Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

Online social networks (OSNs) have become a powerful tool to study collective human responses to extreme events such as earthquakes. Most previous research concentrated on a single platform and utilized users’ behaviors on a single platform to study people’s general responses. In this study, we explore the characteristics of people’s behaviors on different OSNs and conduct a cross-platform analysis of public responses to earthquakes. Our findings support the Uses and Gratification theory that users on Reddit and Twitter are engaging with platforms that they may feel best reflect their sense of self. Using the 2019 Ridgecrest earthquakes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissions and 44,333 comments) to answer the following research questions: (1) What were the similarities and differences between public responses on Twitter and Reddit? (2) Considering the different mechanisms of Twitter and Reddit, what unique information of public responses can we learn from Reddit as compared with Twitter? By answering these research questions, we aim to bridge the gap of cross-platform public responses research towards natural hazards. Our study evinces that the users on the two different platforms have both different topics of interest and different sentiments towards the same earthquake, which indicates the necessity of investigating cross-platform OSNs to reveal a more comprehensive picture of people’s general public responses towards certain disasters. Our analysis also finds that r/conspiracy subreddit is one of the major venues where people discuss the 2019 Ridgecrest earthquakes on Reddit and different misinformation/conspiracies spread on Twitter and Reddit platforms (e.g., “Big one is coming” on Twitter and “Nuclear test” on Reddit).

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Mediating Role of Green Supply Chain Management Between Lean Manufacturing Practices and Sustainable Performance

Manufacturing companies in today's industrial world are seeking to use the new manufacturing process methods. The primary goal of corporations is to achieve optimum production while deploying minimal capital. The fundamental purpose of this study is to examine the influence of various lean manufacturing practices on the sustainability performance of companies and the mediating role of green supply chain management (GSCM). The data was gathered using questionnaires from 250 Pakistani manufacturing firms and analyzed using AMOS 25. Results demonstrate that process and equipment, product design, supplier relationships, and customer relationships significantly affect sustainable performance. It is also recognized that Green Supply Chain Management mediates the interaction between HR processes, product design, supplier relationship, customer relationship, and environmental performance. The findings of this study will enable managers and decision-makers of manufacturing companies to increase sustainable efficiency and reduce waste through the use of lean manufacturing and GSCM implementation.

Library makerspace in Turkey: Public and university libraries

This study is intended to determine the developments in Turkey and to reveal the quality of the libraries that offer makerspace service. In the light of the data, obtained from the Ministry of Culture and Tourism at the time of the study, one university library and three public libraries where this service was offered in Turkey were examined. However, one public library was examined in detail within the scope of the study. Qualitative research methods were used, and the data collected by the semi-structured interview technique were evaluated with the descriptive analysis method. According to the results not much progress has been observed in Turkey regarding the services offered by the library makerspace. The fact that the public libraries evaluated in this study offer only one service, however, shows that they cannot fully realize the maker philosophy.

Exploring the university research scholars’ information literacy competency: a focus on the knowledge and skills

Purpose

This study aims to determine the level of knowledge in searching, evaluating, information management competency and to find differences among research scholars’ in terms of their information literacy (IL) competency with regards to gender and academic designation.

Design/methodology/approach

A survey method was conducted to collect the data from the research scholars’ pursuing their doctoral degrees in Manonmaniam Sundaranar University, Tamil Nadu, India.

Findings

The results of this study revealed that the majority have used simple search and were not using the Online Public Access Catalogue. They were not able to assess the information retrieved and the assistance from the library staff to locate the resources was found to be minimal. This study also highlighted some of the problems faced by the research scholars and provided some suggestions based on the findings. This study highlights the need to adopt a new IL framework and conduct user literacy training programmes for effective use of the resources.

Practical implications

This study also has practical implications in terms of assessing the IL competency among the research scholars and significant variations which identified through demographics and assessment of IL competency will enable policymakers to develop a framework for brand new literacy instructions. The results may help to frame long-term strategies to motivate and train the research scholars in the effective use of technologies for improving their academic performance.

Originality/value

This study used a questionnaire as tool to collect the data. The outcome of the results will help the librarians and authorities to discuss research scholars’ IL competency and take appropriate decisions to improve their IL skills.

Role of Social Media Marketing Activities in Influencing Customer Intentions: A Perspective of a New Emerging Era

The aim of this study is to explore social media marketing activities (SMMAs) and their impact on consumer intentions (continuance, participate, and purchase). This study also analyzes the mediating roles of social identification and satisfaction. The participants in this study were experienced users of two social media platforms Facebook and Instagram in Pakistan. A self-administered questionnaire was used to collect data from respondents. We used an online community to invite Facebook and Instagram users to complete the questionnaire in the designated online questionnaire system. Data were collected from 353 respondents, and structural equation modeling (SEM) was used to analyze the data. Results show that SMMAs have a significant impact on the intentions of users. Furthermore, social identification mediates the relationship between social media activities and satisfaction, and satisfaction mediates the relationship between social media activities and the intentions of users. This will help marketers how to attract customers to develop their intentions. This is the first novel study that used SMMAs to address the user intentions with the role of social identification and satisfaction in the context of Pakistan.

