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Have stakeholders increased their influence over firms' behavior in the digital age? In a recent article in this journal, Barnett, Henriques, and Husted (2020) suggested that, contrary to expectations, digital media has limited the influence of stakeholders on firms as information overload and heterogeneous demands make it difficult for stakeholders to synchronize their interests and achieve one coherent and persistent voice. In this exchange we contend that, while the evolution of digital media has made it harder for stakeholders to achieve synchronicity, it has made it easier for them to achieve what we refer to as heterogeneous convergence. Heterogeneous convergence entails the progressive coming together of heterogeneous stakeholders around a broader demand for change while maintaining different identities and views. We argue that, through heterogeneous convergence, secondary stakeholders can exert strong pressure over firms. [ABSTRACT FROM AUTHOR]

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When parents are expected to play a significant role in the management of their children's health perioperatively, information overload for parents could have particularly detrimental consequences. Our study investigated information communication and overload in 380 parents of children undergoing any elective surgical procedure at our institution. Participants completed an online questionnaire and were asked to respond to a newly designed Information Overload scale based on a modified 5 item Cancer Information Overload Scale and an 8-item atrial fibrillation information overload scale. Nineteen respondents (5%) identified as Aboriginal or Torres Strait Islander. More than a quarter of respondents (n = 102, 27%) primarily spoke a language other than English at home. 56% of respondents (n = 211) indicated that neither themselves nor any of their children had undergone a similar surgery in the past. Most respondents disagreed or strongly disagreed with the majority of the 5-item Information Overload scale statements. University undergraduates had lower total information overload score on average (-1.63, p = 0.002). People who spoke a language other than English had higher total score on average (0.98, p<0.001). Semi-structured qualitative interviews including the BRIEF health literacy screening tool were conducted with 24 parents. 23 interviewees scored 17–20 points in the BRIEF health literacy screening tool, indicating they were able to read and comprehend patient education materials. Overall, parents were satisfied with the amount of information that they received. Very rarely did any parent complain of 'information overload'. Our results show that parents of children undergoing elective paediatric surgery are not suffering from information overload in general, but they do want more information on immediate and late postoperative recovery. [ABSTRACT FROM AUTHOR]

The editorial discusses the exponential growth in scientific research publications, highlighting the challenges and opportunities it presents. The surge in publications is attributed to factors like the expansion of the global research community and the pressure to publish in academia. While the increase in research output can lead to more knowledge and discoveries, it also poses challenges such as information overload, concerns about research quality, and the need for new approaches to research evaluation. The editorial emphasizes the importance of adapting to the evolving research landscape to ensure genuine progress and quality academic research. [Extracted from the article]

In the UK, minority ethnic groups were reported to face the greatest risk from COVID-19. Yet, little is known about their perception and behaviour towards COVID-19 campaigns. This study used 35 semi-structured, in-depth interviews with minority ethnic adults aged 60 or over in England and Wales to explore their motivations for complying with or disregarding official guidance and their evaluations of various campaign content and channels used early in the pandemic. Using Template Analysis, key themes in the interviewees' discourse were identified, which were subsequently analysed using the Theory of Planned Behaviour (TPB). This study found that the predominant motivations for non-compliant behaviour included fatigue from information overload, government- and socially-induced distrust, as well as self-reported personality traits. In contrast, compliant individuals emphasised community-based obligations and their trust in official communications. Although participants recognised the pandemic as a challenging time, they were generally unsatisfied with the credibility, consistency, and accessibility of public health messaging. This investigation contributes to health communications studies by adopting a qualitative approach and providing insights into an under-researched demographic. [ABSTRACT FROM AUTHOR]

While live commerce provides consumers with a new shopping experience, it also leads them to experience shopping failures and to develop a self-protection mechanism to prevent wrong purchases. To address this issue, merchants have attempted to explore new marketing methods for live commerce, giving rise to an offense and defense game between streamers and consumers. In this study, we sought to confirm the effectiveness of consumer protection mechanisms and the impact of streamers' information overload marketing strategy in live commerce. Accordingly, we constructed a hypothetical model based on protection motive theory and information overload theory. In addition, we analyzed the data from the simulated live streaming marketing on seven hundred people through partial least squares structural equation modeling. The results indicate that product utilitarian value uncertainty, consumers' experiential efficacy, and response costs, which are the main factors in the formation of consumer protection mechanisms, influence consumers' intention to stop their purchases. Streamers can circumvent consumer self-protection mechanisms through information overload marketing by reducing utilitarian value uncertainty and consumers' experiential efficacy and increasing consumers' response costs. However, consumers would be able to rebuild their self-protection mechanism through consumer resilience, which moderates the effects of information overload. This study's results provide important theoretical perspectives and new ideas for formulating marketing strategies for live commerce. [ABSTRACT FROM AUTHOR]

