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Now a day’s recommendation system take care of the issue of the massive amount of information overload problem and it provides the services to the candidates to concentrate on relevant information on job domain only. The job recommender system plays an important role in the recruitment process of fresher as well as experienced today. Existing job recommender system mainly focuses on content-based filtering to extricate profile content and on collaborative filtering to capture the behaviour of the user in the form of rating. Dynamic nature of job market leads cold start and scalability issues. This problem can be addressed by item-based collaborative filtering with a machine learning technique, it learns job embedding vector and finds similar jobs content-wise. Existing model in job recommender domain uses the confining model to address the cold start and scalability issue and provide better recommendation, but they fail to accept the complex relationships between job description and candidate profile. In this paper, we are proposing a Deep Semantic Structure Algorithm that overcome the issue of the existing system. Deep semantic structure modelling (DSSM) system uses the semantic representation of sparse data and it represent the job description and skill entities in character trigram format which increases the efficacy of the system. We are comparing the results to three variation of DSSM model with two different dataset (Naukari.com and CareerBuilder. com) and it gives satisfactory results. Experimental results shows that the DSSM Embedding model and its other variants are provides promising results in solving cold start problem in comparison with several variants of embedding model. We used Xavier initializer to initialise the model parameter and Adam optimizer to optimize the system performance.

News recommendation plays an important role in alleviating information overload and helping users find their interesting news. Most of the existing news recommendation methods make a recommendation based on static data. They ignore the time dynamic characteristics of the interaction between users and news, that is, the order in which users click on news implicitly indicates the user’s interest in news. In this paper, we propose a time sensitive heterogeneous graph neural network for news recommendation. The network consists of two subnetworks. One subnet utilizes convolutional neural network and improved LSTM to learn a user’s stay period on the page and click sequence characteristics as the temporal dimension feature. The other subnet constructs an attention-based heterogeneous graph to model the user-news-topic associations, and apply graph neural network to learn the structural features of the heterogeneous graph as spatial dimensional features. Experiments conducted show that our model outperforms the state-of-the-art models in accuracy and has better interpretability.

Information overload in recent years has tremendously sparked recommender systems (RSs). An RSs usually recommends valuable information for users based on historical experience high-dimensional and incomplete (HDI) data. Since each user cannot mark whole items, extracting latent factors (LF) learned by the stochastic gradient descent (SGD) optimization method is frequently used. However, interference from the adjustment of parameters induces an inferior convergence rate and low efficiency. To address this issue, the paper proposes a b rain storm optimization (BSO)-based a daptative l atent f actor a nalysis (BALFA) model consisting of the following three essential ideas: 1) divergent mechanism to avoid premature convergence, 2) particle retention technique to prevent invalid search, and 3) self-adaptive multidimensional leaning rate for more efficient application on varying data. Moreover, the convergence of the modified BSO used in BALFA is proofed based on the Markov chain . Comparison experiments on six HDI datasets in the SGD-based LF model indicate that the BALFA achieves state-of-art convergence rate and computational efficiency for HDI data analysis.

The recommendation systems in the online platforms often suffer from the rating data sparseness and information overload issues. Previous studies on this topic often leverage review information to construct an accurate user/item latent factor. To address this issue, we propose a novel confidence-aware recommender model via review representation learning and historical rating behavior in this article. It is motived that ratings are consistent with reviews in terms of user preferences, and reviews often contain misleading comments (e.g., fake good reviews, fake bad reviews). To this end, the interaction latent factor of user and item in the framework is constructed by exploiting review information interactivity. Then, the confidence matrix, which measures the relationship between the rating outliers and misleading reviews, is employed to further improve the model accuracy and reduce the impact of misleading reviews on the model. Furthermore, the loss function is constructed by maximum a posteriori estimation theory. Finally, the mini-batch gradient descent algorithm is introduced to optimize the loss function. Experiments conducted on four real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods. The proposed method also further promotes the application in learning resource adaptation. The source Python code will be available upon request.

