Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

Artificial intelligence-powered chatbot usage continues to grow worldwide, and there is ongoing research to identify features that maximize the utility of chatbots. This study uses the multi-criteria decision-making (MCDM) method to find the best available alternative chatbot for task completion. We identify chatbot evaluation criteria from literature followed by inputs from experts using the Delphi method. We apply CRITIC to evaluate the relative importance of the specified criteria. Finally, we list popular alternatives of chatbots and features offered and apply WASPAS and EDAS techniques to rank the available alternatives. The alternatives explored in this study include YOU, ChatGPT, PerplexityAI, ChatSonic, and CharacterAI. Both methods yield identical results in ranking, with ChatGPT emerging as the most preferred alternative based on the criteria identified.

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.

Big data has emerged as an important resource for generating wealth in society along with capital and labour, as data analytics generates valuable information and provides critical insights to gain competitive advantage. In Investment management, access to information is vital. As analytics causes information asymmetry among those who use it and others, it has become a key result area in the domain.IM involves multi-criteria decision-making necessitating managers to acquire core capability in analytics. The field of IM is passing through rapid changes, with varying customer preferences, advancing technologies, diminishing margins, acute competition, in the midst of increase in compliances. Cock-pit monitoring and goal-based portfolio preferences by some large customers have complicated IM. The paper explores the rationale for implementing big data analytics and identifies evolving tools and technologies that are applicable in the domain. The paper also highlights few emerging areas of research in the field using both bibliometric analysis and systemic literature review techniques.

Government of India’s ‘Digital India’ initiative intends to build robust digital ecosystem that fosters innovation & entrepreneurship enabling better citizen service & citizen empowerment. Digitization in India involves geo-demographic & socio-economic dependency, choice of smart technologies undergoing rapid innovation, strategic roll-out planning & flawless implementation as prerequisite of technology diffusion & benefit realization. This study identifies technical & non-technical determinants of smart digital framework roll out that can accelerate digital diffusion in urban public services in India. This study follows inductive exploratory method, combining grounded theory & text mining for primary data analysis. Study reveals digitization is an ecosystem of private & public enterprises and citizen participation, identifies integrated use analytics & IoT can enable connected smart city, whereas technology cost, digital literacy & sustainable innovation as non-technological determinant towards resilient urban digital infrastructure in India.

Today, the advent of social media has provided a platform for expressing opinions regarding legislation and public schemes. One such burning legislation introduced in India is the Citizenship Amendment Act (CAA) and its impact on the National Citizenship Register (NRC) and, subsequently, on the National Population Register (NPR). This study examines and determines the opinions expressed on social media regarding the act through a Twitter analysis approach that extracts nearly 18,000 tweets during 10 days of introducing the scheme. The analysis revealed that the opinion was neutral but tended to a more negative reaction. Consequently, recommendations on improving public perception about the scheme by suitable for interpreting the Act to the public are provided in the paper.

The paper aims to examine the factors that influence employee attrition rate using the employee records dataset from kaggle.com. It also aims to establish the predictive power of Deep Learning for employee churn prediction over ensemble machine learning techniques like Random Forest and Gradient Boosting on real-time employee data from a mid-sized Fast-Moving Consumer Goods (FMCG) company. The results are further validated through a regression model and also by a multi-criteria Fuzzy Analytical Hierarchy Process (AHP) model which takes into account the relative variable importance and computes weights. The empirical results of the machine learning models indicate that Deep Neural Networks (91.2% accuracy) are a better predictor of churn than Random Forest and Gradient Boosting Algorithm (82.3% and 85.2% respectively). These findings provide useful insights for human resource (HR) managers in an organizational workplace context. The model when recalibrated by the human resource team of organizations helps in better incentivization and employee retention.

The disparity in consumer and organization preferences of information channels is a major concern. Further, making decisions in the presence of a wide range of conflicting criteria through the use of a multiple criteria decision-making (MCDM) approach has gained increased prominence in recent years and research in this area has become an important consideration for business operations that involve dealing with complex decision problems. This paper describes how an integrated approach can be applied to a decision-making problem that combines a fuzzy analytical hierarchy process (AHP) and TOPSIS for identifying preferences consumers of information search channels according to demographic factors such as gender.

The evolution of information technology has resulted in increasingly fragmented digital media and multiple information channels. Organizations can develop comprehensive insights into consumer behavior and preferences by evaluating customers' perceptions of the various Internet channels that are available. Such insights can be used to identify which information channels can be employed to effectively reach and communicate with a target market and, thus, to optimize marketing strategies. This paper commences with a comprehensive literature review of existing research on consumer information search patterns and strategies, with a particular focus on Internet channels. The literature review is employed to develop a set of criterion by which consumer search preferences can be better understood. This criterion is subsequently used to develop a optimization model for organization that can effectively align marketing practices with customers' search processes and preferences during their pre-purchase information search.