Background: In the information age of health care, nurses often face information overload, leading to negative emotions, e.g., anxiety that may impede the adoption of evidence-based practice and clinical decision-making process. Nurses with higher digital health literacy can effectively process and manage information. Despite this, no research has explored the relationship between information anxiety, digital health literacy, and core competency among nurses. Therefore, this study aims to investigate the mediating effects of digital health literacy on information anxiety and core competency among nurses. Methods: From July to October 2023, the data for this cross-sectional study were collected. The study surveyed a total of 608 nurses from three tertiary hospitals in Fujian Province, and the survey instruments included a sociodemographic information questionnaire, Chinese revision version of the Digital Health Literacy Instrument (CR-DHLI), Information Anxiety Scale (IAS), and Competency Inventory for Registered Nurses (CIRN). Descriptive statistics and Pearson correlation analysis were conducted using SPSS 29.0, and the mediating effect of digital health literacy was examined using Mplus. Results: The mean score of nurses' information anxiety, digital health literacy, and core competency was 3.03 ± 0.91, 2.46 ± 0.56, 2.72 ± 0.88, respectively. And the mediation model of information anxiety on core competency for nurses showed a good model fit index (χ²/df = 2.207, CFI = 0.985, TLI = 0.982, RMSEA = 0.045, SRMR = 0.035). Digital health literacy was positively correlated with nurses' core competency but negatively correlated with information anxiety. The results of path analysis revealed that information anxiety had negative and significant direct effects on NCC (β = -0.119, P = 0.004) and DHL (β = -0.297, P < 0.001). DHL had a positive effect on NCC (β = 0.306, P < 0.001). Digital health literacy played a partial mediating role, accounting for 43.54% of the relationship between information anxiety and nurses' core competency. Conclusions: Information anxiety among nurses was at relatively high levels, which had a negative impact on the core competency of nurses. This issue requires attention from nursing managers. The mediating role of digital health literacy in the relationship between information anxiety and core competency among nurses has been established. Nursing managers should strengthen the evaluation of nurses' DHL and devise effective support strategies to enhance DHL, thus improving the core competence of nurses in information age. [ABSTRACT FROM AUTHOR]

Background and aim: Continuing Medical Education (CME) is crucial for physicians to stay current in the rapidly advancing field of medicine. A WhatsApp (WA) based community of physicians was initiated in 2016 'WhatsApp CME India Group' to facilitate learning, knowledge sharing, and discussion among physicians. Due to participant size constraints of the technology, it evolved into seven distinct WA groups, overseen by a central administrator. A survey undertaken in the group's 7th year aimed to evaluate its effectiveness in achieving its goals, measure participant engagement, and uncover the primary factors driving its usage. Methods: The survey questionnaire was distributed to 3500 members across the 07 WhatsApp CME groups for voluntary participation. Data collected was analysed using SPSS version 24.0 (SPSS Inc., Chicago, IL, USA) and reported using descriptive statistics. Results: Among the 581 survey respondents, the study found 43% of physicians use the WhatsApp academic groups for CME content, with 32% accessing their group over four times daily. The primary motivation for 77% was to discuss challenging cases and to gain knowledge updates from fellow physician (70%). Medical websites (57%), referral books (49%), and Google (43%) were other significant resources. Every participant (100%) found the WhatsApp CME India group the most beneficial resource for daily medical science updates. A significant portion (57%) of the respondents found the group valuable for real-time information exchange. Over 78% stated it kept them current with knowledge and guidelines. Notably, 94% viewed WhatsApp CME as complementary to physical conferences, not a replacement. The post-conference/webinar summaries were appreciated by 81% participants. Case discussions (31%) and update posts from fellow physicians sharing their insights and learnings (24%) were noted as activities of great academic interest. Conclusion: This study underscores the potential of digital platforms like WhatsApp in supplementing CME in India and potentially other comparable settings. The blend of digital and traditional resources suggests a balanced learning approach. While real-time engagement is a strength, challenges like information overload and privacy concerns require careful management. Striking a balance is crucial for ensuring content quality, structured discussions and privacy. As the digital age advances, professionals must critically assess shared information on these platforms to guarantee evidence-based and reliable knowledge dissemination. Trial registration: ARK 001. [ABSTRACT FROM AUTHOR]

This study examines the information-seeking behavior of the clergy using parish pastors in the Redeemed Christian Church of God (RCCG) in Agbarho community in Delta State as a case study. The study seeks to identify the information needs, the sources of information, the purpose of the use of information, and the challenges to information seeking among RCCG parish pastors in Agbarho. A descriptive survey research design was adopted for the study. The total population for the study comprises 44 parish pastors of the RCCG in two Zones of RCCG Delta Province 4 in Agbarho Community. The entire population was used as a sample using the total enumeration sampling technique. The instrument used for data collection was a structured questionnaire. The data collected for this study were analyzed using simple percentage/frequency counts. The study found that RCCG pastors in Agbarho need and use the information for several issues such as sermon preparation, personal and church growth, church administration, evangelism, conflict resolution, meetings/programs, and personal/members' welfare among others. They get the information they need from formal sources such as the Bible, books including all RCCG publications such as Open Heavens and Sunday school manuals, magazines (RCCG Bulletins), etc. Also, RCCG pastors in Agbarho community, Delta State, Nigeria also gets information from informal sources such as senior pastors, colleagues, friends in the ministry, church members, and family among others. In their effort to seek information, RCCG pastors in Agbarho community, Delta State, Nigeria faces some challenges such as limited financial resources, lack of access to ICT facilities, language barriers, information overload, time factor, and poor awareness of information sources. The study recommends that the RCCG authorities in conjunction with other churches and community leaders should establish and equip a library in every community where they are present as a way of advancing their Christian Social Responsibilities (CSR) as this will allow their pastors to have access to free and timely information among others. [ABSTRACT FROM AUTHOR]

