Search

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]

This comprehensive review of concept-supported interpretation methods in Explainable Artificial Intelligence (XAI) navigates the multifaceted landscape. As machine learning models become more complex, there is a greater need for interpretation methods that deconstruct their decision-making processes. Traditional interpretation techniques frequently emphasise lower-level attributes, resulting in a schism between complex algorithms and human cognition. To bridge this gap, our research focuses on concept-supported XAI, a new line of research in XAI that emphasises higher-level attributes or 'concepts' that are more aligned with end-user understanding and needs. We provide a thorough examination of over twenty-five seminal works, highlighting their respective strengths and weaknesses. A comprehensive list of available concept datasets, as opposed to training datasets, is presented, along with a discussion of sufficiency metrics and the importance of robust evaluation methods. In addition, we identify six key factors that influence the efficacy of concept-supported interpretation: network architecture, network settings, training protocols, concept datasets, the presence of confounding attributes, and standardised evaluation methodology. We also investigate the robustness of these concept-supported methods, emphasising their potential to significantly advance the field by addressing issues like misgeneralization, information overload, trustworthiness, effective human-AI communication, and ethical concerns. The paper concludes with an exploration of open challenges such as the development of automatic concept discovery methods, strategies for expert-AI integration, optimising primary and concept model settings, managing confounding attributes, and designing efficient evaluation processes. [ABSTRACT FROM AUTHOR]

AbstractIn today’s era of information overload, efficiently extracting valuable information from a large volume of textual data has become a crucial challenge in reading. This study aimed to explore the enhancement of Chinese readability in the digital domain through intelligent typesetting method. This study involved two experiments. The purpose of the first experiment was to achieve the machine learning-based assessment of the importance of individual words in Chinese articles and to automate typesetting based on the importance of words. For the second experiment, readability tests and eye-tracking reading tests were performed and the reading performance and reading attention between intelligent typesetting and general typesetting style was compared. This work proposed three Chinese typesetting methods that distinguished the importance of Chinese text information, based on font size and color. The results showed that first, when reading Chinese text, visual attention was more likely to be drawn to larger font sizes, darker brightness, or warmer-colored characters. Second, intelligent Chinese typography that distinguishes information importance through font size, color brightness, and color hue can enhance Chinese reading comprehension accuracy and subjective evaluation. It was concluded that using the TextRank model to distinguish importance of Chinese vocabulary and intelligent typesetting methods based on visual features of font could obviously improve text readability. Specifically, readability achieved through intelligent typesetting method distinguishing information importance through font color brightness surpasses that of general typesetting significantly. These intelligent typesetting methods can be widely applied in Chinese reading scenarios, such as web pages, e-books, information visualization, and other Chinese reading contexts. [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]

AbstractDrawing on the stressor–strain–outcome (SSO) framework, this study examined how information-related stressors (perceived information overload, information quality deterioration, and information narrowing) and privacy-related stressor (perceived privacy risk) in using news platforms’ recommendation algorithms trigger users’ psychological reactance, which leads to their active coping, expressive coping, or avoidance coping with their stress in using such recommendation algorithms. Using a sample of 352 valid research responses, this study found that users’ psychological reactance is positively influenced by perceived information quality deterioration, followed by perceived information overload and perceived privacy risk. Meanwhile, perceived information narrowing has no significant impact on psychological reactance. In turn, psychological reactance exerts a stronger impact on expressive coping and avoidance coping responses than on active coping response. This study enriches the literature on the stress in algorithm usage at the individual level and provides practical insights for the providers of news platforms which have applied recommendation algorithms. [ABSTRACT FROM AUTHOR]

The use of Internet for constant connectivity with social networking sites has negative consequences in the form of higher mental pressure, low physical and offline activities, and mental tiredness and fatigue. Using these given phenomena, this research explores how system complexity, information overload, and communication overload are decisive factors of tiredness of social networking sites among Chinese individuals. The study also adds the role of workfamily balance as a predictor in determining the tiredness levels of the use of social networking sites. A survey questionnaire was conducted over a sample of 507 respondents, drawn through non-probability purposive sampling techniques from employees of major Chinese organizations. The Structural Equation Modelling measurements revealed that, except for system complexity, the impact of information overload, communication overload, and work-family balance is significant in causing tiredness of social networking sites. However, while both overload factors were responsible for greater tiredness, a higher balance between work and family tends to reduce it. The study suggests that policymakers should develop strategies through to reduce such tiredness. It was also felt that a more work-family balance should be a panacea for such tiredness among the Chinese people. [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]