Mapping the quantity, quality and structural indicators of Asian (48 countries and 3 territories) research productivity on cloud computing

Purpose

The purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent.

Design/methodology/approach

To achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h, g and m indexes; Bradford's and Lotka's laws; and other scientific mappings.

Findings

Results of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.”

Originality/value

The study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.

Sentiment Analysis of Social Survey Data for Local City Councils

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

The Empirical Investigation Between Ethical Leadership and Knowledge-Hiding Behavior in Financial Service Sector: A Moderated-Mediated Model

This study aimed to investigate the impact of ethical leadership on knowledge-hiding behavior of the employees working in the financial services sector under the mediating role of meaningful at work and moderating role of ethical climate. For this purpose, data were collected from two hundred and fifteen employees of financial services providing organizations. The already-established scales were followed to develop an instrument that was used to obtain responses from the respondents. Collected data were analyzed by applying the structural equation modeling through Smart PLS and Process Macro. The results indicate that ethical leadership and meaningful work (MW) reduce knowledge-hiding behavior of employees at work, while ethical leadership positively impacts the influential work of employees at the workplace. Further, the relationship between ethical leadership and knowledge-hiding behavior is partially mediated by MW. Similarly, ethical climate moderated the relationship between ethical leadership and knowledge-hiding behavior. This research makes valuable contributions to the existing literature on leadership and knowledge management. From a practical point of view, this study stresses that managers at work should promote ethical leadership styles to promote MW, which will reduce knowledge hiding. Thus, in this way, it will enhance the innovation and creativity within organizational circuits. The limitations and future directions of this study are also listed.

Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model

The advent of Internet heralded the rise of scalable educational technology dubbed as massive open online course (MOOC). Easy to use, access, economical as well as flexible, provide students lot of freedom and the advantage of self-paced learning. Despite all these merits, MOOC adoption is low in the higher educational institutions (HEIs) of India. The aim of this study is to explore the factors affecting the behavioural intention to adopt MOOCs among Generation Z (Gen Z) enrolled in the Indian HEIs. The study uses the extended UTAUT2 model with additional constructs of language competency and teacher influence to explore MOOC adoption among the Gen Z. Using online survey, data of 483 students was collected from HEIs of India using stratified random sampling and analysed using partial least square-structure equation modelling (PLS-SEM) technique. The results establish the general applicability of UTAUT2 model in context of MOOC in Indian settings with explanatory power of 69.9% and highlights the positive influence of price value, hedonic motivation, facilitating conditions, performance expectancy and effort expectancy on MOOC adoption. However, the constructs of social influence, habit, language competency, and teacher influence unexpectedly do not have an impact on Behavioural Intention of Gen Z towards MOOC adoption. Based on the research findings, study implications and future directions of the research have been suggested.

Palmprint Recognition System Based on Multi-Block Local Line Directional Pattern and Feature Selection

In this paper, we deal with multimodal biometric systems based on palmprint recognition. In this regard, several palmprint-based approaches have been already proposed. Although these approaches show interesting results, they have some limitations in terms of recognition rate, running time and storage space. To fill this gap, we propose a novel multimodal biometric system combining left and right palmprints. For building this multimodal system, two compact local descriptors for feature extraction are proposed, fusion of left and right palmprints is performed at feature-level, and feature selection using evolutionary algorithms is introduced. To validate our proposal, we conduct intensive experiments related to performance and running time aspects. The obtained results show that our proposal shows significant improvements in terms of recognition rate, running time and storage space. Also, the comparison with other works shows that the proposed system outperforms some literature approaches and comparable with others.

Periodismo de investigación: análisis bibliométrico, conceptual e intelectual (2000-2020)

Resumen La relevancia del periodismo de investigación ha aumentado recientemente. Este artículo analiza la producción científica sobre esta rama de la actividad periodística a través de los artículos (N=331) publicados entre 2000 y 2020 en revistas indexadas en Scopus. Se aplica para ello un análisis bibliométrico de productividad y rendimiento en el campo, así como un análisis sociométrico de su estructura conceptual (red de co-palabras) e intelectual (red de co-citación). Los resultados apuntan a una producción científica creciente al respecto pero que muestra rasgos de inmadurez, especialmente una dispersión en la autoría que indica que el periodismo de investigación no constituye aún una línea preferente en la disciplina. Además, la producción científica sobre el tema se concentra en revistas del área de Periodismo y no ha conseguido atraer aún la atención de otras disciplinas. Los principales temas identificados son el papel de las redes sociales como fuente y el impacto del periodismo de investigación en la vida democrática. Intelectualmente, la producción científica se apoya en estudios sobre la financiación del periodismo, los valores informativos tras la producción de noticias y el estudio de actores y estructuras históricas que influyen en su actividad.

The Dark Side of Mobile Learning via Social Media: How Bad Can It Get?