In recent years, online education platforms have seen rapid growth, attracting an increasing number of students to digital learning environments. In online education, learners can choose learning content and plan their own learning path more freely. Although the online education platform gives learners a high degree of freedom, it reduces the learning guidance for learners, which leads to problems such as "information overload" and "knowledge loss". The main manifestation is that learners don't know how to plan their learning path, resulting in reduced learning efficiency and poor learning effects. To address these challenges, this paper proposes a learning path recommendation algorithm based on reinforcement learning called RLLP. The RLLP model takes into account the learner's learning goals, knowledge level, and the relationships between knowledge points. Simultaneously, it also considers the smoothness of the learning path and the learner's engagement, aiming to recommend efficient and sensible learning paths to learners. Extensive experimental results demonstrate the effectiveness of RLLP model. [ABSTRACT FROM AUTHOR]

The inclusion of usability heuristics into the metaverse is aimed at solving the unique issues raised by virtual reality (VR), augmented reality (AR), and mixed reality (MR) environments. This research points out the usability challenges of metaverse user interfaces (UIs), such as information overloading, complex navigation, and the need for intuitive control mechanisms in these immersive spaces. By adapting the existing usability models to suit the metaverse context, this study presents a detailed list of heuristics and sub-heuristics that are designed to improve the overall usability of metaverse UIs. These heuristics are essential when it comes to creating user-friendly, inclusive, and captivating virtual environments (VEs) that take care of the needs of three-dimensional interactions, social dynamics demands, and integration with digital–physical worlds. It should be noted that these heuristics have to keep up with new technological advancements, as well as changing expectations from users, hence ensuring a positive user experience (UX) within the metaverse. [ABSTRACT FROM AUTHOR]

Public policy initiatives demand transparency of information privacy practices (TIPP) since the 1970s, at least, when a first version of the fair information practice principles (FIPP) was published. Decades later, information system (IS) providers are still struggling to meet this demand. Apparently, there is a tension between socially desirable guardrails for privacy practices (e.g., allowing for anonymity) and economic value creation (e.g., exploiting personalization surplus). To inform IS designs that can change such avaricious states to the benefit of consumers, we present a theoretical foundation (TIPP theory) explaining and prescribing what needs to be built to establish TIPP in the real world. On the one hand, TIPP theory explains why IS providers, who have privacy practices to hide, should not go beyond IS designs that meet legal demands. More importantly, TIPP theory explains and prescribes what IS providers need to build to actually establish TIPP and be able to differentiate from competitors by doing so. TIPP theory will be useful for all involved parties: IS providers are enabled to avoid privacy notice fallacies, public policy makers can give more details in their related guidance and laws published, and consumers can, ultimately, interact with IS that actually establish TIPP. The rising diffusion of information systems (IS) throughout society poses an increasingly serious threat to privacy as a social value. One approach to alleviating this threat is to establish transparency of information privacy practices (TIPP) so that consumers can better understand how their information is processed. However, the design of transparency artifacts (e.g., privacy notices) has clearly not followed this approach, given the ever-increasing volume of information processing. Hence, consumers face a situation where they cannot see the "forest for the trees" when aiming to ascertain whether information processing meets their privacy expectations. A key problem is that overly comprehensive information presentation results in information overload and is thus counterproductive for establishing TIPP. We depart from the extant design logic of transparency artifacts and develop a theoretical foundation (TIPP theory) for transparency artifact designs useful for establishing TIPP from the perspective of privacy as a social value. We present TIPP theory in two parts to capture the sociotechnical interplay. The first part translates abstract knowledge on the IS artifact and privacy into a description of social subsystems of transparency artifacts, and the second part conveys prescriptive design knowledge in form of a corresponding IS design theory. TIPP theory establishes a bridge from the complexity of the privacy concept to a metadesign for transparency artifacts that is useful to establish TIPP in any IS. In essence, transparency artifacts must accomplish more than offering comprehensive information; they must also be adaptive to the current information needs of consumers. History: Suprateek Sarker, Senior Editor; Joe Nandhakumar, Associate Editor. Funding: This work was supported by the Helmholtz Association of German Research Centers and KASTEL Security Research Labs. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the ‘information noise’ phenomenon in the context of modern cinema. As a comprehensive art form, cinema in the current era confronts this multifaceted issue. This study critically analyses the concept of information noise scrutinizing its manifestations across diverse cultural and social contexts. The author highlights various dimensions of information noise and assesses its impact on the perception and interpretation of cinematic productions, encompassing aspects like historical authenticity, value stereotyping, the preservation or substitution of core ideas, censorship influence, narrative complexity, and verbal imperfections. The article also addresses the current state of scientific development of the topic under study. It underscores people’s limitations in processing information and how perception is influenced by their socio-cultural origins. The necessity for a standardized approach to identify characteristics of information noise in film production is also emphasized.

