COVID-19 information seeking and utilization among library and information science professionals in Nigeria

Like many countries, Nigeria’s library services were affected by COVID-19. This study (conducted in 2020) examined the priorities and barriers for information seeking and use by library and information science professionals in Nigeria, including what type of information was sought, the sources used and how these were evaluated. An online survey, with questions informed by a literature survey, was sent to library and information science professionals in Nigeria with WhatsApp accounts (individual and library and information science group accounts). The responses were automatically collected and saved into Google Sheets for further analysis using descriptive statistics. There were 167 responses (61% response rate). The main information needs were causes of the virus, COVID-19 symptoms, providing library services during physical library closure, the timing of reopening, and staff and user safety measures required for library reopening. The respondents were also concerned about COVID-19 test procedures and transmission mechanisms, treatment (including herbal medicines), vaccines, government policy and restrictions on movement. The main information sources used were the Internet (including government and World Health Organization websites), social media, television and radio. The authority, reliability, currency and relevance of the information were considered when evaluating COVID-19 information consulted, and were using the information primarily to ‘keep safe’ in accordance with government regulations, to provide library services on time and to plan for safe reopening. The barriers to information seeking and use were technical (poor telecommunications), financial (lack of funds to purchase resources) and physical (library closures). One barrier was the volume of information (both reliable and unreliable). Verification was viewed as important but seemingly difficult to do. The respondents were concerned about the safe and effective operation of library services. Library and information science professionals in Nigeria may need advice from health professionals on the most reliable sources of information on COVID-19 and how to use them for themselves and their users.

Adolescents' Social Media Use and Their Voluntary Garbage Sorting Intention: A Sequential Mediation Model

Encouraging adolescents to sort garbage is vital for the sustainable development of the ecological environment. This study investigates the relationship between adolescents’ social media use and their garbage sorting intention. A survey, in both online and paper-based versions, was distributed in 2020 in Shanghai and a total number of 605 valid responses were obtained. This study supports the proposed sequential mediation model, indicating that adolescents’ social media use increased their garbage sorting intention via the serial mediation effect of objective and subjective knowledge and the perceived importance of garbage sorting. The current study and its findings provide important insights into the link between adolescents’ social media use and their garbage sorting intention, particularly its underlying mechanism, by distinguishing knowledge into two specific types and introducing perceived importance into the conceptual model. Practical implications for improving adolescents’ intention to sort garbage are also discussed.

What older adults do in online communities when they co-create and co-destruct value

Purpose

This study aims to explain the value co-creation and co-destruction practices of older adults in an online community (OC).

Design/methodology/approach

Adopting practice theory and service-dominant logic as a theoretical perspective, this paper examined an OC of older adults by conducting an inductive thematic analysis of the interactions of the participants in the community.

Findings

The analysis revealed older adults engage with three value co-creation plus one value co-destruction practices in the OC including, communal coping practices, happiness creation practices, social capital generation practices and disparaging practices for older adults.

Research limitations/implications

Illustrated in a conceptual model, this study extends previous work evidencing OCs serve as a platform for value co-creation and value co-destruction activities in the context of older adults. Further, it suggests OCs facilitate resilience of older adults through value co-creation practices. Recognition of value co-destruction in OCs is critical as it is detrimental to the resilience of older adults. This study provides the needed foundation to advance knowledge on the use of OCs by older adults and suggests future research directions.

Practical implications

Identifying co-creation and co-destruction practices of older adults in OCs enables service providers (e.g. caregivers) to engage better in online value co-creation practices. Further, the findings of this study address one of the main priorities of service science to investigate the impact of value co-creation on well-being.

Originality/value

Despite the significant engagement of older adults in OCs, there is a lack of enough knowledge in the literature regarding value co-creation and co-destruction practices of older adults in OCs. This study addressed this gap by explaining how older adults co-create and co-destruct value in online spaces.

Digital Competence Analysis of University Students Using Latent Classes

The objective of this study is to characterize Latent Classes emerging from the analysis of the level of digital competences, use and consumption of applications and/or services through the Internet. For this purpose, the results of the survey Basic Digital Competences (Competencias Básicas Digitales-COBADI®) applied to university students, with more than 60 categorical variables, were considered. A total of 4762 undergraduate and graduate students from five Spanish universities participated in this survey: Complutense University of Madrid (UCM), Pablo de Olavide University (UPO), Almeria University (UAL), National University of Distance Education (UNED) and Rey Juan Carlos University (URJC). The application of the questionnaire was done through the Internet, from the Institute for Research in Social Sciences and Education of University of Atacama—Chile. The methodology used is mixed, because the questions of the questionnaire provide qualitative information that can be interpreted and elaborated from the results. It is also quantitative because basic statistical techniques are used for the exploratory analysis of the data, and later Latent Class Analysis (LCA), to complement the description of the data set and the variables considered in the study, thus allowing us to group the classes of variables that do not appear explicitly in the set of observed variables, but which nevertheless affect them. The results of the study show that regardless of the gender and age range of the participants, there are four clearly differentiated groups or classes in the use and consumption of ICTs in different ways for their activities, both personal and academic, which allows for identifying different developments of digital competences. This study allows establishing a baseline in order to be able to elaborate later, in the development of the digital competences currently needed, which should be developed by university students.