As the COVID-19 pandemic continues to spread at an unprecedented rate, many universities around the world halted physical forms of teaching and learning to stop the spread of the virus. As a result, many university students were forced to utilize online learning through channels such as mobile social media. Due to the novelty of this situation, there are many unknowns particularly with the negative influences of mobile learning via social media on university students. Thus, this study looks to examine this subject matter from the perspective of the stimulus-organism-response theory. The uniquely developed research model included four stimuli (i.e., social overload, information overload, life invasion, and privacy invasion), two organisms (i.e., technostress and exhaustion) as well as a response in terms of reduced intention to use mobile learning via social media. The responses were collected from 384 university students via an online survey and analyzed with the Partial-Least-Square-Structural-Equation-Modelling. It was found that the antecedents for both technostress and exhaustion were able to account for more than half of their respective variances. Furthermore, technostress and exhaustion were significant facilitators of the students' reduced intention to use mobile learning via social media. In addition to the practical insights for stakeholders in the education industry, this study also posited several theoretical implications for researchers.

Abusive Supervision and Its Impact on Knowledge Hiding Behavior Among Sales Force

The purpose of this study is to test the relationship between abusive supervision and employee's knowledge hiding behavior (evasive hiding, playing dumb, rationalized hiding) among sales force of insurance companies in Pakistan. The paper also strives to theoretically discuss and then seek empirical evidence to the mediational paths of psychological contract breach that explain the focal relationship between abusive supervision and knowledge hiding. To test the proposed hypotheses, the study draws cross-sectional data from sales force of insurance companies working in Pakistan. Data were collected through structured questionnaire and using convenient sampling technique. The final sample of 340 valid and complete responses analyzed using structured equation modeling (partial least square) approach. Results showed that abusive supervision is positively related to employee's knowledge hiding behaviors. Also, mediating variable psychological contract breach partially mediates the abusive supervision-knowledge hiding behavior linkage. Current study has tested the positive relationship between abusive supervision and knowledge hiding behaviors unlike most of the previous investigations that have focused on knowledge sharing behavior. The study also empirically investigated the mediational route of psychological contract breach, that explains the blame attributed by the beleaguered employee that led to covert retaliatory behavior, such as knowledge hiding. This paper contributes to knowledge hiding literature which is an important part of knowledge management from the perspective of abusive supervision based on both reactance theory and SET theory.

Digital Resources Integration and Performance Evaluation Under the Knowledge Management Model in Academic Libraries

Leveraging a knowledge management model (KMM), organizing digital resources, and measuring performance beyond digital libraries continue to be hot topics for many organizations. Given its growing importance, the aim of this study was to design a robust integrated research model based on social cognitive theory and KMM to organize resources and improve the productivity of academic librarians to make services effective. Using a quantitative method based on key data collected by librarians at Pakistani universities, the researchers reviewed previously published literature and sought expert opinions to develop a research tool. The authors distributed 350 printed questionnaires to professionals from 190 universities, of which 339 were returned. These were analyzed to test the hypotheses and assess the accuracy of the measurement and structural models. After undergoing rigorous statistical analysis, the seven constructs using Social Cognitive Theory and the Knowledge Management Model have proven to be suitable for digital asset integration, employee productivity measurement, and high-quality academic services. In addition, the model based on social cognitive theory supports the application of KMM to promote knowledge culture, resource organization, and successful integration and innovation of academic services by enhancing the technical and professional skills of staff in academic institutions.

Computer self-efficacy, computer anxiety, cognitive skills, and use of electronic library resources by social science undergraduates in a tertiary university in Nigeria

This study investigated the influence of computer self-efficacy, computer anxiety, and cognitive skills on the use of electronic library resources by social science undergraduates in a tertiary institution in Nigeria. Survey research design was adopted and stratified random sampling technique was used to select 869 sample size from a population of 1452 social science undergraduates across five departments. A total of 793 questionnaire was properly filled and collated which equals a response rate of 91.3% from the population sample. Findings from the study revealed that there were significant relationships among computer selfefficacy, computer anxiety, cognitive skills, and use of electronic library resources by the respondents. Computer self-efficacy, computer anxiety, and cognitive skills individually and jointly had a significant influence on the use of electronic library resources of the respondents. Therefore, library management in the tertiary institution should give due consideration to computer self-efficacy, computer anxiety, and cognitive skills of the respondents when planning to enhance their use of electronic library resources among others.

Does the COVID-19 Vaccine Still Work That "Most of the Confirmed Cases Had Been Vaccinated"? A Content Analysis of Vaccine Effectiveness Discussion on Sina Weibo during the Outbreak of COVID-19 in Nanjing

In July 2021, breakthrough cases were reported in the outbreak of COVID-19 in Nanjing, sparking concern and discussion about the vaccine's effectiveness and becoming a trending topic on Sina Weibo. In order to explore public attitudes towards the COVID-19 vaccine and their emotional orientations, we collected 1542 posts under the trending topic through data mining. We set up four categories of attitudes towards COVID-19 vaccines, and used a big data analysis tool to code and manually checked the coding results to complete the content analysis. The results showed that 45.14% of the Weibo posts (n = 1542) supported the COVID-19 vaccine, 12.97% were neutral, and 7.26% were doubtful, which indicated that the public did not question the vaccine's effectiveness due to the breakthrough cases in Nanjing. There were 66.47% posts that reflected significant negative emotions. Among these, 50.44% of posts with negative emotions were directed towards the media, 25.07% towards the posting users, and 11.51% towards the public, which indicated that the negative emotions were not directed towards the COVID-19 vaccine. External sources outside the vaccine might cause vaccine hesitancy. Public opinions expressed in online media reflect the public's cognition and attitude towards vaccines and their core needs in terms of information. Therefore, online public opinion monitoring could be an essential way to understand the opinions and attitudes towards public health issues.

