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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.

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.

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.

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.

The study embellishes the existing literature on momentum investing by contributing the first bibliometric review on alternative momentum approaches.

Research in the domain of behavioral finance has proven that investors demonstrate irrational behavior while making investment decisions. In a similar domain, the primary objective of this research is to prioritize the behavioral biases that influence cryptocurrency investors' investment decisions in the Indian context.

A fuzzy analytic hierarchy process (F-AHP) was used to prioritize the behavioral factors impacting cryptocurrency investors' investment decisions. Overconfidence and optimism, anchoring, representativeness, information availability, herding, regret aversion, and loss aversion are among the primary biases evaluated in the present study.

The findings suggested that the two most important influential criteria were herding and regret aversion, with loss aversion and information availability being the least influential criteria. Opinions of family, friends, and colleagues about investment in cryptocurrency, the sale of cryptocurrencies that have increased in value, the avoidance of selling currencies that have decreased in value, the agony of holding losing cryptocurrencies for too long rather than selling winning cryptocurrencies too soon, and the purchase of cryptocurrencies that have fallen significantly from their all-time high are the most important sub-criteria.

This survey only covered active cryptocurrency participants. Additionally, the study was limited to individual crypto investors in one country, India, with a sample size of 467 participants. Although the sample size is appropriate, a larger sample size might reflect the more realistic scenario of the Indian crypto market.

The study is relevant to individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, market regulators, and society at large.

To the best of the authors' knowledge, no prior research has attempted to explain how the overall importance of various criteria and sub-criteria related to behavioral factors that influence the decision-making process of crypto retail investors can be assessed and how the priority of focus can be established, particularly in the Indian context.

Environmental impact and changes are becoming essential in textile and yarn industries, where reliable measurement of parameters related to processing harmful substances needs to be examined. Such findings can be cumulated using smart assessment like life cycle analysis. The ecological impact category, supply chain, and climate-changing factors were considered for the necessary assessment.

This paper applies the Life Cycle Assessment technique in the textile and yarn industry to estimate critical environmental potentials. The critical input for the fabric and yarn industry was put in the GaBi software model to estimate various environmental potentials.

Global warming potential, electricity, and raw cotton consumption in the fabric and yarn industry were critical concerns where attention should be focused on minimizing environmental potentials from cradle to gate assessment.

This qualitative study is made via the industry case-wise inputs and outputs, which can vary with demographic conditions. Some machine and human constraints have not been implemented in modelling life cycle model for smart simulation. Smart simulation helps in linking different parameters and simulates their combined effects on the product life cycle.

This modelling approach will help access pollution constituents in different supply chain production processes and optimize them simultaneously.

The raw data used in this analysis are collected from an Indian small scale textile industry. In the textile fabrication industry, earlier assessments were carried out in cotton generation, impact of PET, cradle to grave assessment of textile products and garment processing only. In this research the smart model is drawn to consider each input parameter of yarn and textile fabric to determine the criticality of each input in this assessment. This article mainly talks about life cycle and circular supply assessment applied to first time for both cotton to yarn processing and yarn to fabric industry for necessary estimation of environment potentials.

In today's competitive environment, sustainability is talked out in every sphere of life. Sustainability is a key to stability and for that roots are being focused by incorporating sustainability in higher education. The basic purpose of this paper is to prioritize the sustainability drivers in the higher education system. This research will provide fruitful insight into the sustainability drivers in the higher education system to the education industry and policymakers.

The present research is conducted on the 400 students studying in four major universities in the state of Punjab. Fuzzy analytical hierarchy process was applied to prioritize the sustainability drivers in the higher education system. The primary factors considered for the present study include social-people (social responsibility), environmental-planet (sustainable environmental practices) and financial-profit (economic value created).

The most influential criteria were environmental-planet (sustainable environmental practices) and social-people (social responsibility). The five most influential subcriteria were “Student engagement in eco co-curricular activities (C21)”, Energy efficiency measures (C23)”, “The HEI as a job driver in the city (C11)”, “Total direct energy consumption (C31)” and “Support from the HEI for local initiatives and help in growing the sustainability of the community or region (C12)”.

Although the sample survey conducted in this study was focused on a small sample selected from the state of Punjab which genuinely represented the total population, it is still considered as a limitation for the present study.

The outcome of this research provides policymakers with a better understanding of the sustainability drivers in higher education. This will further help them toward achieving the aim of sustainability.

The present research is based on the available literature on sustainability and the results of the study would add value to the existing knowledge base.

The primary objective of the study is to discover the most prominent criteria and sub-criteria among environmental issues, social dimensions and corporate governance factors that may impact individual equity investors' investment decisions.

The present study collected data from 438 individual equity investors from the North Indian region. To achieve the objectives of the study, a fuzzy analytic hierarchy process (Fuzzy AHP) was applied. The key considerations of the study were environmental, social and governance (ESG) factors.

The governance criterion was discovered to be the most significant factor influencing individual equity investors' investment decisions among the three ESG factors, followed by environmental criteria, while social criteria were shown to be the least influential.

The present study solely looked at ESG issues as drivers of stock investors' investment decisions. In the current world, however, many other factors, including behavioral biases, accounting information, ownership structure and fundamental analysis, can have a substantial influence on investors' investment decisions.

The study's findings widen the theoretical contribution in the field of responsible investment by asserting how ESG factors influence investors' investment decisions in the equity market. From a practical standpoint, this study applies to retail and institutional investors, portfolio managers, financial advisors, market regulators, corporations and society at large.

To the best of authors knowledge, no attempt has been made to prioritize the ESG issues that impact the investment decisions of individual equity investors. Ergo, this study contributes to the existing literature on socially responsible investment.

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.

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.

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.

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.

Research in the area of behavioral finance has demonstrated that investors exhibit irrational behavior while making investment decisions. Investor behavior usually deviates from logic and reason, and consequently, investors exhibit various behavioral biases which impact their investment decisions. The purpose of this paper is to rank the behavioral biases influencing the investment decision making of individual equity investors from the state of Punjab, India. This research would provide valuable insight into the different behavioral biases to investors and other participants of the capital market and help them in improving investment decisions.

The research is conducted on the individual equity investors of Punjab, India. Fuzzy analytic hierarchy process was applied to rank the factors influencing the decision making of individual equity investors of Punjab. The primary factors considered for the study are overconfidence bias, representative bias, anchoring bias, availability bias, regret aversion bias, loss aversion bias, mental accounting bias and herding bias.

The three most influential criteria were herding bias, loss aversion bias and overconfidence bias. The five most influential sub-criteria were “I readily sell shares that have increased in value (C61),” “News about the company (Newspapers, TV and magazines) affects my investment decision (C84),” “I invest each element of my investment portfolio separately (C71)” and “I usually hold loosing stock for long time, expecting trend reversal (C52).”

Although sample survey conducted in the present study was based on a limited sample selected from a particular area that truly represented the total population, it is considered as the limitation of this study.

The outcome of this research provides investors with a better understanding of behavioral biases that influence their decision making. This study provides them a guideline on different behavioral biases that they should consider while making investment decisions.

The research model is based on the available literature on behavioral finance and the research results and findings would add value to the existing knowledge base.