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This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.

From more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.

First, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.

Performing sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

A particular feature that makes foresight powerful is its capability to learn from past trends to help guide decision‐making for future policy. However, in studying both past and future trends, network perspectives are often missing. Since networks are capable of revealing the structure that underpins relationships between stakeholders, key issues and actions in the past, they are powerful to help envisage the future. The purpose of this paper is to propose a methodological framework to incorporate network analysis in foresight.

The paper develops a generic framework to incorporate network analysis into foresight's five stages. Trends identified by respondents of the Big Picture Survey are used to demonstrate how we operationalize this framework.

A network perspective can enrich foresight analysis in that it helps reveal structural linkages between trends and thus can better identify emerging future issues, both of which are critical in foresight.

The inclusion of network analysis can shed light on the process of understanding complex data and assist in building a model based on links and relationships. Network analysis can reveal otherwise unobservable structural features of the data and can help boundary setting discussions in foresight.

Network concepts and measures could usefully enrich the interpretation of foresight data for further analysis, or plausible scenarios.

Network analysis offers a new way of looking at the foresight data by disentangling complicated issue webs. As shaping the future becomes more essential because of the complexity of science, technology and society interrelationships, the incorporation of network perspectives in foresight might be one of the ways to propel future studies.

This study aims to identify the trends that have changed in the field of construction management over the last 20 years.

In this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.

In this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.

This study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.

There has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.

This paper aims to track how ecotourism has been presented in a digital world over time using geotagged photographs and internet search data. Ecotourism photographs and Google Trends search data are used to evaluate tourist perceptions of ecotourism by developing a categorization of essential attributes, examining the relation of ecotourism and sustainable development, and measuring the popularity of the ecotourism sites.

The researchers collected geotagged photographs from Flickr.com and downloaded Google search data from Google Trends. An integrative approach of content, trend and spatial analysis was applied to develop ecotourism categories and investigate tourist perceptions of ecotourism. First, the authors investigate ecotourism geotagged photographs on a social media to comprehend tourist perceptions of ecotourism by developing a categorization of key ecotourism attributes and measuring the popularity of the ecotourism sites. Second, they examined how ecotourism has been related with sustainable development using internet search data and investigate the trends in search data. Third, spatial analysis using GIS maps was used to visualize the spatial-temporal changes of photographs and tourist views throughout the world.

This study identified three primary themes of ecotourism perceptions and 13 categories of ecotourism attributes. Interest over time about ecotourism was mostly presented as its definitions in Google Trends. The result indicates that tracked ecotourism locations and tourist footprints are not congruent with the popular regions of ecotourism Google search.

This research follows the changing trends in ecotourism over a decade using geotagged photographs and internet search data. The evaluation of the global ecotourism trend provides important insights for global sustainable tourism development and actual tourist perception. Analyzing the trend of ecotourism is a strategic approach to assess the achievement of UN sustainable development goals. Factual perspectives and insights into how tourists are likely to seek and perceive natural attractions are valuable for a range of audiences, such as tourism industries and governments.

研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的，文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知，通过对关键要素的分类，审视生态旅游和可持续发展的关系，以及衡量生态旅游基地的受欢迎程度等方法。

本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析，来开发生态旅游类别和游客对生态旅游的感知。首先，我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况，以此搭建了关键生态旅游要素的类别体系，和衡量生态旅游基地的受欢迎程度。第二，我们通过使用互联网搜索数据，检测了生态旅游如何与可持续发展相连接，以及研究了搜索数据中的趋势。第三，我们使用了GIS软件来操作空间分析，对图片的空间-时间改变和游客对世界的观点做了可视化处理。

本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化，根据谷歌趋势上的定义，被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。

本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法，对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见，游客如何选择和感知自然景观，这对于很多群体，比如旅游行业和政府，都有着重要意义。

The purpose of this paper is to provide a framework for understanding core technological competencies and identifying the trends on the technological convergence of a business ecosystem using the patent information of leading firms in the system.

The proposed framework is composed of two steps: time-sequential text clustering analysis for comprehending changes in general technological fields and association rule analysis for identifying the trends of convergences in each field. The authors applied the proposed framework to the patents applied to United States Patent Trademark Office by Samsung Electronics, a market leader of the electronics industry, during the period from 2000 to 2011.

In the sequential text clustering analysis, trends of 14 technological fields such as data storage medium and data processing, mobile, lights and heats and memory are identified. Moreover, changes of technological convergence in each field are identified using association rule analysis. For instance, in the case of technologies related to lights and heats, convergences occurred between radio transmission systems and modulated-carrier systems during the period from 2000 to 2001. However, recent convergences appeared between technologies regarding controlling lights and liquid crystal materials since 2008.

Utilization of the framework will suggest new business opportunities to SMEs in a business ecosystem by identifying the trends of technological convergences.

The objectives of the study were to demonstrate the suitability of methodology based on a text mining toolset for detecting trends in scientific papers and to find trends that were present in the field of project management during the research time span (2000–2019).

An approach based on text mining tools supported by expert analysis was adopted due to an extensive number of publications in the field of project management. The novelty of the approach lies in the proposed method of trends discovery instead of the commonly used trends predefinition. The use of computer support allowed the full texts of papers, and not only abstracts, to be analysed, which significantly increased the reliability of the achieved results. Overall, 3,544 papers from seven journals were analysed.

As a result, 43 trends were discovered including seven long-lasting, four declining, 17 emerging and 15 ephemeris trends. Trends were analysed in comparison with the results of previous studies and project management frameworks. New trends and potential gaps were discussed.