The Effects of Transformational Leadership on Organizational Performance: Testing the Mediating Effects of Knowledge Management

Transformational leadership is a sustainable and exemplary form of leadership that liberates subordinates’ ideas, enables subordinates to view problems from a different perspective, helps adaptation to changing environments, and increases Human Resources Management effectiveness in public and service institutions. The effects of transformational leadership on organizational performance by means of knowledge management have been examined in this study on the northern Cyprus Security Forces. In addition, the study aims to analyze the relationships between transformational leadership, knowledge management, organizational performance, job satisfaction, organizational learning, and knowledge creation processes. The questionnaire was conducted in three different periods using a time-lag method, and data were collected from 1229 employees for this study. The collected data were analyzed using structural equation modeling (Lisrel 8.54). The findings of the research include 17 hypotheses that were supported, and 1 hypothesis that was not. In addition, it was determined that organizational learning, knowledge management, and job satisfaction mediate the effect of transformational leadership on organizational performance.

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

Monitoring students in Learning Management Systems (LMS) throughout the teaching-learning process has been shown to be a very effective technique for detecting students at risk. Likewise, the teaching style in the LMS conditions, the type of student behaviours on the platform and the learning outcomes. The main objective of this study was to test the effectiveness of three teaching modalities (all using Online Project-based Learning -OPBL- and Flipped Classroom experiences and differing in the use of virtual laboratories and Intelligent Personal Assistant -IPA-) on Moodle behaviour and student performance taking into account the covariate "collaborative group". Both quantitative and qualitative research methods were used. With regard to the quantitative analysis, differences were found in student behaviour in Moodle and in learning outcomes, with respect to teaching modalities that included virtual laboratories. Similarly, the qualitative study also analysed the behaviour patterns found in each collaborative group in the three teaching modalities studied. The results indicate that the collaborative group homogenises the learning outcomes, but not the behaviour pattern of each member. Future research will address the analysis of collaborative behaviour in LMSs according to different variables (motivation and metacognitive strategies in students, number of members, interactions between students and teacher in the LMS, etc.).

Saudi scholars’ perceptions and use of open government data portals at Shaqra University, Saudi Arabia

This article reports the findings of a study on open data usage among Saudi scholars. It investigates the sample’s perceptions of open data and open data portals. The study reported the factors affecting the participants’ decision to use open data and aimed to understand Saudi researchers’ practices and perceptions related to the use and sharing of open data. It adopted a quantitative approach and a questionnaire was distributed to and collected from 190 Saudi academic staff to measure their perceptions, their open data usage, the benefits of open data, and the factors that significantly impacted their open data utilization. The findings reveal that 42.1% of Saudi researchers used government open data portals and regularly visited open data portals provided by the university and the government mainly for research purposes. The results also indicate that open data portals enabled Saudi researchers to obtain useful data for their research while giving them the tools to visualize and understand the data.

Revealing at-risk learning patterns and corresponding self-regulated strategies via LSTM encoder and time-series clustering

Purpose

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

Design/methodology/approach

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Findings

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

Research limitations/implications

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

Originality/value

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.

Detecting the public’s information behaviour preferences in multiple emergency events

Due to the frequent occurrence of public emergency events and the extensive use of social media in recent years, the public is more and more involved in the communication of public emergencies. As important members of the public, the rapid and massive information sharing of social media users makes them play an increasingly crucial role in the emergency processing. Hence, it is necessary to analyse information behaviours of social media users in emergency events. This article mined the information behaviours of users in multiple events to reflect the public’s behaviour preferences, aiming to provide information support for emergency handling. Specifically, we collected the user-generated contents related to emergency events, and then analysed the user-generated contents from multiple dimensions to obtain the corresponding information behaviours. Finally, based on the comparative analysis of four events, the information behaviour preferences of the public during emergencies were obtained. The experimental results indicate that the public’s behaviours in emergencies are related to their own interests and economic status, and curiosity about the details of events is the consistent appeal of the public.

WeChatting for Health: What Motivates Older Adult Engagement with Health Information

Although WeChat has become increasingly popular among Chinese elderly people as a tool to engage with health information, little research has examined their motivations for health purposes and their engagement with health information on the site. By applying the two-stage Use and gratification (U&G) approach, we first conducted in-depth interviews (n = 20) to explore older adults’ distinctive motives. Based on the 22 motives found in the qualitative research, we developed a questionnaire for an online survey (n = 690) to further investigate how these motives affect older adults’ engagement with health information on WeChat. As the result, six motive typologies were identified: information needs, social support, surveillance, social interaction, self-agency building, and technological convenience. Together, these six types of motivations jointly account for 59.9% of the variance in older adults’ engagement with health information (M = 2.71, SD = 0.79, adjusted R² = 0.59, p < 0.001). Social support and information needs were significant predictors, suggesting that the older WeChat users’ active engagement is driven by personal instrumental gratification. This study examines the explanation power of U&G theory in a health context, as well as provides the practical implication for leveraging mobile social media to improve older people’s healthcare management.

The use of OPAC query logs to support evidence-informed information literacy (IL) training at The University of Dar es Salaam

This paper reports the results of the study which analysed OPAC users’ searching behaviour through the use of search queries. The study analysed how OPAC search queries can be used to assess the effectiveness of the information literacy programme offered by the University of Dar es Salaam. Data were collected unobtrusively and stored in the OPAC web server’s log file. OPAC search logs were qualitatively and quantitatively analysed to determine search patterns and search query formulation. Information literacy course contents were also qualitatively analysed and compared with users’ search activities. The results suggest that search query logs are essential and high quality evidence that can be used to inform information literacy pedagogical methods and course contents improvement. A comparison between OPAC users’ search behaviours and information lteracy course content identified some areas for readjusting the way information literacy instructions are delivered and improving OPAC search functionalities. The study has contributed to the conceptual understanding and the use of evidence to support evidence-informed practices (EIPs) in delivering information literacy courses.

