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This chapter aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads. MAR models have been successfully implemented on commodity prices as they allow to generate nonlinear features such as locally explosive episodes (denoted here as bubbles) in a strictly stationary setting. The authors consider multiple detrending methods and investigate, using Monte Carlo simulations, to what extent they preserve the bubble patterns observed in the raw data. MAR models relies on the dynamics observed in the series alone and does not require economical background to construct a structural model, which can sometimes be intricate to specify or which may lack parsimony. The authors investigate oil prices and estimate probabilities of crashes before and during the first 2020 wave of the COVID-19 pandemic. The authors consider three different mechanical detrending methods and compare them to a detrending performed using the level of strategic petroleum reserves.

This study employs bibliometric analysis to map the research landscape of social media trending topics during the COVID-19 pandemic. The authors aim to offer a comprehensive review of the predominant research organisations and countries, key themes and favoured research methodologies pertinent to this subject.

The authors extracted data on social media trending topics from the Web of Science Core Collection database, spanning from 2009 to 2022. A total of 1,504 publications were subjected to bibliometric analysis, utilising the VOSviewer tool. The study analytical process encompassed co-occurrence, co-authorship, citation analysis, field mapping, bibliographic coupling and co-citation analysis.

Interest in social media research, particularly on trending topics during the COVID-19 pandemic, remains high despite signs of the pandemic stabilising globally. The study predominantly addresses misinformation and public health communication, with notable focus on interactions between governments and the public. Recent studies have concentrated on analysing Twitter user data through text mining, sentiment analysis and topic modelling. The authors also identify key leading organisations, countries and journals that are central to this research area.

Diverging from the narrow focus of previous literature reviews on social media, which are often confined to particular fields or sectors, this study offers a broad view of social media's role, emphasising trending topics. The authors demonstrate a significant link between social media trends and public events, such as the COVID-19 pandemic. The paper discusses research priorities that emerged during the pandemic and outlines potential methodologies for future studies, advocating for a greater emphasis on qualitative approaches.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2023-0194.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

This paper aims to aims to extend emotional labor research by exploring whether the impact of emotional labor on customer satisfaction depends on the order in which different emotional labor strategies are used by employees. Specifically, the authors explore how the order effects of two emotional labor strategies – deep and surface acting – impact customer satisfaction.

The authors conducted two experimental studies in which participants interacted with service employees who systematically switched between surface and deep acting strategies during the service episode. In Study 1, participants watched a video clip depicting a service encounter in a bookstore. In Study 2, participants partook in a simulated career-counseling session.

The four different emotional labor strategy order effects differentially impact customer satisfaction. Consistent with theories of gain–loss effects, improvement and decline trends positively or negatively impact customers, respectively. Furthermore, results show that these trends impact customer satisfaction growth differently over time.

The authors only focused on two emotional labor strategies, and future research may benefit from extending the research to additional regulation strategies and/or specific discrete emotions.

The results suggest that managers may train employees in recognizing that customer satisfaction is not just driven by customers’ overall assessment of the interaction but also by their experience at different stages of the interaction.

Service marketing and management scholars have largely explored emotional labor from a between-person or within-person perspective, with little empirical attention paid to within-episode processes that focus on how employee behavior varies within a single service episode. To the best of the authors’ knowledge, this study is one of the first to demonstrate that surface and deep acting can be used simultaneously and dynamically over the course of a single service interaction in impacting customer satisfaction.

The paper evaluates trends shaping the post-pandemic reality. The framework adopted is a case study of the V4 region (Poland, the Czech Republic, Slovakia and Hungary) that illustrates broader trends, their direction of change and their influence on the entire region. This paper aims to identify key trends and analyse how they can facilitate or hinder sustainable development.

The paper is based on a multidisciplinary literature review and an online real-time Delphi study carried out across four European countries.

The results indicate that the influence of negative trends on sustainability is much stronger than that of positive ones. Concerning the trends’ driving factors, the blockers of negative trends have a much higher influence on sustainability than the blockers of positive ones. The study shows that the most significant trends affecting sustainability are distributed throughout various fields of human activity, including geopolitics, social issues, education, the environment, technology and health.

The findings presented below can be used primarily by decision makers from the V4 region, who are responsible for crafting strategies regarding post-COVID recovery. The study illustrates trends that V4 countries and other European Union member states might be facing in the future and analyses how they relate to sustainability. The conclusions indicate that the most effective path to the desired level of sustainability is one that incorporates policies built around the blockers of negative trends.

The importance of this study lies in its focus on countries that had previously received little attention in scientific analyses. The paper shows their possible developmental pathways and sheds light on the framework of integrated foresight and its applications in sustainability-related areas.

