categories, while credibility and congruence both influence consumer purchase intention for brands. Similarly, the celebrity recommendation of brands generates greater purchase intention due to celebrity congruence with the endorsed product.

To the best of the authors’ knowledge, the present research is one of the first endeavors to compare the effectiveness of celebrity brand endorsement versus celebrity product endorsement on purchase intention.

The purpose of this paper is to explore the influence of culture on the sense of self-efficacy in teaching English as a Foreign Language of a group of university teachers in Vietnam. Research exploring the relationship between culture and self-efficacy is extremely rare despite the acknowledged importance of culture in the formation of self-efficacy beliefs.

This study took the form of qualitative research with diverse, data collection instruments: individual interviews, focus group discussions, observations and journaling.

Findings indicate that certain features of the Vietnamese cultural context impacted on the way the study teachers constructed their sense of self-efficacy. Specifically, under the influence of a Vietnamese sense of belonging, the study teachers tended to rely more on efficacy-building information from other people rather than from themselves. The perception of inequality in power may have heightened negative emotional arousal, thus contributing to a negative sense of self-efficacy among the teachers. The Vietnamese concept of face and the high status of teachers in the social hierarchy in part mediated teachers’ sense of self-efficacy.

The perceived burden of performing both parenting and teaching roles and responsibilities may have diminished the self-efficacy in teaching of female teachers.

The contribution and implications of the study are discussed.