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The study aims to investigate the time for social media posts and reviews in order to determine the best timing to ensure maximum outreach and interactions from users. The study intends to analytically investigate a company's Facebook and Instagram pages to get meaningful insights for effective post management.

“Great Deal Tires” company’s Facebook and Instagram business pages were taken as the case study and patterns and analytical insights for different posts using Facebook and Instagram analytics were identified. The study categorically selected posts from Facebook pages and Instagram pages that were posted at different times and assessed on their impact.

According to the study, Tuesday and Wednesday have higher engagement on the Great Deal Tires Facebook page, while Friday has higher engagement on Instagram. The study also provided valuable insights into post content and timing in order to increase the marketing impact of the posts.

The study provides an analytical framework for analyzing post and review timing on various company business pages, allowing marketers to initiate more user visits and interactions.

The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.

This study investigates the communication behavior of public health organizations on Twitter during the COVID-19 vaccination campaign in Brazil. It contributes to the understanding of the organizational framing of health communication by showcasing several instances of framing devices that borrow from (Brazilian) internet culture. The investigation of this case extends the knowledge by providing a rich description of the organizational framing of health communication to combat misinformation in a politically charged environment.

The authors collected a Twitter dataset of 77,527 tweets and analyzed a purposeful subsample of 536 tweets that contained information provided by Brazilian public health organizations about COVID-19 vaccination campaigns. The data analysis was carried out quantitatively and qualitatively by combining social media analytics techniques and frame analysis.

The analysis showed that Brazilian health organizations used several framing devices that have been identified by previous literature such as hashtags, links, emojis or images. However, the analysis also unearthed hitherto unknown visual framing devices for misinformation prevention and debunking that borrow from internet culture such as “infographics,” “pop culture references” and “internet-native symbolism.”

First, the identification of framing devices relating to internet culture add to our understanding of the so far little addressed framing of misinformation combat messages. The case of Brazilian health organizations provides a novel perspective to knowledge by offering a notion of internet-native symbols (e.g. humor, memes) and popular culture references for misinformation combat, including misinformation prevention. Second, this study introduces a frontier of political contextualization to misinformation research that does not relate to the partisanship of the spreaders but that relates to the political dilemmas of public organizations with a commitment to provide accurate information to citizens.

The findings inform decision-makers and public health organizations about framing devices that are tailored to internet-native audiences and can guide strategies to carry out information campaigns in misinformation-laden social media environments.

The findings of this case study expose the often-overlooked cultural peculiarities of framing information campaigns on social media. The report of this study from a country in the Global South helps to contrast several assumptions and strategies that are prevalent in (health) discourses in Western societies and scholarship.

This study uncovers unconventional and barely addressed framing devices of health organizations operating in Brazil, which provides a novel perspective to the body of research on misinformation. It contributes to existing knowledge about frame analysis and broadens the understanding of frame devices borrowing from internet culture. It is a call for a frontier in misinformation research that deals with internet culture as part of organizational strategies for successful misinformation combat.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

This paper aims to identify, synthesise and critically examine the extant academic research on the relation between big data analytics (BDA), corporate accountability and non-financial disclosure (NFD) across several disciplines.

This paper uses a structured literature review methodology and applies “insight-critique-transformative redefinition” framework to interpret the findings, develop critique and formulate future research directions.

This paper identifies and critically examines 12 research themes across four macro categories. The insights presented in this paper indicate that the nature of the relationship between BDA and accountability depends on whether an organisation considers BDA as a value creation instrument or as a revenue generation source. This paper discusses how NFD can effectively increase corporate accountability for ethical, social and environmental consequences of BDA.

This paper presents the results of a structured literature review exploring the state-of-the-art of academic research on the relation between BDA, NFD and corporate accountability. This paper uses a systematic approach, to provide an exhaustive analysis of the phenomenon with rigorous and reproducible research criteria. This paper also presents a series of actionable insights of how corporate accountability for the use of big data and algorithmic decision-making can be enhanced.

This paper discusses how NFD can reduce negative social and environmental impact stemming from the corporate use of BDA.