This study examines the impact of message length and audience's perceived information overload on the effectiveness of a fact-check in reducing belief in fake news within the COVID-19 vaccination context. Through an online experiment (N = 374) conducted in Singapore, we found an interaction effect between one's level of information overload and the type of fact-check that was being shown (short, medium, or long). The findings from this study help to extend the literature on the effectiveness of fact-checks in reducing general public's belief in fake news. [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]

Purpose: Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance. Design/methodology/approach: Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized. Findings: The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down. Practical implications: This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development. Originality/value: Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events. [ABSTRACT FROM AUTHOR]

Purpose: Based on the C-A-C framework, this article examined users' information avoidance intention in social media platforms. Design/methodology/approach: The authors conducted data analysis using a mixed method of the SEM and fsQCA. Findings: The results indicated that information overload, functional overload and social overload influence fatigue and dissatisfaction, both of which further determine users' information avoidance intention. The results of the fsQCA identified two paths that trigger users' information avoidance intention. Originality/value: Extant studies have examined the information avoidance in the contexts of healthcare, academics and e-commerce, but have seldom explored the mechanism underlying users' information avoidance in social media. To fill this gap, this article will empirically investigate users' information avoidance in social media platforms based on the C-A-C framework. [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]

Purpose: The discontinuous usage behavior of short video social media presents an ongoing challenge to platform development. The purpose of this study is to investigate the antecedents of intentions to short media discontinuous usage. Design/methodology/approach: This study adopts a Cognition–Affection–Conation (CAC) framework to analyze short video social media discontinuous intention on the basis of cognitive dissonance theory (CDT) and self-efficacy theory. The empirical evaluation of the research model was conducted using SmartPLS 2.0 and was based on questionnaire data obtained from participants in China. Findings: The results show information overload and user addiction have a significant positive association with cognitive dissonance, which is, in turn, found to significantly impact discontinuous usage intention. Self-efficacy moderates the relationships between information overload, user addiction, cognitive dissonance and discontinuous usage. Originality/value: This study contributes to the understanding of the factors that influence short video discontinuous usage intention and it achieves this by engaging from a CDT perspective and by applying Self-Efficacy Theory. Theoretical implications for future short video platform research, as well as practical suggestions for short video platform operators and users, are also discussed. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales 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.)

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]

Successful innovation requires firms to search beyond their organizational confines for complementary knowledge resources ahead of their competitors. While such proactive boundary‐spanning search (PBS) provides relevant knowledge resources for green innovations, it can also lead to information overload that derails green innovation efforts. Therefore, there is a need to understand how and when PBS translates into green innovation. This study combines the dynamic capabilities theory with the organizational information processing theory to develop a moderated mediation model that espouses the role of green dynamic capability and analytics capability in the relationship between PBS and green innovation. The study's hypothesis is tested using firm‐level survey data from 200 manufacturing firms in Ghana. Partial least squares structural equations modeling (PLS‐SEM) and PROCESS were used to evaluate the research model. The results confirm a positive relationship between PBS and green dynamic capabilities, and also between green dynamic capabilities and green innovation. More importantly, PBS drives green innovation through green dynamic capability, particularly at moderate and high levels of analytics capability. The theoretical and managerial implications of the study are outlined in the text. [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]

The importance of managing complexity in innovation has been highlighted both in research and practice, however the question remains: how can this be accomplished? Although there have been answers to this question, the practical alignment of process (project management) and system (technical management) viewpoints remains understudied. We responded to this challenge with an in‐depth case study in high‐tech industry. In this paper we applied Human Centered Design (HCD) and Action Research (AR) principles in a novel context, namely systems engineering. We identified main barriers and key players, essential information elements, and solution requirements. Using co‐design, we iteratively generated solution concepts, out of which we selected and evaluated one candidate concept. We highlight two main areas in our results: 1) how participatory approaches can support solving systems engineering challenges in practice; and 2) how to align the process and system viewpoints. For the first, we discuss the implications, considering aspects like the time and effort investment, along with the benefits of ownership, empathy, democracy, and collective learning. Our insights show potential to further build on the application of participatory approaches in systems engineering to match existing solutions to the practice. For the latter one, we reflect on issues such as information overload, human aspects, and the new relationship between project managers and systems engineers. Considering the current complexity demands, it is crucial to establish a better alignment between these roles and between process and system viewpoints, as they cannot be left undefined and unsupported any longer. [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]

