Diverse Information Sources Research Articles

Stakeholder diversity and collaborative innovation: Integrating the resource-based view with stakeholder theory

This paper uses Stakeholder Theory to build on the Resource-Based View (RBV) of the firm – and its extension, the Knowledge-Based View (KBV) – and theoretically and empirically assesses how a choice of stakeholders with a diverse set of interests and motives affects the development of collaborative innovation. Theoretically, the paper discusses how collaborating with stakeholders with diverse motives and interests affects the development of diverse types of innovations; and whether and how a focal organisation's access to diverse information sources may influence the behaviour of different types of collaborators. The empirical analysis is based on Spanish Technological Innovation Panel (2015–2016) data. The results show that collaborations with secondary (primary) stakeholders are typically associated with the likelihood of developing eco-innovations (product and process innovations) and increased demand for skilled workers. We also find that access to diverse information sources is associated with the likelihood of collaborating with primary stakeholders only.

Role of Indigenous and local knowledge in seasonal forecasts and climate adaptation: A case study of smallholder farmers in Chiredzi, Zimbabwe

Accessible, reliable and diverse sources of climate information are needed to inform climate change adaptation at all levels of society, particularly for vulnerable sectors such as smallholder farming. Globally, many smallholder farmers use Indigenous knowledge (IK) and local knowledge (LK) to forecast weather and climate; however, less is known about how the use of these forecasts connects to decisions and actions for reducing climate risks. We examined the role of IK and LK in seasonal forecasting and the broader climate adaptation decision-making of smallholder farmers in Chiredzi, Zimbabwe. The data were collected from a sample of 100 smallholder farmers. Seventy-three of the 100 interviewed farmers used IK and LK weather and climate forecasts, and 32% relied solely on IK and LK forecasts for climate adaptation decision-making. Observations of cuckoo birds, leaf-sprouting of Mopane trees, high summer temperatures, and Nimbus clouds are the main indicators used for IK and LK forecasts. The use of IK and LK climate forecasts was significantly positively associated with increasing farmer age and farmland size. Farmers using IK and LK forecasts implemented, on average, triple the number of adaptation measures compared with farmers not using IK and LK. These findings demonstrate the widespread reliance of farmers on IK and LK for seasonal forecasts, and the strong positive link between the use of IK and LK and the implementation of climate adaptation actions. This positive association between IK and LK usage and the implementation of adaptation actions may be widespread in smallholder farming communities throughout Africa and globally. Recognition and inclusion of IK and LK in climate services is important to ensure their continued potential for enhancing climate change adaptation.

Multidisciplinary baselines quantify a drastic decline of mussel reefs and reveal an absence of natural recovery

AbstractThe onset of the Anthropocene has coincided with enormous global declines in natural ecosystems, leading to losses in the valuable goods and ecosystem services they provide. This global decline, in conjunction with growing recognition of the ecological importance of natural ecosystems, has generated a pressing need for restoration. Effective ecosystem restoration relies on accurate identification of the cause of decline and clear metrics of success, which are only possible with baseline data of both the pre‐degradation and pre‐restoration ecosystems. However, the establishment of these baselines can be difficult as different potential information sources each have benefits and drawbacks. Determining an efficient method to balance these diverse information sources and generate robust baselines is vital to achieving the United Nations' goal of massively scaled‐up ecosystem restoration. Here we expand on the concept of multidisciplinary baselines, or the combined use of sources and methods across a wide disciplinary spectrum to establish comprehensive and reliable ecosystem baselines, and use mussel reefs in the South Island of New Zealand as a test case. Using a combination of comprehensive historical review, extensive shoreline surveys, and local ecological knowledge, we demonstrate that local mussel abundances decreased by 97% since the mid‐1960s as a result of overharvesting, leaving the extant populations scattered, small, and without recovery. This study demonstrates that harnessing multidisciplinary baselines allows for the consolidation of qualitative and quantitative estimates of ecosystem change over hundreds of years, as well as confirmation of causes of ecosystem degradation, and clear documentation of current ecosystem state beyond what is possible from any individual source. This approach to establishing ecosystem baselines also provides valuable avenues for the advancement of restoration by quantifying the temporal and geographic scales of ecosystem decline, identifying areas for intervention, and establishing clear metrics of success.

