Singh, Amit

In the current landscape, smartwatches have gained popularity as wearable devices thanks to their fitness tracking and health monitoring capabilities. However, the abundance of features and options has made it challenging to select the right alternative. In this regard, we propose a text analytics-based product recommender system that leverages online reviews as peers' recommendations and creates a shortlist of available alternatives based on existing users’ perceptions. It uses a pre-trained transformer-based aspect-level sentiment analysis algorithm, InstructABSA, to quantify consumer sentiments expressed in textual reviews, which are analysed using the integrated House of Quality (HoQ) and Preference Ranking Organisation Method for Enrichment Evaluation-II (PROMETHEE-II) to construct a relative performance index for the selected manufacturers. The proposed framework may assist potential customers in making well-informed purchase decisions and help manufacturers understand their relative position in the market. It also helps customers compare the alternatives concerning selected features and associated consumer perceptions. In addition, manufacturers may use it to discover their perceived strengths and weaknesses. The proposed framework is tested on a review dataset pertaining to 12 smartwatch manufacturers, and their relative ranks are proposed. © 2024 Elsevier Ltd

Purpose: As an emerging technology, Radio Frequency IDentification (RFID) and blockchain have the potential to disrupt many areas of business and social structure. However, it is loaded with significant technical, social, legal, financial and ethical complications that bring difficulty in its widespread use within the public distribution system (PDS). This research aims to analyze the barriers to integrated RFID and blockchain adoption in developing countries' PDS. Furthermore, this study also aims to validate the proposed framework against the Indian PDS. Design/methodology/approach: The proposed framework consists of 10 potential barriers to integrated RFID and blockchain adoption. To identify the barriers, this study referred to the extant literature followed by consultations with domain experts. This study prepared the DEMATEL-based questionnaires, collected the data from four domain experts and analyzed them using an integrated Grey-DEMATEL approach. Findings: The obtained results provide a precise list of barriers and the correlations among them. From the results, it is concluded that the unavailability of a skilled workforce at affordable cost, lack of knowledge about privacy level and unclear return on investment and benefits are the most critical blockchain adoption barriers in the context of Indian PDS. Originality/value: This research proposes a framework consisting of 10 integrated RFID and blockchain adoption barriers in relation to Indian PDS. It also proposes a method for analyzing causal interrelationships between the barriers while allowing for data input from domain experts. Consequently, the framework is capable of coping with experts' biases and data scarcity.

As the metaverse emerges as a new frontier of human interaction, understanding public perceptions of crime in virtual spaces becomes crucial. This study delves into public perceptions of such crimes, focusing on reported incidents of sexual assault in virtual reality environments. We uncover complex dynamics, shaping threat perception in immersive digital realms by analysing YouTube comments through an innovative mixed-methods approach combining machine learning and empirical analysis. Our findings reveal that social norms and expectations are pivotal in influencing perceptions of threats, while technology-mediated interactions correlate with reduced perceived risks. Surprisingly, the oft-discussed blurring of virtual and physical realities shows no significant impact on threat perception. This research contributes to the expanding literature on the social construction of reality and public perception of emerging technologies. The results have implications for the development and governance of metaverse platforms, highlighting the necessity for comprehensive user education initiatives and culturally sensitive approaches to community guidelines and safety features. © 2024 Elsevier Ltd

AI companion applications are transforming how people form and maintain relationships in the digital world, with millions of users now engaging in emotional and social interactions with AI agents. Understanding what drives user satisfaction becomes crucial as these applications become increasingly integrated into users' daily lives. Drawing on Orlikowski's practice lens theory and employing text mining and hierarchical clustering methodologies on user reviews enhanced with focus group discussions, this study identifies two key determinants of user satisfaction: Functional Capability Perception and Affective Social Attunement. The analysis of 156,637 user reviews and insights from diverse participants reveals that satisfaction emerges through users' simultaneous negotiation of technical proficiency and emotional boundaries in their AI interactions. Functional capabilities positively influence satisfaction, while deeper emotional engagement creates a paradoxical effect where users become more sensitive to AI limitations. The study demonstrates how societal conditions shape these dynamics, with evaluation criteria evolving from simple acceptance to deeper interpersonal connections, reflecting changing attitudes toward human-AI relationships. Our mixed methods approach uncovers the contextual factors and usage patterns that shape how users integrate these technologies into their daily routines and emotional ecosystems. These insights advance our understanding of human-AI relationships while providing practical guidance for developers on creating adaptive systems that respond to evolving user needs. By understanding these complex dynamics, stakeholders can develop AI companions that enhance human relationships rather than attempting to replace them. © 2025 Elsevier Ltd