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Purpose This comprehensive study aims to take a punctilious approach intended to present qualitative and quantitative knowledge on the emerging concept of noise trading and identify the emerging themes associated with noise trading. Design/methodology/approach This study combines bibliometric and content analysis to review 350 publications from top-ranked journals published from 1986 to 2020. Findings The bibliometric and content analysis identified three major themes: the impact of noise traders on the functioning of the stock market, traits of noise traders and different proxies used to measure the impact of noise trading. Research limitations/implications This study undertakes research papers related to the field of finance, published in peer-reviewed journals and that too in the English language. Practical implications This study shall accommodate rational traders, portfolio consultants and other investors to gain deeper insights into the functioning of noise traders. This will further help them to formulate their trading/investment strategies accordingly. Originality/value The successful combination of the bibliometric and content analysis revealed major gaps in the literature and provided future research directions.

PurposeMassive open online courses (MOOCs) are among the most recent e-learning initiatives to gain widespread acceptance among universities. However, despite MOOCs' “much-documented” benefits, many questions are being raised late regarding the long-term sustainability of the open online teaching e-learning model. With high dropout rates in MOOCs courses, recent research has focused on the challenges limiting MOOCs’ growth. But most of the research is directed toward students’ perspectives, leaving the instructors’ perspective. One of the most important aspects of instructors’ perspective is the motivation for MOOCs' development and delivery.Design/methodology/approachThe present study collected the data from 25 MOOC developers of Indian origin. To prioritize or rank the motivational factor behind developing a MOOC, a fuzzy-analytical hierarchical process (F-AHP) technique was applied to the data set. The primary motivational factors considered for the study were professional development, altruism, personal development, institutional development, intrigue, monetary benefits and peer influence.FindingsThe results showed that professional development and personal development are two prime motives that drive MOOCs development. Monetary benefits and peer influence were the least important factors among all the factors considered for the study.Originality/valuePrevious studies have identified and modeled the motivational factors that contribute toward developing MOOCs. However, there was little knowledge about the hierarchy among the motivating factors. The present study fills this gap by establishing the ranking of motivational factors responsible for MOOCs development.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0205.

Owing to radical advances in the area of technology, the health sector has gained access to technologies to meet patient requirements; for example, mobile health (mHealth) care. Despite the possible benefits of mHealth, the effectiveness of these interventions ultimately depends on whether citizens use those initiatives. The current study have thus sought to explore mHealth applications’ adoption behavior. A cross-sectional investigation process has been employed on 440 potential consumers from North India to enumerate the consumers’ intention and their adoption. In addition to the core constituents highlighted in previous studies, this research focuses more on the relationship of several other factors affecting behavioral intentions and their eventual impact on the acceptance of mHealth apps. The key considerations in the current study are the inclusion of trust in the existing UTAUT model and the mediating role of behavioral intentions in determining mHealth services adoption.

Purpose The advocates of behavioural finance have denounced the existing literature on investors’ rationality in the decision-making process and questioned the existence of efficient markets and rational investors. Although diversified research has been conducted in the area of behavioural finance, yet there is a need of further explorations into the field as the available knowledge base is confined to one or a few behavioural biases confronted by investors while making investment decisions. Hence, this study aims to develop a comprehensive, reliable and valid scale to measure the behavioural biases affecting investors’ decision-making process. Design/methodology/approach To develop a comprehensive, reliable and valid scale for measuring the behavioural biases affecting investors’ decision-making process, rigorous multi-stage scale development methodology has been followed. Stage one started with an extensive review of the literature followed by interviews from experienced stockbrokers to clarify construct and getting novel insights about dimensions of behavioural biases. In stage two, 52 items measuring the dimensions of behavioural biases were generated and got evaluated from panel of judges. Pilot testing was done in the third stage which gave a set of 39 items. Finally, in fourth stage, data were collected from 332 individual equity investors on a 7-point Likert scale using the snowball sampling technique. Findings The results of the study highlighted that behavioural biases is a multidimensional phenomenon that significantly affects investors’ decisions and has different dimensions, namely, Availability Bias, Representativeness Bias, Overconfidence Bias, Market Factors, Herding, Anchoring, Mental Accounting, Regret Aversion, Gamblers’ Fallacy and Loss Aversion. The present research has developed a comprehensive, reliable and valid scale for measuring behavioural biases affecting equity investors’ decision-making process. Originality/value Behavioural finance is an emerging area in the field of research particularly in the Indian context which needs further exploration. The present research concentrates on rendering an empirically tested scale to the researchers for measuring the behavioural biases and its impact on investor’s decision-making. Such an instrument can contribute to making progress in the area of behavioural finance and other research studies may also find it useful to achieve their goals.

