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The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

The increased digital interactions in the B2B industry have enhanced the importance of customer engagement as a measure of firm performance. This study aims to map and analyze temporal and spatial journeys for customer engagement in B2B markets from a bibliometric perspective.

The extant literature on customer engagement research in the B2B context was analyzed using bibliometric analysis. The citation analysis, keyword analysis, cluster analysis, three-field plot and bibliographic coupling were used to map the intellectual structure of customer engagement in B2B markets.

The research on customer engagement in the B2B context was studied more in western countries. The analysis suggests that customer engagement in B2B markets will take centre stage in the coming times as digital channels make it easier to track critical metrics besides other key factors. Issues like digital transformation, the use of artificial intelligence for virtual engagement, personalization, innovation and salesforce management by leveraging technology would be critical for improved B2B customer engagement.

The study provides a comprehensive reference to scholars working in this domain.

The study makes a pioneering effort to comprehensively analyze the vast corpus of literature on customer engagement in B2B markets for business insights.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

Given the dearth of thorough summaries in the literature, this systematic review and bibliometric analysis attempt to take a meticulous approach meant to present knowledge on the constantly developing subject of stock market volatility during crises. In outline, this study aims to map the extant literature available on stock market volatility during crisis periods.

The present study reviews 1,283 journal articles from the Scopus database published between 1994 and 2022, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram. Bibliometric analysis through software like R studio and VOSviewer has been performed, that is, annual publication trend analysis, journal analysis, citation analysis, author influence analysis, analysis of affiliations, analysis of countries and regions, keyword analysis, thematic mapping, co-occurrence analysis, bibliographic coupling, co-citation analysis, Bradford’s law and Lotka’s law, to map the existing literature and identify the gaps.

The literature on the effects of crises on volatility in financial markets has grown in recent years. It was discovered that volatility intensified during crises. This increased volatility can be linked to COVID-19 and the global financial crisis of 2008, as both had massive effects on the world economy. Moreover, we identify specific patterns and factors contributing to increased volatility, providing valuable insights for further research and decision-making.

The present study is confined to the areas of economics, econometrics and finance, business, management and accounting and social sciences. Future studies could be conducted considering a broader perspective.

Most of the available literature has focused on the impact of some particular crises on the volatility of financial markets. The present study is not limited to some specific crises, and the suggested research directions will serve as a guide for future research.

This paper aims to focus on exploring and understanding the practice of analyzing the determinants of the Moroccan Dirham by foreign exchange professionals in trading rooms in the context of transitioning to a more flexible regime initiated by Moroccan authorities. The objective of this study is to highlight how foreign exchange operators analyze the determinants of the Moroccan Dirham in the context of exchange rate liberalization, focusing primarily on qualitative data rather than quantitative data.

Therefore, this paper opted for a methodological approach using interview surveys to understand the underlying behavior of Moroccan foreign exchange operators, conducting a content analysis. This paper targeted six foreign exchange operators from nine Moroccan banks authorized as market makers by Bank Al-Maghrib.

The results indicate that the fluctuations of the Moroccan Dirham are closely linked to two main factors: the analysis of the EUR/USD exchange rate and market liquidity analysis. Furthermore, content analysis revealed five essential aspects regarding the practice of analyzing the determinants of the Dirham: “Dirham determinants,” “complementarity between technical analysis and fundamental analysis,” “trends and reversals,” “utility of macroeconomic models” and “psychological factors.”

Certainly, this methodology allows for exploring and understanding the underlying behavior of currency operators but inherently generates a certain degree of subjectivity that can affect the research validity. Indeed, the subjectivity can arise from the responses of the currency operators themselves. They may present the phenomenon coherently or selectively choose the elements they remember to respond to. On the other hand, the validity of this type of research relies on the researcher's ability to cultivate empathy throughout the knowledge creation process. The empathetic stance adopted in this study proved to be complex due to the uniqueness of operators and interaction, sometimes making it challenging to combine empathy, respect and critical thinking (Olivier De Sardan, 2004). Furthermore, the researcher is often faced with an interpretation bias, which can manifest not only during the coding of collected data but also during the analysis of the constructed content. To mitigate this interpretation bias, this paper subjected the collected data to a double coding procedure.