A high-quality recommender system (RS) can effectively alleviate information overload by producing recommendations. The quality of the recommender system not only depends on the recommendation algorithm but also on the quality of collected data. Since users are often affected by environmental and accidental factors during the rating process, natural noise is probably brought into the data of RS by non-malicious users, which will lead to deviations in prediction results. In this paper, we propose a scheme based on fuzzy theory to manage the natural noise in RS. We first classify the ratings into three fuzzy categories with variable boundaries. Then, the fuzzy profiles of users and items are built to detect the natural noise in ratings. Finally, once the noisy ratings are detected, we replace them with the rating threshold values according to the Maximum membership principle. The proposed scheme is tested in two benchmark datasets and experimental results verify that the scheme can significantly improve the recommendation quality and has higher efficiency than the schemes based on re-predication.

There exist situations of decision-making under information overload in the Internet, where people have an overwhelming number of available options to choose from, e.g. products to buy in an e-commerce site, or restaurants to visit in a large city. Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on the user’s preferences, needs and/or behavior. Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end user’s reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users. Hence, both the end user and the user being recommended should accept the “matching” recommendation to yield a successful RRS performance. The operation of an RRS entails not only predicting accurate preference estimates upon user interaction data as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preference information. This paper presents a snapshot-style analysis of the extant literature that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches. Representative RRS models are likewise highlighted. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

A common university joke goes that in order to complete the first-year program at a faculty of philology, one has to read not less than a thousand pages daily during the academic year. The number of works and commentaries increases every year and catalogues are swelling, putting modern philologists in an ever more difficult position. The rapid digitalizing of old texts and the exponentially growing number of new ones during the last decade hardly allow any works to escape the professional eye. Accessibility is turning into information overload, and philologists are becoming unable to see the forest behind the trees. The abundance of texts, provoked exclusively by technology, cannot be conquered merely by recommending perseverance or cultivating the love of reading: there is a need for a whole new way of being a philologist. This article examines the new quantitative formalism from a historical critical perspective. Formalism did not evade “biological bias” even at the time of its formation and specification as a method: it would be odd to expect that it could be evaded at the stage of the hybridization of literary science and methodological convergence.

Mitigating perceived uncertainty and risk is critical for successful exchanges in an online B2B marketplace. However, scant empirical research has been devoted to the topic, and even less to small- and medium-sized enterprises (SMEs). We investigate the use of quality signals and recommendations to foster buyer-supplier matching in an online B2B marketplace of SMEs. Unique proprietary data from a leading B2B e-commerce platform suggest that such a matching is significantly related to signals of SME buyers' quality and credibility embedded in how projects are described. Moreover, appropriately designed recommendation system can increase the matching rate because it helps reduce buyers' information overload. This research highlights the importance of integrating online channel data with traditional offline channel data to accurately assess the drivers of buyer-supplier matching. Our findings provide valuable insights into how to design and manage an online B2B marketplace for SMEs to yield more successful matches.

With the development of information technology such as mobile Internet and social media applications, network information is growing rapidly and leads to the problem of information overload. Keywords help to filter and find interesting information for users from massive text. Automatic extraction of keywords from text as tags of text help to improve recommendation and keyword-based information retrieval. This paper proposes a novel keyword extraction approach from text that combines features such as word frequency and association. Experiment results show that the precision rate, recall rate and F-measure are all better than those of TextRank and TF-IDF.

Today, lots of personalization can be done in applications or even websites. It is possible to change the look and feel, to group often used functionalities or to filter relevant information. To get the same behavior for security monitoring in large scaled enterprise companies, one aspect of the research project IUNO-iSOC is to develop a dashboard system where every user can create his own custom overview of all needed information. A second part IUNO-iSOC aims to handle the information overload a security analyst is faced by analyzing alerts of different types of security management systems. This paper adresses the mentioned problem and describes the way from collecting security data from different sources to the concept of supporting the users creating their own view to the data currently needed as well as dealing with lots of incoming security alerts.

