Online Information Review

Publisher:: Emerald Group Publishing Limited
Publication date:: 2021-02-01

ISBN:: 1468-4527

Issue Number

Latest documents

Corporate disclosure via social media: a data science approach
Purpose: The purpose of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information. Design/methodology/approach: This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation topic modeling to identify financial disclosure tweets. Panel, Logistic and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics. Findings: Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity and board tenure. Originality/value: The extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive vs non-executive directors relating to disclosure decisions.
Analysis of content topics, user engagement and library factors in public library social media based on text mining
Purpose: The purpose of this paper is to explore topics of Facebook posts created by public libraries using the bi-term topic model, and examine the relationships between types of topics and user engagement. The authors further investigated the effects of three library factors, namely, staff size, budget and urbanization degrees, on Facebook content and user engagement based on multilevel generalized linear modeling. Design/methodology/approach: This study suggested a novel method, a combination of the bi-term topic modeling and MGLM, to enhance the understanding of social media in the context of public libraries. Findings: The findings revealed that posts related to community events, awards and photos were likely to receive more likes and shares, whereas posts about summer reading programs received relatively more comments. In addition, the authors found that a larger staff size and the inclusion of multimedia had positive impacts on user engagement. Originality/value: This study analyzed the content of public library-generated social media based on text mining. Then, the authors examined the effects of contextual library-level factors on social media practice in public libraries. Based on empirical findings, the study suggested a range of practical implications for effective use of social media in public libraries.
Exploring the antecedents of mobile payment service usage. Perspectives based on cost–benefit theory, perceived value, and social influences
Purpose: The number of smartphone users has increased with the maturity of mobile networks, which has not only led to a new lifestyle but has also facilitated the development of mobile application services. Smartphones are regarded as essential communication devices. Currently, diverse groups of people are considering using mobile payment services. Thus, the motives for using mobile payment as well as individual motives for continuing usage are of great research interest. The purpose of this paper is to examine the behavioral motivations underlying individual intentions to continue using mobile payment. Design/methodology/approach: To explore the factors affecting the intention to use mobile payment services, this study constructed a theoretical framework based on cost-benefit theory that also considers social influences to form an integrated research model that explains the intentions of individuals to use mobile payment services. Online questionnaires were used to evaluate individuals with experience using mobile payment services. A total of 302 questionnaires were collected. Structural equation modeling was employed to assess the relationships among factors included in the research model. Findings: Perceived value, social norms and social self-image played crucial roles in the intention to use mobile payment services. Furthermore, perceived benefits (relative advantage and service compatibility) and perceived costs (security risks and perceived fees) determined users’ perceived value. Social self-image positively affected users’ perceived value; in the context of a mobile-oriented information system, the ability of a mobile payment service to satisfy a user’s demands with respect to social self-image influenced the user’s perceived value of using such services. Originality/value: This study contributes to a theoretical understanding of factors that explain users’ intention to use mobile payment services.
How to enhance solvers’ continuance intention in crowdsourcing contest. The role of interactivity and fairness perception
Purpose: The purpose of this paper is to explore the underlying mechanisms through which interactivity and fairness perception impart influence on solvers’ continuance intention in crowdsourcing contest settings. Design/methodology/approach: On basis of self-determination theory and social exchange theory, this study focuses on the mediating roles of motivation and platform trust to explain the underlying influence processes of interactivity and fairness perception on continuance intention. A sample of 306 solvers was obtained from an online crowdsourcing platform through two separated surveys. The hypotheses were tested using the partial least squares method and bias-corrected bootstrapping method. Findings: The empirical results indicate that motivation and platform trust together fully mediate the effect of interactivity on continuance intention, and the effect of fairness perception on continuance intention is also fully mediated by motivation and platform trust. While motivation is found to have a stronger mediating effect than platform trust does. Originality/value: This study contributes to the crowdsourcing research by figuring out the pathway through which interactivity and fairness perception influence solvers’ continuance intention.
The myth of knowledge within a robust nutrition online training course
Purpose: The purpose of this paper was to develop a predictor model for an online nutrition course on sugar reduction. The proposed model is based on health knowledge, healthy behavior, social support, self-efficacy, attitude and the health belief model in relation to people’s behavior within a Facebook group. Subsequently, the model can be used to design a robust online training course for human resources, thereby reducing the training costs which managers have experienced as being expensive. Design/methodology/approach: A single pre-post experimental group design was used. Pre and post data were collected from 100 Facebook users using an online questionnaire, within a three-week intervention. Findings: The results show a significant difference between pre- and post-test scores of health knowledge and healthy behavior, indicating an effective intervention. In addition, perceived barriers, attitude, self-efficacy and emotional support were significant predictors of the healthy behavior model, predicting 70 percent of healthy behavior. However, knowledge had no significant relationship with any of the three dependent variables (self-efficacy, attitude and healthy behavior) proposed. Practical implications: This model has proved to be an effective intervention which can be used in online training of human resources, because the content of the training is known from the predictor model, thereby greatly reducing the training cost, since everything is done online. Moreover, the provided model and predictors show that the content to be delivered in the training program is not knowledge but perceived barriers, attitude, self-efficacy and emotional support. Originality/value: This study is one of the first to propose an integrative model that suggests attitude and self-efficacy as key predictors of healthy behavior whereas knowledge is not.
Evaluation of institutional repositories of South Asia