A New Multi-Dimensional Framework for Start-Ups Lifespan Assessment Using Bayesian Networks

As historical data are typically unavailable for a start-up, risk assessment is always complex and challenging. Traditional methods are incapable of capturing all facets of this complexity; therefore, more sophisticated tools are necessary. Using an expert-elicited Bayesian networks (BNs) methodology, this paper aims to provide a method for combining diverse sources of information, such as historical data, expert knowledge, and the unique characteristics of each start-up, to estimate the default rate at various stages of the life cycle. The proposed method not only reduces the cognitive error of expert opinion for a new start-up but also considers the learning feature of BNs and the effect of lifespan when updating default estimations. In addition, the model considers the impact of investors’ risk appetite. Furthermore, the model can rank the most effective risk factors at various stages. The receiver operating characteristic (ROC) curve was utilized to assess the model’s explanatory power. Moreover, three distinct case studies were used to demonstrate the model’s capabilities.

Evolution of the Cotton Genus, Gossypium, and Its Domestication in the Americas

Gossypium, the cotton genus, includes ∼50 species distributed in tropical and sub-tropical regions of all continents except Europe. Here we provide a synopsis of the evolutionary history of Gossypium and domestication of the American allopolyploid species, integrating data from fundamental taxonomic investigations, biogeography, molecular genetics, phylogenetic analysis, and archaeology. These diverse sources of information provide a temporal and phylogenetic perspective on diversification among the diploids and on polyploid formation, uncover multiple previously cryptic interspecific hybridizations, clarify and contribute to the taxonomy of the genus, and offer a firm foundation for understanding parallel domestications in Mesoamerica and South America, which led to the globally important cotton crop species G. barbadense and G. hirsutum. Gossypium thus offers a testimonial example of the importance and utility of fundamental botanical discovery combined with modern technological capabilities to generate genomic insights into evolutionary history. We also review the current state of our knowledge regarding the archaeological history of cotton domestication and diffusion in the Americas, a seemingly unlikely story entailing parallel domestication origins and parallel directional selection tracing to 8,000 (G. barbadense) and 5,500 (G. hirsutum) years ago, transforming two geographically isolated wild short-day perennial shrubs having small capsules and seeds covered by short, tan-colored epidermal trichomes into modern daylength-neutral annuals bearing abundant, fine, strong white fibers. This dual domestication was followed several millennia later by unintentional and more recently intentional interspecific introgression, as the two species came into contact following their initial domestication in different hemispheres. Thus, the cycle of species divergence and biological reunion was reiterated, this time at the allopolyploid level. Understanding this evolutionary history is vitally important to our understanding of the genomic architecture of the world’s most important fiber plant and contributes substantially to our understanding of general biological principles.

Microlearning

Microlearning is a strategy of delivering short, stand-alone instruction with one or two knowledge or skill-based objectives as part of or within formal, non-formal and informal learning environments through any modality. There are varying definitions of the term microlearning in the literature. Paul (2016), for example, refers to microlearning as a form of e-learning delivered in small chunks, focused on delivering skill-based and just-in-time learning, which is competency-based and immediacy-focused (see Figure 1). Others define the term from a problem-centred, and connectivist view that engages students to “solve a problem, direct their own learning, apply their knowledge or connect with others” (Major and Calandrino, 2018, p. 2). From a connectivist view (De Gagne et al., 2019), microlearning prioritizes the development of learners’ capacity to connect and associate multiple ideas and resources from different microlearning objects. As a result, learners can connect with diverse sources of information and their peers, leading to a deeper understanding of the subject matter.