Susceptibility of falling behind current medical knowledge among health-care professionals: the extended parallel process model

Purpose

The purpose of this paper is to appraise the possible response outcomes (no response, fear control or danger control) of a fear-based message on falling behind current medical knowledge among health-care professionals (HCPs).

Design/methodology/approach

This study was conducted using a quantitative research design. A cross-sectional survey was carried out in 2,873 health-care facilities across 36 districts of Punjab. The Extended Parallel Process Model (EPPM) provided a theoretical framework for this study. The important components to EPPM such as threat, efficacy and outcomes were used to collect the data. The collected data was analyzed using Statistical Package for Social Sciences (SPSS) version 22.

Findings

HCPs perceived high susceptibility and threat of falling behind current medical knowledge. A majority of the HCPs were at the danger control process and engaged in a behavior that is recommended for adaptive behavioral changes. Clinical experience and enrollment in post-graduation programs (e.g. FCPS, MCPS) were significant factors as to HCPs’ perceived response efficacy.

Research limitations/implications

The responses were obtained using a structured questionnaire, which is always subject to respondents’ personal biases and ability to understand the question’s statement.

Practical implications

This study has important implications in terms of introducing promotional, educational and logistical interventions that could help in HCPs overcoming the fear of falling behind current medical knowledge and develop productive and adoptive information behavior thus improving patient care and outcome.

Originality/value

This is the first large-scale empirical study in Pakistan that measured the level of threat and efficacy among HCPs using the EPPM. It proposes a framework for developing long-lasting adaptive information behavioral changes that may result in informed patient care and better decision-making.

The Impact of Coding Apps to Support Young Children in Computational Thinking and Computational Fluency. A Literature Review

Data that shows that young children can learn and acquire Computational Thinking (CT) skills has led governments and policymakers internationally to integrate CT into the curriculum, starting in the earliest grades. Researchers support the idea that this introduction must not solely focus on a problem-solving process skill (CT) but instead provide children with new ways to express themselves, supporting their cognitive, language, and socio-emotional development (Computational Fluency-CF). Coupled with the media and government’s rhetoric and an increasing number of apps offering various programming lessons, puzzles, and challenges, educators have been responsible for introducing young children to CT and CF using touchscreen technology. This paper presents a literature review (N = 21) of empirical studies on applying four coding apps to support young children’s learning of CT and CF. The main conclusion is that all apps positively affect the development of children’s CT skills. None of the apps can ultimately support the development of CF, although ScratchJr, with a “sandbox” approach, can better help students express themselves.

Student-Performulator: Predicting Students' Academic Performance at Secondary and Intermediate Level Using Machine Learning

Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. The in hand research work focuses on students' grade and marks prediction utilizing supervised ML approaches. The data-set utilized in this research work has been obtained from the Board of Intermediate & Secondary Education (B.I.S.E) Peshawar, Khyber Pakhtunkhwa. There are 7 areas in BISEP i.e., Peshawar, FR-Peshawar, Charsadda, Khyber, Mohmand and Upper and Lower Chitral. This paper aims to examine the quality of education that is closely related to the aims of sustainability. The system has created an abundance of data which needs to be properly analyzed so that most useful information should be obtained for planning and future development. Grade and marks forecasting of students with their historical educational record is a renowned and valuable application in the EDM. It becomes an incredible information source that could be utilized in various ways to enhance the standard of education nationwide. Relevant research study reveals that numerous methods for academic performance forecasting are built to carryout improvements in administrative and teaching staff of academic organizations. In the put forwarded approach, the acquired data-set is pre-processed to purify the data quality, the labeled academic historical data of student (30 optimum attributes) is utilized to train regression model and DT-classifier. The regression will forecast marks, while grade will be forecasted by classification system, eventually analyzed the results obtained by the models. The results obtained show that machine learning technology is efficient and relevant for predicting students performance.

How privacy concerns and cultural differences affect public opinion during the COVID-19 pandemic: a case study

Purpose

COVID-19, an infectious disease first identified in China, has resulted in an ongoing pandemic all over the world. Most of the countries have been experiencing a difficult period during the fighting of this pandemic. The purpose of this study is to explore the effect of privacy concerns and cultural differences on public opinion related to the pandemic. The authors conducted a comparative analysis of public opinion in the US and in China as a case study, in order to determine the results.

Design/methodology/approach

National policies on important issues faced during the COVID-19 pandemic in the US and in China were examined through a comparative analysis. The authors used text clustering and visualization to mine public opinion on two popular social media platforms, Twitter and Weibo. From the perspectives of concern for privacy and of national culture, this study combines qualitative and quantitative analysis to discover the acceptance level of national policies by the public in the two countries.

Findings

The anti-pandemic policies and measures of the US and China reflect the different characteristics of their respective political systems and national cultures. When considering the culture of the US, it is hard to establish and enforce a rigorous regulation on either mask wearing in public or home quarantine on the national level. The opinions of US people are diverse, regarding national COVID-19 policies, but they are rather unified on privacy issues. On the other hand, Chinese people show a high acceptance of national policies based on their mask-wearing customs and their culture of collectivism.

Originality/value

Prior studies have paid insufficient attention to the ways in which user privacy and cultural difference affect public opinion on national policies between the US and China. This case study that compares public opinion on current and topical issues which are closely bound up with public life shows originality, as it innovatively provides a cross-cultural perspective on the research of public opinion dissemination during emergencies by considering the ongoing COVID-19 pandemic.