Abstract Background In the information age of health care, nurses often face information overload, leading to negative emotions, e.g., anxiety that may impede the adoption of evidence-based practice and clinical decision-making process. Nurses with higher digital health literacy can effectively process and manage information. Despite this, no research has explored the relationship between information anxiety, digital health literacy, and core competency among nurses. Therefore, this study aims to investigate the mediating effects of digital health literacy on information anxiety and core competency among nurses. Methods From July to October 2023, the data for this cross-sectional study were collected. The study surveyed a total of 608 nurses from three tertiary hospitals in Fujian Province, and the survey instruments included a sociodemographic information questionnaire, Chinese revision version of the Digital Health Literacy Instrument (CR-DHLI), Information Anxiety Scale (IAS), and Competency Inventory for Registered Nurses (CIRN). Descriptive statistics and Pearson correlation analysis were conducted using SPSS 29.0, and the mediating effect of digital health literacy was examined using Mplus. Results The mean score of nurses’ information anxiety, digital health literacy, and core competency was 3.03 ± 0.91, 2.46 ± 0.56, 2.72 ± 0.88, respectively. And the mediation model of information anxiety on core competency for nurses showed a good model fit index (χ²/df = 2.207, CFI = 0.985, TLI = 0.982, RMSEA = 0.045, SRMR = 0.035). Digital health literacy was positively correlated with nurses’ core competency but negatively correlated with information anxiety. The results of path analysis revealed that information anxiety had negative and significant direct effects on NCC (β = -0.119, P = 0.004) and DHL (β = -0.297, P < 0.001). DHL had a positive effect on NCC (β = 0.306, P < 0.001). Digital health literacy played a partial mediating role, accounting for 43.54% of the relationship between information anxiety and nurses’ core competency. Conclusions Information anxiety among nurses was at relatively high levels, which had a negative impact on the core competency of nurses. This issue requires attention from nursing managers. The mediating role of digital health literacy in the relationship between information anxiety and core competency among nurses has been established. Nursing managers should strengthen the evaluation of nurses’ DHL and devise effective support strategies to enhance DHL, thus improving the core competence of nurses in information age.

Introduction: With the boom in social media, many people spend a lot of time on these platforms. Among them, some developed negative emotions, such as fatigue, depression, or disinterest in communicating, and used social media temporarily or permanently. Therefore, this study aims to explore the antecedents of social media fatigue, including social media helpfulness, social media self-efficacy, online subjective well-being, social comparison, compulsive social media use, privacy concerns, fear of missing out, and information overload, and to further discuss the determinants of social media fatigue on social anxiety and lurking. Methods: An online questionnaire was distributed to social media users, and 659 valid samples were obtained with the help of a purposive sampling strategy. The data was analyzed by the partial least square (PLS) method. Results: The study found that social media self-efficacy had a significant negative effect on social media fatigue; compulsive social media use, fear of missing out, and information overload had a significant positive effect on social media fatigue; and social media fatigue had a significant positive effect on social anxiety and lurking. Discussion: The research results can be used as a reference for social media marketers and internet service providers in developing business strategies. [ABSTRACT FROM AUTHOR]

The fast growth of the web generates a significant amount of heterogeneous information such as images, text, audio, and video through various applications. These applications use different layouts to represent significant information. The layouts of table information are overloaded with anomalies that have given rise to intensive research into the semantification of web content and organizing tabular data for knowledge sharing and acquisition. Moreover, there are many anomalies present in tabular layouts that lead to the lack of semantic representation in tabular form and new challenges in data modeling. In this paper, we have discussed the various anomalies present in the tabular data that pertain to ontology learning and population tasks and provide the semantification of tabular data. To complete this task, (1) we provide the list of anomalies that pertain to semantification and provide the resolution to anomalies along with the semantification of tabular data, and (2) we have established the algorithm to interpret the table structure into a formal representation to analyze anomalies and provide the resolution. Furthermore, the proposed approach has been compared with existing approaches using ontology elements, the ability to resolve the anomalies, and the time complexity of the ontology population. [ABSTRACT FROM AUTHOR]

Patients consistently rate cancer-related fatigue (CrF) as the most prevalent and debilitating symptom. CrF is an important but often neglected patient concern, partly due to barriers to implementing evidence-based interventions. This study explored what an ideal intervention for CrF would look like from the perspectives of different stakeholders and the barriers to its implementation. Three participant populations were recruited: healthcare providers (HCPs; n = 32), community support providers (CSPs; n = 14), and cancer patients (n = 16). Data were collected via nine focus groups and four semi-structured interviews. Data were coded into themes using content analysis. Two main themes emerged around addressing CrF: "It takes a village" and "This will not be easy". Participants discussed an intervention for CrF could be anywhere, offered by anyone and everyone, and provided early and frequently throughout the cancer experience and could include peer support, psychoeducation, physical activity, mind–body interventions, and interdisciplinary care. Patients, HCPs, and CSPs described several potential barriers to implementation, including patient barriers (i.e., patient variability, accessibility, online literacy, and overload of information) and systems barriers (i.e., costs, lack of HCP knowledge, system insufficiency, and time). As CrF is a common post-treatment symptom, it is imperative to offer patients adequate support to manage CrF. This study lays the groundwork for the implementation of a patient-centered intervention for CrF in Canada and possibly elsewhere. [ABSTRACT FROM AUTHOR]