Objective To explore how women describe efforts to seek, appraise and interpret information during the diagnostic phase of her breast cancer care. Methods Qualitative interviews with 35 women with breast cancer across Canada, using audio/video recording. Thematic analysis was used to identify topics important to participants (original results published: www.healthexperiences.ca ). Secondary analysis of transcripts to identify how women described information flow, content, and management strategies. Results Women adopt different strategies to optimize access to information, while acknowledging the negative effects of information overload and lack of relevant information. They propose small steps towards gathering and managing information, and to focus initially on understanding their illness. Conclusion Different strategies can help to ensure that women have the right information, in the right format, at the right time. Some of these strategies include developing guidance on how to ‘handle’ information, helping healthcare professionals identify patient’s information preferences, improving the availability, quality and access to experiential information, and facilitating acces to electronic information that can tailor information. Further research to understand how women handle information can inform strategies to help newly-diagnosed patients navigate available information. Practice implications Healthcare professionals can work in partnership with patients to tailor reliable information to support informed decision-making.

This article aims to review the important roles of health knowledge organization systems (KOSs) during the COVID-19 pandemic. Different types of knowledge organization systems, including term lists, synonym rings, thesauri, subject heading systems, taxonomies, classification schemes, and ontologies are widely recognized and applied in both modern and traditional information systems. Apart from their usage in the management of data, information, and knowledge, KOSs are seen as valuable components for large information architecture, content management, findability improvement, and many other applications. After introducing the challenges of information overload and semantic conflicts, the article reviews the efforts of major health KOSs, illustrates various health coding schemes, explains their usages and implementations, and reveals their implications for health information exchange and communication during the COVID-19 pandemic. Some general examples of the applications, services, and analysis powered by KOSs are presented at the end. As revealed in this article, they have become even more critical to aid the frontline endeavors to overcome the obstacles due to information overload and semantic conflicts that can occur during devastating historic and worldwide events like the COVID-19 pandemic.

This paper studies the information processing behavior of a decision maker (DM) who can only process a subset of all information he receives: before taking an action, the DM receives sequentially a number of signals and decides whether to process or ignore each of them as it is received. The model generates an information processing behavior consistent with that documented in the psychological literature: first, the DM chooses to process signals that are strong; second, his processing strategy exhibits confirmation bias if he has a strong prior belief; third, he tends to process signals that suggest favorable outcomes (wishful thinking). As an application I analyze how the Internet and the induced change in information availability affects the processing behavior of the DM. I show that providing more/better information to the DM could strengthen his confirming bias.

Efficient response to machine failures is crucial for improving machine availability. In practice, the number of alarms occurring during the operation of manufacturing systems could overwhelm even experienced machine operators. Manufacturing execution systems (MES) therefore often allow for alarm data acquisition as well as its filtering and aggregation to reduce the occurring information overload. However, manual configuration of these systems may be cumbersome and time-consuming. Hence, we propose an event-discrete method for temporal alarm data analysis to support the configuration process.

Objective While the ability to recall medical information is crucial, it is known to be a considerable challenge for many patients. Consequently, we aimed to investigate whether replay could enhance information recall and to explore the extent of information recall in a group of Danish outpatients. Methods This study utilized a mixed-methods approach and evaluated patients’ recall by comparing seven key themes between the interviews and the recordings. A total of 33 patients were included from three outpatient clinics. Results Overall, 61% of the information was recalled. However, the study could not confirm an effect of replay on patients’ information recall. Information recall was associated with age and information load. Accordingly, patients younger than 70 years had a 2.46 higher probability of recall (95%CI: 1.1–5.5, p = 0.027), whereas an increase in information load negatively influenced recall. Conclusion The study power is insufficient to provide a definite answer to the hypothesis regarding a positive association between replay and recall. Patients’ information recall depended on the information theme, their age, and amount of information provided in the consultation. Practice implications The critical consequences of information overload necessitate an increased awareness of how to prioritise information, particularly when communicating with older patients.