The Fragmentation of the Mainstream and Communication in Economics: A View from the Top

The decline of Neoclassical dominance has paved the way to a more fragmented Mainstream. In this article, we empirically explore the Mainstream’s thematic structure and its fragmentation’s effects on within-discipline communication. For this purpose, we exploit a dataset containing 10,064 articles published in economics in seven Blue Ribbon Eight journals between 1985 and 2006 and their citations. Articles are assigned to 18 topics created via Latent Dirichlet Allocation to represent specialities within economics. Results show that the economic Mainstream has yet to experience a dramatic increase in fragmentation in the observed period, and the prevailing topics are related to market equilibrium and econometrics. Moreover, the Mainstream’s thematic structure does not include any heterodox approach. Regarding communication, economists increasingly tap into diverse sources of information, and such diversity positively impacts the citational patterns of articles. The same result holds for the articles written by Nobel Prize winners and the most cited articles in the discipline, which exhibit a higher diversity than the whole sample.

Factors associated with knowledge about ‘Undetectable HIV viral load is Untransmittable’ among Zambian adults on antiretroviral therapy: a mixed method approach

Background: Knowledge of ‘Undetectable HIV viral load (VL) is Untransmittable’ (U=U) motivates optimal adherence to antiretroviral therapy (ART). Objective: This study assessed factors associated with knowledge about U=U among Zambian people living with HIV on ART. Design: The study used questionnaires and in-depth interviews (IDIs) concurrently between December 2018 and January 2019. Three high volume health facilities, Chilenje, Chipata, and Kalingalinga, were selected. A single proportion of 63.4% of HIV VL suppression (VLS) in Lusaka Province was used to calculate a sample size of 362 respondents. Probability proportional to size procedure was used to apportion the sample to the study sites while a simple random technique was employed in selecting respondents aged 18–59 years and on ART for over 9 months. Stata 14 was used to run descriptive and chi-square statistical analyses. Fifty-five respondents were drawn from 362 respondents for IDIs. The selection was based on varied VL levels. Interviews were audio-recorded, transcribed and analysed thematically, and the findings merged in the discussion. Results: Approximately, 34.5% of the 362 respondents (n = 125) had knowledge about U=U. The VLS status was significantly associated with U=U knowledge (P = 0.01). Discussion: Diverse information sources, symbiotic understanding of VL, and treatment-as-prevention benefits were enablers of acquiring more knowledge about U=U, while inadequate health discussion about VL and congestion and waiting time were barriers. Conclusions: The findings suggest that increased information sharing on the implication of suppressed VL being able to prevent HIV transmission could motivate people living with HIV to adhere to ART, thus sustaining VLS.

Strengthening Creative Thinking Ability: A Project-Based Physics Assessment Instrument

Creative thinking is one of the skills needed in education in the 21st century which requires students to think appropriately in accordance with relevant and diverse sources of information. This study examines students' creative thinking skills during project-based physics learning In the subject of work and energy. The study was conducted by 36 students in their first year of high school. It was obtained using a simple random sampling technique in one of the high schools in Kotaagung, Tanggamus. The assessment tool utilized in the study was a project assessment tool supported by a worksheet on business and energy materials. It was a valid, reliable, and useful instrument. According to the research's findings, 36 students achieved the following levels of creativity in each indicator: 1) fluency (69%); 2) flexibility (80%); 3) originality (69%); and 4) elaboration (68%).

Use of WeChat to gratify information needs of the Chinese community in Portugal during the COVID-19 outbreak

