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Anthropomorphism in Artificial Intelligence (AI)-powered devices is being used increasingly frequently in consumer-facing situations (e.g., AI Assistants such as Alexa, virtual agents in websites, call/chat bots, etc.), and therefore, it is essential to understand anthropomorphism in AI both to understand consequences for consumers and to optimize firms' product development and marketing. Extant literature is fragmented across several domains and is limited in the marketing domain. In this review, we aim to bring together the insights from different fields and develop a parsimonious conceptual framework to guide future research in fields of marketing and consumer behavior.

We conduct a review of empirical articles published until November 2021 in Financial Times Top 50 (FT50) journals as well as in 41 additional journals selected across several disciplinary domains: computer science, robotics, psychology, marketing, and consumer behavior.

Based on literature review and synthesis, we propose a three-step guiding framework for future research and practice on AI anthropomorphism.

Our proposed conceptual framework informs marketing and consumer behavior domains with findings accumulated in other research domains, offers important directions for future research, and provides a parsimonious guide for marketing managers to optimally utilize anthropomorphism in AI to the benefit of both firms and consumers.

We contribute to the emerging literature on anthropomorphism in AI in three ways. First, we expedite the information flow between disciplines by integrating insights from different fields of inquiry. Second, based on our synthesis of literature, we offer a conceptual framework to organize the outcomes of AI anthropomorphism in a tidy and concise manner. Third, based on our review and conceptual framework, we offer key directions to guide future research endeavors.

Carbon emission is one of the most important problems of today. In this framework, it is important for countries to take the necessary actions to solve this problem. Energy use is one of the most important causes of carbon emissions. Choosing fossil fuels in this process increases the carbon emission problem. Therefore, it is understood that countries should be more sensitive about energy types. In this context, renewable energy (RE) sources are recommended by experts. However, due to some problems of these energy types, it does not seem possible to meet all energy needs from these sources. It is thought that nuclear energy will produce a permanent solution to the carbon emission problem. In this context, it is recommended that the use of nuclear energy be put on the agenda by countries.

Green tourism and ecotourism have close meanings focusing on environmental factors and are sometimes related to rural tourism in general or a type of ecotourism itself. These forms emerged due to the concerns to protect the environment, and greater consideration has been given to tourism development and the notion that it should be approached carefully in light of recognizing its impacts.

The thematic concepts of green and responsible tourism raised questions about the definitions of both notions and their nexus of sustainability. Thus, the current chapter looks for an overview to understand green and responsible tourism, their emerging, definitions, practices and their role in the current scenario in the post-pandemic era.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

The victim’s rights movement and restorative justice (RJ) have gained momentum around the world. More laws and policies have focused on crime victims and their families. Western literature suggests that the victim’s family suffers physical, emotional, and financial tolls and that the power of the victim’s family in pursuing justice for their loved ones remains limited. This is particularly concerning within the political and legal context of the abolitionist movement, innocence project, and human rights groups’ campaigns against police torture. Grounded in the perspectives of RJ and Chinese legal culture, this study examines the victim’s family, represented by Ding and senior Yu, of the Nian Bin capital murder case. Drawing on published reports and using the thematic content analysis method, this study examines the following aspects of victim’s family in a death penalty case: 1) victim family’s physical, emotional, and financial tolls; 2) victims’ family and the criminal justice system; 3) victims’ family and the media; and 4) the relationship between the victims’ and the accused’s families. This study concludes with discussions of the competing goals of families impacted by a crime and RJ practices that would help mitigate the loss of the victim’s family and enhance their confidence in the criminal justice system.

The research question is how home country corruption and nationalism may affect operations of BRIC multinational enterprises. BRIC composition permits a comparison of two authoritarian regimes and two constitutional democracies. Each BRIC features a different combination of corruption and nationalism. The chapter adds South Africa information for two limited reasons. First, from 2010 South Africa is a member of the BRIC summit process. South Africa is an important entry point to Africa, for BRIC multinationals and particularly for China. Second, concerning corruption and nationalism South Africa is analytically useful as a control context that helps illustrate but does not appear to change highly exploratory BRIC findings.

The chapter draws on limited literature and information concerning corruption and nationalism in BRICs to suggest tentative possibilities. Transparency International provides bribe payers index estimates for 28 large economies, with important multinational enterprises, and corruption perceptions index estimates including those 28 countries. These estimates include the four BRICs and South Africa. The available sources suggest some suggested findings about varying impacts of home country corruption and nationalism on operations of BRIC multinationals.

China and Russia are authoritarian regimes in transition from central planning-oriented communist regimes. They are global military powers, expanding influence in their respective regions. Brazil, India, and South Africa are constitutional democracies. India, a nuclear-armed military power, seeks a regional leadership role in South Asia. Brazil and South Africa are key countries economically in their regions. BRIC multinationals are positioned between home country and host country conditions. Chinese and Russian multinationals may reflect a stronger nationalistic tendency due to home country regimes and ownership structure.

The chapter is an original but highly exploratory inquiry into impacts of corruption and nationalism on BRIC multinationals. Extant BRIC literature tends to understudy effects of home country corruption and nationalism on managerial mindset and incentives in either commercial or state-owned enterprises.