Online network platforms provide great convenience for users to obtain information. However, it’s challenging to select the required information from enormous texts. Automatic text headline generation methods not only guide users to select the information they are interested in, but also solve the problem of information overload. Nevertheless, the existing works mainly utilize the grammar rules to obtain the key information of the source text, while ignoring the dwell time of user’s attention on different text contents. To address this issue, this paper proposes an abstractive text headline generation model based on the eye-tracking attention mechanism. Specifically, this model first relies on the eye-tracking data to establish the mapping relationship between text words and the words’ reading time. Then, an eye-tracking attention mechanism is constructed to judge the importance of different words. Finally, this attention mechanism is integrated into the encoder-decoder framework to generate a high-quality headline. Experimental results obtained from different datasets demonstrate that the headline generated by our model is more concise. Moreover, our proposed model outperforms significantly the classical headline generation models on ROUGE-1, ROUGE-2 and ROUGE-L.

Within the last years new online resources such as blogs and podcasts are making their way into medical education. With the introduction of the internet in the last decades, the field of medical education and training has also changed. Consumption of information independent of time and location through blogs and podcast is now a part of everyday life. The FOAM (“Free Open Access Medical Education”) community in particular is playing a pioneering role here. We discuss the roles of blogs and podcasts in medical education and possible applications such as the “flipped classroom” concept. We explain the many advantages, like their function as a filter against information overload or their role in the so called “knowledge translation gap” but we also highlight points such as a cult of personality that should be viewed critically. Last but not least, we describe the reasons why we run a blog ourselves and the challenges we face.

Complex tasks such as treating patients with chronic conditions and developing software products are typically accomplished by teams that collaborate over an extended time duration. To remain coordinated, team members need to be aware of others' activities if those activities are likely to affect their own actions. However, identifying such interactions and sharing information appropriately is challenging, especially when the activities of team members are loosely-coupled. In practice, team members often either lack important information about others' activities, or are overwhelmed by the need to review too much information. This paper presents Personalized Change Awareness, a new approach for supporting team coordination which aims to automatically identify and share the subset of information about others' activities that is most relevant to each of the team members. The paper formally defines the computational problem of information sharing in loosely-coupled teamwork, which underlies the personalized change awareness approach. It defines a new representation, Mutual Influence Potential Networks (MIP-Nets) and an algorithm, MIP-DOI, that uses this representation to determine the information that is most relevant to each team member. In contrast to existing information sharing algorithms in multi-agent teams, MIP-DOI does not assume the availability of a priori knowledge of a team's possible plans, because human teams rarely explicitly define detailed long-term plans in advance. We demonstrate the ability of MIP-DOI to identify relevant information using simulations of collaborative activities. We further evaluated the contribution of personalized change awareness to team performance in a controlled user study. To this end, we developed a personalized change awareness mechanism for collaborative writing, which used MIP-DOI to determine which changes to share with each author. This evaluation demonstrates that the Personalized Change Awareness approach resulted in higher productivity and lower perceived workload without any change in final quality compared to the currently prevalent approach of sharing all change information. Our results also demonstrate that merely reducing the amount of information shared with co-authors is not enough: sharing a random subset of changes resulted in significantly lower quality of work than sharing a personalized subset of changes.

This study aims to develop an unobtrusive measure for experienced stress in a digital serious gaming environment involving decision making in crisis management, using only in-game measures in a digital decision game called the Mayor Game. Research has shown that stress has an influence on a decision-maker's behavior, and also on the learning experience in training scenarios. Being able to assess unobtrusively the level of stress experienced would allow manipulation of the game so as to improve the learning experience. An experiment was conducted with two conditions, one paced and one non-paced. In the paced condition, participants were exposed to in-game changes that aimed to induce stress by creating information overload, uncertainty and time pressure. While pacing caused differences between the conditions with respect to in-game performance for analytical skills, several simple unobtrusive in-game measures were not consistent enough to serve as indicators for experienced stress. Further, physiological measurements of stress did not show significant differences between the conditions, indicating that the employed methods to induce stress did not work sufficiently. These results call for testing of more sophisticated methodologies to unobtrusively assess experienced stress in the given type of serious game.

