ABSTRACT Consumer research has largely treated loneliness as a chronic, trait‐level deficit that consumers compensate for through consumption. Much less is known about situational loneliness, the transient yet emotionally charged form of loneliness that arises within consumption itself, particularly in extraordinary, liminal experiences that suspend ordinary roles, routines, and relational anchors. Drawing on netnography combined with autonetnography across two large online communities of solo female travelers and using Interaction Ritual Chain theory as an analytical lens, we identify three discursive rituals through which members collaboratively rework the meaning of loneliness: (1) Escaping loneliness, (2) embracing loneliness, (3) evolving through loneliness. We make two contributions. First, we reconceptualize situational loneliness not as a failure of extraordinary experience but as a central affective characteristic of liminality. Second, we extend the Interaction Ritual Chain theory to asynchronous digital communities. We show that discursive entrainment sustains ritual dynamics without bodily co‐presence, and we develop the concept of stored emotional energy—archived, high‐emotional‐energy narratives that act as ritual repositories, initiating newcomers into the community's emotional logic long after the original encounter.
Categorization strategies differ based on an item's origins, for example, those that are natural versus human-made. However, it is unclear if and how foods fit into these categories since these can be both natural and human-made (processed), and both types of foods have a history of human intervention. Across two studies, 189 four- and five-year-olds (M-age = 5.09, > 50% White), six- and seven-year-olds (M-age = 6.69, > 50% White), and adults (M-age = 19.34, > 50% White) were recruited from a Northeast community in the United States. They viewed pictures or were told about natural and processed foods, natural kinds, and artifacts undergoing transformations, and were asked if the transformed item remained the same. Category membership judgments differed for foods and non-foods, and for older participants, between processed and natural foods. This suggests that food differs from non-food domains, which requires more than a single categorization strategy.
This study aims to address the convergence of boundaries in Tourism, Hospitality, Events, and Leisure (THEL) fields. We offer a synthesized definition of THEL and discuss a set of meta-concepts that define the core and knowledge structure of THEL. We argue that hospitality, experience, and place are unifying constructs upon which all THEL scholarship activities and practice fields are established. The paper invites a system-wide conversation to stimulate thinking regarding academic discipline building at a time when technological and societal forces are transforming work, life, and education. This research contributes to modernization of THEL and engenders new perspectives to guide future scholarship. This paper invites a reassessment of curricular structures to stay relevant and adaptive to changing needs of contemporary societies.
Artificial intelligence (AI) is a fast-growing field focused on modeling and machine implementation of various cognitive functions with an increasing number of applications in computer vision, text processing, robotics, neurotechnology, bio-inspired computing and others. In this chapter, we describe how AI methods can be applied in the context of intracranial electroencephalography (iEEG) research. IEEG data is unique as it provides extremely high-quality signals recorded directly from brain tissue. Applying advanced AI models to these data carries the potential to further our understanding of many fundamental questions in neuroscience. At the same time, as an invasive technique, iEEG lends itself well to long-term, mobile brain-computer interface applications, particularly for communication in severely paralyzed individuals. We provide a detailed overview of these two research directions in the application of AI techniques to iEEG. That is, (1) the development of computational models that target fundamental questions about the neurobiological nature of cognition (AI-iEEG for neuroscience) and (2) applied research on monitoring and identification of event-driven brain states for the development of clinical brain-computer interface systems (AI-iEEG for neurotechnology). We explain key machine learning concepts, specifics of processing and modeling iEEG data and details of state-of-the-art iEEG-based neurotechnology and brain-computer interfaces.
