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The behavioural decision-making process of individuals highlights the importance of investors’ sentiment and their correlation with the real economy. This paper aims to contribute to the literature of behavioural finance by examining the influence of contextual factors on investment decision-making.

Using a questionnaire, a total of 445 valid responses were collected from March to May 2021 through online sources. The current study uses a technique of Fuzzy-analytical hierarchical process (AHP) to assign relative weights to various contextual factors influencing investment decision-making. Harman’s single factor test was used to check common method bias.

Results of the study reveal that accounting information, self-image/firm-image coincidence, and neutral information as the top-ranked factors in influencing investment decisions, whereas advocate recommendation and personal financial needs emerged as less important factors in influencing investment decisions.

The current study collects data from Indian stock market investors, which may limit the generalization of the study to India only. Moreover, this study is cross-sectional in nature, and there are numerous factors that are not part of the study but might significantly influence the investors’ decision-making process.

The research has implications for both academicians working in the area of behavioural finance and practitioners’ who are active in stock markets, more specifically dealing with retail investors and in the domain of personal finance. Also, the current study will accommodate different groups, i.e. policy makers, financial advisors, investors, investment professionals, etc. in carrying out their professional work.

The current study will provide a comprehensive overview of individual investor behaviour. To the best of the authors’ knowledge, the present study is one of its kind to use the Fuzzy-AHP technique for evaluating the relative ranks of contextual factors influencing investment decision-making.

While national public policies such as performance contracts and disinvestment affect the dynamics of large- and medium-scale state-owned enterprises in emerging market economies, the purpose of this paper is to analyze the performance of India’s public sector undertakings (PSUs) and suggest options to improve their outcomes.

Using firm-level data on India’s 235 PSUs with total assets of around $500 billion over the past two and half decades (1990-2015), the study empirically tests the effect of performance contracts, measured by memorandum of understanding (MOU) and disinvestment, measured by private equity share, on PSUs performance indicator such as return on capital (ROC). Data were collected from the Public Enterprises Survey Reports released by the Department of Public Enterprises under India’s Ministry of Heavy Industries and Public Enterprises, Department of Disinvestment, Bombay Stock Exchange and Capitaline database. By controlling firm-, industry- and macro-level factors in regression models, the results were presented in several aspects like service sector, non-service sector and individual and joint effects.

Empirical estimations indicate that performance contracts such as MOUs have had a positive impact on PSU performance by increasing their ROC by 8-9 percent. This result holds more strongly for the non-service sector (manufacturing, mining) but less so for service sector firms. In the case of service sector firms, partial privatization (share sales) has a significant impact on performance, making them ideal candidates for more aggressive disinvestment. Larger PSUs (Maharatnas) appear to perform better than smaller PSUs and even better than private firms of similar size. Smaller PSUs (Navratnas and Miniratnas) perform worse than private companies and should be good candidates for strategic disinvestment (privatization). PSUs that do not have Ratna status – and are loss makers – should be disposed of their asset value.

The study recommends that India should change the public sector balance sheet by raising capital through strategic disinvestment (privatization), disinvestment and liquidation of PSUs and re-investing it, in public infrastructure through the National Infrastructure Investment Fund and not into the budget as a revenue-raising measure. It should also transform Maharatnas into world class companies with greater commercialization.

The paper makes significant contributions to the academic literature on the changing dynamics of state-owned enterprises in emerging economies by examining the effect of performance contracts and disinvestment on India’s PSUs performance. It is one of unique longitudinal-empirical studies on India’s PSU performance in several dimensions.

The purpose of this study is to recognise the role of information and communication technology (ICT) tools in different sectors like Education, Health Care, Business, FMCG and Entertainment in the phase of social distancing. This study also attempts to provide a quantitative review of the scholarly literature on this topic.

