Dr. ALEEM ANSARI
@siu.edu.in
Assistant Professor
Symbiosis International University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Aleem Ansari, Vikrant Vikram Singh, and Aditya Kumar Gupta
IEEE
This research investigates the sluggish adoption of disruptive digital technologies (DDTs) in the global real estate sector, with a specific focus on the Indian market. Analysing barriers to digital transformation, the study applies Pythagorean Fuzzy Sets methodologies to quantify challenges, prioritizing technological, organizational, and environmental obstacles. Key hindrances include high implementation costs, limited market data access, organizational resistance to innovation, and external environmental factors. The study advocates for a shift toward Industry 4.0 standards, emphasizing the need to address technological complexities and internal organizational challenges. Insights from experts in telecommunications, information technology, and smart city development inform proposed strategies to facilitate the integration of digital innovations in the Indian real estate landscape. This research contributes theoretical and practical implications, offering actionable strategies for policymakers, industry leaders, and innovators to navigate complexities and promote the adoption of smart real estate technologies on a global scale.
David Campbell, Aleem Ansari, and Vikrant Vikram Singh
Productivity Press
Harendra Singh, Aleem Ansari, and Vikrant Vikram Singh
Productivity Press
Sana Fatima, Vartika Kapooor, and Aleem Ansari
Productivity Press
Vikrant Vikram Singh, Aleem Ansari, and Aditya Kumar Gupta
IEEE
In recent years people have felt the importance of technology-driven infrastructure in many areas including higher education. Teachers' researchers and students have felt a very strong need for advanced technology-driven platforms in higher education scenarios. The main focus of this study is on how successful metaverse-driven learning systems are. This study looks at the influencing factors of metaverse technology-based digital learning platforms in higher education institutions. To properly examine the various driving factors of Metaverse-based digital learning platforms used by various higher education institutions, several important aspects of these systems were gleaned from the available literature, and several noteworthy articles were chosen from the literature. Several influencing factors of metaverse-based learning systems are identified and ranked using the analytical hierarchy process (AHP) according to important variables and their auxiliary elements. Pythagorean Fuzzy-Delphi was used to address the option's ambiguity. A threshold value of 0.6 was applied and various capabilities were recognized and accepted based on the $\\mathrm{d}_{\\mathrm{f}}\\ (\\alpha)$ value. The difference matrix, interval multiplicative matrix, determinacy value matrix, and normalized priority weight were created using the Pythagorean Fuzzy-Delphi method. Capability's priority rating was established as Push > Mooring > Pull based on Normalized Weights of 0.271, 0.402 and 0.327 respectively. Further various sub-criteria were ranked based on their global ranks obtained from their Pythagorean fuzzy weights and de-fuzzified values. The study's findings showed why metaverse-based systems in different higher education institutions are successfully used to create digital learning systems.
Aleem Ansari and Valeed Ahmad Ansari
Emerald
PurposeThe purpose of this study is to empirically examine the presence of herding behavior of Indian investors using daily sample data drawn from the Standard and Poor's (S&P) Bombay Stock Exchange-500 Index over the period 2007–2018.Design/methodology/approachThe study employs the model proposed by Chang et al. (2000), taking stock return dispersion as a measure to capture herding. The empirical results demonstrate the absence of herding behavior in all market states, that is, normal, up and down market conditions for the overall period.FindingsContrastingly, the study found negative herding behavior, which underlines that individuals are taking the decision away from the market consensus. The subperiod analysis corroborates the negative herding behavior. The results remain invariant across large, mid and small-capitalization firms except in one year, that is, 2009 for small firms. While using liquidity and sentiment as variables to examine herding, the study finds some evidence of herding behavior for high market liquidity state and sentiment. The findings of negative herding shed new light on herding behavior in the Indian stock market.Originality/valueThis pattern of behavior may indicate irrationality of investor behavior and the presence of noise traders who mistrust market-wide information. Behavioral factors such as overconfidence may explain this pattern of behavior.