Electrooculography (EOG)-based human–computer interface (HCI) systems have a wide range of applications due to their usability in inferring user's intention through eye movements. Because of limited eye activities, these systems generate limited commands, such as looking up, down, left, and right. The challenge is to utilize EOG signals in strategy-based applications, such as game-playing robots. This article presents a novel asynchronous controlled single-channel EOG-based HCI system that uses eyeblink signals to control a robotic manipulator in solving Guarini's puzzle. A puzzle board and three control buttons are presented in a graphical user interface. The user navigates to a target through a single eyeblink and selects it through a double eyeblink. Two data analysis algorithms, specifically logistic regression model and eyeblink detection module, are used to detect eyeblinks. Two online experiments were conducted with ten healthy subjects. In the screen-based puzzle-solving experiment, we achieved an average accuracy of 95.19% with a short response time (RT) of 0.96 s and an information transfer rate (ITR) of 268.14 bits/min. In the robot-assisted experiment, the proposed system achieved an average accuracy of 92.7%, with an RT of 0.98 s and an ITR of 252.92 bits/min. In both experiments, during the decision (idle) state, the average false positive rate (FPR) is found to be 0.02 events/min. This system generates sufficient commands to control a robotic manipulator in solving the puzzle on the physical puzzle board. In comparison to existing EOG-based gaming systems, the proposed system achieves high accuracy, low RT, lower FPR, and high ITR with fewer electrodes.

This paper explores the role of AI and database management in organizational transformation using insights from Twitter data. By analyzing 30,000 English-language tweets with methods such as word analysis, topic modeling, network analysis, sentiment analysis, and emotion analysis, the study reveals a strong correlation between AI and digital transformation. The findings show positive sentiment and optimism about AI's potential. This research highlights the importance of social influence, perceived trust, and awareness in AI adoption, offering valuable insights for researchers and practitioners. Despite relying on Twitter data, the study provides practical guidance for leveraging AI in digital transformation efforts.

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

There is widespread agreement that information systems are the lifeblood of the global economy, providing businesses with a crucial competitive edge in international markets and giving the world's governments and corporations of the 21st century the foundation they need to ensure the safety of their citizens and deliver superior services. Despite their importance, information systems face significant risks within and outside of the company. The best security methods can be circumvented by professional hackers, as the complexity of security attacks, the number of vulnerabilities, and the absence of implementation of quality information security management within companies have expanded substantially in recent years. For the sake of this discussion, the authors examine several security paradigms designed by scientists to protect the operations of multinational corporations.

Timely intelligent decision support systems (DSS) are increasingly important for the sustainable development of entrepreneurship. Global information management plays an important role in accurate DSS. Judgments can be made more quickly, accurately, and objectively thanks to the availability of large data and sophisticated artificial intelligence in the realm of quantitative smart decisions. In this context, this research analyzes the contribution of global information management for sustainable business development through DSS. This paper used the Scopus database to collect relevant research papers related to the research topic. This research helps researchers analyze the recent trend and development in the field of DSS in the context of global information management.

Blended learning is widely adopted by organizations where a blend of online component complements the traditional face-to-face learning. Despite its popularity, research on developing a model for blended learning effectiveness is limited. This paper develops and validates a research model for blended learning effectiveness by investigating the role of engagement on perceived learning effectiveness. Further, the role of internet self-efficacy (personal factor) and interactions (environmental factors) on various dimensions of engagements are examined through the lens of social cognitive theory. A total of 246 postgraduate Indian students participated in a Management Information Systems course. Structural equation modeling is used to validate the research model empirically. It is found that the internet self-efficacy and the interaction factors are positively related to the engagement dimensions, which further positively affects the perceived learning effectiveness. Moreover, perceived learning effectiveness is positively related to student scores. Theoretical and practical implications are discussed.

This article describes how knowledge discovery is a frontier research issue of knowledge management, and social media provides an opportunity for knowledge discovery that was at no other time as virtuous as the present. Despite the fact that, the articulations in national dialects via web-based networking media is mounting day by day. This discovery endeavor in regional languages is rare. The usage of Hindi, the Indian National language, is also observing the similar trend. Any expression in social media contains multiple features. Discovering the hidden sentiments behind these features have wider functions. This article is the first attempt to mine the opinion at the features level in the Hindi language. The principle contribution of this article is the development of context specific corpus in the Hindi language. Based on this corpus authors extract the sentiments on one of the prominent leader of India at the feature level. Opinion mining conclusion based on present work is reproduced likewise in the subsequent election results.

When citizens are actively engaged in e-government processes as co-creators it leads to e-government development. Citizen participation in e-government is a recent phenomenon where government is seeking more involvement of citizens through different platforms. Whether citizens' participation through social media platforms leads to e-government development has been a debate in literature. In this research study, the authors investigate whether social media usage influences government's role in e-government development. Power distance between government and citizen also plays a role in deciding the extent of e-participation in e-government. In this study, the authors examine the relationship between social media, power distance, citizen engagement and e-government development. Using customer focus theory and agency theory, this study establishes that e-participation initiative by government acts as a mediator in the relation between social media usage by citizen as well as power distance to e-government development.