The results highlight the topics of research that gain popularity among researchers, and which are related to the current problems that arise in project management. Therefore, the results can help focus studies on the most important areas, as well as find new ones which are not so popular yet. The knowledge of current trends is also important for those project managers who seek to improve the efficiency of their work.

The purpose of this paper is to investigate trends in the use of research methodologies and publications in manufacturing strategy (MS) literature across geographical regions and suggests possible future research opportunities.

This literature review is based on a sample of 512 subject-relevant journal articles and uses content analysis as the primary method for data analysis. The paper investigates developments in the use of research methodologies – in terms of research design, data collection methods, country of data collection, sample size, respondent type, statistical techniques used and time horizon of studies; and publication trends in terms of authorship type, authorship collaboration, most prolific authors, top journals, most prolific universities, and citation analysis.

Research in MS has substantially changed from conceptual quantitative to empirical quantitative designs. NA and Europe show a declining research interest. However, other regions of the world are consistently showing higher interest. Significant opportunities and synergies exist for collaborative research among regions.

Though the literature review is limited in its selection of articles and journals it sketches a picture that may surrogate the whole research community in MS.

Trends in publications and use of research methodologies provide directions for designing research projects relevant to various geographical regions. This will help develop a holistic understanding of MS that is meaningful for managers of today’s organizations.

This paper provides broader and deeper review of the MS literature. Complex patterns in data are revealed using cross-tabulations and advanced cross-tabulations that have not been performed in previous content-analysis–based literature reviews in MS. These patterns will help position future research studies.

The purpose of this paper is to document the total return on investment (ROI) of a comprehensive worksite health program from 1999 to 2007 through two different analytic approaches.

Two analytical techniques were used: time period analysis and historical trend analysis of the entire study period. The time‐period analysis of ROI was performed among employees in four time periods: 1999‐2001; 2002‐2003, 2004‐2005; and 2006‐2007. The historical trend analysis on participation‐related savings was used to compare the financial trend differences between participants and non‐participants as well as the three different participation levels of continuous, sporadic, and non‐participants since the year 2000 among 2,753 employees who worked for and were covered by the company‐sponsored health plans for the entire study period.

The ROI from health care costs and time away from work ranged from 1.29 to 2.07 for the four time periods with a cumulative ROI of 1.66 over nine years. The historical trend analysis of 2,753 long‐term employees resulted in a 1.57 ROI for 2,036 program participants (t‐test: p<0.005) with statistically significant annual saving of $180 per participant per year.

The returns on comprehensive worksite health program were greater than the program investment as documented by both time‐period and historical trend analyses. Organizations seeking ways to manage the increases in health care and absenteeism costs of employees will be encouraged to see that positive returns can be generated by investments in employee health and wellness and steady or consistent participation is one key to generating success.

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 the study was to detect trends in human resource management (HRM) research presented in journals during the 2000–2020 timeframe. The research question is: How are the interests of researchers changing in the field of HRM and which topics have gained popularity in recent years?

The approach adopted in this study was designed to overcome all the limitations specific to the systematic literature reviews and bibliometric studies presented in the Introduction. The full texts of papers were analyzed. The text-mining tools detected first clusters and then trends, moreover, which limited the impact of a researcher's bias. The approach applied is consistent with the general rules of systematic literature reviews.

The article makes a threefold contribution to academic knowledge. First, it uses modern methodology to gather and synthesize HRM research topics. The proposed approach was designed to allow early detection of nascent, non-obvious trends in research, which will help researchers address topics of high value for both theory and practice. Second, the results of our study highlight shifts in focus in HRM over the past 19 years. Third, the article suggests further directions of research.

In this study, the approach designed to overcome the limitations of using systematic literature review was presented. The analysis was done on the basis of the full text of the articles and the categories were discovered directly from the articles rather than predetermined. The study's findings may, however, potentially be limited by the following issues. First, the eligibility criteria included only papers indexed in the Scopus and WoS database and excluded conference proceedings, book chapters, and non-English papers. Second, only full-text articles were included in the study, which could narrow down the research area. As a consequence, important information regarding the research presented in the excluded documents is potentially lost. Third, most of the papers in our database were published in the International Journal of Human Resource Management, and therefore such trends as “challenges for international HRM” can be considered significant (long-lasting). Another – the fourth – limitation of the study is the lack of estimation of the proportion between searches in HRM journals and articles published in other journals. Future research may overcome the above-presented limitations. Although the authors used valuable techniques such as TF-IDF and HDBSCAN, the fifth limitation is that, after trends were discovered, it was necessary to evaluate and interpret them. That could have induced researchers' bias even if – as in this study – researchers from different areas of experience were involved. Finally, this study covers the 2000–2020 timeframe. Since HRM is a rapidly developing field, in a few years from now academics will probably begin to move into exciting new research areas. As a consequence, it might be worthwhile conducting similar analyses to those presented in this study and compare their results.

The present study provides an analysis of HRM journals with the aim of establishing trends in HRM research. It makes contributions to the field by providing a more comprehensive and objective review than analyses resulting from systematic literature reviews. It fills the gap in literature studies on HRM with a novel research approach – a methodology based on full-text mining and a big data toolset. As a consequence, this study can be considered as providing an adequate reflection of all the articles published in journals strictly devoted to HRM issues and which may serve as an important source of reference for both researchers and practitioners. This study can help them identify the core journals focused on HRM research as well as topics which are of particular interest and importance.