[Objectives] This paper seeks to characterize the main themes of COVID-19-related posts delivered by WeChat official accounts targeting Portuguese news, and explore the information needs and preferences of their users. Furthermore, the current study set out to evaluate the influence of coronavirus situation in Portugal on the number of posts, view frequency and total engagement on WeChat platforms. [Methods] Following the selection of COVID-19-related posts published in 2021 by four official accounts targeting Portuguese news, Chi-square tests were conducted to quantitively describe the differences between the four developmental stages of the COVID-19 pandemic in Portugal. Negative binominal regression was employed to establish an association between category variables and count data. Regression analysis was used to explore the relationship between COVID-19 infection cases in Portugal and the metrics of selected posts. [Results] A total of 3292 COVID-19-related posts remained. The posts regarding the latest epidemic situation in Portugal, COVID-19 vaccination and the economic news were more prevalent. However, the predominant posts were not consistent with followers’ information demands, and the most in demand information did not generate more engagement. In addition, thematic categories were found to be significantly associated with view frequency and total engagement. Furthermore, a significant difference in thematic categories was identified among the four developmental stages, which in turn was found to be related to the overall engagement of posts. The COVID-19 official case counts in Portugal is predictive of post frequency, view frequency and total engagement. [Conclusions] The results of this study support the view that social media provided diverse information sources for migrants during the ongoing COVID-19 pandemic. However, it is evidently clear from the findings that communicators have to leverage the characteristics of social media, not only for delivering real-time news, but also to gratify public information needs and attract more users.

Digital Bicycling Planning: A Systematic Literature Review of Data-Driven Approaches

To increase the amount of bicycling as a mode of transport, many countries are developing placed based bicycling plans and strategies. However, this approach necessitates considering a fine-scale mapping of bicycling patterns and a detailed description of urban spaces. The rise of new data and technologies offers much promise to planners and researchers to access diverse and richer sources of information to optimise the bicycling network design. This review aims to comprehensively examine the role of data and technology in bicycling planning, historical changes in using data-driven approaches, and current domains in the existing body of research in bicycling planning from 1990 to 2021. For this, a systematic literature review has been conducted according to PRISMA framework. A total number of 1022 studies was analysed and synthesised with the VOS Viewer and CiteSpace platforms. Upon completing the review, we extracted the most-used datasets, tools, and methodologies. The results of the systematic review reveal three evolutionary phases in using data-driven approaches from 1990 to 1999, 2000 to 2009, and 2010 to 2021. In addition, we identified six knowledge domains in using data-driven approaches in bicycling planning that is (i) smart city, (ii) infrastructure, (iii) built environment, (iv) decision making, (v) people, and (vi) safety.

A Bayesian two-stage approach to integrate simulator data and expert judgment in human error probability estimation

With the ongoing efforts to collect new data for Human Reliability Analysis (HRA) (in particular, from nuclear power plant control room simulators), it becomes important that the coming data will be processed traceably, addressing its underlying variability, eventually in combination with expert judgment. In this direction, this work presents a two-stage Bayesian model to integrate expert-elicited probability estimates and empirical evidence from simulator data in the quantification of HEP values and of the associated variability distributions. The general aim is to provide a data aggregation framework able to mathematically combine diverse information sources throughout the HEP estimation process, in a systematic and reproducible way, contributing to strengthening the empirical basis of future HRA methods. The Bayesian model can be used to produce reference values and bounds for HRA methods as well as to improve the quality of plant-specific HEP estimates for use in Probabilistic Safety Assessment applications. The model is first verified with artificial data and then applied to quantify the HEP of human failure events from literature. Model sensitivity to biases in expert estimates is also investigated.

What Drives Innovation in Family Farms? The Roles of Socioemotional Wealth and Diverse Information Sources

Family business literature barely addresses family farms and their innovation behavior. Innovation can be key to mitigate typical threats family farms are faced with, e.g., global competition and climate change. This article investigates socioemotional wealth (SEW) and diversity of information sources as innovation drivers. It also explores the role of diversity of information sources as a moderator. A sample of 911 family farms was used for linear regression analysis. The SEW dimension identification of the family members with the farm positively affects the implementation of innovation measures because the stronger the family members identify with the business, the more important is it for them to preserve the identity endowments. Since innovation is a way to do that, strong identification will motivate family members to innovate. Diversity of information sources is also positively linked to innovation measures. However, it has a negative moderating effect on the relationship between identification and innovation measures. While diverse information sources seem to increase a family farm’s ability to innovate by supporting the opportunity identification and utilization, it can also mitigate the farms willingness to innovate when information is ambiguous. The study integrates knowledge from agricultural, innovation and family business research and contributes to a better understanding of the peculiar business type “family farms” and SEW as a multidimensional concept.

