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This scoping review article examined research on information behavior in communities over the past two decades (2000–2023). The review aims to uncover the characteristics and types of communities studied, the featured information behaviors, and the research methods employed.

The PRISMA-ScR guidelines were followed to conduct this review. Five databases were selected to search for relevant empirical research. A total of 57 studies met the inclusion criteria for review. Thematic synthesis was used to analyze the multidimensional findings of included studies.

A steady increase in the number of articles is evident in the past two decades. The review suggests that information behavior in community studies involved collaboration from other disciplines, such as public health and business management. More than half of the communities studied are virtual communities (56.1%), followed by communities of identity, professional communities and support communities, communities of interest, geographic communities, and academic communities. There are overlaps among these categories. Information sharing (63.2%) and information seeking (57.9%) were the most studied behavior of communities, followed by information use, information needs, and information judgment. Questionnaires (38.6%) and interviews (35.1%) were the most commonly used data collection techniques in studying information behavior in communities. It is noteworthy that eleven (19.3%) mentioned utilizing community-engaged approaches.

This is the first scoping review to explore the intersecting constructs of community research and information behavior studies. We call for further research to understand the contextual factors that shape the community’s information environments and to increase awareness of the partnership between communities and researchers.

To gain a deeper understanding of users’ health information adoption and to promote the effectiveness of health information spread in the context of online limited information, this paper aims to examine how the information-motivation-behavioural (IMB) skills model can be used to organize online health information by experimenting how different IMB elements (information, motivation and behavioural skills) affect users’ intention to adopt health information.

The authors conducted an experiment with 48 participants who received health articles with various combinations and sequences of IMB elements, analysing the impact on information adoption intention to share and practice. The authors also examined the mediation effect of information usefulness and the moderating effect of perceived health status.

The authors found that: users’ adoption intention of information was influenced by the order of used IMB elements, not the number of elements used; users were more likely to adopt information that started with behavioural skills rather than the model-prescribed IMB sequence; and perceived usefulness mediated the relationship between IMB elements and users’ adoption intention, which means users with different levels of health status all pay more attention to information usefulness and practicability.

The study contributes to research on health communication by showing how the IMB model can be applied online to enhance the effectiveness of health information dissemination. It can also help online health communities arrange more effective and engaging health messages to promote users’ willingness to adopt.

This study examines the temporal dynamics shaping our understanding of search in education and the role language plays in legitimising these dynamics. It critiques the way online search is discursively constructed using home-education as a case study, and problematises how particular discourses are privileged, whom this privileging serves, as well as the likely consequences.

The study employs Faircloughian Critical Discourse Analysis (CDA) as its methodological framework. Search and discursive practices were recorded during observations, search-tasks and interviews with five Australian home-educating families. Discursive features from the Google interface were also analysed.

A discursive privileging of hasty search practices was identified. This was found alongside largely ineffectual search, but participants continued to discursively represent search as fast and easy. The study highlights the complex co-option of discourses surrounding online search that privilege particular temporal and commercial landscapes.

This study contributes new knowledge regarding time as a context for understanding search behaviours, locating the perception of temporal scarcity in education within broader discursive and social structures. To date, no studies are found which investigate the temporal factors surrounding search in home-education. Increasing global reliance upon online search means the findings have broad significance, as does the proliferation of home-education induced by COVID-19. Additionally, while much work problematises the power search engines wield to privilege certain discourses, few investigate the day-to-day discursive practices of searchers affording Google and others this power.

This study aims to explore the Facebook communication strategies of Spanish hospitals during health emergencies, focusing on their role in crisis management and public information dissemination.

Automatic topic modelling and deep learning sentiment analysis were applied to analyse 151,738 posts from 274 hospital Facebook pages (March 2020–Feb 2022). Regression analyses were used to explore the relationships between topics, sentiment scores and hospital characteristics.

