Exploring information dissemination effect on social media: an empirical investigation

Operators make profits by publishing information. If they can know the influencing factors in the process of information dissemination, they can provide new insights for practical operations and formulate corresponding operation strategies for different types of accounts. The purpose of this article is to discuss the information dissemination process of WeChat public accounts and what factors will affect the reading rate and sharing rate of the article. In this paper, the "feedback-sympathize-identify participant-share" (FSIPS) two-stage model is used to analyze the characteristics of information dissemination, and the negative binomial regression model is used to analyze which factors have a significant impact on the two stages of the dissemination model. Our data is obtained from a company that specializes in operating WeChat Official Accounts, and the data is authentic and more valuable. We collectively consider the roles of users, message content, interactions between content and individuals, and the context of social media in information dissemination behaviors (i.e., reading and sharing). In this process, some new variables, such as environment-related variables, are involved and incorporated into the two stages of information dissemination for analysis. The results show that the like rate, which is one of the feedback dimensions, has the greatest impact on the reading rate, while the favorite rate has the greatest impact on the sharing rate. Article type also plays a crucial role in the dissemination of information. In addition, the content relevance between the title and the content also largely affects the share rates of the three types.

Pricing Decisions with Social Interactions: A Game-Theoretic Model

For media or digital products with quality uncertainty like online games, movies, theater plays, software, and smartphone applications, online customers may strategically delay their purchase waiting for online reviews and their peers’ purchase decisions. Thus, a firm needs to consider both social learning and positive network externality to anticipate the customers’ purchasing decisions and set a good pricing strategy over time. This paper investigates how these dual concerns affect the strategic interaction between a firm using preannounced pricing or responsive pricing and strategic customers in a two-period game-theoretic model. Deviating from conventional wisdom suggesting that social learning and externality work in a similar way, our results highlight their differences and provide valuable managerial insights. Although social learning and externality play a similar role in expanding the increasing-price-optimal region, they are different in other aspects: The firm will be worse off with learning if the externality gets stronger, whereas it will be worse off or better off with learning if learning gets stronger. In addition, we characterize the condition under which responsive pricing may outperform preannounced pricing. We further find that the firm’s discount factor has an influence on the firm’s pricing strategy selection.

Funding: X. Wang and Y. Yang acknowledge financial support from the National Natural Science Foundation of China [Grant 72071204].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2022.0463 .

Social media analytics in tourism: a review and agenda for future research

Purpose

The significance of social media in our lives is manifold. The tourism sector closely interacts with existing and potential tourists through social media, and therefore, social media analytics (SMA) play a critical role in the uplift of the sector. Hence, this review focus on the role of SMA in tourism as discussed in different studies over a period of time. The purpose of this paper to present the state of the art on social media analytics in tourism.

Design/methodology/approach

The review focuses on identifying different SMA techniques to explore the trends and approaches adopted in the tourism sector. The review is based on 83 papers and discuss the studies related to different social media platforms, the travelers' reactions to a particular place and how the tourism experience is enriched by the way of SMA.

Findings

Findings indicate different sentiments associated with tourism and provides a review of tourists’ use of social media for choosing a travel destination. The various analytical approaches, areas such as social network analysis, content analysis, sentiment analysis and trend analysis were found most prevalent. The theoretical and practical implications of SMA are discussed. The paper made an effort to bridge the gap between different studies in the field of tourism and SMA.

Originality/value

SMA facilitate both tourists and tourism companies to understand the trends, sentiments and desires of tourists. The use of SMA offers value to companies for designing quick and adequate services to tourists.