This study examines how intrinsic message characteristics (i.e. call to action, negative emotions, and informality) drive online information engagement (i.e. the numbers of likes and retweets). We also explore how the veracity of information (i.e. misinformation vs. accurate information) is intertwined with negative emotions in promoting online information engagement. Analyses of 472 tweets demonstrate that tweets with higher anger-laden words were more likely to be retweeted. By contrast, those with higher anxiety-laden words and more informal language were more likely to receive likes. Regarding the interaction between distinct emotions and the veracity of information, the results showed that anger-laden misinformation tended to be retweeted more and received more likes from users whereas anger-laden accurate information received fewer likes. Further, anxiety-laden accurate information received more likes. This study contributes to existing scholarship surrounding online information engagement by illustrating how intrinsic message features are differentially associated with liking and retweeting behaviours. Our findings also provide practical implications on how messages should be tailored to encourage engagement on social media and devise effective countermeasures to curb the potentially detrimental effects of anger when used for the purposes of misinformation. [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]

The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference. [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]

Young children are biased to treat new information communicated to them as conventional, shareable, and known by others in their community. However, some information is privileged in the sense that is not intended to be shared with or known by all. The current study compared judgements regarding sharing conventional versus privileged information. Seventy‐four 3‐ to 5‐year‐olds and adults responded to vignettes in which a protagonist had to decide whether to share conventional (an object name) or privileged information (surprise). Consistent with our hypothesis, there was no developmental change in sharing judgements for conventional information but a clear decrease with age for sharing privileged information. Nonetheless, even 3‐year‐olds were more likely to judge that conventional information should be shared more than privileged information, though this difference increased with age. While children overall treat information as shareable, there is an emerging ability to distinguish how conventional versus privileged information should be shared. [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 excess of mystery and 'meaning' in Twin Peaks: Season 3 (2017) reflects the post-truth ontological dissonance of information overload, tantalising the thirst for answers that dominates digital communication. Combining its clues and codes with payoffs for 'long-term fan investments' (Hills, 2020: 197) situates the series between two modes of fan participation: the curative puzzle-solving of complex 'Quality TV', and cult television's 'hyperdiegesis'. Season 3 is complex cult television, encouraging forensic and creative engagement. Replicating the polarisation of contemporary discourse while simultaneously promoting algorithmic literacy informs Mark Frost's politicised vision for Twin Peaks, and David Lynch's aesthetic experiments amplify this. [ABSTRACT FROM AUTHOR]

Nowadays, deep learning models are used to summarize the large volume of text data to understand its intent effectively. Processing huge amounts of data can lead to an Information overload, where the models may generate text summaries that miss out on the important information of actual text content. Such problems in business document summaries can impact progressive business growth. This study employs a dataset comprising business articles sourced from BBC News to conduct an extensive comparative analysis of three prominent text summarization algorithms: Bidirectional and Auto-Regressive Transformers, Text-to-Text Transfer Transformer, and Pre-training with Extracted Gap-sentences for Abstractive Summarization, within the domain of business news summarization. The primary objective is to assess the accuracy of these models in generating concise and coherent summaries, utilizing ROUGE and METEOR scores as the benchmark for evaluation. Each model's proficiency in distilling business narratives while retaining crucial insights is carefully examined. This study analyzes the summaries generated and compares them with the already existing summaries. From the result analysis it observed that BART and PEGASUS show ROUGE-I score of 0.308 and 0.245, and METEOR score 0.28 and 0.25 respectively. The outcomes of this study show that the T5 excelled in the ROUGE-1 and METEOR scores which were 0.354 and 0.35 respectively. outcomes of this research offer significant implications for both researchers and practitioners, equipping them with advanced summarization techniques for extracting information effectively from business-related content. In an age where information overload is prevalent, the findings from this study can guide the selection and deployment of text summarization models to enhance information extraction processes, ultimately facilitating more efficient decision-making and information dissemination in the business domain. [ABSTRACT FROM AUTHOR]