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

ABSTRACT A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.

A linked data approach to multi-scale energy modelling

Interactions between built infrastructure are complex and nuanced; changes to any one component can have disproportionate effects on the system as a whole. For instance, adoption of heat pumps or electric vehicles by a significant proportion of a population in an urban centre would place new demands on both electricity transmission and distribution networks. It is essential that planners – both national and local – can understand and share information about the resource demands that this type of change places on national and local infrastructure. Access to integrated sources of information – from building component to national levels – is key to supporting policy makers and decision takers. However, over time, information – and as a consequence, the software that manages it – has evolved into functional silos; this has, in turn, affected the definition of data exchange standards. This limits the ability of experts in functional areas to exchange data and implement broader decision support systems. This paper describes the use of linked data approaches to permit queries across large, diverse information sources to provide reasoning about complex questions at multiple scales. The methodology defines a central context to which various external sources can be attached. These distributed sources are, in themselves, registered in a central catalogue; they remain, however, under the control of their source organisations. In this way a large, extensible, interconnected network of distributed data describing, for example, a built environment or electricity transmission network; this network of data resources can be queried centrally to provide customised views of subsets of the data, and so provide a richer view than one source in isolation. The approach was applied to prepare and integrate information about Ireland’s transmission grid and administrative boundaries, along with domestic housing stock into a single data source. The resulting data network is queried by a scenario exploration tool. This tool successfully allows analysis, at a national level by economists, of the effects of the adoption of new technologies on the national grid of Ireland.

Developing Students’ Multimodal Communicative Competence Through Multiliteracies Pedagogy

With the rise of globalization, digitization, and the emergence of the fourth industrial revolution, meaning-making modes are now increasingly multimodal in which written linguistics modes of meaning interface with oral, visual, audio, gestural, tactile, and spatial patterns of meaning. In the EFL context, this situation unavoidably urges changes in the goal of English language teaching to include multimodal communicative competence. In response to such challenges, multiliteracies pedagogy offers plausible solutions. In a multiliteracies classroom, students are supported through a pedagogy that provides them with ample opportunities to develop their knowledge and skills to multimodally examine diverse information sources and content, critically explore the information, and become active meaning-makers and effective communicators. This study aims at investigating the implementation of multiliteracies pedagogy in English language teaching to foster students’ multimodal communicative competence. This is a qualitative case study involving twenty students of the English language department of IAIN Curup. The data of the study are gained through observation, interviews, and students’ artefacts. The data collected from these instruments were examined qualitatively using thematic and categorical coding. The result of this study demonstrates the enactment of multiliteracies pedagogy can develop five competences of multimodal communicative competence: linguistic competence, sociocultural competence, interactional competence, discourse competence, and multimodal competence. Meanwhile the other two competences, strategic competence and formulaic competence, are somewhat hindered due to lack of exposure to authentic English language.

Democracy, Authoritarianism and the Inflow of Foreign Direct Investments: A Critical Examination of a Debate and its Associated Consequences for Social Science Research

Are debates of value to social science research? To what extent has the debate on the preconditions and determinants of foreign direct investments (FDI) helped in the understanding of the totality of the forces, factors, and processes of international capitalism? How does the knowledge of the preconditions and determinants present themselves as the knowledge of the debate on how best to attract and stimulate FDI? To what degree/extent do regimes necessarily influence and shape the determinants and preconditions of FDI attractions and stimulations? How can the research on the preconditions and determinants of FDI be designed in such a way and manner that the purpose of the scholarship is best served? What are the associated consequences of the choice and technique of the research design? The article attempts a review of the significant initiatives that give meaning to the debate on the affinities of FDI to either democracy or authoritarianism and presents the debate within the intellectual foundation stones of the understanding and grasp of international political economy. The method of data collection is qualitative and scattered in the diverse sources of information on the subject of study. The objective is to contribute meaningfully to understanding the debate and chart future research directions. The study finds out that while the debate on the subject matter is scintillating, it is, however, convoluted by three interrelated failures and shortcomings: (i) lack of clear specification of the research period, (ii) lack of detailed examination of the domestic economy of study, and (iii) lack of comparative study of the periods, which altogether compound the likelihood of in-depth knowledge and generalization, the ultimate goal of academic debates.