The volume of flow demand in cyber-physical power systems (CPPSs) fluctuates unevenly across coupled networks and is susceptible to congestion or overload due to consumers' energy demand or extreme disasters. Therefore, considering the elasticity of real networks, communication links with excessive information flow do not immediately disconnect but have a certain degree of redundancy. This paper proposes a dynamic cascading failure iterating model based on the distribution of information flow overload in a communication network and power flow betweenness in the physical power grid. First, a nonlinear load capacity model of a communication network with overload and weighted edges is introduced, fully considering the three link states: normal, failure, and overload. Then, flow betweenness substitutes for branch flows in the physical power network, and power flow on failed lines is redistributed using the load capacity model, simplifying the calculations. Third, under the influence of coupling relations, a comprehensive model based on improved percolation theory is constructed, with attack strategies formulated to more accurately assess the coupled networks. Simulations on the IEEE-39 bus system demonstrate that considering the overload capacity of communication links on a small scale enhances the robustness of coupled networks. Furthermore, deliberate link attacks cause more rapid and extensive damage compared to random attacks. [ABSTRACT FROM AUTHOR]

Some leaders tend to use their intuition to think and make decisions more than others do. This individual difference (i.e., an intuitive cognitive style) may have important implications for the collectives of people they lead. Unfortunately, scholars lack a clear understanding of the conditions under which intuition is effective, especially in the context of large and complex organizational structures such as multiteam systems (MTSs). We argue that the effects of leaders' intuitive cognitive style on MTS performance depends on the amount of information load they face in their roles, as well as their level of introversion: a personality characteristic that is associated with an inward and reflective focus. Using a sample of 222 MTSs comprised of Captains in the United States Air Force, our study demonstrated that the intuitive cognitive style of leaders in high information load roles positively affected MTS performance, especially if they were high in introversion. In low information load roles, however, an intuitive cognitive style had a negative effect on MTS performance, especially for leaders low in introversion. We also argued and found that MTS coordination effectiveness played a key mediating role. Our study advances research in multiple areas of work, including intuition, MTSs, and leadership. [ABSTRACT FROM AUTHOR]

Over investigations contribute to escalating health costs driven by multiple factors including physician decisions, patient requests, information overload, technological advances, marketing, hospital management policies, insurance requirements and defensive practices. The reconciliation between knowledge and clinical wisdom while dealing with uncertainties in medicine is the primary way forward through this ethical maze. A case scenario illustrates what pediatricians need to reflect upon while facing decisions on rational investigations to maximize beneficence while being aware of economics of healthcare delivery. [ABSTRACT FROM AUTHOR]

This article develops a model showing how information reporters influence information load among decision makers and generate supplier-induced information demand (SID). The intra-corporate information-providing process is an expert market with information asymmetry. I show that information overload occurs as an SID and is the result of informational overconsumption deliberately caused by the supplying reporter. My analysis highlights that the information overload depends on the specificity of information. It also shows that the decision maker may face a hold-up situation in light of switching costs. The more specific the information needed, the higher the threat of information overload. The strategic content of information tempts reporting managers to overload the decision maker for the purpose of increasing their reporting transfer price and to discourage the decision maker from getting the information from another reporting manager. Although the decision maker knows a part of the information demand, information overload involves the cost of using unnecessary inputs, information overload occurs as an SID of information, even if other competing reporting managers exist. My analysis demonstrates that information overload can occur due to uncertainty and opportunism of both the decision maker and reporting managers. [ABSTRACT FROM AUTHOR]

The rapid digitization of information has transformed traditional libraries into dynamic, easily accessible digital platforms, commonly referred to as digital libraries. This paper explores the significant impact of digital libraries on english language academic writing. Specifically, it examines how digital libraries have influenced the quality, depth, and efficiency of academic writing, with a focus on english language studies. The study analyzes the role of digital libraries in enhancing research capabilities, promoting diversity in resources, and fostering improved academic writing skills through the availability of a vast range of online scholarly materials. Furthermore, the paper highlights challenges such as information overload, digital literacy issues, and concerns related to the credibility of sources. Through qualitative and quantitative research, the findings demonstrate that digital libraries have not only facilitated easier access to academic resources but also have become instrumental in shaping more informed, structured, and coherent academic writing. Ultimately, this study concludes that while digital libraries have made notable contributions to the improvement of english language academic writing, it is imperative to develop effective strategies to address the challenges that accompany this digital transformation. [ABSTRACT FROM AUTHOR]

Background: The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. Results: To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. Conclusion: The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module (https://pypi.org/project/manamodeller/) and jupyter notebooks (https://github.com/LouisonF/MANA). [ABSTRACT FROM AUTHOR]

In today's information age, personalized recommendations have permeated every aspect of people's lives, becoming an indispensable service on major mainstream websites. By considering users' past interaction habits and preferences, personalized recommendation systems cater to the unique needs of individuals, presenting content that aligns with their interests. However, research on news recommendations, compared to fields such as ecommerce and music, remains relatively scarce and faces significant challenges, particularly in identifying news choices that may engage readers amidst information overload. This study designed and implemented a personalized hot news recommendation mechanism based on collaborative filtering technology. The system was developed using the SSM framework, incorporating software engineering principles with UML modeling. The front-end interface was built and deployed using the Bootstrap framework, while the back-end interface construction was supported by the Layui front-end framework. MySQL served as the storage system for news information. The core services of the personalized news push system include user account creation and login, personal information management, information search, news content evaluation, customized content push, and administrator-end information supervision. The customized push service, in particular, relies on collaborative filtering technology, tag-based recommendation strategies, and mechanisms for tracking current hot topics to achieve its goals. [ABSTRACT FROM AUTHOR]