Why open government data initiatives fail to achieve their objectives: categorizing and prioritizing barriers through a global survey

Purpose

Existing overviews of barriers for openly sharing and using government data are often conceptual or based on a limited number of cases. Furthermore, it is unclear what categories of barriers are most obstructive for attaining open data objectives. This paper aims to categorize and prioritize barriers for openly sharing and using government data based on many existing Open Government Data Initiatives (OGDIs).

Design/methodology/approach

This study analyzes 171 survey responses concerning existing OGDIs worldwide.

Findings

The authors found that the most critical OGDI barrier categories concern (in order of most to least critical): functionality and support; inclusiveness; economy, policy and process; data interpretation; data quality and resources; legislation and access; and sustainability. Policymakers should prioritize solving functionality and support barriers and inclusiveness barriers because the authors found that these are the most obstructive in attaining OGDI objectives.

Practical implications

The prioritization of open data barriers calls for three main actions by practitioners to reduce the barrier impact: open data portal developers should develop advanced tools to support data search, analysis, visualization, interpretation and interaction; open data experts and teachers should train potential users, and especially those currently excluded from OGDIs because of a lack of digital skills; and government agencies that provide open data should put user-centered design and the user experience central to better support open data users.

Originality/value

This study contributes to the open data literature by proposing a new, empirically based barrier categorization and prioritization based a large number of existing OGDIs.

A distributional semantics-based information retrieval framework for online social networks

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

Purpose

Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world.

Design/methodology/approach

This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample.

Findings

This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world.

Research limitations/implications

The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue.

Social implications

The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing.

Originality/value

This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

E-learning adoption in higher education: A review

In recent years, e-learning has become pivotal in higher education sectors. Researchers are correlating novel approaches with e-learning to facilitate education. However, despite the increase in e-learning research, there is still a lack of comprehensive literature analysis of e-learning in the higher education sector. Thus, this study aims to conduct a systematic literature review of the literature on e-learning in higher education. This study classifies the selected studies according to the focus of the study, utilizes a theoretical model and framework, and research methods. Also, it presents limitations and future research directions of e-learning in the higher education sector. A systematic approach is conducted, and a total of 47 relevant articles published between the year 2011 and 2019 were selected based on the inclusion and exclusion criteria. The findings on selected studies focus on the adoption, acceptance, readiness, and user insight, as well as e-learning expansion and challenges in the higher education sector. This study also classified theoretical models and frameworks based on their usage in the pre-adoption, adoption, and post-adoption stages. The findings revealed that most of the theoretical models and frameworks were used at the post-adoption stage. Nevertheless, this study revealed that most of the current studies in this domain were conducted using a quantitative research approach. Finally, this study highlighted limitations and presented possible future research directions as a guide for further enhancement in e-learning and higher education studies.

Research of influence mechanism of corporate social responsibility for smart cities on consumers' purchasing intention

Purpose

The main purpose of this paper is to explore the influence mechanism of corporate social responsibility (CSR) for smart cities on consumers' purchase intention. The authors aim to identify the key components of CSR for smart cities based on the perspective of consumers, namely responsibility toward consumers, environment and community and validate their relationship.

Design/methodology/approach

The authors exploit data collected by questionnaire surveys to estimate the effects of CSR for smart cities on consumers' purchase intentions and to investigate the statistical causality between them. The multilinear regression model is used to figure out the different impact levels of the three dimensions of CSR for smart cities on consumers' purchase intention.

Findings

The results illustrate that CSR for smart cities and its three dimensions all have significant positive impacts on consumers' purchase intentions. Besides, consumer–corporate identity (CCI) exerts a partial mediation effect on this influence mechanism.

Research limitations/implications

This research is based on a rather small sample size. Besides, due to the time limitation and other factors, some other control variables are neglected in the regression model. Therefore, the impact level could be distorted.

Practical implications

The authors put forward management implications according to research conclusions. Corporates should actively fulfill the CSR in the field of consumer responsibility to boost consumers' purchase intention. Corporate should strengthen the interaction with consumers to improve their corporate identity.

Originality/value

The main contribution of this paper is to provide convincing evidence of the impacts of CSR for smart cities on consumer purchase intention (CPI), thus proposing effective measures for corporates to win more consumers by taking on social responsibility for smart cities. This paper takes CCI as mediating variable to deepen the understanding of the impacts of CSR for smart cities on CPI, which is innovative and beneficial to enriching literature in related fields.

Factors influencing college students’ behaviours of spreading internet public opinions on emergencies in universities

Purpose

This study aims to explore the influencing factors on college students’ behaviours of spreading Internet public opinion on emergencies in colleges and universities. This study provides a reference for these institutions to cope with and reduce the influence of Internet public opinion on emergencies and maintain their normal teaching order.

Design/methodology/approach

In this study, a research model by using motivation theory and design a questionnaire on the basis of relevant literature are constructed. This paper surveys college students and collects a total of 317 valid questionnaires. On the basis of the reliability and validity of the questionnaire, this study verifies the proposed model by using Smart PLS.