Social Media Strategy Processes for Centralized Payment Network Firms after a War Crisis Outset

From the outset of the war in Ukraine, extensive crises in many sectors of the world economy have occurred, with firms offering services and products both online and through physical stores facing serious problems. These problems are mainly related to higher operational costs and the lack of website visibility. For this research study, centralized payment network organizations (CPNs), firms providing online payment services through their networks, were selected and analytical data from their websites were collected for a period of 6 months. The main focus of this research study is to evaluate benefits and the role of social media strategies for CPNs’ digital marketing performance during crisis events and to also assess their utility as a risk-management tool. Following data collection, the authors performed statistical processes (regression and correlation analysis) and stationary modeling with Fuzzy Cognitive Mapping (FCM) tools; finally, dynamic simulations were performed by utilizing Agent-Based Models (ABM). The authors suggest that various variables of CPNs’ social media platforms can aid in improving their digital marketing performance and, using proper analysis, can lead to higher user social engagement, thus rendering social media strategy a useful risk-management tool.

LASSO Regression Modeling on Prediction of Medical Terms among Seafarers’ Health Documents Using Tidy Text Mining

Generally, seafarers face a higher risk of illnesses and accidents than land workers. In most cases, there are no medical professionals on board seagoing vessels, which makes disease diagnosis even more difficult. When this occurs, onshore doctors may be able to provide medical advice through telemedicine by receiving better symptomatic and clinical details in the health abstracts of seafarers. The adoption of text mining techniques can assist in extracting diagnostic information from clinical texts. We applied lexicon sentimental analysis to explore the automatic labeling of positive and negative healthcare terms to seafarers’ text healthcare documents. This was due to the lack of experimental evaluations using computational techniques. In order to classify diseases and their associated symptoms, the LASSO regression algorithm is applied to analyze these text documents. A visualization of symptomatic data frequency for each disease can be achieved by analyzing TF-IDF values. The proposed approach allows for the classification of text documents with 93.8% accuracy by using a machine learning model called LASSO regression. It is possible to classify text documents effectively with tidy text mining libraries. In addition to delivering health assistance, this method can be used to classify diseases and establish health observatories. Knowledge developed in the present work will be applied to establish an Epidemiological Observatory of Seafarers’ Pathologies and Injuries. This Observatory will be a collaborative initiative of the Italian Ministry of Health, University of Camerino, and International Radio Medical Centre (C.I.R.M.), the Italian TMAS.

Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System

The coronavirus pandemic has affected everyday life to a significant degree. The transport sector is no exception, with mobility restrictions and social distancing affecting the operation of transport systems. This research attempts to examine the effect of the pandemic on the users of the public transport system of London through analyzing tweets before (2019) and during (2020) the outbreak. For the needs of the research, we initially assess the sentiment expressed by users using the SentiStrength tool. In total, almost 250,000 tweets were collected and analyzed, equally distributed between the two years. Afterward, by examining the word clouds of the tweets expressing negative sentiment and by applying the latent Dirichlet allocation method, we investigate the most prevalent topics in both analysis periods. Results indicate an increase in negative sentiment on dates when stricter restrictions against the pandemic were imposed. Furthermore, topic analysis results highlight that although users focused on the operational conditions of the public transport network during the pre-pandemic period, they tend to refer more to the effect of the pandemic on public transport during the outbreak. Additionally, according to correlations between ridership data and the frequency of pandemic-related terms, we found that during 2020, public transport demand was decreased while tweets with negative sentiment were being increased at the same time.

Cutoff Threshold Decisions for Classification Algorithms with Risk Aversion

Classification algorithms predict the class membership of an unknown record. Methods such as logistic regression or the naïve Bayes algorithm produce a score related to the likelihood that a record belongs to a particular class. A cutoff threshold is then defined to delineate the prediction of one class over another. This paper derives analytic results for the selection of an optimal cutoff threshold for a classification algorithm that is used to inform a two-action decision in the cases of risk aversion and risk neutrality. The results provide insight to how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for both the risk neutral and risk averse decision makers. The optimal risk averse threshold is not reliably above or below the optimal risk neutral threshold, but the relation depends on the parameters of a particular application. The results further show the risk averse optimal threshold is insensitive to the size of the data set or the magnitude of the costs, but instead is sensitive to the proportion of positive records in the data and the ratio of costs. Numeric examples and sensitivity analysis derive further insight. Results show the percent value gap from a misspecified risk attitude increases as the specificity of the classification algorithm decreases.

Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

Among services, the immense growth of Indian tourism in the last years has attracted the interest of practitioners, researchers, and governments. Service experiences at the point of encounter can impact the consumption of these tourism services extensively. However, measuring the service experience at the point of service encounter becomes a bit difficult. The tourists who visit India often share their experiences immediately regarding their service encounter in social media. These tweets often have high sentiments and emotional content. In this study, we attempt to identify factors which impact customer service experience, at the point of service encounter, by mining social media discussions. After removing spurious tweets, 7,91,804 tweets were identified and analysed in this study. Factors such as accessibility, accommodation, assurance, cultural attraction, Jugaadu service flexibility, cleanliness, hospitality, price, restaurant, and security were identified using topic modelling, topic association mining, and sentiment analysis. We attempt to model these experiences and their drivers across five zones of India, namely North, South, East, West, and North-East India. Our inferential analysis highlights that the importance and impact of these factors differ significantly zone wise across India, which indicates high location specificity of factors which impact the customer service experience. The study elaborates implications for theory and practice based on our findings.

How did COVID-19 impact working professionals – a typology of impacts focused on education sector

Purpose

The pandemic COVID-19 brought in large challenges globally among the workforce. There were reports of how employee layoffs and pay-cuts were gradually becoming prominent across industries based on media reports. However, there were no attempts to develop a typology of challenges faced by the workforce.

Design/methodology/approach

This study mined user-generated content from Twitter to bring out a typology of challenges due to the sudden turbulence that is faced from the pandemic. A case study has also been conducted by taking in-depth interviews in the academic sector to deep dive into the nature of these problems.

Findings

The study findings indicate that these challenges are basically stemming from challenges surrounding infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses.

Research limitations/implications

There is a need to explore the linkages through inferential research infrastructure readiness, digital readiness, changing nature of deliverables, workforce demand versus supply problems and challenges surrounding job losses on employee welfare during pandemics.

Originality/value

The authors provide inductive insights based on a data-driven research methodology surrounding the sudden challenges faced and possible mechanisms to address these issues faced by a stressed workforce catering to multiple stakeholders.

How do network attributes impact information virality in social networks?

Purpose

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

Design/methodology/approach

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Findings

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

Research limitations/implications

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

Originality/value

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

A review of machine learning applications in human resource management

Purpose

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

Design/methodology/approach

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

Findings

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Originality/value

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

Examining Post COVID-19 Tourist Concerns Using Sentiment Analysis and Topic Modeling

The COVID-19 pandemic has had a destructive effect on the tourism sector, especially on tourists’ fears and risk perceptions, and is likely to have a lasting impact on their intention to travel. Governments and businesses worldwide looking to revive and revamp their tourism sector, therefore, must first develop a critical understanding of tourist concerns starting from the dreaming/planning phase to booking, travel, stay, and experiencing. This formed the motivation of this study, which empirically examines the tourist sentiments and concerns across the tourism supply chain. Natural Language Processing (NLP) using sentiment analysis and Latent Dirichlet Allocation (LDA) approach was applied to analyze the semi-structured survey data collected from 72 respondents. Practitioners and policymakers could use the study findings to enable various support mechanisms for restoring tourist confidence and help them adjust to the’new normal.’

What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the "Digital Service Usage Satisfaction Model"

Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The "Digital Service Usage Satisfaction Model" has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.