Visual analysis of global research trends in social bots based on bibliometrics

Purpose

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Design/methodology/approach

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

Findings

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Originality/value

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.

Modeling and simulation of microblog-based public health emergency-associated public opinion communication

With the advent of the era of “we media,” many people's opinions have become easily accessible. Public health emergencies have always been an important aspect of public opinion exchange and emotional communication. In view of this sudden group panic, public opinion cannot be effectively monitored, controlled or guided. This makes it easy to amplify the beliefs and irrationality of social emotions, that threaten social security and stability. Considering the important role of opinion leaders in micro-blogs and users’ interest in micro-blog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information. The parameters of the model are calculated by combining the actual crawl data from the novel coronavirus pneumonia epidemic period, and the trends in the evolution of public opinion are simulated by MATLAB. The simulation results are consistent with the actual development of public opinion dissemination, which shows the effectiveness of the model. These research findings can help the government understand the principles that guide the propagation of public opinion and advise an appropriate time to control and correctly guide public opinion.

Dynamic Distributed and Parallel Machine Learning algorithms for big data mining processing

Purpose

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

Design/methodology/approach

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

Findings

The authors got very satisfactory classification results.

Originality/value

DDPML system is specially designed to smoothly handle big data mining classification.

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

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A comprehensive systematic review on social big data analytic approaches is provided.

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The main methods, pros, cons, evaluation methods, and parameters are discussed.

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A scientific taxonomy of social big data analytic approaches is presented.

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A detailed list of challenges and future research directions is outlined.

Social Networking Services (SNSs) connect people worldwide, where they communicate through sharing contents, photos, videos, posting their first-hand opinions, comments, and following their friends. Social networks are characterized by velocity, volume, value, variety, and veracity, the 5 V’s of big data. Hence, big data analytic techniques and frameworks are commonly exploited in Social Network Analysis (SNA). By the ever-increasing growth of social networks, the analysis of social data, to describe and find communication patterns among users and understand their behaviors, has attracted much attention. In this paper, we demonstrate how big data analytics meets social media, and a comprehensive review is provided on big data analytic approaches in social networks to search published studies between 2013 and August 2020, with 74 identified papers. The findings of this paper are presented in terms of main journals/conferences, yearly distributions, and the distribution of studies among publishers. Furthermore, the big data analytic approaches are classified into two main categories: Content-oriented approaches and network-oriented approaches. The main ideas, evaluation parameters, tools, evaluation methods, advantages, and disadvantages are also discussed in detail. Finally, the open challenges and future directions that are worth further investigating are discussed.

Impact of Role Conflict on Intention to Leave Job With the Moderating Role of Job Embeddedness in Banking Sector Employees

This study investigates why some employees intend to leave their jobs when facing conflict between family responsibilities and job routines. The present study also reveals the moderating role of on-the-job embeddedness between role conflict and intention to leave the job. Drawing on conservation of resources theory, the paper investigates the buffering effect of the three on-the-job embeddedness components (fit, links, and sacrifice). Data were collected from banking officers because most of the employees have to face role conflict between family and job responsibilities, as banking is considered among the most stressful jobs. Collected data were analyzed by applying structural equation modeling. Results indicate that the role conflict significantly influences intention to leave the job. Furthermore, the study shows that on-the-job embeddedness moderates the relationship between role conflict and intention to leave. The results suggest that organizations can reduce turnover intention during times of work and life conflict by developing employee on-the-job embeddedness. This study provides some insights to managers on why many employees leave their jobs and how to overcome this problem. Management should also offer extra and available resources in periods of greater tension to minimize early thinking regarding quitting.

A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning

The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

The Relationship Between Social Media Digitalization and Coronavirus Disease 2019 Fear Among Service Sector Employees

In the age of digitalization, social media has played a significant role in quickly spreading the news about current affairs. From December 2019 to now, coronavirus disease 2019 (COVID-19), with its several mutated shapes, has more transmissible potential catastrophe and has become a severe phenomenon issue worldwide. The international spread of the epidemic has created fear among people, especially employees working physically in different organizations. The present research aimed to measure the impact of social media on its users in the China. The social media users more often were influenced by shocking news instructively and destructively. The research analysis was based on service sector employees and data collected from 630 respondents via a structured questionnaire. This research was confirmed the negative impact of fear on social media on the performance of employees. This research was also confirmed the moderation impact of the COVID-19 vaccine on the relationship between social media fear and employee performance. This research recommends that the China Censor Board checked the news and its validity to reduce the fear of COVID-19 among employees. This research will become a roadmap for organizations and media controllers to understand the impact of social media during an intense situation. The telecommunication sector will reduce psychological disease and enhance the work capability of employees by controlling unnecessary and unapproved material about sensitive issues.