The present study investigates what drives Lending inquisitors’ judgement & decision-making behaviour that influences credit risk assessment in Indian banks. This research investigates three aspects: risk attitude & information acquisition behaviour, the effect of experience on lending, and desirable attributes of lending inquisitors. For the first area, Kruskal–Wallis non-parametric test is applied, for second area correlation and Cramer’s V is applied on fictitious case analysis and for third aspect 27 attributes of inquisitors through unstructured personal interviews are then analysed by applying Kruskal Wallis Test, Factor analysis and Fuzzy analytic hierarchy process (FAHP). Fuzzy AHP technique was applied to understand the key personal attributes and sub-attributes, which play a major role in Lending inquisitors’ judgement & decision-making behaviour. The risk attitude and information acquisition provided no substantial relationship between the two. Whereas, in the second area, which is assessing the impact of experience on decision-making behaviour, the result shows that the senior and junior credit inquisitors are cautious in acquiring the information as compared to outsourced credit inquisitors.

The present study examines the existing knowledge and intellectual structure on contagious diseases and tourism to map the development of the concept through collaborative networks. Utilising the Scopus and Web of Science bibliometric databases, 328 research records were extracted through keyword searching and forward referencing approaches. Based on these records, the study conducted bibliometric and content analysis to diagnose core themes in the field. The present study's findings are helpful for academia and industry to aid their existing knowledge about contagious diseases, particularly its timeline, geographical spread, and development of coherent themes. A rigorous literature review revealed that the scholarly work in the domain of contagious diseases and tourism revolves around four important themes, namely COVID-19, SARS, Crisis management, and Sustainability. The first theme revolves around COVID-19, highlighting about the impact of COVID-19 on different sectors of the TTH industry, countries, stakeholders, and contexts. Also, researchers foresee COVID-19 as a catalyst to reshape the tourism industry. The next group of studies explained the handling of SARS, particularly by Asian countries. The third cluster elaborated on different stages of crisis and strategies adopted by organizations and countries to manage the crisis. Lastly, a handful of studies in the corpus stated that sustainability in tourism needs to be understood beyond saving the environment and aspiring prosperous travel and tourism industry. The study also suggested the scope of future work.

Relative momentum strategies yield large and substantial profits in the Indian Stock Market. Nevertheless, relative momentum profits are negatively skewed and prone to occasional severe losses. By taking into consideration 450 stocks listed on the Bombay Stock Exchange, the present study predicts the timing of these huge momentum losses and proposes a simple risk-managed momentum approach to avoid these losses. The proposed risk-managed momentum approach not only doubles the adjusted Sharpe ratio but also results in significant improvements in downside risks. In contrast to relative momentum payoffs, risk-managed momentum payoffs remain substantial even in extended time frames. The study’s findings are particularly relevant for asset management companies, fund houses and financial academicians working in the area of asset anomalies.

PurposeThis study aims to recognize the current dynamics, prolific contributors and salient trends and propose future research directions in the area of alternative momentum investing.Design/methodology/approachThe study uses a blend of electronic database and forward reference searching to ensure the incorporation of all the significant studies. With the help of the Scopus database, the present study retrieves 122 research papers published from 1999 to 2020.FindingsThe results reveal that alternative momentum investing is an emerging area in the field of momentum investing. However, this area has witnessed an exponential growth in last ten years. The study also finds that North American, West European and East Asian countries dominate in total research publications. Through network citation analysis, the study identifies five major clusters: industrial momentum, earnings momentum, 52-week high momentum, time-series momentum and risk-managed momentum.Research limitations/implicationsThe present review will serve as a guide for financial researchers who intend to work on alternative momentum approaches. The study proposes several unexplored research themes in alternative momentum investing on which future studies can focus.Originality/valueThe study embellishes the existing literature on momentum investing by contributing the first bibliometric review on alternative momentum approaches.

Research on behavioural biases has witnessed a momentous growth in the last two decades, supported by rising interest and publication thrust shown by academic scholars. Present study maps the academic literature on the role of behavioural biases in investment decision-making. With the help of bibliometric tools, the paper highlights the current state-of-the-art and identifies significant gaps in the existing literature on behavioural biases. Through keyword and reference searching approaches, the study retrieved 212 research papers from the Scopus database. Application of performance analysis techniques has helped in identification of influential journals, prolific authors, countries and affiliations enriching the literature on behavioural biases. Scientific mapping approaches such as bibliographic coupling and thematic mapping has provided valuable insights about the conceptual and intellectual structure of the field. Finally, the research directions proposed in this review will provide a roadmap for future research.

This comprehensive research study aims to highlight the evolution of momentum investing research and identify the mature and emerging themes in momentum investing. This study reviews 532 research studies published between 1993 and 2019. The study uses a combination of various bibliometric and network analysis tools to identify the most influential research studies, key journals and leading authors. Bibliometric and network analysis also help in the broader classification of research studies into four major categories. Further, a rigorous investigation of these research studies identifies various loopholes and propose actionable themes for next-generation research.