In the era of big data, it is extremely challenging to decide what information to receive and filter out in order to effectively acquire high-quality information, particularly in social media where large-scale User Generated Contents (UGC) is widely and quickly disseminated. Considering that each individual user in social network can take actions to drive the process of information diffusion, it is naturally appealing to aggregate spreading information effectively at the individual level by regarding each user as a social sensor. Along this line, in this paper, we propose a framework for effective information acquisition in social media. To be more specific, we introduce a novel measurement, the preference-based Detection Ability to evaluate the ability of social sensors to detect diffusing events, and the problem of effective information acquisition is then reduced to achieving social sensing maximization through discovering valid social sensors. In pursuit of social sensing maximization, we propose two algorithms to resolve the longstanding problems in traditional greedy methods from the perspectives of efficiency and performance. On the one hand, we propose an efficient algorithm termed LeCELF, which resolves the redundant re-evaluations in the traditional Cost-Effective Lazy Forward (CELF) algorithm. On the other hand, we observe the participation paradox phenomenon in the social sensing network, and proceed to propose a randomized selection-based algorithm called FRIENDOM to choose social sensors to improve the effectiveness of information acquisition. Experiments on a disease spreading network and real-world microblog datasets have validated that LeCELF greatly reduces the running time, whereas FRIENDOM achieves a better detection performance. The proposed framework and corresponding algorithms can be applicable in many other settings in resolving information overload problems.

This study focuses on consumers' processing of online reviews as an empowering experience. We investigate how decision support information (i.e., online reviews) can lead to information overload and decision difficulty, and ultimately affect decision satisfaction. A series of three experimental studies with subjects from a professional marketing agency examine the influence of self-determined review quantity and perceived review quality on satisfaction, as well as the mediating effects of information overload and decision difficulty. Our results show that the effect of perceived review quality on information overload is enhanced when the consumer chooses to read more reviews. Individual characteristics are also relevant; consumers' product knowledge moderates the mediating effect of decision difficulty on satisfaction. This study contributes to the literature by (1) investigating how the characteristics of decision support information affect decision process and outcome, and (2) exploring the boundary conditions wherein online reviews can empower but also overload consumers under various decision contexts.

Despite the increasing popularity of mobile applications in commerce, there exists an emerging trend where users are abandoning these applications due to the abuse of push notification. This study applies the stress-coping theory to investigate this phenomenon. A 2 × 2 × 2 factorial design experiment with manipulations on perceived information overload, perceived intrusiveness, and perceived reward is conducted in this study. The results show that users are affected by perceived information overload and perceived intrusiveness from push notifications in shopping applications and they deal with these stresses by using disturbance handling strategies and self-preservation strategies. The application varies with different reward systems. Specifically, high levels of reward can encourage disturbance handling strategies and decrease self-preservation strategies. In addition, with high perceived information control and self-efficacy in using mobile devices, users prefer to take actions to handle the disturbances. Finally, disturbance handling strategies prohibit discontinuance behavior, whereas self-preservation strategies encourage quitting behavior. The results of this study benefit both mobile shopping apps users who are in danger of stressors to develop proper strategies and actions to protect themselves and retailers who use applications on smartphones to provide products and services to obtain access to users more efficiently.

Government departments have limited resources but they are responsible for the healthy functioning of whole markets. This tension is amplified by the opportunities to generate, share and use information from new data sources and digital technologies. Huge increases in volumes and types of data produced by sensors and firms' IT systems can potentially be shared between firms which can cause information overload. This paper uses government orchestration theory to investigate the problems and opportunities of the UK's maritime transport ministry as it supports resilience planning for the whole country's ports system. We build on the developing Lean Government (l- Government) literature by theorizing on the differences between government and other stakeholders. We use a case study to investigate how these differences hinder as well as support the role of a government department. And how the special perspective of an orchestrator can integrate and filter information, motivate diverse collaborators and support the use of orchestration platforms in l-Government.

This conceptual paper provides a brief review of hospitality consumers’ information search behavior and the factors that are likely to influence their information search behavior and their utilization and processing of online reviews. This study focuses on the factors such as information overload, confusion, information processing, information presentation format, trust and evaluation mode that has not received much attention from hospitality scholars in addition to discussing the impacts of perceived risk and familiarity on information search and information processing approaches utilized by hospitality consumers. This study also discusses opportunities for hospitality researchers to empirically examine the extent to which each of the factors discussed might influence hospitality consumers information search, information processing and especially their utilization of online reviews in their decision making process.