Using data mining to track the information spreading on social media about the COVID-19 outbreak

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Analysis of the Evolution of User Emotion and Opinion Leaders' Information Dissemination Behavior in the Knowledge Q&A Community during COVID-19

Since its emergence in 2019, COVID-19 has quickly triggered widespread public discussion on social media. From 26 February 2020 to 26 September 2020, this study collected data on COVID-19-related posts in the knowledge Q&A community, identified 220 opinion leaders of this community, and used social network analysis and sentiment analysis to analyze the information exchange behavior and emotional evolution of the opinion leaders during COVID-19. The results show that the COVID-19 topic community could be divided into seven main categories. The information dissemination of opinion leader information dissemination network had low efficiency, multiple paths, and a high degree of control. In addition, the emotional evolution of users showed obvious phased characteristics. User emotion changed from initially strong negative to strong positive over the course of the pandemic and eventually tended to be objective and neutral as time passed and the event stabilized.

Analyzing COVID-19 vaccine tweets following vaccine rollout: A sentiment-based topic modeling approach

Background

COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public’s conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public’s vaccine awareness through sentiment–based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines.

Objective

In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved.

Methods

We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter’s application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment–based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns.

Results

Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign.

Conclusions

To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment–based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.

The complementarities of big data and intellectual capital on sustainable value creation; collective intelligence approach

It is evident in the literature that both intellectual capital and big data analytics create value to the organizations independently, but how threats, opportunities, capabilities and value creation for intellectual capital change with big data adoption is largely unexplored. This paper aims to develop an analytical framework for identifying challenges, opportunities, capabilities and value creation in the face of complementarity between big data and components of intellectual capital. The paper uses a Collective Intelligence approach as a theoretical background. Based on Structured Literature Review, the current study has developed an analytical framework for organizations to be used as a decision-making tool while making investment in big data and managing intellectual capital. Findings suggest that the scope of human capital has changed largely as now employees are expected much more than in the past with strong analytical, dynamic, technical and IT capabilities. Structural capital calls for new practices, routines and procedures to be adopted and old methods to unlearn whereas relational capital stresses the importance of network building and social media to create sustainable value for the society.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

BACKGROUND

With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated.

OBJECTIVES

This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020.

METHODS

Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized.

RESULTS

Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics.

CONCLUSIONS

Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

Student learning outcomes and online engagement in time of crisis: the role of e-learning system usability and teacher behavior

Purpose

This research explores the effect of e-learning Moodle-based system usability on students' learning outcomes with the possible intervening role of teacher's behavior and online engagement.

Design/methodology/approach

In this research, the authors followed a quantitative methodology and a deductive research approach. Data were collected from 433 students at different study levels and academic specializations in higher education institutions (HEIs) in Oman. The data have been analyzed using partial least squares structural equation modeling via Smart-PLS.

Findings

The findings of this research show that e-learning system usability affects students' learning outcomes. Moreover, the relationship between these two variables is mediated by teacher behavior and students' online engagement.

Originality/value

This study is important as it adds to the understanding of the role of e-learning system usability in predicting student outcomes. From practical perspectives, especially during the spread of the COVID-19 pandemic, this study also helps practitioners at private HEIs use e-learning systems more efficiently and effectively to improve students' engagement and learning outcomes.

Effects of LEGO robotic on freshmen students' computational thinking and programming learning attitudes in Taiwan

Purpose

Advancements in technology require that everyone is skilled with computational thinking (CT), problem-solving and computer programming skills. This study aims to examine the development of CT in problem-solving skills (PSS) and programming learning attitude by integrating LEGO robotics kits in a project-based learning course.

Design/methodology/approach

This study examines the development of CT in PSS and programming learning attitude by integrating LEGO robotics kits in a project-based learning course. This study consists of a single group pre-post-test research design with 32 freshmen university students. Quantitative and qualitative data were collected by pre-post-tests and recording of classroom discussions, respectively.

Findings

Therefore, this finding implies that robotics can be used to develop CT in university students; however, there is a need for designing curricula with advanced robotic kits as artificial intelligence (AI) has become more prevalent. Hence, programming knowledge learned will help students to understand the application of robots in AI.

Originality/value

The study creates educators' awareness that CT skills might be developed in freshmen university students through robotics. However, many still consider them toys rather than learning aids.

Information system for visually impaired in Indian universities: a user’s perspectives

Purpose

This study aims to comprehend the perceptions of visually impaired users in development of information systems in Indian universities. This will help higher educational institutions in India to fulfil information requirements of visually impaired users.

Design/methodology/approach

A twofold method was used in the study: survey of visually impaired students in four universities; design and development of an online information system for visually impaired (ISVI). A structured questionnaire was used for survey of visually impaired users in Indian universities. The questionnaire with 33 open-ended questions was distributed among 603 visually impaired users and 316 responses were received. Stratified sampling technique was used in data collection. Response rate was 52.4%. The received responses were analysed using Statistical Package for Social Sciences version 19 and presented through tables and figures.

Findings

Majority of respondents revealed that lack of content availability in vernacular language, poor information resources access mechanism, less volume of course content and poor maintenance of assistive software(s) and devices and non-standardized e-resources compatible with the assistive software(s) are some major inhibits faced. Majority of respondents revealed that they prefer scribes in examinations because use of assistive technology (AT) is time-consuming. Lack of training and confidence in using AT is another reason for preferring scribes in examinations.

