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Barriers to employment are a significant issue in the United States and abroad. As civil rights legislation continues to be enforced and as employers seek to diversify their workplaces, it is incumbent upon the management field to offer insights that address obstacles to work. Although barriers to employment have been addressed in various fields such as psychology and economics, management scholars have addressed this issue in a piecemeal fashion. As such, our review will offer a comprehensive, integrative model of barriers to employment that addresses both individual and organizational perspectives. We will also address societal-level concerns involving these barriers. An integrative perspective is necessary for research to progress in this area because many individuals with barriers to employment face multiple challenges that prevent them from obtaining and maintaining full employment. While the additive, or possibly multiplicative, effect of employment barriers have been acknowledged in related fields like rehabilitation counseling and vocational psychology, the Human Resource Management (HRM) literature has virtually ignored this issue. We discuss suggestions for the reduction or elimination of barriers to employment. We also provide an integrative model of employment barriers that addresses the mutable (amenable to change) nature of some barriers, while acknowledging the less mutable nature of others.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

The study examines how the tourism concept developed amongst Chinese students in the United States from 1905 to current juncture. Through the contrasting views presented in two landmark mega-reviews of Chinese students in the United States and France, the authors concluded that tourism enhances understanding of the host countries resulting in more comprehensive and overall success of Study Abroad Program. After the reopening, China encouraged touring the host country but with extreme financial constraints in the beginning. Tourism of Chinese students became popular and fashionable only in late 1990s with China’s economic prosperity and policy changes to open tourism to foreign countries. As tension with China grew during the COVID pandemic, Chinese students in the United States were used by the Trump Administration as a lever in trade and diplomatic negotiation, and touring became wishful.

According to the U.S. Committee for Refugees (2002) there were approximately 15 million refugees in the world in 2001, of which over three million were African. Refugees are persons who flee to a different country to escape persecution based on personal or group characteristics such as race, ethnicity, religion, nationality, social group, political opinion, or armed conflict, and lack of a durable solution (U.S. Committee for Refugees, 2001). For example, in Burundi, a small African country of about 6 million people, the civil war between the Tutsi and the Hutu has forced over half a million refugees to seek shelter in other African countries, Europe, and the United States (UNHCR, 2000).

To demonstrate why leaving the ethnographic field provides an excellent opportunity for the researcher to engage in reflexivity on all aspects of the research and especially on issues of power, age and gender.

An autobiographical reflection on a 40 year career as an ethnographer.

The autobiographical literature and the methods literature on ethnography has neglected leaving the field, and the opportunities that process provides for reflectivity. The author reflects on issues of power, age and gender as they have been implicated in the various fieldsites studied in her career. The particular field site featured centrally is two martial arts, savate and capoeira.

To improve the quality of reflexive writing on leaving the field.

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.