Findings

The results show that social motivation and information source preference have significant positive influences on college students’ willingness to spread Internet public opinion on emergencies in colleges and universities. Moreover, information source preference has a significant moderating effect on the relationship between social motivation and dissemination willingness. If college students’ information source preference is high, then the moderating effect is significant. The extent of college students’ interaction and involvement has a significantly positive influence on their trust in the dissemination platform for Internet public opinion on emergencies in colleges and universities. Egoism has a significantly positive influence on the social motivation of college students to spread Internet public opinion on emergencies in colleges and universities. Involvement degree has a significant moderating effect on the relationship between social motivation and trust. If college students’ involvement degree is low, then the moderating effect is significant. Thus, when the involvement of college students in Internet public opinion on emergencies in colleges and universities is low, the influence of social motivation on trust is great.

Originality/value

This study increases the influencing factors in the literature on Internet public opinion, enriches the research theory of Internet public opinion on emergencies in colleges and universities and expands the application scope of the theory of social motivation. The conclusion provides guidance for colleges and students to govern Internet public opinion on emergencies and improve the ability of these institutions in dealing with Internet public opinion on emergencies.

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.

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.

Towards developing library and information science practicum supervision competency framework

The purpose of this article is to discuss competencies required for practicum supervision within the field of library and information science (LIS) education. In doing so, this study attempts to propose Practicum Supervision Competencies Framework for professionals working in libraries and similar information organisations. The study aims to contribute to the body of knowledge on supervision of experiential learning programmes such as practicum and internships in the field of library and information science. Constructivist Grounded Theory approach was used for collection and analysis of data. Qualitative data was collected through semi-structured interviews with 43 participants that included library and information science professionals and students. Using constant comparative method, suggested in the Constructivist Grounded Theory literature, the components of the framework were developed and then validated and strengthened through the literature on competencies for library and information science professionals. Qualitative data analysis software NVivo 12 pro was used to support coding, category development and constant comparison methods in the data analysis process. Results of this study propose a framework for library and information science practicum supervision competencies and signify its importance for the academia and industry. The proposed framework is composed of five distinct but interrelated components, that is, Interactive, Management, Pedagogical, Professional and Technology which are important for practicum supervision in the field of LIS. The study also discusses empirical insights about the significance of research on practicum supervision within the field of library and information science as a distinct area. Practical implication of research encompasses several dimensions: methodological, theoretical, managerial and academic. For example, the framework should attempt to help better understand gaps between potential and actual competencies required by practicum supervisors in the field of library and information science. Research results may lack generalisability because of the chosen research method. However, researchers in other regions of the world are encouraged to test the proposed framework further.

Relationships between research supervisors and students from coursework-based master’s degrees: information usage under social media

Purpose

Existing studies reflect that traditional teaching–learning relationships between supervisors and graduate students have become disjointed with actuality seriously. In particular, there are practical difficulties in handling many students from coursework-based postgraduate degrees under current university curricula. Therefore, this study aims to explore the relationship between research supervisors and graduate students on social media, which is popular among students.

Design/methodology/approach

This study surveyed 109 graduate students from two majors (population around 100 each) of a university in Hong Kong to explore their information usage for research on social media, related attitudes and their perceived supervisor relationships. The differences between the two majors were also compared.

Findings

The authors’ findings indicated that graduate students were active on social media, and social media has successfully provided effective alternate ways for students to communicate with their research supervisors. Social media could improve relationships between supervisors and research students and among fellow students. Besides education purposes, students also discussed their personal affairs on social media with supervisors, demonstrating enhanced trusted relationships. Graduate students also showed confidence in the further application of social media in higher education. Some differences between respondents from the two programs were also found in terms of communication contents, strengths, personal preferences and purposes for using social media.

Originality/value

Scant studies focus on the relationship between supervisors and graduate students under the current social media environment, especially for students from coursework-based postgraduate degrees. At a deeper level, for the widespread use of social media in the information age, this study explores the specific changes brought about by social media. Therefore, this study is of great theoretical and practical value to graduate education under the current social media environment.

Factors influencing scholarly publication productivity: a systematic review

Purpose

The importance of scientific publications has enhanced the research activities at all aggregate levels. The purpose of this paper is to conduct an in-depth review of studies examined the factors that influence the publication productivity among researchers, institutions and countries.

Design/methodology/approach

This paper uses a systematic literature review approach that is based on relevant literature retrieved from two authentic and well-known databases named Web of Science and SCOPUS. Kitchenham protocol of systematic review was applied, and relevant studies were searched by selected keywords search. Inclusion and exclusion criteria were used to select high quality studies. A rigorous study selection procedure was designed.

Findings

Findings reveal that several factors play a significant role in the publication productivity growth. All of the identified factors were grouped into three categories. Most of the factors belong to personal category while two top dominant factors, funding and collaboration fall into situational category. Other most influential factors, time, academic rank and academic qualification are personal. Findings highlight that publication output at individual level is strongly influenced by personal factors while few environmental and situational factors also affect the number of publications. Publication productivity of institutes and countries is affected by environmental and situational factors.

Originality/value

This paper contributes by providing a detailed review to explore the influencing factors of scholarly publication of researchers, institutions and countries. This study will be supportive for researchers and policymakers for making strategies to stimulate the research publication practices at researchers, institutions and national level.

The effects of social media usage on loneliness and well-being: analysing friendship connections of Facebook, Twitter and Instagram

Purpose

This study aims to clarify the effects of different patterns of Facebook, Twitter and Instagram usage on user loneliness and well-being in Japan.

Design/methodology/approach

Based on responses to a self-report questionnaire in Japan, 155 university students were separated into 4 groups: users of Twitter only, users of Twitter and Facebook, users of Twitter and Instagram and users of all three social media. The effects of social media usage on loneliness and well-being for each group were analysed.

