Scholarly research on virtual reality (VR) is characterized by a dynamic tension between VR's potential and the challenges impeding its adoption. Grounded in a mixed‐methods systematic review, this research examines the drivers influencing consumer VR adoption by rigorously combining qualitative and quantitative analyses of 158 scholarly articles ranging from 1996 to 2023. Based on an extensive analysis of VR adoption literature, we introduce the Virtual Reality Integrated Adoption Framework (VRIAF), which is the first mixed‐methods systematic review focusing exclusively on VR adoption. This empirically substantiated model integrates key determinants of VR adoption such as consumer attitudes, perceived enjoyment, ease of use, social influences, and previous user experiences. The research identifies four pivotal themes through qualitative exploration, further elucidated by quantitative meta‐analyses and weight analyses. These themes encompass the user experience in VR environments, the role of VR in construction and design, the immersive attributes of VR technologies, and the ongoing technological advancements influencing adoption patterns. This research contributes significantly to the theoretical understanding of VR adoption and provides practical insights for VR professionals. By delineating future research directions, the study bridges the gap between theoretical exploration and practical application, offering a valuable resource for both scholars and practitioners in the field of VR.

Purpose: This study aims to examine YouTube comments relevant to sustainable fashion posted on fashion haul videos over the past decade (2011–2021). It is guided by two research questions: (1) How have sustainable fashion-related comments posted on YouTube fashion haul videos changed over time? and (2) What themes are relevant to sustainable fashion in the comments posted on fashion haul videos?Design/methodology/approach: A data set of comments from 110 fashion haul videos posted on YouTube was refined to only include comments with keywords related to sustainable fashion.Leximancer, a machine learning technique, was employed to identify concepts within the data and co-occurrences between concepts.Linguistic Inquiry and Word Countsoftware was employed to assess the prevalence of concepts and identify sentiment over time.Findings: Over the decade, the authors identified increased comments and conversations relevant to sustainable fashion. For instance, conversations surrounding sustainable fashion were linked to “waste” and “addicted” between 2011 and 2013, which evolved to include “environment” and “clothes” between 2014 and 2016, to “buy” and “workers” between 2017 and 2019 and “sustainable” between 2020 and 2021, demonstrating the changes in conversation topics over time.Practical implications: With increasing engagement from YouTube viewers on sustainable fashion, retail-affiliated content that promotes sustainable fashion is proposed as one approach to engage viewers and promote sustainable practices in the fashion industry, whereby content creators can partner with retailers to feature products and educate viewers on the benefits of sustainable fashion.Originality/value: The findings suggest that consumers are becoming more aware of and responsive to sustainable fashion. The originality of this research stems from identifying the source of this interest.

Purpose: The rise of conversational artificial intelligence (AI) bots such as ChatGPT highlights users’ anxieties and high expectations. This study aims to explore consumers’ views of AI conversational bots and examines their societal implications, emphasizing public perception as a fundamental factor in their acceptance and integration. Design/methodology/approach: This study combines manual and automated thematic analysis to understand public sentiment by analyzing 45,844 YouTube comments. The comments are collected from the top five nonsponsored English-language YouTube videos on ChatGPT, with comments extracted using Octoparse. Key themes and their relationships are identified through manual coding and further analyzed using Leximancer to enhance the depth and accuracy of the analysis by detecting patterns in large data sets. Findings: The analysis reveals three primary areas: empowerment through AI-enhanced capabilities, anxiety over AI-induced societal shifts and negotiating human–AI collaboration. Concerns are expressed about misinformation, privacy and the impact of AI on employment and human skills. Conversely, positive perceptions highlight AI’s role in education, personal productivity and medical diagnosis. These themes categorize public sentiment into techno-skepticism, techno-realism and techno-optimism, demonstrating the complex and diverse opinions on AI technology. Originality/value: This research bridges AI’s technical aspects with its social and ethical dimensions, providing a comprehensive understanding of public sentiment towards ChatGPT. It underscores the importance of examining consumer views as a foundational step in understanding AI’s broader societal impacts.

This research seeks to profile consumer segments formed during the COVID-19 pandemic via a set of psychographic consumption traits: Narcissism, Psychological Entitlement, Status Consumption, Fear of Embarrassment, and Fear of Missing Out. Based on a cluster analysis of 281 consumers, the data generated four distinct groups: Egalitarians, Agentic Egoists, Communal Egoists, and Conformists. Further, we compared the segments in their acquisition behavior as it pertains to importance of purchase, quantity of purchase, sharing of purchase, and willingness to pay for essential items. Our results showed that each cluster was associated with a unique set of consumer preferences. For instance, Egalitarians placed less importance on medical items. Conformists placed greater importance on acquiring disposable masks than others. Communal Egoists were interested in food-related items such as bottled waters and snacks. Agentic Egoists reported that they would spend more money on cold/cough medicines than Egalitarians and Conformists. Overall, our findings provide key insights and recommendations to retail managers. Some limitations include our sampling approach (i.e. US consumers) and determining clusters based on select psychographic traits. We acknowledge that there are other characteristics that can differentially influence consumers’ acquisition behavior during the pandemic.