Purpose: The purpose of this paper is to explore the status of institutional repositories (IRs) in the South Asian region. The various characteristic features of IRs are studied. Design/methodology/approach: Open directory of open access repositories (DOAR) as a data-gathering tool was consulted for extracting the desired data. Findings: India, Sri Lanka and Bangladesh lead other South Asian nations in terms of IRs count. Majority of the IRs are operational in nature with higher number of operational IRs from India. In terms of record count, India leads the list. “Journal articles” outscore other content type and majority of the IRs have OAI-PMH as their base URL. DSpace stays a prioritized software for content management in IRs. Majority of the IRs have not defined their content management policies. English stays a prioritized language of the content dotting the South Asian IRs and majority of the IRs not providing usage statistics. A good score of IRs has incorporated Web 2.0 tools in them with RSS as the preferred Web 2.0 tool. A good count of the IRs has not customized their interface. Majority of the IRs have interface in two languages. Research limitations/implications: The main limitation of the study is that the findings of the research are based on the data collected through the repositories indexed by Open DOAR. Originality/value: The study tries to explore the characteristic features of IRs from the South Asian region.
The impact of personality in recognizing disinformation
Purpose: The purpose of this paper is to investigate and quantify the effects of personality traits, as defined by the five-factor model (FFM) on an individual’s ability to detect fake news. The findings of this study are increasingly important because of the proliferation of social media news stories and the exposure of organizational stakeholders and business decision makers to a tremendous amount of information, including information that is not correct (a.k.a. disinformation). Design/methodology/approach: The data were collected utilizing the snowball sampling methodology. Students in an Management Information Systems course completed the survey. Since a diverse sample was sought, survey participants were instructed to recruit another individual from a different generation. The survey questions of the FFM identify particular personality traits in respondents. Survey respondents were given a collection of nine news stories, five of which were false and four that were true. The number of correctly identified stories was recorded, and the effect of personality traits on the ability of survey respondents to identify fake news was calculated using eta-squared and the effect size index. Findings: Each of the five factors in the FFM demonstrated an effect on an individual’s ability to detect disinformation. In fact, every single variable studied had at least a small effect size index, with one exception: gender, which had basically no effect. Therefore, each variable studied (with the exception of gender) explained a portion of the variability in the number of correctly identified false news stories. Specifically, this quantitative research demonstrates that individuals with the following personality traits are better able to identify disinformation: closed to experience or cautious, introverted, disagreeable or unsympathetic, unconscientious or undirected and emotionally stable. Originality/value: There is scant research on an individual’s ability to detect false news stories, although some research has been conducted on the ability to detect phishing (a type of social engineering attack to obtain funds or personal information from the person being deceived). The results of this study enable corporations to determine which of their customers, investors and other stakeholders are most likely to be deceived by disinformation. With this information, they can better prepare for and combat the impacts of misinformation on their organization, and thereby avoid the negative financial impacts that result.
A comprehensive framework to rank cloud-based e-learning providers using best-worst method (BWM). A multidimensional perspective
Purpose: Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market. Design/methodology/approach: To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM). Findings: The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM. Practical implications: The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations. Originality/value: Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.
Tell me who you are and I will tell you which SNS you use: SNSs participation
Purpose: Social networking sites (SNSs) have become an essential part of our lives. The purpose of this paper is to explore how demographic variables, SNS importance, social and informational usage, and personality traits (extroversion/introversion, openness, neuroticism, internal and external locus of control) can explain participation frequency of the four biggest SNSs in Israel: Facebook, WhatsApp, Instagram and Twitter. Design/methodology/approach: The research was conducted in Israel during the Fall semester of the 2017–2018 academic year and encompassed 244 students. Researchers used six questionnaires to gather data: a demographic questionnaire, a participation frequency questionnaire on four different SNSs, four SNSs importance questionnaire, social and informational usage on four different SNSs questionnaire, personality questionnaire (extroversion, openness and neuroticism) and the locus of control questionnaire. Findings: The findings revealed that different social network sites play distinct roles for various individuals. WhatsApp, the most frequently used platform, is used more by women and people with internal locus of control. Facebook is more frequently used by open people and Instagram is more frequently used by women, younger adults and neurotic people. Twitter is more frequently used by men. In addition, for all SNSs, the higher the social and informational usage is, the more important the SNSs are to the users, which significantly explains participation frequency. Originality/value: The differences between social networks can be evidence that each social network serves a different group and does not compete with other SNSs. This may well explain why many people make use of several social networks and have a tendency to move from one to another.
“Less is more”. Mining useful features from Twitter user profiles for Twitter user classification in the public health domain
Purpose: This work studies automated user classification on Twitter in the public health domain, a task that is essential to many public health-related research works on social media but has not been addressed. The purpose of this paper is to obtain empirical knowledge on how to optimise the classifier performance on this task. Design/methodology/approach: A sample of 3,100 Twitter users who tweeted about different health conditions were manually coded into six most common stakeholders. The authors propose new, simple features extracted from the short Twitter profiles of these users, and compare a large set of classification models (including state-of-the-art) that use more complex features and with different algorithms on this data set. Findings: The authors show that user classification in the public health domain is a very challenging task, as the best result the authors can obtain on this data set is only 59 per cent in terms of F1 score. Compared to state-of-the-art, the methods can obtain significantly better (10 percentage points in F1 on a “best-against-best” basis) results when using only a small set of 40 features extracted from the short Twitter user profile texts. Originality/value: The work is the first to study the different types of users that engage in health-related communication on social media, applicable to a broad range of health conditions rather than specific ones studied in the previous work. The methods are implemented as open source tools, and together with data, are the first of this kind. The authors believe these will encourage future research to further improve this important task.