The analysis revealed nine main topics, with the three most prevalent related to COVID-19: vaccine information, security measures and situational updates. This indicates that Spanish hospitals significantly relied on Facebook to manage the emergency. The communication strategies dynamically adapted to the intensity of the pandemic and varied across hospital types. Sentiment analysis showed a negative tone for posts about security measures and situational updates. These findings align with the Agenda-Setting Theory, suggesting that hospitals influenced public discourse. Vaccine information posts were more positive, resonating with the Uses and Gratifications Theory by fulfilling the audience’s need for reassurance and guidance.

Using replicable machine learning techniques, this study elucidates the communication strategies employed by Spanish hospitals to manage healthcare emergencies, such as the COVID-19 pandemic. It highlights factors that potentially influence these strategies and provides theoretical justifications for them. The variation in communication strategies on Facebook among different hospital categories underscores the imperative for stricter guidelines and regulations to guarantee consistent and reliable communication during emergencies. This research provides valuable insights for practitioners and policymakers aimed at developing effective health communication strategies on social media.

This paper aims to explore the underlying mechanism whereby information-induced stress, resulting from the burdensome nature, questionable information reliability, misleading content and diffuse characteristics of infodemic pressure, impacts individuals’ online information-related behavior and health-related preventive behavior.

We conducted a cross-sectional survey organized with social media users during the post-pandemic period. Based on the 342 valid responses, structural equation modeling was employed to validate the research model.

The results substantiate our multidimensional view of infodemic stress, which encompasses dimensions including information overload, uncertainty, diffusivity and insecurity. We found that the infodemic stress contributes to pandemic fatigue, then engenders both negative information behavior and reduced engagement in preventive behavior. Furthermore, infodemic stress has also been found to have a direct positive effect on individuals’ engagement in preventive behavior.

This study introduces the concept of “infodemic stress” and presents a comprehensive framework to capture its various dimensions. This study offers novel insights into the psychological and behavioral repercussions of the infodemic stress transmitted through social media.

Public service announcements (PSAs) have been shown to be effective instruments that raise awareness, educate society, and change behaviors and attitudes. Many governments and organizations have utilized PSAs on social media to promote online safety among children and youth. However, we have limited understanding of the range of topics that these PSAs address and how they present their content to audiences. This study provides an inventory of the types of online safety topics that current PSAs address and a catalogue of the types of persuasive features employed by PSAs.

A content analysis of 220 YouTube PSA videos on online safety was conducted. Various topics under the umbrella of online safety were identified. Guided by the prospect theory and exemplification theory, different persuasive features employed in the PSAs were sought.

The findings highlight that the primary focus of these PSAs is on online safety behaviors and general instructions on online hygiene. Interestingly, nearly half of the videos employ a neutral frame, while a significant portion provides no evidential support. Additionally, video length was associated with the number of views and likes it gathered but not with the number of comments.

The inventory of PSAs can help researchers, practitioners, and policymakers better understand the type of content being produced and disseminated online as well as identify topics that are either over or under-represented. Further, the catalogue of the types of persuasive features employed by PSAs would be helpful in guiding research, practice, and policymaking in the context of creating effective online safety videos.

The mounting health misinformation on social media triggers heated discussions about how to address it. Anchored by the influence of presumed influence (IPI) model, this study investigates the underlying process of intentions to combat health misinformation. Specifically, we analyzed how presumed exposure of others and presumed influence on others affect intentions to practice pre-emptive and reactive misinformation countering strategies.

Covariance-based structural equation modeling based on survey data from 690 Chinese participants was performed using the “lavaan” package in R to examine the proposed mechanism.

Personal attention to health information on social media is positively associated with presumed others’ attention to the same information, which, in turn, is related to an increased perception of health misinformation’s influence on others. The presumed influence is further positively tied to two pre-emptive countermeasures (i.e. support for media literacy interventions and institutional verification intention) and one reactive countermeasure (i.e. misinformation correction intention). However, the relationship between presumed influence and support for governmental restrictions, as another reactive countering method, is not significant.