Modulation of brain activity by psycholinguistic information during naturalistic speech comprehension and production

Language processing requires the integration of diverse sources of information across multiple levels of processing. A range of psycholinguistic properties have been documented in previous studies as having influence on brain activation during language processing. However, most of those studies have used factorial designs to probe the effect of one or two individual properties using highly controlled stimuli and experimental paradigms. Little is known about the neural correlates of psycholinguistic properties in more naturalistic discourse, especially during language production. The aim of our study is to explore the above issues in a rich fMRI dataset in which participants both listened to recorded passages of discourse and produced their own narrative discourse in response to prompts. Specifically, we measured 13 psycholinguistic properties of the discourse comprehended or produced by the participants, and we used principal components analysis (PCA) to address covariation in these properties and extract a smaller set of latent language characteristics. These latent components indexed vocabulary complexity, sensory-motor and emotional language content, discourse coherence and speech quantity. A parametric approach was adopted to study the effects of these psycholinguistic variables on brain activation during comprehension and production. We found that the pattern of effects across the cortex was somewhat convergent across comprehension and production. However, the degree of convergence varied across language properties, being strongest for the component indexing sensory-motor language content. We report the full, unthresholded effect maps for each psycholinguistic variable, as well as mapping how these effects change along a large-scale cortical gradient of brain function. We believe that our findings provide a valuable starting point for future, confirmatory studies of discourse processing.

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, we argue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.

The Relationship Between Information Sources, Health Literacy, and COVID-19 Knowledge in the COVID-19 Infodemic: Cross-sectional Online Study in Japan.

BackgroundThe COVID-19 pandemic has caused not only a disease epidemic but also an infodemic. Due to the increased use of the internet and social media, along with the development of communication technology, information has spread faster and farther during the COVID-19 infodemic. Moreover, the increased choice of information sources has made it more difficult to make sound decisions regarding information. Although social media is the most common source of misinformation, other forms of media can also spread misinformation. However, the media sources used by people with high health literacy and COVID-19 knowledge to obtain information are unclear. Furthermore, the association between the use of multiple information sources and health literacy or COVID-19 knowledge is ill-defined.ObjectiveThis study aims to examine the following 3 aspects regarding the COVID-19 infodemic: (1) the relationship between health literacy, COVID-19 knowledge, and the number of information sources used; (2) the impact of media use on health literacy; and (3) the impact of media use on COVID-19 knowledge.MethodsAn online cross-sectional study was conducted in November 2021. Participants were 477 individuals aged 20-69 years. After obtaining consent to participate in the study, participants were asked about sociodemographic indicators, sources of health-related information, health literacy, and COVID-19 knowledge. Sources of health-related information were categorized into 4 types: mass media, digital media, social media, and face-to-face communication. The Spearman rank correlation test was conducted to determine the relationship between health literacy, the number of correct answers to COVID-19 knowledge, and the number of information sources used. Multiple regression analysis was conducted with health literacy and the number of correct answers as dependent variables, the 4 media types as independent variables, and age and sex as adjustment variables.ResultsMass media was the most frequently used source of information, followed by digital media, face-to-face communication, and social media. Social media use was significantly higher among individuals aged 20-29 years than among other age groups. Significant positive correlations were found between health literacy, the number of positive responses to COVID-19 knowledge, and the number of information sources used. Multiple linear regression analysis showed that health literacy is associated with access to information from digital media and face-to-face communication. Additionally, COVID-19 knowledge was associated with access to information from mass media, digital media, and face-to-face communication.ConclusionsHealth literacy and COVID-19 knowledge could be improved using diverse information sources, especially by providing opportunities to use digital media and face-to-face communication. Furthermore, it may be important to improve health literacy and provide accurate knowledge about COVID-19 to young adults.