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This study examines the impact of social media investor sentiment on the stock market performance through qualitative and quantitative proxies.

The authors use a sample of daily stock performance related to S&P 500 Index for the period from December 18, 2017, to December 18, 2018. The social media investor sentiment was assessed through qualitative and quantitative proxies. For qualitative proxies, the study relies on three social media resources”: Twitter, Trump Twitter account and StockTwits. The authors proposed 3 methods to reflect investor sentiment. For quantitative proxies, the number of daily messages published from Trump Twitter account and StockTwits is considered as a signal of investor sentiment. For regression model, the study adopts the autoregressive distributed lagged to determine the relationships between the nonstationary series.

Empirical findings provide evidence that quantitative measures of investor sentiment have significant effects on S&P’500 performances. The authors find that Trump's tweets should be interpreted with caution. The results also show that the number of Trump's tweets on t−1 day have a positive effect on performance on day t.

Social media sentiment contains information for predicting stock returns and transaction activity. Since, the arrival of new information in capital markets triggers investor sentiment on social media.

This study investigates the investors’ sentiment through social media and explores quantitative and qualitative measures. The amount of information on social media reflects more the investor sentiment than content analysis measures.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0818

The main purpose is to explore the impact of major stock markets on China's market where major markets are represented by former G8 nations (current G7 and Russia).

The article makes use of: stationarity tests (ADF and PP unit root); long-run correlation tests (Johansen integration involving trace and maximum eigenvalue); impact of G8 markets on China (VECM test); influence of G8 markets on volatility in China's market (variance decomposition analysis) and, effect from shocks in G8 markets on China (impulse response function).

Using a period of 2009–2019 that avoids detecting linkages caused by interdependencies created by two major international crises, the article offers four major findings. First, except for Germany and Russia, G8 markets have a significant causal influence on China with UK having the greatest. Second, G8 markets are not the major source of short-run fluctuation in China's market but over time exercise a noteworthy collective impact with UK having the greatest impact. Third, there are occasions for international portfolio diversification with China's market providing greater diversification than G8 nations. Fourth, all markets provide a short-run window of abnormal profit.

The indexes used to represent national markets are assumed to be adequate representations.

Short-term abnormal profits exist. Investing in China, compared to G8 countries, offers greater portfolio diversification possibilities.

Removal of trade and investment barriers cause greater market integration.

By using recent data, this study reveals that G8 stock markets influence China's market.

The study focuses on micro, small and medium-sized enterprises (MSMEs) perceptions about using Fintech instruments to meet their financial requirements.

Methodology: The research utilises a quantitative method, which entails collecting primary data from 400 participants using an organised questionnaire. Chi-square tests, principal component analysis and multiple linear regression are some of the statistical tools utilised to analyse the findings.

Findings: The study’s findings indicate that only a small percentage of business owners know that Fintech instruments can be a source of financing for their companies. In addition, the items utilised in the research led to the generation of three different aspects: banking, MSME and Fintech.

Originality: The previous study indicates that several scholars have focused on various facets of the application and utilisation of Fintech. In the category of digital payments, the total transaction value is anticipated to reach $8.49 trillion in 2022. By 2027, it is expected that the entire transaction value will have increased by 12.31 per cent each year (CAGR 2022–2027), totaling US$15.17 trillion. Digital commerce is expected to have a total transaction value of US$5.49 trillion in 2022, making it the market’s largest segment. On the other hand, there isn’t much research that concentrates on developing countries, and the standards those studies need to meet are relatively stringent. In the context of MSMEs, this is an innovative approach taken towards the State of Punjab.

Need: Fintech services have an important role in bringing about significant change by providing innovative financial services, addressing credit gaps and reshaping how financial services are delivered. Fintech instruments are gaining very high popularity in terms of functions and novelty and can be predicted as the future of operations in the financial sector. Moreover, Fintech instruments can be seen to have a close connection to the MSME-guided programmes. Thus, it would be pretty interesting to note if there lies a need for the optimum implications of Fintech in MSMEs and upcoming start-ups.

Purpose: The goal of this study is to delve into the causes behind the Fintech sector’s rise in various areas and its prospects. Fintech is rapidly expanding because of government legislation, multiple schemes, consumer expectations, a cashless economy, digitisation, globalisation, innovation, and other drivers.

Need for the Study: Fintech firms are forming alliances with traditional financial organisations to stay afloat and compete. India is becoming a superpower regarding e-startups, especially unicorns. Many startups are undergoing initial public offerings (IPOs). Fintech is an emerging space in India, spreading its wings rapidly in every sector.