Many consumers feel overwhelmed by the unwieldy glut of information on peer-review websites (e.g., Yelp). Review helpfulness as a peer-rating mechanism on these websites enable consumers to quickly identify the most informative reviews and thus decreasing the information overload. The purpose of the study was to examine the influential factors on review helpfulness for restaurants on Yelp from affective content and communication style perspectives. The affective content was evaluated with eight emotional dimensions (joy, sadness, anger, fear, trust, disgust, anticipation, and surprise) in Plutchik’s emotion wheel. The communication style perspective was assessed with linguistic style matching (LSM). 262,205 pieces of consumer reviews were analyzed with negative binomial model. Both LSM and six out of the eight emotional dimensions (except anticipation and surprise) were found to have significant impact on review helpfulness. The study contributes to the knowledge body of review effectiveness from an innovative angle and provide pertinent managerial implications.

Modern recommender systems appeared during the last decade of the 20th century and have already proven their importance in tackling with information overload and content or services personalisation in the modern big-data world. This is markedly suggested by their adoption by major industry parties in a wide variety of domains. The application of recommender systems in the Cultural Heritage domain has appeared relatively recently, particularly during the advent of the 21st century. Nevertheless, a large number of works appeared in the scientific literature, driven primarily from large-scale cultural heritage collaborative projects. This paper presents a review of the current recommender system approaches, including some basic mathematical formalism, and focuses on recommender system applications in Cultural Heritage, to identify the advantages, the benefits and prospects, the disadvantages and pitfalls. The paper discusses the diversity and some common assumptions in the reviewed works, outlines some proposals for future development, and takes a position on the way forward.

Background Fatalism – beliefs about the causes and controllability of disease – has been negatively associated with prevention behaviors. Fatalism has been suggested as a mechanism for health disparities because ethnic minorities are especially likely to hold fatalistic beliefs. However, the construct has been criticized: Fatalism fails to account for structural barriers to health faced by vulnerable populations that also score highly on measures of fatalism. Another critique suggests that operationalizations of fatalism expose communication failures: “Fatalism” rather reflects information overload from an environment riddled with misinformation and contradictions. This study aimed to expand understanding of one mechanism through which communication may contribute to disparities by considering the context of nutrition among bicultural Latinas, who face increased risk from dietary acculturation. Method Mixed-methods (semi-structured in-depth interview, survey) with Mexican-American women ages 18–29 (n = 24) in rural California. Results Contrary to previous studies, the majority of this sample of Mexican-American women did not endorse fatalistic beliefs; most demonstrated clear understanding of the link between diet and risk of diseases: Diabetes and heart disease were understood to result from behaviors within one's control. Yet despite articulating links between diet and disease, participants felt overloaded and confused about conflicting information from public and interpersonal sources. Moreover, despite reporting feeling inundated with information, participants noted critical information gaps, distinguishing between information available and information needed. Conclusions We found minimal support for fatalistic beliefs among a sample of Mexican-American women, but considerable information overload and confusion, together with a desire for specific knowledge and skills. Results extend understanding of how communication may influence disparities: Information overload may be conflated with fatalism, challenging the notion that fatalism is a cultural belief. Moreover, inequalities in access to and ability to process information compound overload effects. We discuss opportunities to improve the clarity of communication about nutrition science and dietary recommendations.

Background COVID-19 has brought unprecedented demands to general practitioners (GPs) worldwide. We examined their knowledge, preparedness, and experiences managing COVID-19 in Australia. Methods A cross-sectional online survey of GPs members of the Royal Australian College of General Practitioners (RACGP) was conducted between June and September 2020. Results Out of 244 survey responses, a majority of GPs (76.6%) indicated having good knowledge of COVID-19, relying mostly on state/territory department of health (84.4%) and the RACGP (76.2%) websites to source up-to-date information. Most felt prepared to manage patients with COVID-19 (75.7%), yet over half reported not receiving training in the use of PPE. The majority were concerned about contracting SARS-CoV-2, more stressed than usual, and have heavier workloads. Their greatest challenges included scarcity of PPE, personal distress, and information overload. Conclusion Access to PPE, training, accurate information, and preparedness are fundamental for the successful role of general practices during outbreaks.