With growing numbers of workers relying on the digital workplace to get work done, attention is increasingly focused on the well-being impacts of digital working. This study explored the stress, burnout and mental health implications of employees' digital workplace experience. Using the Job Demands-Resources model as a theoretical foundation, 142 workers were surveyed about their experiences of dark side of digital working effects (stress, overload, anxiety, and Fear of Missing Out) and well-being (exhaustion and mental health). Results from regression analyses indicated that the fear of missing out on information (IFoMO) is a risk factor for employee mental health and, along with information overload (IO), may lead to greater exhaustion. Additionally, both IFoMO and IO elevate digital workplace stress, further impacting well-being negatively. The results suggest that organizations need to optimize the flow of work-related information in the digital workplace and support employees to leverage information effectively. Plain language summary: Impacts to employee well-being due to information in the digital workplace With growing numbers of workers relying on the digital workplace to get work done, attention is increasingly focused on the well-being impacts of digital working. This study explored stress, burnout and mental health issues that can arise for employees as a result of demands they experience when using technology at work. A total of 142 workers were surveyed about stress, overload, anxiety, and Fear of Missing Out relating to technology use. They were also asked about negative well-being impacts namely exhaustion and mental health issues. Results indicated that the fear of missing out on information is a risk factor for employee mental health and, along with information overload, may lead to greater exhaustion. In addition, both of these information related issues were found to elevate digital workplace stress, further impacting well-being negatively. The results suggest that organizations need to optimize the flow of work-related information in the digital workplace and support employees to leverage information effectively. [ABSTRACT FROM AUTHOR]

While comprehensive knowledge networks can be instrumental in finding solutions to complex problems or supporting the development of detailed simulation models, their large number of nodes and edges can become a hindrance. When the representation of a network becomes opaque, they stop fulfilling their role as a shared representation of a system between participants and modelers; hence, participants are less engaged in the model-building process. Combating the information overload created by large conceptual models is not merely a matter of changing formats: shifting from an unwieldy diagram to enormous amounts of text does not promote engagement. Rather, we posit that participants need an environment that provides details on demand and where interactions with a model rely primarily on a familiar format (i.e., text). In this study, we developed a visual analytics environment where linked visualizations allow participants to interact with large conceptual models, as shown in a case study with hundreds of nodes and almost a thousand relationships. Our environment leverages several advances in generative AI to automatically transform (i) a conceptual model into detailed paragraphs, (ii) detailed text into an executive summary of a model, (iii) prompts about the model into a safe version that avoids sensitive topics, and (iv) a description of the model into a complementary illustration. By releasing our work open source along with a video of our case study, we encourage other modelers to use this approach with their participants. Their feedback and future usability studies are key to respond to the needs of participants by improving our environment given individual preferences, models, and application domains. [ABSTRACT FROM AUTHOR]

In international travel, customers are willing to express their experiences and feelings about the trip by posting reviews on platforms in their native language. According to Customer-based Discrepancy Theory, customers with different language backgrounds will form different anchors when browsing or reading eWOM in the corresponding language due to the differences in expression forms, nouns, and other information in different languages, and even if they have consistent offline consumption experience, customers with different language backgrounds may have different satisfaction levels due to the differences in established anchors. Prior studies have been leveraging reviews to understand the information contained in eWOM in different languages, however, the analyses of multilingual reviews still face challenges. The current joint linguistic and statistical analysis methods suffer from information overload in terms of massive online data. In this paper, we address the above challenges by utilizing cross-linguistic deep learning and multiple linear regression model of attribute-level effects on customer satisfaction. The results of the data experiment in English, Spanish, French & Dutch based on online restaurant reviews from both Yelp and TripAdvisor platforms show that there are significant differences in restaurant satisfaction across customer groups with different language backgrounds. Furthermore, there are differences in the impact of attributes level satisfaction such as restaurant service between customer groups with different language backgrounds. Our findings contribute to the development of effective marketing strategies for corporate policies for international travel services by providing a more responsive experience for customers from different language backgrounds. [ABSTRACT FROM AUTHOR]

Recommender Systems (RSs) have recently emerged as a practical solution to the information overload problem users face when searching for digital content. In general, RSs provide their respective users with specialized advice and guidance in order to make informed decisions on the selection of suitable digital content. This paper is a systematic literature review of recent (2011–2023) publications on RSs designed and developed in the context of education to support teachers in particular—one of the target groups least frequently addressed by existing RSs. A body of 61 journal papers is selected and analyzed to answer research questions focusing on experimental studies that include RS evaluation and report evaluation results. This review is expected to help teachers in better exploiting RS technology as well as new researchers/developers in this field in better designing and developing RSs for the benefit of teachers. An interesting result obtained through this study is that the recent employment of machine learning algorithms for the generation of recommendations has brought about significant RS quality and performance improvements in terms of recommendation accuracy, personalization and timeliness. [ABSTRACT FROM AUTHOR]