A comprehensive literature evaluation was undertaken using a database encompassing 150 English-language papers with publication dates ranging from 2019 to 2021. The research profile and thematic analysis are presented through a comprehensive content analysis, resulting in four themes. The study reviews various research articles and reports related to social distancing and opens a discussion on the growing importance of ICT tools during this COVID-19 era.

ICT acts as a surviving tool for the economy by creating a virtual environment and helping people to stay socially connected during this pandemic. There is a lack of empirical evidence to support the facts so further research is required.

There are two drawbacks to the current study. Firstly, this study established a rigorous review methodology in which the researchers opted to exclude any grey literature, non-peer-reviewed articles, books, notes and book chapters from consideration. These sources could have had pertinent literature. Secondly, even after protocol’s rigour and numerous rounds of checks by a team of academicians and researchers, an anomaly may have sneaked into the evaluation.

The current study contributes to the growing literature on ICT tools particularly in this phase of social distancing. This paper highlights the need for future research in this area supported by different statistics.

The primary purpose of this study is to examine the factors leading to shopping cart abandonment and construct a model depicting interrelationship among them using interpretive structural modeling (ISM) and Matriced Impact Croises Multiplication Appliquee an un Classement (MICMAC).

Initially, 20 factors leading to shopping cart abandonment were extracted through a systematic literature review and expert opinions. Fifteen factors were finalized using the importance index and CIMTC method, for which consistency has been checked in SPSS software through a statistical reliability test. Finally, ISM and MICMAC approach is used to develop a model depicting the contextual relationship among finalized factors of shopping cart abandonment.

The ISM model depicts a technical glitch (SC8), cash on delivery not available (SC4), bad checkout interface (SC9), just browsing (SC11), and lack of physical examination (SC12) are drivers or independent factors. Additionally, four quadrants have been formulated in MICMAC analysis based on their dependency and driving power. This facilitates technical managers of e-commerce companies to focus more on factors leading to shopping cart abandonment according to their dependency and driving power.

Taking an expert’s opinion as a base may affect the results of the study due to biases based on subjectivity.

This study’s outcomes would accommodate practitioners, researchers, and multinational or national companies to indulge in e-commerce to anticipate factors restricting the general public from online shopping.

For the successful running of an e-commerce business and to retain the confidence of e-shoppers, every e-commerce company must make a strategy for controlling factors leading to shopping cart abandonment at the initial stage. So, this paper attempts to highlight the main factors leading to shopping cart abandonment and interrelate them using ISM and MICMAC approaches. It provides a clear path to technical heads, researchers, and consultants for handling these shopping cart abandonment factors.

The main objective of this study is to compare the service quality of two retail chains of hypermarkets, namely, Big Bazaar and Spencer's, using the trapezoidal fuzzy approach.

Customers from Big Bazaar and Spencer's of Andhra Pradesh, India, have been surveyed through a well-designed questionnaire. The study attempts to compare the service quality of two major retail giants (Spencer's and Big Bazaar) in Andhra Pradesh by using the trapezoidal fuzzy approach to prioritize the attributes of service quality of retail outlets.

The result of the study indicates that the expectations of Big Bazar customers are higher as compared to Spencer's. Further, the study reveals – that Spencer's need to improve in the dimension of tangibility while Big Bazar needs to focus more on responsiveness.

As the data taken for the study are primary in nature, chances of bias may arise on the part of respondents, which may affect the validity of results. Further, the study is confined to two retail stores in Andhra Pradesh, India only, which may not reflect the broader picture.

Retailers may provide more importance to two major service quality dimensions, i.e. tangibility and responsiveness while preparing for their service and marketing strategies.

As the study relates to the comparative analysis of service quality of Big Bazar and Spencer's, the findings will be of additional value to these specific retailers. Therefore, it is expected that this study will fill the gap in the literature by prioritizing the expectations and perceptions of customers of Big Bazar and Spencer's.

The primary objective of this study is to prioritize the main intentions behind investment in cryptocurrency, in spite of its volatile nature and no regulatory framework.

