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The field of library and information sciences (LIS) is crucial to our educational system. Across the globe, the LIS systems operate at varying levels and rates of efficiency. One of the developing nations is Pakistan, which has LIS systems in all of its colleges, universities and schools. This multimethod study aims to identify and quantify elements that are detrimental to LIS progress as well as evaluate the faculty and infrastructure profiles of universities that offer LIS undergraduate and graduate level programmes.

Data was collected from the study's participants, who were mainly LIS professionals and faculty at 17 different universities, using survey questionnaires and in-person interviews. This study used a descriptive survey methodology, gathering information through a Google Survey and filling it out with a premade survey proforma. The survey responses were examined using content analysis. The development of LIS instructional and scholarly output is influenced by a variety of factors, which were investigated using a generalized linear model (GLM). To determine whether there was a statistically significant difference in opinion between faculty members and working professionals, as well as between men and women, the outcomes of an independent sample t-test were examined.

According to our data, the factors that have the biggest impact on the caliber and output of LIS research are “poor writing skills” (3.43), “lack of journal publication fees” (3.51) and “lack of research skills” (3.78). The top GLM model identified poor writing skills, a lack of publication fees and a lack of research expertise as bottleneck characteristics for producing high-quality LIS research. The aforementioned factors were 3.62, 2.41 and 2.19 times more significant than the average factor, respectively, to put it another way.

This study’s analysis revealed that there is no real distinction between the two groups' viewpoints. The results of this study can be applied to problems and challenges associated with LIS education in Pakistani educational institutions.

This study aims to investigate the impact of terrorism on financial inclusion that is achieved through automated teller machine penetration and bank branch expansion.

Eight countries that are the most terrorized countries in the world were analysed using the panel fixed effect regression model and the generalized linear model.

The results provide evidence that terrorism reduces the level of financial inclusion in countries experiencing terrorism, but the presence of strong legal institutions, accountability governance institutions and political stability governance institutions mitigate the adverse effect of terrorism on financial inclusion.

A growing literature has shown that terrorism affects the economy, yet little is known about its impact on financial inclusion.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

The study aims to analyze consumer reaction to counterintuitive communication linked to the LGBTQIAP+ cause.

The theoretical framework focused on marketing communication, counterintuitive communication and the insertion of the LGBTQIAP+ issue in them. A hypothetical-deductive approach was adopted, carried out through an online experiment with a factorial design 2 (high involvement product − wedding ring × low involvement − perfume) × 2 (homosexual couple − counterintuitive × heterosexual − intuitive), with two subsequent steps: eye tracker collection (n = 21) and questionnaire application (n = 136).

It was possible to attest to the differences between traditional (intuitive) and counterintuitive marketing communications, identified both in terms of visualizations and fixations (eye tracking), and objective responses (online questionnaire), finding more positive consumer behavior in the case of counterintuitive communication with insertion of the LGBTQIAP+ cause.

Marketing communication has great potential to create “new/other” values and worldviews, thus having an important role in social responsibility that goes beyond the marketing sphere. Precisely in this context, the study contributes to endorse the literature concerning counterintuitive communication, reinforcing the increasing importance and favorable scenario for its practice, as well as highlighting its importance as an efficient marketing strategy.

The study extends the understanding of counterintuitive communication with the scope of an investigation linked to the LGBTQIAP+ cause, especially from the perspective of the receiving public, the consumers. Furthermore, it advances in the sense of aggregating empirical evidence by means of both exploratory (with neuroscientific technique as eye tracking) and explanatory factors.

This study examines whether corporate social responsibility (CSR) initiatives positively impact customers’ selection of retail banks in Hong Kong (HK) and identifies which CSR domains affect customers’ selection of banks.

This study adopted a quantitative approach. Primary data were collected from 416 customers of 22 retail banks in HK. The theoretical framework of this study was developed from a literature review, prior studies by Oberseder et al. (2013 and 2014), and CSR initiatives implemented by leading retail banks in HK. Descriptive statistics and statistical tests were used to analyze the data.

The study found that CSR initiatives positively affect customers’ bank selection. CSR initiatives related to the customer and environment domains are likely to have a greater impact on customers than those related to the society domain and are not likely to significantly impact customers’ bank selection.

This study contributes to the CSR literature by offering enhanced insight into the dynamics of CSR and its effects on customer bank selection. Furthermore, this study tests consumers’ perceptions of CSR initiatives in each CSR domain in the banking sector in Hong Kong – a novel approach that has not been previously explored in existing studies. These findings can help banks review the effectiveness of their CSR initiatives and make informed decisions on which initiatives should pursue improved CSR performance and efficient resource allocation.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

Based on the social interaction theory and trust theory, this study investigates the switching of users on social question and answer (Q&A) platforms from knowledge seekers to knowledge contributors.

We used Python to gather data from Zhihu, performed hypothesis testing on the models using Poisson regression and finally conducted a mediation effect analysis.

The findings reveal that knowledge seeking impacts users' motivation for information interaction, emotional interaction and trust. Notably, information interaction and trust exhibit a chained mediation effect that subsequently influences knowledge contribution.

Current studies on user knowledge behavior typically examine individual actions, rarely connecting knowledge seeking and knowledge contribution. However, the balance of knowledge inflow and outflow is crucial for social Q&A platforms. To cover this gap, this paper empirically investigates the switching between knowledge seeking and knowledge contribution based on the social interaction theory and trust theory.

The purpose of this paper is to conduct a comprehensive study on building, fire and evacuation, so as to effectively improve the efficiency of building fire evacuation and the management level of fire evacuation site. Make up for the difficulties of BIM technology in effectively connecting building information and fire data.

First, this paper establishes a fire model and an evacuation model based on BIM information. Then, the safety index (SI) is introduced as a comprehensive index, and the IRI is established by integrating the SI function to evaluate the safety of evacuation routes. Based on these two indices, the IRI-based fire evacuation model is established.

This study offers an Improved Risk Index (IRI)-based fire evacuation model, which may achieve effective evacuation in fire scenes. And the model is verified by taking the fire evacuation of a shopping center building as an example.

This paper proposes a fire evacuation principle based on IRI, so that the relevant personnel can comprehensively consider the fire factors and evacuation factors to achieve the optimization of building design, thereby improving the fire safety of buildings.

Federation analytics approaches are a present area of study that has already progressed beyond the analysis of metrics and counts. It is possible to acquire aggregated information about on-device data by training machine learning models using federated learning techniques without any of the raw data ever having to leave the devices in the issue. Web browser forensics research has been focused on individual Web browsers or architectural analysis of specific log files rather than on broad topics. This paper aims to propose major tools used for Web browser analysis.

Each kind of Web browser has its own unique set of features. This allows the user to choose their preferred browsers or to check out many browsers at once. If a forensic examiner has access to just one Web browser's log files, he/she makes it difficult to determine which sites a person has visited. The agent must thus be capable of analyzing all currently available Web browsers on a single workstation and doing an integrated study of various Web browsers.

Federated learning has emerged as a training paradigm in such settings. Web browser forensics research in general has focused on certain browsers or the computational modeling of specific log files. Internet users engage in a wide range of activities using an internet browser, such as searching for information and sending e-mails.

It is also essential that the investigator have access to user activity when conducting an inquiry. This data, which may be used to assess information retrieval activities, is very critical. In this paper, the authors purposed a major tool used for Web browser analysis. This study's proposed algorithm is capable of protecting data privacy effectively in real-world experiments.

After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation.

This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population.

Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country.

These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models.