Research limitations/implications

This study was conducted on a limited number of visually impaired uses in Indian universities. Also, the online system ISVI contains a sample collection related to undergraduate courses only.

Practical implications

This study will help Indian universities to identify problems faced by visually impaired users. Findings of the study can help faculty members, decision-makers to understand the perspective of visually impaired. Besides this, the study can also help government agencies in formulating policies. Findings will help in developing robust information resources suiting the needs of visually impaired.

Originality/value

This study developed an online system ISVI (www.isvi.in) suitable to address the requirements of visually impaired students. Furthermore, it can guide policymakers, faculty members and information professionals in developing seamless open access information systems.

Research on influencing factors of information diffusion in online social networks under different themes

Purpose

This paper aims to study the factors influencing online social network (OSN) information diffusion under different themes helps to understand information diffusion in general.

Design/methodology/approach

This study collects data from the Web of Science, use the strategic consulting intelligent support system for word frequency analysis and use keyword clustering to classify themes, then research information themes as influencing factors of OSN information diffusion.

Findings

Five themes of “natural disaster”, “political event”, “product marketing”, “sport and entertainment” and “health-disease” have been identified. It is found that the research objects, research methods and research theories used by scholars under different themes have different focuses, and the factors affecting information diffusion are different.

Research limitations/implications

The limitation of this paper is that it only focuses on five typical themes, and there may be more themes.

Practical implications

The research helps other scholars to conduct in-depth research on the diffusion of OSN information under different topics and focus on the content of the research on OSN information diffusion under different topics.

Social implications

The research helps other scholars to conduct in-depth research on the diffusion of social network information under different topics, so as to better understand and predict the law of information diffusion.

Originality/value

The research summarizes the research on information diffusion in OSNs from the theme level and analyses the key points and theories and further enriches the research system on information diffusion in OSNs.

Availability and preferences for information services to visually impaired tertiary students in Delhi

Purpose

The study aims to understand the information services provided to visually impaired users in Indian universities. Further, the study identifies the nuances of information system for visually impaired (ISVI) development suitable to the requirements of the visually impaired community in Indian higher educational institutions.

Design/methodology/approach

A two-fold methodology was followed in the study: (1) empirical survey and (2) information system design and development. The empirical survey was conducted in Indian higher educational institutions wherein 316 responses were collected including 185 (58.5%) undergraduate and 131 (41.5%) postgraduate students. A stratified random sampling technique was used in data collection. Empirical study findings were further used as a basis for ISVI design and development for addressing the information requirements of visually impaired users.

Findings

The study found that 232 respondents (73.2%) access the Internet every day. “Surprisingly, 143 respondents (45.3%) revealed that no digital infrastructure is available in their institutions, while 173 respondents (54.7%) stated digital infrastructure is available. A total of 210 respondents (75.9%) highlighted that the interface of open access (OA) resources is not designed as per visually impaired (VI) students' requirements. information and communication technology (ICT) infrastructure is not up to the mark revealed as by 34 respondents (10.7 percent) and was rated ‘poor.’” A total of 268 respondents (90.2%) expressed that their institution should develop the information system to full their information needs.

Research limitations/implications

The study is conducted in only one state in India due to financial hindrances. Visually impaired students studying in other states of India are not covered in the study, which may have different perception about the design and development of information system for the visually impaired.

Practical implications

An online ISVI is designed and developed under the study, which is accessible at http://www.isvi.in. Furthermore, the findings of the study may also be used by administrators of the higher educational institutions for building ICT infrastructure for visually impaired students.

Originality/value

No study has been conducted in Indian higher educational institutions for understanding the availability and preferences of information services to visually impaired tertiary students.

Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study

Background

Racial and ethnic diversity in clinical trials for cancer treatment is essential for the development of treatments that are effective for all patients and for identifying potential differences in toxicity between different demographics. Mining of social media discussions about clinical trials has been used previously to identify patient barriers to enrollment in clinical trials; however, a comprehensive breakdown of sentiments and barriers by various racial and ethnic groups is lacking.

Objective

The aim of this study is to use an innovative methodology to analyze web-based conversations about cancer clinical trials and to identify and compare conversation topics, barriers, and sentiments between different racial and ethnic populations.

Methods

We analyzed 372,283 web-based conversations about cancer clinical trials, of which 179,339 (48.17%) of the discussions had identifiable race information about the individual posting the conversations. Using sophisticated machine learning software and analyses, we were able to identify key sentiments and feelings, topics of interest, and barriers to clinical trials across racial groups. The stage of treatment could also be identified in many of the discussions, allowing for a unique insight into how the sentiments and challenges of patients change throughout the treatment process for each racial group.

Results

We observed that only 4.01% (372,283/9,284,284) of cancer-related discussions referenced clinical trials. Within these discussions, topics of interest and identified clinical trial barriers discussed by all racial and ethnic groups throughout the treatment process included health care professional interactions, cost of care, fear, anxiety and lack of awareness, risks, treatment experiences, and the clinical trial enrollment process. Health care professional interactions, cost of care, and enrollment processes were notably discussed more frequently in minority populations. Other minor variations in the frequency of discussion topics between ethnic and racial groups throughout the treatment process were identified.