This research paper has worked on collective constructs of the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and social support theory with an added construct of financial literacy. A fuzzy analytical framework has been applied to prioritize the intentions of investors.

The result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)” is the least influencing factor considered by investors. The subdimensions ranked in the top priority by investors are as follows: “I want to invest in cryptocurrencies because I have a good level of financial knowledge (FL1)”; “The people who are important to me will think that I should use cryptocurrencies (SI2)”; “I have the necessary resources to use cryptocurrencies (FC2).” The least importance is given to “It will be easy for me to become an expert in the use of cryptocurrencies (EE3).”

Few of the constructs of the UTAUT, the TAM and social support theory have been considered while prioritizing intentions. Different other intentions also prevail under different theories that need to be researched further.

Unlike previous studies, this research adds the archetype of social commerce, social support and utility theories to analyze and prioritize the behavioral perspective of using cryptocurrencies in digital transactions.

This paper fills the gap in the research study, along with assisting the regulators and cryptocurrency practitioners to widen their knowledge base and to recognize the prioritized intentions.

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.

This study combines bibliometric and content analysis to review 350 publications from top-ranked journals published from 1986 to 2020.

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.

This study undertakes research papers related to the field of finance, published in peer-reviewed journals and that too in the English language.

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.

The successful combination of the bibliometric and content analysis revealed major gaps in the literature and provided future research directions.

The aim of this work was to study the effect of aerobic packaging on storage quality of functional restructured spent hen meat slices (FRSHMS) at refrigerated temperature (4 ± 1°C).

FRSHMS were prepared by replacing the lean meat with different binders and extenders in pre-standardized restructured spent hen meat slices formulation. The slices of control and FRSHMS were aerobically packaged in LDPE and analyzed for physicochemical, microbiological and sensory quality.

The pH of FRSHMS was significantly higher (p = 0.03) than control on 0 and 7th day. Thiobarbituric acid (TBA) values of both control and FRSHMS increased significantly on 21st day of storage as compared to 0 day. TBA value of FRSHMS was significantly lower (p = 0.04) as compared to that of control on 21st day. Psychrophils were detected on 14th day of storage and thereafter increased significantly (p = 0.00) in both control and FRSHMS. Coliforms were detected only on 21st day of storage in both control and FRSHMS. The sensory attributes of control, and FRSHMS were affected from 14th day onwards, but the products remained quite acceptable with sensory rating between good to very good up to 21 days during refrigerated storage.

The trials can be further carried in modified atmospheric packaging to evaluate storage stability of developed restructured spent hen meat slices.

The paper has demonstrated that the FRSHMS could be stored safely in aerobic packaging for 21 days at refrigeration temperature.

Introduction: With a rapid increase in the application of artificial intelligence and machine learning, new Artificial Intelligence (AI) technologies are now playing a vital role in evolving green human resource management practices in the sustainable development of organizations. A thorough analysis of the part of AI technologies in Green Human Resource Management (GHRM) is still an ongoing process.

Purpose: This chapter/study aims to provide an overview of the changing paradigm of human resource management practices into Green Human Resource Management Practices and the role of Artificial Intelligence interventions in various aspects of talent management, talent acquisition, and talent retention.

Design/methodology/approach: This study is based on the systemic review of 37 research studies and reports on human resource management practices, green human resource management, artificial intelligence, and talent management strategies.

Findings: The findings below show a change in human resource management practices encouraging new technologies, software solutions, and artificial intelligence. Green management practices are overtaking the traditional mindsets and work environment. This transformation has synergized the adoption of artificial intelligence and G-HRM practices, encouraging innovative and positive work behaviors.

Originality/value: The originality of this study lies in fulfilling the existing gap in the studies of the role of artificial intelligence and its effects on green human resource management. Its value counts in showing the aspects of innovation in HRM with sustainability and advanced technology.