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Examines trend rules in conjunction with other well‐known supplementary runs rules to assess their impact when used in control charting. Focuses on a set of 613 trend rules deemed as potential candidates to increase the sensitivity of the control chart. The examined rules are viewed in the light of a stable environment, which determines the false alarm rate, and then in an environment in which the process mean is subjected to drift. Results indicate that there are subsets of trend rules that aid in the detection of out‐of‐control conditions depending on the severity of the drift and the number of zonal‐based supplementary runs rules used.

This paper aims to contribute to the understanding of the concept of a trend and the discourse of trend analysis.

This paper concisely discusses the concept of trends, the value of trend analysis for strategic planning and hierarchical trend pyramids as a tool to scan and analyse trends.

The examples will be given of how specific mega, meso and micro trends are related within a hierarchic trend pyramid.

The tool of trend pyramids helps to structurally analyse and understand trends and developments. Such analysis and understanding are relevant for strategic foresight and scenario planning in leisure and tourism.

The literature on trend levels and pyramids is scarce and varies in interpretation. The aim of this paper is to integrate the various viewpoints into a useful instrument for the scanning and analysis of trends and developments.

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.

This study aims to provide a comprehensive analysis on the reasons of the observed trend in the GAAP ETR over 1960–2016.

The authors use a linear tax function which allows for time-varying coefficients to track the trend in GAAP ETR over 1960–2016. This approach can decompose the ETR trend into the trends of the statutory tax rate, the propensity to recognize taxes, the tax-related firm characteristics and their coefficients. Thus, the authors can quantify the contribution of each factor in the tax function to the ETR trend.

Before 1988, the declining trend in tax expense is mainly driven by changes in the statutory tax rate; in contrast, after 1988, the trend is completely explained by firms’ decreasing propensity to recognize tax expense. While prevalent across different groups of firms, the decreasing propensity to recognize tax expense in the recent 30 years is more pronounced among firms that have higher needs for tax savings or greater tax-saving advantages.

To the best of the authors’ knowledge, this study is the first one that uses a trend analysis to examine the reasons for the downward trend in tax expense over a long period (1960–2016). The results show that, although the trend appears for the full sample period, it is driven by different forces between the first and second half of the time window. A decreasing propensity to recognize tax expense is the main reason for only the trend in recent years, which calls for attention from academia and policymakers. The results also show which firms have had faster trends in their propensity to recognize tax expense, suggesting targets for tax enforcement and tax researchers.

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.

This research aims to intuit the trending technology hashtags (artificial intelligence (AI), blockchain, big data, cloud, enterprise resource planning (ERP), information and communication technology (ICT)) in the field of accounting by providing a bibliometric overview of research articles published in 28 years.

A bibliometric analysis of R software was used in this study. The Scopus database was considered to identify the broad research trends related to technology hashtags in the field of accounting from 1984 to 2021, as well as to gain a better understanding of the growing technology research hashtags in that period.

The bibliometric analysis reveals that the trending research topic focused on technology hashtags (AI, blockchain, big data, cloud, ERP and ICT) in the field of accounting has become important after 2010, as references and number of publications found before that year are scarce. The six trending technology hashtags (AI, blockchain, big data, cloud, ERP and ICT) framework for accounting were outlined in this study.

This research examined the trending technology hashtags in the field of accounting. Only scientific papers published in the Scopus database were analyzed.

This research will be able to understand the trending technology hashtags in the field of accounting, as it identifies the most frequent words, cloud (146), big data (91), AI (65), ERP (76), blockchain (6) and ICT (14). It also aids practitioners and scholars in detecting gaps in the existing literature as well as future research trends.

This research result could be a useful resource for researchers, practitioners and accounting professionals who are interested in the latest technology hashtags in the field of accounting. The current research establishes a new framework on the role of trending technology hashtags in accounting research (accounting technology hashtags, framework) comprising of six research pathways (RP) in the four branches of accounting that future researchers can use to think about and construct their study designs in the field of accounting.

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 study aims to demonstrate the suitability of text-mining toolset for the discovery of trends in quality management (QM) literature in 2000-2019. The hypothesis was formulated that as the field of study is mature, the most important trends are related to deepening and broadening of the knowledge.

A novel approach to trend discovery was proposed. The computer-aided analysis of full-texts of papers led to increased reliability and level of detail of the achieved results and helped significantly reduce researchers’ bias. Overall, 4,833 papers from 8 journal dedicated to QM were analysed.

Trends discovery led to the identification of 45 trends: 17 long-lasting trends, 4 declining trends, 11 emerging trends and 13 ephemeris trends. They were compared to the results of earlier studies. New trends and potential gaps were discussed.

The results highlight the trends that gain or lose popularity, thus they can be used to focus studies, as well as find new subjects, which are not so popular yet. The knowledge about emerging trends is also important for those quality managers who strive for improvement of their efficiency.

The research was designed to bypass the limitations of previous studies. The use of text mining methods and analysis of full texts of papers delivered more detailed and reliable data. Resignation from predefinition of classification criteria significantly reduced researchers’ bias and allowed the discovery of new trends, not identified in previous studies.

The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.

In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.

The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.