Conclusions

This study demonstrates the power of digital search technology in health care research. The results are also valuable for identifying the ideal content and timing for the delivery of clinical trial information and resources for different racial and ethnic groups.

Consumer Knowledge Sharing Behavior and Consumer Purchase Behavior: Evidence from E-Commerce and Online Retail in Hungary

The twenty-first century has been full of fundamental changes in consumers’ behavior patterns, especially with the use of diverse social media knowledge-sharing platforms. Therefore, companies have highlighted the significance of knowledge sharing and the importance of social network use in purchasing processes. Accordingly, his paper tries to reveal how consumer purchase behavior (CPB) can be affected by consumer knowledge sharing behavior (CKSB) and the moderating role played by value co-creation dimensions, which are citizenship behavior (CB) and participation behavior (PB), within a sustainable e-commerce field. To test our hypotheses deducted from the literature review, we opted for the PLS-SEM method. We also employed other innovative approaches, such as the IPMA matrix, MAICOM test, FIMIX approach, and CTA analysis, to evaluate the outer and inner model. Our statistical population covered individuals living in Hungary with at least one online purchase involvement. We distributed the questionnaire via various online platforms and, finally, 433 completed questionnaires were prepared for analysis. The results showed that CPB, CB, and PB are positively influenced by the CKSB. However, the link between CPB and CB was not confirmed. As for the moderating role of gender, the permutation test was applied to compare male and female groups and see the difference between them. With a focus on CKSB, this study contributes to the success of international marketing strategies to attain higher competitive advantages.

Visual Expression of Emotion in Dynamic 3D Painting System Based on Emotion Synthesis Model

Emotion is a unique ability possessed by human beings as advanced creatures. Emotions give people a unique physical and mental experience. Assigning emotions to computer systems is one of the latest topics in artificial intelligence research. The purpose is to allow machines to achieve natural coordination between humans and computers. This article focuses on the visual expression of emotion in the dynamic three-dimensional painting system, creating an intelligent painting system and realizing a good user experience. In this paper, the discrete method is used to qualitatively analyze emotions, and the continuous method is used to quantify basic emotions, and emotional modeling and emotional quantitative analysis are proposed to realize quantitative analysis of emotions. Combining these two methods, a comprehensive method is proposed, which uses a continuous method to quantify the basic emotions of each discrete dimension, and finally superimposes them into a comprehensive emotional synthesis model. Emotion modeling is the basis of emotion visualization. Borrowing the relationship between emotion synthesis model and visual emotion elements, this article puts forward the concept of qualitative and quantitative visual emotion elements, and expounds that the multidimensional superposition of visual emotion elements makes dynamic three-dimensional painting system emotions. The experimental results in this article show that the emotional visualization scheme of 100 samples is tested by quantitative statistical methods to demonstrate its effectiveness. Starting from 5 points of concern, the emotion visualization method discussed in this article can indeed convey or suggest a certain positive emotion (the average value of experience, transitivity, and infectiousness > 2.5, and the variance is close to 0), but we also found this recognition at the same time The degree is not high enough, and individual differences are large (mean value < 2.5, variance close to 1). This can indicate that different subjects have different feelings and evaluations of this emotional visualization. As long as the difference is within a reasonable range, this emotional visualization also has practical value, and has the ability to convey or suggest emotions.

Information search behavior among Chinese self-drive tourists in the smartphone era

Purpose

The Chinese economy has grown rapidly over the past decade, making self-drive tours more affordable among Chinese tourists. Thus, self-driving tours using online tourism-related information have also been popular. This study aims to investigate information search behaviors among different segments of Chinese self-drive tourists under the influence of contemporary mobile internet technologies and compares the results with prior related studies from a global standpoint.

Design/methodology/approach

This paper investigates the relationships between tourist demographics and information search behavior in Mainland China based on a quantitative method. This study collected 228 surveys from users with self-drive tour experience. This study also conducted a series of online interviews to explore the potential barriers when self-drive tourists search for information.

Findings

The results showed variances within pre-decision and post-decision stages between different age groups, social classes and experience levels. Preference for up-to-date information and contemporary platforms were also observed.

Originality/value

Research paucity exists on self-drive tourists’ information search behavior, using data collected from Mainland Chinese tourists using mobile devices. The findings offer insights to industry practitioners, such as travel agencies and liaison websites, for refining their services to meet the information needs of Chinese self-drive tourists through more comprehensive marketing strategies.

Information-seeking behavior in the digital age: use by faculty members of the internet, scientific databases and social networks

Purpose

The study aims to survey information-seeking behavior of the Iranian medical faculty members and to identify the reasons and obstacles of their usage via the internet, scientific databases and online social networks.

Design/methodology/approach

In this cross-sectional study, a structured questionnaire used to collect data. The research population includes faculty members of four faculties of Hamadan University of Medical Sciences in Iran, including health, nursing – midwifery, paramedicine and rehabilitation. SPSS software was used to analyze data in descriptive and analytical levels.

