In the last two decades, research on behavioural biases has grown dramatically, fuelled by rising academic interest and zeal for publication. The present study explores the mediating role of risk perception on the relationship between heuristic biases and individual equity investors’ decision-making. The study uses Partial Least Square Structural Equation Modelling (PLS–SEM) to examine the survey data from 432 individual equity investors trading at the National Stock Exchange (NSE) in India. Risk perception is found to play a partial mediating role in the relationship amid overconfidence bias and investment decision-making, availability bias and investment decision-making, gamblers’ fallacy bias and investment decision-making and anchoring bias and investment decision-making, whereas it is found to play the full mediating role in the relationship between representativeness bias and investment decision-making. The result of the present study provides valuable insights into the different behavioural biases of capital market participants and other stakeholders such as equity investors, financial advisors, and policymakers. The present study solely relied on the heuristic biases of individual equity investors. However, in the real world, many other factors may impact the investment decision of individual equity investors. This has been considered a limitation of the study. The present study solely relied on the heuristic biases of individual equity investors. However, in the real world, many other factors may impact the investment decision of individual equity investors. This has been considered a limitation of the study.
The purpose of this study was to know the importance of certain fitness variables and Phase Ratios with the Triplejump performance. Sixteen male triple jumpers of National level were selected as subjectsfor this study. Speed tests (30m, 50m), jumping ability tests (standing triple jump & Two hops and Twosteps and jump) and strength tests(Snatch, Bench press and overhead backward throw) were Administered to collect the data. Trials were conducted to assess the Triple Jump Performance. The result indicates that there is no relationship of 30meter run (−.382) and 50meter run (−.138) Test performances with Triple jump performance. The “r” values of 0.643 and 0.687 for Standing Triple Jump and 2 Hop-2 Step Jumprespectively, with triple jump performance are highly significant, at 0.01 levels. Strength parametershave shown significant relationship with Triple jump performance. A highly significant relationshipbetween Snatch and triple jump performance (0.759 at p<0.01 level)indicates that snatch is an importantExercise to improve Triple jump performance. The mean value of Phase Ratio of Indian Triple Jumper is36.82%: 29.57%: 33.61% which indicates that Indians are Hop dominating jumpers.
Purpose This 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/approach The 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. Findings The 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/implications The 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/value The study embellishes the existing literature on momentum investing by contributing the first bibliometric review on alternative momentum approaches.
Purpose Massive 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/approach The 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. Findings The 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/value Previous 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 review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0205 .
The objective of this article is to forecast India's crude oil output patterns in the future. Specifically, this article examines whether data on crude oil output in India may be modified to ARIMA models for the purpose of estimate and forecasting. This article makes use of monthly data on Indian crude oil output. The Box Jenkins autoregressive integrated moving average (ARIMA) forecasting method was used to anticipate future patterns in the Indian crude oil market. The ARIMA model clearly indicates that the average monthly percentage rise in crude oil output will be.01 percent between December 2019 and November 2020, corresponding to a 0.36 percent increase in crude oil prices over the same time. The results of the study would be helpful for the investors of the commodity market to make their investment strategies keeping in mind the predicted future fluctuations. Further, it will also assist the Indian government to make necessary policies to absorb the volatility and to control prices of crude oil.
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