Payments bank are a new addition to the family of digital payment services and are largely unexplored by the consumers. Hence there is a need to comprehend the underlying critical success factors that play a crucial role in the mass adoption of Payments bank. Further, to make Payments bank a standard digital payment option, converting imprecise information acquired from the respondents into key business insights is required. Hence, the present study aims to explore and prioritize the key factors responsible for the adoption of Payments bank. The study used the Fuzzy Analytic Hierarchy Process on the sample obtained from the northern region of India to identify the structure of the pertinent concerns and determine the criteria and sub-criteria of Payments bank services. Results of the study postulated that ease of use and facilitating conditions are the most prioritized factors. At the same time, personalization is ranked as the least preferred factor among all the factors. The present study sample is limited to a few parts of India, so generalizations of results should be made with caution. However, the current study strengthens the literature on Payments bank as it is one of the early studies in this area. Further, the insights generated by the study will help marketers and policymakers to pay broader attention to key parameters responsible for the success of Payments bank.

Alzheimer’s disease (AD) is a human brain disease that remains as a common cause of dementia, which occurs mainly in middle-aged or grown-up individuals. AD results in cognitive decline and memory loss. AD is caused by the decomposition of plaques around the nerves of brains or around the brain cells, where the brain cells get neurofibrillary tangled and result in various instability and mental illness. AD is a chronic and irreversible disease; the reasons and disease identification are still not known, but research says it can be identified during the early stages. In spite of that fact, this research work has proposed a computer-aided Alzheimer’s classification method that will classify the class of an image either in normal class or demented class. The method uses the hybrid strategy of ant colony optimization (ACO) and feed forward convolutional neural network (CNN or ConvNet); however, identifying the architecture of CNN requires lots of expertise and is time-consuming. Henceforth, this research work has used the bio-inspired optimization strategy, which will identify the optimal combination of hyper-parameters, i.e. it recommends the configuration for the CNN model, and with that, configuration of hyper-parameters with the CNN model is trained with the training dataset, and CNN performs feature extraction alongside classification for arranging the gatherings possibly, when the model undergoes validation, where the performance metric of the model is evaluated and to identify whether the validating images are falling in the category of normal class (i.e. non-demented) or Alzheimer’s class (i.e. demented class) with good results or not, the classification error is measured during this phase and is backpropagated to ACO optimizer, iteratively ACO is used to minimize the classification error by tuning the hyper-parameters, and after few iterations, a CNN architecture with optimal hyper-parameters combinations is obtained to result in least classification errors. The method was applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which constitutes the fMRI images of Alzheimer’s affected patients and resulted in developing efficient and state-of-the-art method for the classification of Alzheimer’s disease. The proposed method performance metrics were recorded as 98.67% as accuracy, 97.63% as sensitivity and 99.02% as specificity. The results were seen noticeably better than other proposed methodologies.

Originality and Value: Present study is one of the early attempts that analyzes the literature on contagious diseases and tourism using bibliometric analysis and contributes to the literature by identifying various mature and emerging on contagious diseases and tourism literature. These insights provide a robust map for future investigation in this field and also offer implications for practitioners.

Alzheimer’s Disease (AD) is one of the chronic and irreversible sorts of disease found among grown-ups. AD causes in destroying the ability to learn, think, and remember. It is the main reason behind the death of adults as AD causes harm in the brains, i.e., presence of neurofibrillary tangles and deposition of plaques around the cerebral cortex which leads toward cognitive decline. As indicated by the previous studies around 45 million people have been affected by Alzheimer’s and the rate is exceptionally quicker which may result in double the number today in the next twenty years. The main cause of AD is not known so we need to identify it at its earliest stage, which is also known as Mild Cognitive Impairment (MCI) in the medical field. The main objective of this research is to help in classifying the AD with extreme effectiveness and accuracy during MCI phase. In this research, we developed a best-in-class Restricted Boltzmann Machine (RBM) algorithm, which plays a vital role in Deep Belief Networks which is a stochastic learning method that defines a bunch of rules which is useful in feature reduction, classification, collaborative filtering, and regression methods. Additionally, the method used in this research doesn’t require feature extraction and is equipped for performing Classification with the greatest effectiveness. All the experiments are performed in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset which constitutes old age patients under two classes: Normal Class and Alzheimer Class, i.e., identifying whether Demented or Non-Demented. The methodology was divided into four sections: Normalization, Feature Reduction, Training dataset using RBM with-standing Gibbs Sampling, Contrastive Divergence, and Classification carried out in the last section. In this way, to find if our model could beat the cutting-edge models among already proposed models RBM algorithm was viably applied to the expectation of classification for Alzheimer Disease giving better results during the early phase of diagnosis. The final result shows that our model achieved an accuracy of 98.6%, specificity of 99.01%, and sensitivity of 98.22% and on differentiating the performance metrics among different techniques it shows better results. From now on, Alzheimer’s Disease prediction analysis performance of our methodology recommended a unique strategy to build the predictive model among various Deep Learning strategies and Machine Learning methods. The model can perform more effectively with an increase in the size of the Alzheimer’s dataset and applying optimization strategies along with RBM to reduce the computational operability and increasing effectiveness.

P. Martino, Blockchain and Banking: How Technological Innovations are Shaping the Banking Industry, 2021. Switzerland: Springer Nature, 109 pp., ₹6,468 (Paperback). ISBN: 978-3-030-70969-3.