Findings

Low internet speed, network traffic and lack of time were the main barriers and difficulties. Gender was significantly related with familiarity with databases, problems and barriers to accessing information, barriers to using social network, as well as purpose and motivation of using social network. Search for educational and research resources and use of social network to increase their general information were the main goals of participants to using the internet and databases. A negative significant relationship was observed between usage of social network with age (r = −0.204, P = 0.047) and experience (r = −0.239, P = 0.019).

Originality/value

The usage of faculty members of various digital resources is growing ever more. This study examines the use of faculty members of the internet, scientific databases and online social networks simultaneously.

Six Sigma in Health Literature, What Matters?

Six Sigma has been widely used in the health field for process or quality improvement, constituting a quite profusely investigated topic. This paper aims at exploring why some studies have more academic and societal impact, attracting more attention from academics and health professionals. Academic and societal impact was addressed using traditional academic metrics and alternative metrics, often known as altmetrics. We conducted a systematic search following the PRISMA statement through three well-known databases, and identified 212 papers published during 1998–2019. We conducted zero-inflated negative binomial regressions to explore the influence of bibliometric and content determinants on traditional academic and alternative metrics. We observe that the factors influencing alternative metrics are more varied and difficult to apprehend than those explaining traditional impact metrics. We also conclude that, independently of how the impact is measured, the paper’s content, rather than bibliometric characteristics, better explains its impact. In the specific case of research on Six Sigma applied to health, the papers with more impact address process improvement focusing on time and waste reduction. This study sheds light on the aspects that better explain publications’ impact in the field of Six Sigma application in health, either from an academic or a societal point of view.

Semantic enrichment on large scanned collections through their “satellite texts”: the paradigm of Migne’s Patrologia Graeca

Purpose

This paper aims to present a methodology for the semantic enrichment on the scanned collection of Migne’s Patrologia Graeca (PG), attempting to easily locate on the Web domain the scanned PG source, when a reference of this source is described and commented on another scanned or textual document, and to semantically enrich PG through related scanned or textual documents named “satellite texts” published by third people. The present enrichment of PG uses as satellite texts the Dorotheos Scholarios's Synoptic Index (DSSI) which act as metadata for PG.

Design/methodology/approach

The methodology consists of two parts. The first part addresses the DSSI transcription via a proper web tool. The second part is divided into two subsections: the accomplishment of interlinking the printed column numbers of each scanned PG page with its actual filename, which is the build of a matching function, and the build of a web interface for PG, based on the generated Uniform Resource Identifiers (URIs) of the above first subsection.

Findings

The result of the implemented methodology is a Web portal, capable of providing server-less search of topics with direct (single click) navigation to sources. The produced system is static, scalable, easy to be managed and requires minimal cost to be completed and maintained. The produced data sets of transcribed DSSI and the JavaScript Object Notation (JSON) matching functions are available for personal use of students and scholars under Creative Commons license (CC-BY-NC-SA).

Social implications

Scholars or anyone interested in a particular subject can easily locate topics in PG and reference them, using URIs that are easy to remember. This fact contributes significantly to the related scientific dialogue.

Originality/value

The methodology uses the transcribed satellite texts of DSSI, which act as metadata for PG, to semantically enrich PG collection. Furthermore, the built PG Web interface can be used by other satellite texts as a reference basis to further enrich PG, as it provides a direct identification of sources. The presented methodology is general and can be applied to any scanned collection using its own satellite texts.

UTAUT3 model viability among teachers due to technological dynamism during COVID-19

Purpose

The purpose of this paper is to identify the viability of the extended Unified Theory of Acceptance and Use of Technology Model 3 (UTAUT3) model among the teachers especially during COVID-19 towards the use of technology.

Design/methodology/approach

An extensive primary survey has been conducted through a well-structured tool under UTAUT3 model. The survey is conducted among 450 teachers from various institutions taken for the study. The data was collected from the Northern India. The data analysis will be done through the SmartPLS software with application of structural equation modelling (SEM).

Findings

The results are strong for educators and policy makers. It was found that performance expectancy is positively related to the behavioural intentions among teachers. Teachers consider that usage of technology will boost their job and task performance.

Practical implications

This study has a very strong implications in the field of education in case or replacement of traditional teaching patterns with modern one during pandemic times. It will be effective if teachers would prioritize their work. There will be more effective teaching and learning system in future.

Originality/value

The study validates the constructs of UTAUT3 model in understanding teachers' behaviour and attitude towards technology acceptance. Furthermore, the study invites research from different viewpoint to investigate the role of UTAUT3 model in an individuals' behaviour and attitude towards technology acceptance.

Conceptualising and measuring social media engagement: A systematic literature review

The spread of social media platforms enhanced academic and professional debate on social media engagement that attempted to better understand its theoretical foundations and measurements. This paper aims to systematically contribute to this academic debate by analysing, discussing, and synthesising social media engagement literature in the perspective of social media metrics. Adopting a systematic literature review, the research provides an overarching picture of what has already been investigated and the existing gaps that need further research. The paper confirms the polysemic and multidimensional nature of social media engagement. It identifies the behavioural dimension as the most used proxy for users' level of engagement suggesting the COBRA model as a conceptual tool to classify and interpret the construct. Four categories of metrics emerged: quantitative metrics, normalised indexes, set of indexes, qualitative metrics. It also offers insights and guidance to practitioners on modelling and managing social media engagement.