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The Metaverse has the potential to form the next pervasive computing archetype that can transform many aspects of work and life at a societal level. Despite the many forecasted benefits from the metaverse, its negative outcomes have remained relatively unexplored with the majority of views grounded on logical thoughts derived from prior data points linked with similar technologies, somewhat lacking academic and expert perspective. This study responds to the dark side perspectives through informed and multifaceted narratives provided by invited leading academics and experts from diverse disciplinary backgrounds. The metaverse dark side perspectives covered include: technological and consumer vulnerability, privacy, and diminished reality, human-computer interface, identity theft, invasive advertising, misinformation, propaganda, phishing, financial crimes, terrorist activities, abuse, pornography, social inclusion, mental health, sexual harassment and metaverse-triggered unintended consequences. The paper concludes with a synthesis of common themes, formulating propositions, and presenting implications for practice and policy.
RESUMO
Misinformation or fake news has had multifaceted ramifications with the onset of the Covid-19 pandemic, creating widespread panic amongst people. This study investigates the impact of misinformation/ fake news (on internet platforms) on consumer buying behavior, impact of fear (created by fake news) on hoarding of essential products and consumer spending and finally impact of misinformation-induced panic buying on supply chain disruptions. It draws upon the consumer decision theory and the cognitive load theory for explaining the psychological and behavioral responses of consumers. The study follows an inductive approach towards theory building using a multi-method approach. Initially, a qualitative research method based on interviews followed by text-mining has been used followed by analysis using python for topic modelling using Latent Dirichlet Allocation (LDA). The findings revealed several prominent themes like consumer shift to online buying, two contrasting spending intentions namely financial security and compensatory consumptions, irrational panic buying, uncertainty/ambiguity of government protocol and norms, social media fraudulent practices and misinformation dissemination, personalized buying experience, reduced trust on news and marketers, logistics and transportation bottlenecks, labor shortage due to migration and plant closures, and bullwhip effect in supply chains.
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Monitoring buyer experience provides competitive advantages for suppliers as buyers explore the market before reaching a salesperson. Still, not many B2B suppliers monitor their buyers' expectations throughout their procurement journey, especially in MSMEs and SMEs. In addition, the inductive research on evaluating buyer experience in buyer-supplier relationships is minimal, leaving an unexplored research area. This study explores antecedents of buyer experience during the buyer-supplier relationship in MSMEs and SMEs. Further, we investigate the nature of the influence of extracted precursors on the buyer experience. Firstly, we obtain the possible antecedents from the literature on buyer-supplier experience and supplier selection criteria. We also establish hypotheses based on transaction cost theory, resource-based view (RBV), and information processing view. Secondly, we employ an investigation based on the social media analytics-based approach to uncover the antecedents of buyer experience and their nature of influence on MSMEs and SME suppliers. We found that buyer experience is influenced by sustainable orientation, management capabilities (such as crisis management and process innovation), and suppliers' technology capabilities (digital readiness, big data analytical capability).
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Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines and obsessive-compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81-84%, with a normalized discounted cumulative gain of 79-81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models' treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
RESUMO
Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The "Digital Service Usage Satisfaction Model" has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.
RESUMO
Among services, the immense growth of Indian tourism in the last years has attracted the interest of practitioners, researchers, and governments. Service experiences at the point of encounter can impact the consumption of these tourism services extensively. However, measuring the service experience at the point of service encounter becomes a bit difficult. The tourists who visit India often share their experiences immediately regarding their service encounter in social media. These tweets often have high sentiments and emotional content. In this study, we attempt to identify factors which impact customer service experience, at the point of service encounter, by mining social media discussions. After removing spurious tweets, 7,91,804 tweets were identified and analysed in this study. Factors such as accessibility, accommodation, assurance, cultural attraction, Jugaadu service flexibility, cleanliness, hospitality, price, restaurant, and security were identified using topic modelling, topic association mining, and sentiment analysis. We attempt to model these experiences and their drivers across five zones of India, namely North, South, East, West, and North-East India. Our inferential analysis highlights that the importance and impact of these factors differ significantly zone wise across India, which indicates high location specificity of factors which impact the customer service experience. The study elaborates implications for theory and practice based on our findings.