This study aims to narrow the gap in opinions between academics and practitioners by providing a practical overview for change novices.

This study is the pioneering inquiry exploring the process of determining the Moroccan dirham within the transition to a flexible exchange rate regime, using an exploratory methodological approach.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

The article aims to study the demand for sparkling wines in Europe. The main objective is to estimate the classic demand parameters aggregately for the entire European area (European Union and the United Kingdom) and separately for groups of countries characterized by wine markets with similar characteristics.

Using 15-years market data for different wine categories from the Euromonitor Passport database, the estimation of price and income elasticities is performed through a Quadratic Almost Ideal Demand System. In line with the objectives, the model is applied first to the whole European area and then separately to the considered groups of countries (subareas). To identify homogeneous subareas, a cluster analysis was performed on basic characteristics of the wine market.

When considering the European market as a whole, sparkling wines should be considered a luxury category with a high own-price elasticity. However, the structure of their demand is rather different in different sub-areas. The observed heterogeneity suggests that differentiated policy and marketing considerations should be made. In addition, it widens the possibilities for producers, who can choose the submarkets that respond best to their needs to export their sparkling wines. This seems particularly important in markets, like the sparkling wine ones, that are experiencing a continuous expansion over the last decades.

Despite using a methodology well-established to study wine and alcohol demand, the study fills a considerable gap in the literature. Although the demand for sparkling wine is growing worldwide, so far only a couple of studies have engaged in the analysis of its structure. In Europe, the largest market for sparkling wine, this kind of studies is completely lacking.

The objective of this review article is to meticulously examine and critically evaluate the present body of literature on sensory marketing. The study aims to explore this domain’s latest trends and prospects using diverse metrics such as annual publication trends, authorship patterns, most cited publications, sources analysis, co-citation patterns, keyword analysis and thematic analysis.

In the study, bibliometric analysis is used to assess the different advancements in the field. To perform performance analysis, science mapping and network analysis, VOSviewer, Bibloshiny and MS Excel are used. Additionally, the Scopus database is used to extract literature related to the subject area.

The most cited author of the domain is Biswas D, while the most prolific source of the domain is Plos One, additionally “An integrative review of sensory marketing: Engaging the senses to affect perception, judgment, and behavior” is stated as the most cited publication in the domain. Moreover, experiential marketing is reported as the most important keyword of the domain. Additionally, other findings reported are stated in the main body of the research study.

Practical implications of the study extended to marketing practitioners, future researchers and various stakeholders interested in the subject area.

What stands this research paper apart from other studies is the comprehensive approach to the evaluation of literature using VOSviewer, Bibloshiny and MS Excel and in-depth evaluation of the domain using performance analysis, science mapping, network analysis and thematic analysis.

This study aims to perform the bibliometric analysis of the customer engagement (CE) literature, highlights the major research themes and classifies the subdomains. The study also identifies antecedents and consequences, as well as dimension evolution, and suggests future research directions.

This study used a comprehensive bibliometric approach using Scopus data from 2002 to January 2024. Advanced analytical techniques, including bibliometric and cocitation analysis using R and bibexcel, were used. In addition, machine learning (ML)-based Latent Dirichlet Allocation (LDA) was used to extract latent themes.

This study reveals the domain’s past trend and present research scenario. The thematic analysis of CE is classified into three phases. Document cocitation analysis provided four broad clusters: conceptualization and operationalization, value creation through engagement, building relationships with brands and engagement-social media interface. The antecedents and consequences are categorized and presented along with the evolution of the multidimensional nature of CE.

This study adds to the literature in two key ways. First, the entire scholarly production has been compiled into one frame. Second, multiple methods were used to unravel citation, cocitation and textual data. Furthermore, ML-based LDA was used to extract latent themes from clusters and future research directions were proposed.