To the best of the authors’ knowledge, this paper is the first one to provide a comprehensive synthesis of academic literature, identify research gaps and outline a prospective research agenda on the implications of big data technologies for NFD and corporate accountability along social, environmental and ethical dimensions.

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

This study aims to explore Twitter posts of Turkish government agencies and the public under a specific hashtag, #NotHolidayButDistanceEducation, specifically related to online distance education during the Covid-19 pandemic.

This study used a thematic analysis on 22,547 original tweets posted by 6,970 users during the first month of online distance education in Turkish K-12 schools. Based on like and retweet counts, the study further explored the extent of stakeholders’ engagement with the observed themes.

The findings showed that government agencies and citizens used Twitter to provide technical and psychological support, appreciate and motivate stakeholders, demonstrate sample distance education activities, share information and offer suggestions about the ongoing online distance education. It was also observed that the hashtag has been used for expressing negative views about online distance education and for political purposes. A positive relationship was found between social media engagement and providing technical support or sharing information for online distance education.

This study highlights the role of social media in providing practical and emotional support to education stakeholders in times of crisis. Thus, governments can use social media to provide evidence-based psychological and physical health support to their citizens during a pandemic. Social media can serve to improve education practices in schools through the interactions between the public and policymakers.

To the best of the authors’ knowledge, this study can be considered unique because it demonstrates the civic use of social media for educational crisis management. This study highlights the influence of social media in educational policy and practice development in the contemporary era.

The main purpose of this study is to investigate the mediating effect of trust on the relationship between consumers' social motivation and online purchase intentions in the context of social media marketing in the fashion industry of Sri Lanka.

The sample selection was done using a convenience sampling strategy. An online survey was conducted, and data gathered from consumers who worked for a range of organizations, including universities in the Colombo district (Sri Lanka).

The results illustrated that social motivation has a significant positive effect on trust as well as online purchase intentions. And they further demonstrated that a consumer's level of trust has a significant impact on their online purchase intentions. Trust was also found to partially mediate the relationship between social motivation and online purchase intention.

The study was solely focused on the Sri Lankan fashion industry. Consumer behavior relating to other industries may differ. Therefore, this model can be further developed to encompass other industries in future studies.

The study contributes to practical solutions in the development of consumer behavior (in the context of social media marketing). Stakeholders in the fashion industry may take the suggestions of this research, such as how to incorporate “trust” in social media marketing to attract and retain customers, into consideration in their future decision making.

This study is the first study in the Sri Lankan context to assess the mediating effect of trust on the relationship between consumers' social motivation and online purchase intentions in the context of social media marketing in the fashion industry of Sri Lanka. Overall, the results offer implications that align with existing theories and contribute to practical solutions in the development of consumer behavior (in the context of social media marketing).

Digitalization accelerates the need of tourism and hospitality ecosystems to reframe business models in line with a data-driven orientation that can foster value creation and innovation. Since the question of data-driven business models (DDBMs) in hospitality remains underexplored, this paper aims at (1) revealing the key dimensions of the data-driven redefinition of business models in smart hospitality ecosystems and (2) conceptualizing the key drivers underlying the emergence of innovation in these ecosystems.

The empirical research is based on semi-structured interviews collected from a sample of hospitality managers, employed in three different accommodation services, i.e. hotels, bed and breakfast (B&Bs) and guesthouses, to explore data-driven strategies and practices employed on site.

The findings allow to devise a conceptual framework that classifies the enabling dimensions of DDBMs in smart hospitality ecosystems. Here, the centrality of strategy conducive to the development of data-driven innovation is stressed.

The study thus developed a conceptual framework that will serve as a tool to examine the impact of digitalization in other service industries. This study will also be useful for small and medium-sized enterprises (SMEs) managers, who seek to understand the possibilities data-driven management strategies offer in view of stimulating innovation in the managers' companies.

The paper reinterprets value creation practices in business models through the lens of data-driven approaches. In this way, this paper offers a new (conceptual and empirical) perspective to investigate how the hospitality sector at large can use the massive amounts of data available to foster innovation in the sector.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.