Methodology: This work is based on a literature review. It utilises secondary data from numerous research publications, magazines, newspapers, published reports, relevant websites, Forbes magazine articles, stories from The Economic Times, the RBI Portal, and information from StartupIndia, Assocham, and Pwc, among others, to develop a conceptual framework showing the growth drivers of Fintech.

Findings: The whole world has been affected severely due to COVID-19. Crisis always comes with some opportunity, and it is up to us how to turn the calamities into opportunities that further turn into innovation that has the power to lead the world. Fintech is that fruit that had been born normally but grew abnormally (tremendous growth) during the pandemic. Also, the roots are so deeper that they will flourish more and more. It has been found that the emergence of a cashless economy, ease of internet connectivity, etc., are the major factors that paved the way for growth for Fintech in India.

Practical Implications: This study contains the conceptual framework which can guide the stakeholders, policymakers, management teams, field experts, etc., in knowing about their area expertise and looking for improvement, if any.

Originality: There are many papers on the relationship between Fintech and financial inclusion, but this is the first study that builds the conceptual framework for the growth drivers of Fintech.

The study's objectives are to conduct a comprehensive review of existing knowledge, thoroughly understand the present state of green finance, identify emerging research trends, perform content analysis and offer valuable guidance for advancing this field.

Data has been collected by selecting highly indexed databases, Scopus and Web of Science. These databases are well-known repositories of academic papers, journals and other scholarly publications related to various fields of study. This research uses the PRISMA methodology for conducting a structured literature review and employs a bibliometric approach to summarize the findings of the previous studies. “R” studio and Biblioshiny are used to clean the data and visualize the results. The TCCM framework is utilized to propose potential avenues for future research in the domain of green finance.

The research uncovers the potential areas in the domain of green finance for future work, encompassing green bonds, the green economy, connectivity, forces, constraints and sustainable development. Furthermore, this process enhances the theoretical underpinnings of scholarly investigations within the discipline by succinctly synthesizing and evaluating preexisting literature. This contribution could facilitate more informed and focused research endeavors in green finance.

The research findings have practical implications for researchers, practitioners, regulators, legislators, issuers and investors involved in green finance. The results can take initiatives to improve practices related to issuing and pricing green financial products and enhance the understanding of interconnectedness within the field.

This ground-breaking research sheds light on various emerging areas by taking a new approach, including the most widely read articles, authors and journals and the broader conceptual and intellectual framework. That includes finding and expanding original research streams, summarizing the most seminal works, and suggesting new research pathways.

Governance quality has been a dominant factor to formulate policies for the development of financial institutions in the world. Therefore, this study aims to explore the impact of governance quality on financial institutions along with globalization in the case of Pakistan.

Time series data from 1996 to 2018 are considered for analysis. The NG-Perron is applied to check the order of integration. In addition, Kim and Perron (2009) structural break unit root test is used to identify break years. The autoregressive distributive lags (ARDL) bound testing approach is used to detect the long-run association among governance quality, financial institutions and globalization.

The results of unit root analysis show that all series are stationary at a different level of integration, I(0)/I(1). However, the long-run association is detected in the presence of break years. The authors find a positive impact of governance quality to determine financial institutions in the long-short-run. Similarly, globalization also enhances financial institutions but only in long run.

This study fills the gap in the economic literature by exploring the linkages between the financial institution and disaggregated governance indicators in the case of Pakistan. Moreover, a role of structural break is also captured during analysis. This study also opens some new insights for policymaking.

The purpose of this study is to investigate whether sexual homicide offenders (SHO) who dispose of the victim’s body naked present with particular crime scene characteristics.

This study aims to answer this question through the use of a sequential logistic regression to test the individual effects of each set of crime scene variables against the manner of disposal using a sample of 662 solved cases of extrafamilial sexual homicide from an international database.

Results demonstrated that the modus operandi behaviors of sexual penetration, asphyxiation, dismemberment and overkill were significantly associated with the body being disposed of naked. In addition, removing or destroying evidence from the scene was also significantly associated with a naked victim. In contrast, the body was more likely to be dumped clothed if the contact scene was deserted and the victim was a stranger. These results suggest that SHOs who dispose of the body naked are more in line with the sadistic sexual murderer, while clothed victims are often disposed of by angry offenders.

To the best of the authors’ knowledge, this is the first study to examine the particular manner of disposing the victim’s body naked in cases of sexual homicide.

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.