Findings

No social media usage effects on loneliness or well-being were detected for those who used only Twitter or both Twitter and Instagram. For those using both Twitter and Facebook, loneliness was reduced when users accessed Twitter and Facebook more frequently but was increased when they posted more tweets. Users of all three social media were lonelier and had lower levels of well-being when they accessed Facebook via PC longer; whereas their their access time of Facebook via smartphones helped them decrease loneliness and improve their levels of well-being.

Originality/value

The findings reported here provide possible explanations for the conflicting results reported in previous research by exploring why users choose different social media platforms to communicate with different groups of friends or acquaintances and different usage patterns that affect their loneliness and well-being.

Online Education and the COVID-19 Outbreak: A Case Study of Online Teaching during Lockdown

The COVID-19 pandemic has become a critical challenge for the higher education sector. Exploring the capacity of this sector to adapt in the state of uncertainty has become more significant than ever. In this paper, we critically reflect on our experience of teaching urban design research methods online during the early COVID-19 lockdown in the UK. This is an exploratory case study with a qualitative approach with an aim to inform resilient practices of teaching in the face of public health emergencies. Drawing on the experience of teaching the Research Methods and Techniques subject during lockdown, we discuss the rapid transition from face-to-face to online teaching and point to the challenges and opportunities in relation to the learning and teaching activities, assessment and feedback, and digital platforms. This paper concludes by outlining some key considerations to inform the development of more adaptive and resilient approaches to online teaching in the context of unprecedented global health crises such as the COVID-19 pandemic. We argue that it is critical to move beyond fixed pedagogical frameworks to harness the productive capacities of adaptive teaching.

Identifying the factors influencing digital library use among research students: a case of National Digital Library of Pakistan

Purpose

As the revolution of information takes place, industrialized societies are going to become information societies. Developing countries such as Pakistan are going to change due to technology and, in turn, transform the whole structure of libraries. The concept of digital libraries (DL) has emerged due to technological advancements. This study aims to highlight the factors that encourage and discourage the use of the Higher Education Commission’s (HEC) National Digital Library (NDL).

Design/methodology/approach

A quantitative research approach was selected for the investigation, while the data collection instrument was a questionnaire. Postgraduate research students were the population of the study. A convenient sampling technique was used for sample selection.

Findings

The results of the study indicated that the use of HEC (NDL) was frequent monthly. The display of search results, 24/7 access, the authenticity of items, availability of navigational assistance and up-to-date information encourage researchers to use DL. However, their preference for print material over electronic material, slow downloading speed of the internet and non-availability of older and archival publications were the common reasons for the low use of DL. Furthermore, there are significant differences in terms of using the NDL based on gender, the program of study and the stage of the study.

Originality/value

This study will contribute significantly to the literature from the developing countries and would also helpful for HEC (NDL) authorities and university libraries to design information services.

Factors Driving Citizen Engagement With Government TikTok Accounts During the COVID-19 Pandemic: Model Development and Analysis

Background

During the COVID-19 pandemic, growth in citizen engagement with social media platforms has enabled public health departments to accelerate and improve health information dissemination, developing transparency and trust between governments and citizens. In light of these benefits, it is imperative to learn the antecedents and underlying mechanisms for this to maintain and enhance engagement.

Objective

The aim of this study is to determine the factors and influencing mechanisms related to citizen engagement with the TikTok account of the National Health Commission of China during the COVID-19 pandemic.

Methods

Using a web crawler, 355 short videos were collected from the Healthy China account on TikTok (with more than 3 million followers throughout China), covering the period from January 21, 2020, to April 25, 2020. The title and video length, as well as the number of likes, shares, and comments were collected for each video. After classifying them using content analysis, a series of negative binomial regression analyses were completed.

Results

Among the 355 videos, 154 (43.4%) related to guidance for clinicians, patients, and ordinary citizens, followed by information concerning the government’s handling of the pandemic (n=100, 28.2%), the latest news about COVID-19 (n=61, 17.2%), and appreciation toward frontline emergency services (n=40, 11.3%). Video length, titles, dialogic loop, and content type all influenced the level of citizen engagement. Specifically, video length was negatively associated with the number of likes (incidence rate ratio [IRR]=0.19, P<.001) and comments (IRR=0.39, P<.001). Title length was positively related to the number of shares (IRR=24.25, P=.01), likes (IRR=8.50, P=.03), and comments (IRR=7.85, P=.02). Dialogic loop negatively predicted the number of shares (IRR=0.56, P=.03). In comparison to appreciative information, information about the government’s handling of the situation (IRR=5.16, P<.001) and guidelines information (IRR=7.31, P<.001) were positively correlated with the number of shares, while the latest news was negatively related to the number of likes received (IRR=0.46, P=.004). More importantly, the relationship between predictors and citizen engagement was moderated by the emotional valence of video titles. Longer videos with positive titles received a higher number of likes (IRR=21.72, P=.04) and comments (IRR=10.14, P=.047). Furthermore, for short videos related to government handling of the pandemic (IRR=14.48, P=.04) and guidance for stakeholders (IRR=7.59, P=.04), positive titles received a greater number of shares. Videos related to the latest news (IRR=66.69, P=.04) received more likes if the video title displayed higher levels of positive emotion.

Conclusions

During the COVID-19 pandemic, videos were frequently published on government social media platforms. Video length, title, dialogic loop, and content type significantly influenced the level of citizen engagement. These relationships were moderated by the emotional valence of the video’s title. Our findings have implications for maintaining and enhancing citizen engagement via government social media.

