Search results

Quality of life is dependent on a healthy lifestyle and the self-care behavior of individuals. The study's purpose is to find out the determinants of individuals' self-care behavior. As such, self-care behavior is influenced by several factors that include individual knowledge, available information sources and their use, information-seeking related skills and cognitive state.

A quantitative research design followed using a questionnaire-based survey method. A total of 384 responses from the Pakistani public were collected using the convenience sampling technique. Structural equation modeling (SEM) was performed for examining the possible link between the variables.

Health literacy, Internet and social media use, and health information-seeking behavior had a direct/indirect positive impact on self-care behavior, but health anxiety had a negative impact. Health literacy and health information-seeking behavior positively mediated the relationship among Internet and social media use health anxiety and self-care.

Improving health literacy appears to be key to supporting better self-care, but it is an exploratory study, more research is required to confirm these findings. Policymakers, health professionals and information professionals should work together to improve health literacy and support informed self-care among the population.

Thus far, no previous study has examined the collective role of social media exposure, health anxiety, health literacy and health information-seeking behavior as predictors of self-care behavior. Although self-care behavior among the general population might be different compared to chronic patients, only few studies have examined the former as a unit of analysis.

Grounded in the stressor-strain-outcome model, this study aims to examine the impact of different information sources on information behavior among the Pakistani Z generation during the pandemic.

The study was quantitative, with 344 responses collected from Gen Z (those born in the late 1990s) using an online survey. The proposed structural model was tested with the help of SmartPLS 3.3. Information sources were divided into four categories, i.e. conventional media, personal networks, social media and Internet use through official health websites.

In the Pakistani context, conventional media was found to develop information overload among Zers, whereas social media and personal networks were positively associated with information anxiety. However, Internet use (official and medical websites) for health-related information-seeking significantly reduced information anxiety among people. None of these information sources are the reason for information avoidance but the sources affect either the independent predictor of information avoidance or the mediators. Whereas information overload is a predictor of information anxiety and information, anxiety is a mediator between information overload and avoidance behavior.

To avoid the negative consequences of abundant information, the authors need to promote and encourage the use of authentic information sources to make Gen Z skeptical, independent, critical and scientific thinkers.

Information sources' dynamics in terms of negative consequences of abundant information has not been explored previously at this magnitude, particularly from the perspective of a developing country.

Given the evidence previously presented, it would be odd if the development of new kinds of polities did not result in demands for new standards of political leadership. The emerging global consensus against public corruption is one indication of changing standards (see Chapter 7). In addition to changing standards, the public has also started to look elsewhere than the political system for leaders. In the United States, for example, recent polls show that citizens are favoring the following kinds of people for national political office: businessmen, state governors, and those with no Washington experience or connections (Andrew Kohut, PEW Poll, PBS; June 2, 2011). This is also true in Europe. Habermas (2010) complains about the fact that citizens in Germany have disdain for professional politicians and are turning to amateurs for leadership. This development coincides with the growth of social movement politics across many regions of the world (Meyer & Tarrow, 1999). As we have seen, many citizens have more confidence in social movements than political parties, parliaments, civil services, and national justice systems. Not only are professional politicians often believed to be “out of touch” with members of society, but they are also seen as less attentive to them and out for themselves and interest groups they favor (Dalton, 2006; Chapter 7).

The authors conducted an action research study with the aim of understanding current commercial offerings in modular designs in virtual environments and to explore modularity development based on consumer input for the purpose of personalizing three-dimensional (3D) virtual fashion stores.

Through five phases of diagnosing, action planning, action taking, evaluating and specifying learning, the authors attempted to diagnose the current commercial offerings of modular designs in virtual spaces and to identify the right type and the number of modules and modular options for personalizing 3D virtual stores based on consumers' actual designs and focus group input. The authors then further conceptualized modules to serve as an example for developing modularity in 3D virtual reality (VR) stores.

In the diagnosing phase, the authors investigated the modularity structure of cocreating a retail store in two popular virtual worlds: Second Life and The Sims 4. In the evaluation phase, the authors identified modules and modular options for personalizing 3D virtual stores based on a content analysis of consumers' post-design focus group discussions. In the last phase (specifying learning), the authors conceptualized a total of nine modules and 38 modular options for personalizing 3D virtual stores, including style, price point, product category, color, presence of avatar, virtual product try-on, music, product recommendation and product customization.

The significance of this study lies in the pioneering methodological work of identifying, creating and visualizing 3D VR modular store options based on consumer input and in improving the authors’ understanding of current commercial offerings. This study also enriches design theories on cocreation systems. The authors’ suggested modules for personalizing 3D virtual stores could inspire future evidence-based designs to be readily used by VR retailers as well extend the application of mass customization theory from the realm of product development to retail environments.

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.