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This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

This study explored the influence of Dervin’s sensemaking methodology (SMM).

Citation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.

The influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.

This study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.

This study aims to describe the development of hospitality research in terms of research methods and data sources used in the 2010s.

Content analyses of the research methods and data sources used in original hospitality research published in the 2010s in the Cornell Hospitality Quarterly (CQ), International Journal of Hospitality Management (IJHM), International Journal of Contemporary Hospitality Management (IJCHM), Journal of Hospitality and Tourism Research (JHTR) and International Hospitality Review (IHR) were conducted. It describes whether the time span, functional areas and geographic regions of data sources were related to the research methods and data sources.

Results from 2,759 original hospitality empirical articles showed that marketing research used various research methods and data sources. Most finance articles used archival data, while most human resources articles used survey designs with organizational data. In addition, only a small amount of research used data from Oceania, Africa and Latin America.

This study sheds some light on the development of hospitality research in terms of research method and data source usage. However, it only focused on five English-based journals from 2010–2019. Therefore, future studies may seek to understand the impact of the COVID-19 pandemic on research methods and data source usage in hospitality research.

This is the first study to examine five hospitality journals' research methods and data sources used in the last decade. It sheds light on the development of hospitality research in the previous decade and identifies new hospitality research avenues.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The purpose of this study is to examine the quality of information in articles in the online encyclopedia, Wikipedia, as perceived by readers and content contributors. This study explored several dimensions and characteristics of information presented in Wikipedia by identifying new emerging dimensions in terms of readers’ perceptions of the quality of online information.

Two rounds of online surveys were conducted using a mixed-method approach. In the first survey, the authors conducted content analysis on 197 participants, and in the second survey, the authors conducted factor analysis on 107 study participants. The authors used Qualtrics Panel Services to recruit individuals who read and/or edited the English version of Wikipedia articles and resided in the United States.

The mixed-method approach employed in this study to explore the quality of online information had three core components: users’ perceptions of information quality, content analysis, and exploratory factor analysis of the perceived information quality structure. The study found a new information quality category, social aspect quality. Dimensions include fun, goodness, empowering and user generated.

The results demonstrate the emergence of novel quality attributes for information quality presented online, particularly in social media. Moreover, this study is one of the rare studies to employ a mixed-method approach that offers diverse but reliable perspectives on information quality as perceived by everyday users of Wikipedia.

The purpose of this study is to assess how intergovernmental panel on climate change’s (IPCC’s) sixth assessment report of Working Group I (WGI), a Summary for Policymakers (SPM), has evaluated the current climate change situation. The author uses the qualitative content analysis (QCA) method (i.e. summative content analysis [SCA]) to identify critical points of the SPM.

To better understand the WGI report and its SPM, the author uses the thematic analysis, often called the QCA method. This study takes more steps and uses one of the main qualitative content analysis approaches (i.e. SCA). Therefore, QCA (SCA) can help the author count the occurrence of certain words using computer-assisted qualitative data analysis software that quantifies the words in the data. This process helps the author to understand codes and patterns (e.g. the concept in the results section).

Interestingly, the AR6 says more about adaptation for policy makers in sections C (Climate information for risk assessment) and D (Mitigation of future climate change) than in sections A and D. Finally, this study concludes that the IPCC WGI SPM has addressed evidence on global climate change policymaking for SPMa, SPMd (mitigation-based strategies and mitigation policy [MP]) and SPMb, SPMc (adaptation policy and adaptation-based strategy).

First, this study refers only to the contribution of WGI, a SPM. The findings of this study do not necessarily provide a full understanding of what the AR6 WGI SPM says about climate change. It points out that the QDA Miner software and Voyant tool do not include all variables and examples where mitigation and adaptation-based strategies are discussed. The guidance for coding is based on the approved version of IPCC AR6 WGI SPM. The final limitation is that the relatedness of key words (e.g. confidence, high and warming) is sometimes ambiguous; even experts may disagree on how the words are linked to form a concept. Thus, this method works at the keyword level. A more intelligent method would use more meaningful information than keywords.

This study used qualitative data analysis (SCA) to explore what was said about climate change in four sections of the IPCC AR6 WGI SPM, which may influence current and future global climate change policymaking.

This study aims to review the current status of quality management (QM) research in Arab countries between 2001 and 2020.

The study adopted the content analysis methodology, searching through various databases and search engines for relevant publications using several keywords. The selected publications were classified according to several criteria and the obtained results were presented in the form of frequencies and percentages.

Most research publications regarding QM were journal articles. The number of publications has steadily increased between 2001 and 2020. Moreover, QM research largely uses the quantitative research design. Questionnaire surveys are widely used as a data collection method; basic statistical analysis techniques are commonly employed to analyze the data. There is a tendency toward empirical research versus conceptual research. A few journal articles were published in reputed peer-reviewed international journals with low citation. Overall, Arab scholars research on QM and related topics over the past two decades is not significant for the field considering the number of published papers, citations and the papers published in reputed peer-reviewed international journals.

This study has several limitations. First, it does not cover non-English information sources due to the overall lack of Arabic publication databases. Second, it uses a limited number of criteria to classify the selected publications. Third, it adopts the content analysis methodology to classify the selected publications. This method has several limitations, which may negatively affect the results. Nevertheless, the study offers several implications for research scholars, educators and practitioners.

This is the first study to attempt a comprehensive overview of the state of research on QM in Arab countries between 2001 and 2020 using the content analysis methodology.

Scholars have pointed out the need for improvement and refinement in public management research, also depending on the role of public administration for sustainable development. Because government organizations employ a substantial portion of the workforce, management practices in the public sector are critical areas for designing, implementing and delivering policies that can achieve the goals set forth in the 2030 Agenda for Sustainable Development. For these purposes, and in implementation of the Next Generation EU (NGEU) programme, Italy recently launched an ambitious National Recovery and Resilience Plan (NRRP) which includes, among other things, upskilling goals for staff employed, following the modernization process that has involved the Italian public sector in the last decades, with the Decree n. 150/2009. Aiming both to understand the extent of the application of the reform and to answer some basic questions (why, what and how) concerning Individual Performance Appraisal Systems (IPAS), we conducted a content analysis on the 220 documents already produced by the Italian Ministries. The study has been conducted in two different steps of the reform process and provides solid evidence of the reforms' effects on designing and implementing individual performance systems. The analyzed documents reveal no longer-term vision in implementing the IPAS that involves some critical performance management utilities such as training, development, fair pay and deployment of employees, raising new questions about a sustainable approach to the individual performance management process even in public organizations.

Over the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.

The study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.

This study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.

This work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.