Social Media and E-mail Marketing Campaigns: Symmetry versus Convergence

Companies use social business intelligence (SBI) to identify and collect strategically significant information from a wide range of publicly available data sources, such as social media (SM). This study is an SBI-driven analysis of a company operating in the insurance sector. It underlines the contribution of SBI technology to sustainable profitability of a company by using an optimized marketing campaign on Facebook, in symmetry with a traditional e-mail campaign. Starting from a campaign on SM, the study identified a client portfolio, processed data, and applied a set of statistical methods, such as the index and the statistical significance (T-test), which later enabled the authors to validate research hypotheses (RH), and led to relevant business decisions. The study outlines the preferences of the selected group of companies for the manner in which they run a marketing campaign on SM in symmetry with an e-mail-run campaign. Although the study focused on the practical field of insurance, the suggested model can be used by any company of any industry proving that BI technologies is the nexus of collecting and interpreting results that are essential, globally applicable, and lead to sustainable development of companies operating in the age of globalization. The results of the study prove that symmetrical unfolding (time and opportunity symmetry) of SM marketing campaigns, and using email, could lead to better results compared to two separate marketing campaigns. Moreover, the outcomes of both campaigns showed convergence on SBI platforms, which led to higher efficiency of management of preferences of campaign beneficiaries in the insurance sector.

Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day

Twitter is increasingly used by individuals and organizations to broadcast their feelings and practices, providing access to samples of spontaneously expressed opinions on all sorts of themes. Social media offers an additional source of data to unlock information supporting new insights disclosures, particularly for public health purposes. Systemic lupus erythematosus (SLE) is a complex, systemic autoimmune disease that remains a major challenge in therapeutic diagnostic and treatment management. When supporting patients with such a complex disease, sharing information through social media can play an important role in creating better healthcare services. This study explores the nature of topics posted by users and organizations on Twitter during world Lupus day to extract latent topics that occur in tweet texts and to identify what information is most commonly discussed among users. We identified online influencers and opinion leaders who discussed different topics. During this analysis, we found two different types of influencers that employed different narratives about the communities they belong to. Therefore, this study identifies hidden information for healthcare decision-makers and provides a detailed model of the implications for healthcare organizations to detect, understand, and define hidden content behind large collections of text.

Managerial Responses and Customer Engagement in Crowdfunding

Despite the widespread recognition of the importance of customer behavior in crowdfunding performance, empirical research concerning the importance of managerial responses in user-generated content is scarce. How do managerial responses affect backers’ comments? Does user-generated content affect following backers’ behavior? Using a dataset of backers’ comments and creators’ managerial responses from Kickstarter.com, we attempt to clarify the relationships among creator responses to comments, comment volume, linguistic features of comment text and crowdfunding performance. Our results show creator responses have a significant positive effect on customer engagement and crowdfunding performance. Moreover, creator response is an effective advertising strategy to improve crowdfunding performance.

Experience

Governance of misinformation is a serious concern in social media platforms. Based on experiences gathered from different case studies, we offer insights for the policymakers on managing misinformation in social media. These platforms are widely used for not just communication but also content consumption. Managing misinformation is thus a challenge for policymakers and the platforms. This article explores the factors of rapid propagation of misinformation based on our experiences in the domain. An average of about 1.5 million tweets were analysed in each of the three different cases surrounding misinformation. The findings indicate that the tweet emotion and polarity plays a significant role in determining whether the shared content is authentic or not. A deeper exploration highlights that a higher element of surprise combined with other emotions is present in such tweets. Further, the tweets that show case-neutral content often lack the possibilities of virality when it comes to misinformation. The second case explores whether the misinformation is being propagated intentionally by means of the identified fake profiles or it is done by authentic users, which can also be either intentional, for gaining attention, or unintentional, under the assumption that the information is correct. Last, network attributes, including topological analysis, community, and centrality analysis, also catalyze the propagation of misinformation. Policymakers can utilize these findings in this experience study for the governance of misinformation. Tracking and disruption in any one of the identified drivers could act as a control mechanism to manage misinformation propagation.

Destination Image Analytics through Traveller-Generated Content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.