Recommender systems (RSs) play a pivotal role in mitigating information overload by aiding individuals or groups in discovering relevant and personalized information. An individual's food preferences may vary when dining with friends compared to dining with family. Most of the existing group RSs generally assume users to be associated with a single group. However, in real-world scenarios, a user can be part of multiple groups due to overlapping/diverse preferences. This raises several challenges for traditional RSs due to the inherent complexity of group memberships, degrading the effectiveness and accuracy of the recommendations. Computing user to group membership degrees is a complex task, and conventional methods often fall short in accurately capturing the varied preferences of individuals. To address these challenges, we propose an integrated two-stage group recommendation (ITGR) framework that considers users' simultaneous memberships in multiple groups with conflicting preferences. We employ fuzzy C-means clustering along with collaborative filtering to provide a more flexible and precise approach to membership assignment. Group formation is carried out using similarity thresholds followed by deep neural collaborative filtering (DNCF) to generate the top-k items for each group. Experiments are conducted using a large-scale recipes' dataset, and the results demonstrate that the proposed model outperforms traditional approaches in terms of group satisfaction, normalized discounted cumulative gain (NDCG), precision, recall, and F1-measure. [ABSTRACT FROM AUTHOR]

The research conducts a meta-analysis to explore the effects of multidisciplinary healthcare approaches on patient care quality in hospitals. It includes a comprehensive review of literature on public health initiatives, nursing care, medical devices, and more, emphasizing their impact on patient outcomes. The methodology involves analyzing peer-reviewed articles to assess the impact of these approaches. Results show significant improvements in patient care quality, emphasizing the benefits of team-based, coordinated, comprehensive, and patient-centered care. The discussion highlights the synergy between different care components and the need for strategic planning to address resource allocation and information overload. Recommendations are provided for healthcare practitioners, policymakers, and future research, focusing on enhancing collaboration, continuous education, supportive policies, and resource allocation. The conclusion underscores the importance of multidisciplinary strategies in improving patient care quality and outcomes, advocating for a shift towards integrated team-based care. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Medienwissenschaft: ZFM is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED. Materials and methods We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes. Results Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes. Discussion Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation. Conclusion We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED. [ABSTRACT FROM AUTHOR]

This study examines the challenges and opportunities of using group recommendation systems in an information overload scenario. Social network recommendation systems are increasingly important because they deliver users customized choices. Most existing solutions are geared for single users, making it difficult to propose for a group with different interests. This paper analyses group recommendation systems and exposes their flaws. This study tested whether the suggested approach outperforms the one without tagging information in recall, precision, and user satisfaction. Empirical evidence indicates that the algorithm exhibits appropriate levels of reliability and accuracy compared to conventional methods. The proposed approach has the potential to substantially enhance the existing state of social network group recommendation systems, thereby facilitating users in their quest to identify and participate in groups that align with their preferences. [ABSTRACT FROM AUTHOR]

Digital deliberation has been steadily growing in recent years, enabling citizens from different geographical locations and diverse opinions and expertise to participate in policy-making processes. Software platforms aiming to support digital deliberation usually suffer from information overload, due to the large amount of feedback that is often provided. While Machine Learning and Natural Language Processing techniques can alleviate this drawback, their complex structure discourages users from trusting their results. This paper proposes two Explainable Artificial Intelligence models to enhance transparency and trust in the modus operandi of the above techniques, which concern the processes of clustering and summarization of citizens' feedback that has been uploaded on a digital deliberation platform. [ABSTRACT FROM AUTHOR]

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm. [ABSTRACT FROM AUTHOR]

In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

Online reviews have become an important source of information for consumers, significantly influencing their purchasing decisions. However, the abundance and variety of review formats, especially the mix of text, image, and video elements, can lead to information overload and hinder effective decision-making. This study investigates how different review formats and their combinations affect the perceived helpfulness of reviews. We develop a comprehensive framework to analyze the interactions between text, image, and video elements and their impact on the helpfulness of reviews. We collect and code 8693 online reviews from JingDong Mall Mallacross six product categories, including both experience products and search products, and use multiple regression analysis to test our hypotheses. Our results show that textual review elements significantly increase review helpfulness. However, their effectiveness decreases as the amount of information increases, indicating cognitive overload. Text reviews are more prone to contribute to information overload, while visual elements such as images and videos generally do not contribute to information overload in the coexistence of text, image, and video reviews. Imagery components have a minimal effect on review helpfulness. Video elements are relatively short, which may not be sufficient to convey useful information. We also find that the impact of review formats varies between experience products and search products, and that star ratings moderate the alignment of textual or imagery components with consumer expectations. We conclude that the hybrid of text, image, and video elements in online reviews plays a crucial role in shaping consumer decision-making and information overload. Our research contributes to the literature on online reviews and information overload while providing practical implications for online retailers, review platforms, and consumers to optimize review formats, star ratings, and product types to facilitate informed purchase decisions. [ABSTRACT FROM AUTHOR]

The study investigates the unusual purchase behaviour of priority medical devices, observed during the second wave of the COVID-19 pandemic in India. The study proposes a structural model connecting online information consumption to this unusual purchase. The study also investigates the mediation of perceived severity and information overload, along with testing the direct influence of self-efficacy. To test the proposed model, 205 respondents from India participated in an online survey, and analysis was carried out using PLS-SEM. We found a strong link between online information consumption and unusual purchase behaviour. Perceived severity mediated the relationship between online information consumption and unusual purchase behaviour. Also, the direct relation between self-efficacy on unusual purchase behaviour was significant. The results confirm that increased online information consumption led to unusual purchase behaviour of priority medical devices used for the care and management of COVID-19. [ABSTRACT FROM AUTHOR]