Teaching and Learning Styles on Moodle: An Analysis of the Effectiveness of Using STEM and Non-STEM Qualifications from a Gender Perspective

Teaching in Higher Education is with increasing frequency completed within a Learning Management System (LMS) environment in the Blended Learning modality. The use of learning objects (activities and resources) offered by LMS means that both teachers and students require training. In addition, gender differences relating to the number of students in STEM (Science, Technology, Engineering, and Mathematics) and Non-STEM courses might have some influence on the use of those learning objects. The study involves 13 teachers (6 experts in e-Learning and 7 non-experts) on 13 academic courses (4 STEM and 9 Non-STEM) and a detailed examination of the logs of 626 students downloaded from the Moodle platform. Our objectives are: (1) To confirm whether significant differences may be found in relation to the use of learning objects (resources and activities) on Moodle, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (2) To confirm whether there are significant differences between students regarding their use of learning objects, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (3) To confirm whether there are significant differences for the use of learning objects among students as a function of gender. Differences were found in the use of Moodle learning objects (resources and activities) for teachers and for students depending on the expertise of the teacher. Likewise, differences were found for the use of some learning objects as a function of gender and the degrees that the students were following. Increased technological training for both teachers and students is proposed, especially on Non-STEM qualifications, in order to mitigate the effects of the technological gap and its collateral relation with the gender gap and the digital divide.

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

Purpose

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

Design/methodology/approach

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

Findings

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Research limitations/implications

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

Practical implications

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

Social implications

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

Originality/value

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

Using community information for natural disaster alerts

Recently, the ceaseless rise in the global average temperature has led to extreme climates in which natural disasters, such as droughts, hurricanes, earthquakes and floods, are becoming increasingly serious. Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90 min and a sliding gap of 10 min obtained the best F-measure value ( F = 0.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.

Impact of social media on the academic performance of college students in Kashmir

Purpose

The use of social media is prevalent and having the largest number of members than any other online platform available on the internet. Members of all age groups use social media websites according to their needs. This study aims to investigate the impact of social media usage on the academic performance of college students in Kashmir.

Design/methodology/approach

A structured questionnaire was formulated and a survey was carried out to collect data from the undergraduate students of different government colleges in Kashmir. The questionnaires were distributed randomly among both, male and female of the first, second and third year students of different undergraduate programs.

Findings

The findings show a majority of the students use social media networking sites to fulfill their educational needs. YouTube is the most largely used social media network among undergraduate students. A majority of the students feel that social media networks are easy to use and they have been using these sites for the past three years. The use of social media greatly helps the students to share knowledge and information with others, to improve their academic grades and to improve their reading and writing skills. A majority of the students spend an hour in using social media networks. Students use their laptops, mobile phones and personal computers to access social media websites.

Social implications

This study will help to highlight the important role of social media networks in the pedagogical activities of students.

Originality/value

This study affirms that students do use social media networking sites for academic purposes, which is a good factor to improve their academic skills and grades. Teachers must encourage their students to make the best use of social media networking sites for recreational and academic purposes.

Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design

Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer’s work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined.

Seeking or contributing? Evidence of knowledge sharing behaviours in promoting patients' perceived value of online health communities

BACKGROUND

Health knowledge, as an important resource of online health communities (OHCs), attracts users to engage in OHCs and improve the traffics within OHCs, thereby promoting the development of OHCs. Seeking and contributing health knowledge are basic activities in OHCs and are helpful for users to solve their health-related problems, improve their health conditions and thus influence their evaluation of OHCs (ie perceived value of OHCs). However, how do patients' health knowledge seeking and health knowledge contributing behaviours together with other factors influence their perceived value of OHCs? We still have little knowledge.

OBJECTIVE

In order to address the above gap, we root the current study in social cognitive theory and prior related literature on health knowledge sharing in OHCs and patients' perceived value. We treat health knowledge seeking and health knowledge contributing behaviours as behavioural factors and structural social capital as an environmental factor and explore their impacts on patients' perceived value of OHCs.

DESIGN

We have built a theoretical model composed of five hypotheses. We have designed a questionnaire composed of four key constructs and then collected data via an online survey.

SETTING AND PARTICIPANTS

We have distributed the questionnaire in two Chinese OHCs. We obtained a sample of 352 valid responses that were completed by patients having a variety of conditions.

RESULTS

The empirical results indicate that health knowledge seeking and health knowledge contributing have positive impacts on patients' perceived value of OHCs. The impact of health knowledge seeking on patients' perceived value of OHCs is greater than the impact of health knowledge contributing. In addition, structural social capital moderates the effects of health knowledge seeking and health knowledge contributing on patients' perceived value of OHCs. It weakens the effect of health knowledge seeking but enhances the effect of health knowledge contributing on patients' perceived value of OHCs.

CONCLUSIONS

These findings contribute to the literature on patients' perceived value of OHCs and on the role of structural social capital in OHCs. For OHC managers, they should provide their users more opportunities to seek or contribute health knowledge in their communities.

Exploring the Potentialities of Automatic Extraction of University Webometric Information

Purpose

The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities.

Design/methodology/approach

Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities’ websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, www.bing.com) or from a leading provider of Web analytics (SimilarWeb, http://www.similarweb.com). The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (https://eter.joanneum.at/#/home), a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.

Findings

The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’ websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.

Research limitations

The results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad.

Practical implications

The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems.

Originality/value

This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020).

Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach

Background: The substandard vaccine case of that broke out in July 2018 in China triggered an outburst of news reports both domestically and aboard. Distilling the abundant textual information is helpful for a better understanding of the character during this public event. Methods: We collected the texts of 2211 news reports from 83 mainstream media outlets in China between 15 July and 25 August 2018, and used a structural topic model (STM) to identify the major topics and features that emerged. We also used dictionary-based sentiment analysis to uncover the sentiments expressed by the topics as well as their temporal variations. Results: The main topics of the news report fell into six major categories, including: (1) Media Investigation, (2) Response from the Top Authority, (3) Government Action, (4) Knowledge Dissemination, (5) Finance Related and (6) Commentary. The topic prevalence shifted during different stages of the events, illustrating the actions by the government. Sentiments generally spanned from negative to positive, but varied according to different topics. Conclusion: The characteristics of news reports on vaccines are shaped by various topics at different stages. The inner dynamics of the topic and its alterations are driven by the interaction between social sentiment and governmental intervention.

Learning analytics stakeholders' expectations in higher education institutions: a literature review

Purpose

The objective of this paper is to present a comprehensive review of the literature on learning analytics (LA) stakeholders' expectations to reveal the status of ongoing research in this area and to highlight gaps in research.

Design/methodology/approach

Conducting a literature review is a well-known method to establish knowledge and interpret the domain under examination (Snyder, 2019). In this paper, a descriptive approach was used to investigate the different research methods used to tackle LA stakeholders' expectations, which depends on summarising the studies' content and minimising the reviewer's interpretation.

Findings

Most of the studies have used interviews and focus groups without testing isolated variables. Moreover, it was concluded that LA awareness and perception are affected by a lack of resources, technical skills, trustworthiness, data availability, accessibility, organisational culture, integration and lack of technology. 10;This paper sheds light on research on LA stakeholders' expectations and highlights the need for investigating developing countries' context in order to test the perception and impact of LA. The study has focussed mainly on papers published in journals, which may have led to missing some previous research papers.

Originality/value

Literature review studies provide an overview of the existing LA literature from general focus to analytical methods. As it is a broad topic, this paper focuses on reviewing studies about stakeholders, which will give a fuller understanding of how their expectations are being discussed beyond the currently assumed benefits of LA.

Virtual reality services in academic libraries: deployment experience in Hong Kong

Purpose

Virtual Reality (VR) has become a popular topic recently, and the quality of immersive experience nowadays is beyond our imagination. While VR applications for entertainment are common, it is a new and popular trend in academic libraries. Although many academic libraries in the West have started to provide VR services to catch up with the trend, the deployment is not quite popular in the East. This research aims to identify the reasons behind such phenomenon.

Design/methodology/approach

This research explores this phenomenon by studying two selected cases through interviews, site visits, and website/document analysis: the CAVE of the City University of Hong Kong Library and the VR Experience Zone of the Hong Kong Polytechnic University Library.

Findings

The findings indicated that VR library services are well-received and meeting user needs. However, the major limitations of using VR in academic libraries are technical capability, space and budget, causing libraries to hesitate in introducing and developing VR services.

Originality/value

Scant studies focus on the development, management and user feedback of VR services in academic libraries, especially in the East. Based on the findings, possible solutions for academic libraries interested in taking part in this trend are suggested.

Moodle LMS Integration with Amazon Alexa: A Practical Experience

The frequency of interaction between teachers and students through Learning Management Systems (LMSs) is continuously rising. However, recent studies highlight the challenges presented in current LMSs to meet the specific needs of the student, regarding usability and learnability. With the motivation to support the research of effectiveness when using a Voice User Interface (VUI) for education, this paper presents the work done (RQ1) to build the basic architecture for an Alexa skill for educational purposes, including its integration with Moodle, and (RQ2) to establish whether Moodle currently provides the necessary tools for voice-content creation for develop voice-first applications, aiming to provide new scientific insight to help researchers on future works of similar characteristics. As a result of this work, we provide guidelines for the architecture of an Alexa skill application integrated with Moodle through safe protocols, such as Alexa’s Account Linking Web Service, while our findings ratify the need for additional tooling within Moodle platform for voice-content creation in order to create an appealing voice experience, with the capabilities to process Moodle data structures and produce sensible sentences that can be understood by users when spoken by a voice device.

Social Networks Consumption and Addiction in College Students during the COVID-19 Pandemic: Educational Approach to Responsible Use

Within the framework of digital sustainability, the increase in Internet consumption, and especially online social networks, offers social benefits, but is not without its drawbacks. For example, it can lead to psychological and/or psychiatric disorders in some people. Numerous researches are highlighting the similarities of these addictions with the consumption of toxic substances. University students are heavy users of the Internet and, in certain situations, addiction to online social networks can be the result of depression, harassment, and anxiety, among others, affecting their daily life, including their academic responsibilities. In recent months, an anomaly has occurred that may have contributed to intensifying this problem, namely the confinement produced by the COVID-19 pandemic, which has affected the whole world to a greater or lesser extent. In this cross-sectional study, with a descriptive and quantitative methodology, students from 14 Spanish universities were investigated in the first wave of the COVID-19 pandemic in order to understand the effects of this situation on the problem described. The results show a high consumption of social networks during that time, with significant incidences of addiction. In parallel, the presence of comorbidity has been determined. In this scenario, it would be necessary to implement university educational programs to redirect these addictive behaviors, as well as preventative recommendations and actions to minimize negative impacts. This is a major problem that is growing, exacerbated by the global pandemic produced by the SARS-CoV-2 coronavirus. Situations of this gravity call for the development of preventive and educational measures for the responsible and sustainable use of ICT.