The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potential of AI agents and agentic systems to reshape industries by decentralizing decision-making, redefining organizational structures, and enhancing cross-functional collaboration. Specific applications include healthcare systems capable of creating adaptive treatment plans, supply chain agents that predict and address disruptions in real-time, and business process automation that reallocates tasks from humans to AI, improving efficiency and innovation. However, the integration of these systems raises critical challenges, including issues of attribution and shared accountability in decision-making, compatibility with legacy systems, and addressing biases in AI-driven processes. The paper concludes that while agentic systems hold immense promise, robust governance frameworks, cross-industry collaboration, and interdisciplinary research into ethical design are essential. Future research should explore adaptive workforce reskilling strategies, transparent accountability mechanisms, and energy-efficient deployment models to ensure ethical and scalable implementation.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

Artificial Intelligence (AI) is among the emerging technologies that offer potential competitive advantages, but there is insufficient evidence regarding its use in B2B SMEs in India. This study uses empirical methods to investigate the factors that lead to the successful adoption of AI practices by B2B SMEs in India, as well as the outcomes of this adoption. A conceptual model is formulated using the technology-organization-environment (TOE) framework, which examines how AI enablers and AI readiness influence the competitive advantage with due acceptance of AI practices. To test the theoretical framework, the study used a mixed-method approach; survey data was gathered from 866 employees (managers) of SMEs. The findings suggested that all the AI enablers except perceived benefits and role clarity significantly impacted AI readiness. Further, AI ethics, the moderator of the study, was found to be significant between perceived benefits, role clarity, perceived trust, and competitive advantage.

Cryptocurrencies have increasingly been traded against fiat currencies and as a result, governments globally have been trying to regulate these largely decentralized currencies. In this study, event study methodology has been used to evaluate the effect of regulatory announcements made by 25 countries. Based on cryptocurrency usage and returns of three major cryptocurrencies, namely Bitcoin, Ether, and XRP, this study finds that regulatory news results in significant abnormal returns for Bitcoin and Ether, but not for XRP. The authors find that irrespective of the type of news, abnormal returns are almost always negative. The countries have also been clustered based on their abnormal returns and it has been found that country characteristics such as income level, technological readiness and innovation potential affect the magnitude of abnormal returns. Thus, cryptocurrencies being global in essence, their regulatory oversight in countries do not exist in isolation, but they are also affected by the countries' development.

Suppliers and sellers play a crucial role in the ecommerce ecosystem. Sellers and ecommerce firms use social media to increase user engagement, visibility, and sales. Seller ratings are as important as the product ratings on ecommerce platforms to drive buying decisions. Based on sellers' actions on social media, this study examines seller turnover and disengagement on e-commerce platforms. The study has been supported by the justice theory. Seller reviews and ratings from e-commerce platforms and conversations from social media platforms have been gathered. Using natural language processing, machine learning, partial least squares (PLS) path analysis, and statistical inferences, objectives of the study are met. The study offers recommendations for both practitioners and researchers. The sellers must focus more on interaction and communication than marketing. Through a longitudinal analysis, the study also establishes that ecommerce organizations can use seller social media performance as a predictor of future seller churn and disengagement so they can take the necessary remedial action.

This study identifies the determinants which impact the intention to use the Internet of Things (IoT) enabled devices by the aged consumers of India for household purposes. With the help of the Model of Adoption of Technology in Households (MATH), a conceptual model has been developed. The model has been extended by considering two moderators (age and gender). It has been ascertained that the explanative power of the model after consideration of two moderators has been enhanced. Ten constructs of MATH have been considered which were found relevant to this study. The validation of the greater explainability by including two moderators in this modified MATH is considered as a theoretical contribution of this study. This study provides insight to the policy makers to understand how the aged consumers of India can be motivated to use IoT-enabled devices for their household purposes that would eventually improve their quality of life.

In order to increase and develop overall performance of Modern Network Grids in Smart Cities of India with acceptable levels of security and privacy, the internal and external factors which substantially affect the performance of network grids in Smart Cities without jeopardizing privacy and security issues are needed to be identified. Besides, the interdependencies of these critical success factors are needed to be realized clearly. This paper seeks to identify these critical factors and also takes a sincere attempt to ascertain the main driving forces among these critical success factors and to ascertain inter-relationship among the CSFs. These factors here have been identified by the help of three reliable instruments which are questionnaire based survey, brainstorming and finally consolidation by Principal Component Analysis (PCA). A total of 16 critical success factors eventually have been detected by the help of PCA and finally a pragmatic structure of inter-relationship among the CSFs been developed by the application of Interpretive Structural Model (ISM).

User generated content in the social media platforms are being considered as an important source for information about consumers and other emerging trends by the businesses. Using Twitter analytics, the paper presents insights on trends and discussions about the Internet of Things (IoT). Using relevant hashtags, 40,387 tweets were collected in early 2016. The analysis had followed three major approaches: descriptive analysis, content analysis and network analysis. The tools R and NodeXL were used for the analysis. The findings showed major themes like business concerns, scope of applications, security, emerging smart technologies and manufacturing. The sentiments of emotions and polarity differed across these themes. The top individual and industrial influencers were identified. The analysis also detected the highly-associated words and hashtags, and different user communities and how they are connected. Business implications of the findings and limitations are also elaborated.