An increasingly common practice for policy-makers is to leverage e-participation to collect citizens’ opinions and improve their decision-making processes. This practice, however, is hindered by the large quantity of collected opinions which are often overloading and hard to value. This is referred to as information overload. As a way to mitigate this challenge for policy-makers, this article develops a prioritization framework for citizens’ ideas collected through e-participation. The framework builds on Design Science Research and is validated on a real-world case in collaboration with the European Commission. The resulting contributions are threefold. First, theoretical criteria, popularity and polarization, are developed to prioritize citizens’ proposals. Then, automated and quantitative metrics are proposed to measure these criteria. Finally, a prioritization matrix is developed to visually assess the relative priority of these citizens’ proposals.

The exponential growth of digital information has led to the pervasive problem of information overload, affecting decision-making, productivity, and well-being. This article reviews the existing literature on the various effects of information overload, its underlying causes, and strategies for managing it. A scoping review of English literature up until January 2023 was conducted using Scopus, Web of Science, PubMed, and Emerald. The findings reveal that information overload is caused by personal factors, information characteristics, task parameters, organizational parameters, and information technology parameters. The effects include poor decision-making, decreased productivity, and cognitive pressures. Strategies for managing information overload include learning multiple skills and using filtering, prioritizing, and technology tools. This article provides a foundation for future research and interventions in this area.

Online reviews have become an important source of information for consumers, significantly influencing their purchasing decisions. However, the abundance and variety of review formats, especially the mix of text, image, and video elements, can lead to information overload and hinder effective decision-making. This study investigates how different review formats and their combinations affect the perceived helpfulness of reviews. We develop a comprehensive framework to analyze the interactions between text, image, and video elements and their impact on the helpfulness of reviews. We collect and code 8693 online reviews from JingDong Mall Mallacross six product categories, including both experience products and search products, and use multiple regression analysis to test our hypotheses. Our results show that textual review elements significantly increase review helpfulness. However, their effectiveness decreases as the amount of information increases, indicating cognitive overload. Text reviews are more prone to contribute to information overload, while visual elements such as images and videos generally do not contribute to information overload in the coexistence of text, image, and video reviews. Imagery components have a minimal effect on review helpfulness. Video elements are relatively short, which may not be sufficient to convey useful information. We also find that the impact of review formats varies between experience products and search products, and that star ratings moderate the alignment of textual or imagery components with consumer expectations. We conclude that the hybrid of text, image, and video elements in online reviews plays a crucial role in shaping consumer decision-making and information overload. Our research contributes to the literature on online reviews and information overload while providing practical implications for online retailers, review platforms, and consumers to optimize review formats, star ratings, and product types to facilitate informed purchase decisions.

ABSTRACTUsing a sample of Chinese listed firms over 2011–2020, this study examines the impact of digital finance on accounting information transparency. Our findings reveal an inverted U-shaped relationship between digital finance and accounting information transparency, suggesting that digital finance within certain limits increases accounting information transparency by optimising information acquisition and processing, but excessive digital finance leads to information overload and impairs accounting information transparency. Our findings are robust to a variety of sensitivity tests and are still valid after using two-stage “shift-share” instrumental variable procedures, propensity score matching method and firm-level fixed effect regression to control for the endogeneity issue. Lastly, MD&A and annual reporting disclosure timeliness are two influential channels by which digital finance affects accounting information transparency, and further the inverted U-shaped relationship is more pronounced for firms in provinces with lower marketisation indexes (CEO-chairman duality) than for their counterparts.

This study aims to explore whether social media information overload affects medical staff's information avoidance behavior and the extent to which time pressure mediates and job performance moderates this relationship. We analyze survey data on 586 medical staff from 11 hospitals in China using bootstrapping. The results demonstrate that medical staff's social media information overload has a significant positive effect on their information avoidance behavior and that time pressure plays a partial mediating role. Additionally, job performance moderates the impact of social media information overload on information avoidance. Further, high-performing medical staff's social media information overload has a greater impact on their information avoidance behavior than that of medical staff with low job performance. This study satisfies the call for an in-depth investigation of social media information avoidance by uncovering the relationship between social media information overload and the job performance of medical staff. The findings not only shed light on intention from an informational overload perspective but also provide fresh insights for practitioners on how to strategically manage information overload. Medical staff are suggested to be aware of both the positive and the negative effects of social media on their work and life as well as secure the time to deal with social media information to benefit from the convenience that social media brings to their work and life spheres without spending too much time on social media, which negatively affects working time. Healthcare institution managers should avoid sending too much information to high-performing medical staff, as information overload may cause them to be more inclined to “hide from” this information in reality.

Journalistic routines in the current context of information overload and communicative complexity have resulted in an excessive workload for media professionals. This issue is particularly critical in wartime situations, where the conflicting parties contribute to the problem by disseminating biased information and hoaxes within the official communication under the pretext of safeguarding their military and strategic interests. Safeguarding against disinformation and fake news has become a priority in media newsrooms. This article explores how journalists at the Spanish broadcasters À Punt and Tele 5 address the problem of news coverage concerning the wars in Ukraine and Gaza. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Americana de Ciência da Informação is the property of Revista Ibero-Americana de Ciencia da Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)