This study supplements the misinformation countering literature by examining IPI’s tenability in explaining why individuals engage in combating misinformation. Both pre-emptive and reactive strategies were considered, enabling a panoramic view of the motivators of misinformation countering compared to previous studies. Our findings also inform the necessity of adopting a context-specific perspective and crafting other-oriented messages to motivate users’ initiative in implementing corrective actions.

The aim of this study was to investigate consumer preferences and profile their food-related lifestyles, as well as to identify consumer groups with similar attitudes/behaviours in the Euro-Mediterranean fruit and vegetable market.

A structured questionnaire was designed drawing from the food related lifestyles instrument and including other factors relevant to fruit and vegetable consumer preferences. The data were collected in an online survey with 925 participants in France, Greece, and Italy. A principal component analysis was conducted to interpret and examine consumers' fruit and vegetable related lifestyles. In addition, a cluster analysis was performed to identify different consumer segments, based on the core dimensions of the food-related lifestyle approach.

In each country, three primary consumer segments were distinguished. Health-conscious individuals were predominant in France and Greece, while quality-conscious consumers were prevalent in Italy. These classifications were determined considering various factors such as purchase motivation, perception of product quality, health concerns, environmental certifications, and price sensitivity.

The food-related lifestyle approach has been adapted instrument to create a customised survey instrument specifically designed to capture the intricacies of fruit and vegetable consumer preferences and priorities in three Euro-Mediterranean Countries.

The paper reports on developments in the Information Seeking and Communication Model (ISCM) since it was first presented in 2013, focussing on how the model, derived by explicitly drawing on previous models, frameworks, and concepts, has been applied, adapted and extended. This paper summarises the reception of the ISCM, and describes two new applications in different contexts.

A literature review details applications of, comments on and critiques of, the ISCM since its introduction. Two exploratory studies of health information seeking behaviour show the application of the ISCM in novel contexts.

The literature cited here, and the two studies outlined show the applicability and extendibility of the ISCM, as well as its value in helping to capture the holistic context of information seeking, the interplay between providers and seekers of information, including fluidity of provider/seeker roles, and emotional dimensions of information seeking and use.

The paper responds to the call by Case and Given (2016) for further empirical testing of the ISCM and similar conceptual models. It shows the range of applicability of this model, and models drawn from it, and makes recommendations for further development.

The study investigates the impact of various factors on the number of active investors in digital health startups. Through nine hypotheses, we examine the influence of metrics such as patents, online presence, financial aspects and company valuation on investor interest. The results reveal positive associations between these metrics and investor numbers, highlighting their role in signaling strength and attracting investment. This research enhances the understanding of investor valuation in digital health startups, emphasizing the importance of credible signals for building trust and securing funding. However, we acknowledge limitations in data analysis methods and suggest future research to explore industry signals, longitudinal trends and failed startups for comprehensive insights.

This study delves into the design methodology and approach, aiming to fill gaps in understanding investor roles in valuing digital health ventures. We focus on deciphering factors driving valuations for these startups to secure growth financing. Using signaling theory, we investigate how entrepreneurs communicate their latent strengths to bridge information gaps, aiding investment decisions. We analyze a sample of 482 healthcare startups from the Pitchbook database using Poisson regression in SPSS.

This research sheds light on the factors driving investor interest in digital health startups. Despite the critical role of entrepreneurs in patient care innovations, the relationship between investor characteristics and funding for digital health technologies still needs exploration. We examine factors influencing investor valuation in healthcare startups and identify patents, social followers and financial disclosures as pivotal elements shaping investor interest. The findings show that all factors for active investors are significant for all variables except similar unique visitors.

These results significantly enhance our understanding of investor decision-making in digital health startups. They confirm the importance of various signals, like patent activity, online presence and financial performance, in attracting investor attention. We utilize unique data sources, offering insights into investors' behavior across different funding stages. In conclusion, these findings underscore investors' crucial role in the growth and funding of healthcare tech startups, emphasizing the need for robust signals to attract investment.