The world can manage its path towards better health thanks to the information, community, and support that medical forums offer in the modern digital environment. Integrating subject modelling on a decentralized platform may be essential and innovative along the way. Topic modelling aids in better understanding user requirements, spotting patterns and trends in the medical sector, and taking proactive measures. A centralized platform is typically used to host health forums, but this has several disadvantages, including a lack of security and privacy for sensitive personal health information, the potential for bias and censorship to serve the vested interests of the central authority, and it is significantly more expensive to implement and maintain than a decentralized platform. We therefore suggest a medical forum with topic modelling housed on a decentralized platform to enhance the existing state of medical forums so that we can better understand the current topics of interest in the medical sector and act proactively. Topic modelling analysis speeds up reaction time and aids in better understanding community needs. Blockchain technology offers enhanced privacy and security for healthcare data. However, there are still challenges in ensuring the privacy of sensitive information when conducting topic modeling on blockchain-based healthcare systems. Further research is needed to address privacy concerns, develop privacy-preserving topic modeling algorithms, and establish robust data access control mechanisms. Social media platforms generate a massive amount of healthcare-related content, including posts, comments, and discussions. Without topic modeling, sorting through this overwhelming volume of data becomes a significant challenge. It can lead to information overload, making it hard to identify key trends, topics, or critical issues. The absence of topic modeling in the analysis of healthcare topics on social media results in a lack of structure, organization, and systematic exploration of the information available. Topic modeling provides a valuable solution by automatically identifying, categorizing, and analyzing the diverse range of healthcare-related discussions, enabling more insightful and efficient understanding of the landscape. Current topic modeling approaches often assume static topics and may not capture temporal dynamics and emerging topics in real-time. Research is needed to develop dynamic topic modeling techniques i.e. Cluster Visualized BTM and Cluster Visualized Hierarchical Dirichlet Process that can adapt to evolving healthcare topics and provide timely insights for decision-making in blockchain-based healthcare systems. The forum's host also offers several benefits like privacy, security, affordability, and less bias and restriction. The submitted information is not utilized by a central authority with personal interests. [ABSTRACT FROM AUTHOR]

Prior research suggests that quarterly reports released concurrently with earnings depress trading due to information overload. In this study, we predict that concurrent reports help investors trade when they face uncertainty about how to interpret earnings news. We rely on the implementation of ASC 606 as a quasi-exogenous increase in uncertainty about how to trade in response to earnings news. Specifically, we find that when uncertainty is high in the first quarter of ASC 606 implementation, 10-Qs released concurrently with earnings are associated with increased trading around the earnings announcement. We find the relation is more pronounced when investors face greater uncertainty about earnings and when firms increase disclosure in their revenue recognition footnote. We also find some evidence that our results hold in a broader sample of accounting standard changes and generalize to other proxies for investor uncertainty. Our results suggest that concurrent quarterly reports are informative to investors when uncertainty about earnings is especially high. [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.

This article discusses the history of reference tools in medicine, tracing their evolution from textbooks and reference files to pocket guides and smartphones. It highlights the ways in which physicians have relied on these tools as repositories of knowledge and how they have shaped medical education and practice. The article also explores the challenges and benefits of digitization and the integration of technology in reference tools. Overall, it emphasizes the importance of reference tools in the medical field and how they have evolved over time. [Extracted from the article]

Information, which is the critical resource provided by information systems (IS), is available in increasing volumes due to rapid technological progress. This causes a tension between the cognitive limitations of individuals and their access to large volumes of information. This tension is important not only for individuals but also for teams, which are increasingly making important organizational decisions. Recognizing this tension, this paper investigates the relationship between information volume and decision performance in IS-enabled teams. We use information processing and schema theories to develop a contextualized theoretical model, wherein information volume has a curvilinear (inverted U-shaped) effect on decision performance, and this effect is moderated by three context-relevant attributes of the team members: computer self-efficacy, computer anxiety, and learning goal orientation. We conducted two complementary studies, with similar experimental contexts and procedures but different types of participants and different tasks. In these studies, 236 individuals in 118 dyads (Study 1) and 200 individuals in 60 small teams (Study 2) made decisions over time using an enterprise resource planning system for a competitive virtual firm in a business simulation. We used objective data on system use and decision outcomes, along with survey data, to test the theoretical model. The two complementary studies find consistent results supporting the curvilinear relationship between information volume and decision performance and find that this relationship is flattened by high computer self-efficacy. Study 1 also supports the hypothesized steepening effects of high computer anxiety and learning goal orientation on the curvilinear relationship. Study 2 does not support these effects of computer anxiety and learning goal orientation but highlights some boundary conditions for them. Together, the studies provide insights into how information volume affects decision performance in IS-enabled teams, and how context-relevant team member attributes moderate this relationship and themselves affect decision performance. [ABSTRACT FROM AUTHOR]