To provide a dynamic view on accommodation choice behaviors during the pandemic, this study aims to examine the impact of recent trends on prospective travelers’ preferences for hotels and Airbnb.

The paper adopts a mixed methods approach that incorporates three independent studies (experimental analysis, online search pattern analysis and an econometric event study) to understand customer decision-making behaviors.

The findings indicate that travelers prefer Airbnb entire flats/apartments to hotels when the pandemic is trending upward. This result externally validates travelers’ preference toward Airbnb during periods of high risk. Interestingly, when the trends go downward, however, the same behavioral pattern was not identified.

This study provides important empirical insights into how the evolution of health crises influence customer decision-making for hotels and Airbnb. Future research needs to consider the role of socio-demographic factors in accommodation selection behaviors and examine how travelers react to cleanliness levels between Airbnb and hotels.

As one of initial studies that empirically examine Airbnb customers’ decision-making behaviors in the context of the COVID-19 pandemic’s trends, this study provides a dynamic view on how the evolution of the pandemic influences accommodation choice behaviors.

The purpose of this study is to create a methodological approach for identifying priority areas for science and technology (S&T) development and its empirical application within the city of Moscow. This research uncovers a wide range of multicultural and multidisciplinary global trends that will affect the development of major cities in an era of complexity and uncertainty, including the inherent complexity of urban contexts, demographic and socioeconomic trends, as well as scientific and ecological factors.

The methodological approach is based on classic foresight instruments. Its novelty lays in the blending of qualitative and quantitative methods specially selected as the most appropriate for the identification of S&T areas in an era of complexity and uncertainty, including horizon scanning, bibliometric analysis, expert surveys and the construction of composite indexes with respect to the scope and resources of the research and the selected object for empirical application – Moscow, which is one of the world’s largest megacities. The analysis was performed for the period of 2009–2018 and expert procedures took place in 2019.

As a result, 25 global trends were identified, evaluated and discussed over the course of an expert survey and subsequent expert events. Ten priority areas of S&T development were determined, including 62 technological sub-areas within them and the most important market niches for all identified technological sub-areas, which could be useful for the world’s megacities. The results of this study are illustrated using the construction sector. Based on the conducted research and results, a list of recommendations on S&T policy measures and instruments were suggested, including the creation of the Moscow Innovation Cluster, which by the end of 2023 contained more than 6,000 projects and initiatives, selected using the findings of this investigation.

This research contributes to the existing literature and research agenda of setting priorities for S&T development and shows how it can be done for a megacity. The blended foresight methodology that was created within the study satisfies the criteria of scientific originality, is repeatable for any interested researcher, is applicable to any other city in the world and demonstrates its high efficiency in empirical application. It could be used for creating new agenda items in S&T policy, setting S&T priorities for a megacity and integrating the results into decision-making processes. This study provides recommendations on the further implementation of the designed methodology and results into a policymaking system. Moreover, the example of the Moscow Innovation Cluster, which was created based on the results of our research, demonstrates these recommendations’ practical significance in real life, which is quite valuable. The limitation of this study is that it is not devoted to urban planning issues directly or the promotion of R&D areas; it is about setting promising S&T priorities in an era of complexity and uncertainty for megacities.

This paper examines asymmetric multifractality (A-MF) in the leading Middle East and North Africa (MENA) stock markets under different turbulent periods (global financial crisis [GFC] and European sovereign debt crisis [ESDC], oil price crash and COVID-19 pandemic).

This study applies the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method of Cao et al. (2013) to identify A-MF and MENA stock market efficiency during the COVID-19 pandemic.

The results show strong evidence of different patterns of MF during upward and downward trends. Inefficiency is higher during upward trends than during downward trends in most of the stock markets in the whole sample period, and the opposite is true during financial crises. The Turkish stock market is the least inefficient during upward and downward trends. A-MF intensifies with an increase in scales. The evolution of excessive A-MF for MENA stock returns is heterogeneous. Most of the stock markets are more inefficient during a pandemic crisis than during an oil crash and other financial crises. However, the inefficiency of the Saudi Arabia and Qatar stock markets is highly sensitive to oil price crashes. Overall, the level of inefficiency varies across market trends, scales and stock markets and over time. The findings of this study provide investors and policymakers with valuable insights into efficient investment strategies, risk management and financial stability.

This paper first explores A-MF in the MENA emerging stock markets. The A-MF analysis provides useful information to investors regarding asset allocation, portfolio risk management and investment strategies during bullish and bearish market states. In addition, this paper examines A-MF under different turbulent periods, such as the GFC, the ESDC, the 2014–2016 oil crash and the COVID-19 pandemic.

This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic.

This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021.

The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash.

The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.