COJO for COVID Recovery is a UKRI/AHRC-funded rapid COVID-19 response research project that addresses the role that constructive journalism (COJO), particularly solution-focused journalism, can play in the pandemic recovery. The research set out in this report details the findings of the project’s initial in-depth interviews and survey with UK news audiences. Four key sets of findings regarding COVID-19 news consumption and experience emerge from this analysis. 1. The high interest in COVID-19 news was associated with the need to monitor and navigate the uncertain evolution of the pandemic as it affected audiences. 2. There was a clear fluctuation that followed “an inverse Bell curve” (up-down-up) pattern of COVID-19 news consumption which was in parallel with the severity of the pandemic and its associated lockdown restrictions. 3. Many of our participants reported switching off from the news or avoiding it for reasons linked to mental and emotional health, including negativity, repetitive reporting and information overload. 4. Participants demanded the media provide more positive news that helps to lift the mood of the public and/or offers ways out of the pandemic.

Purpose The purpose of this paper is to examine the effect of visual technology on the price discovery process in listings of residential properties in Singapore from 2015 to 2018. Design/methodology/approach The authors empirically model the effects of 360 virtual tours and drone video on four dimensions in price discovery – buyers’ arrival rate, sale probability, transaction prices and time-on-market – using a comprehensive data set for the residential properties in Singapore. Findings The analysis shows that the availability of virtual tours or drone video in a listing increases the arrival rate from potential buyers, the probability of a successful sale and the selling price. These findings are consistent with the hypothesis that technologically enhanced tools improve the quality of information and the marketability of property. However, listings with virtual tours tend to be associated with longer marketing time, which is consistent with the prediction of the information overload hypothesis. Research limitations/implications This paper extends the housing and price discovery literature by examining how technologically enabled new information affects property transactions. Originality/value To the best of the authors’ knowledge, this is the first paper to consider the impact of drone video on property market outcome.

Online Product Recommendation (OPR) systems offered by Ecommerce website are crucial in providing real-time product recommendations based on customers’ past behavior and references, contributing a significant impact on increasing purchasers’ satisfaction, boosting websites’ revenue through up-selling or cross-selling, and settling a long-term relationship between websites and customers. This research model was developed to investigate how positive and negative characteristics of OPR’s quality influence purchasers’ decision-making process and their time spent while surfing E-commerce websites, and how these two factors conclusively affect customer loyalty. The results point out that customer loyalty is significantly influenced by decision making quality and is negatively affected by customer time wasted. There are positive relationships between Content relevance, vividness and the DMQ; positive association between deceptiveness, information overload and customer time wasted. The results of this research can be utilized by E-commerce websites to optimize their Product Recommendation system to gain more customer loyalty and be a strong player in the market

We define accounting engagement as stakeholders’ actions taken with the intention of influencing corporate reporting. Using this definition, we review the literature on such activism and discuss avenues for research. The evidence reviewed suggests accounting engagement is rare. We reflect on the reasons of this, given evidence on increasing overt engagement on other corporate issues, such as managerial compensation and governance, social, and environmental responsibility. Both information production and information acquisition costs have decreased over time, raising further questions about why engagement has not increased. We consider potential reasons linked to concerns over whether financial reporting meets users’ information needs, particularly, given the emergence of new users and the role of new technologies in the diffusion and processing of information. These concerns have accompanied claims of increasing complexity of financial accounting and the threat of information overload.

Highlights • A research model based on S-O-R framework is proposed to examine the factors that influence health information avoidance intention during the COVID-19 pandemic. • Information avoidance in the COVID-19 pandemic is determined by consumers’ negative affect: sadness, anxiety, and cognitive dissonance. • Information avoidance intention influences consumers’ subsequent intentions of taking preventive behaviors during the COVID-19 pandemic. • Consumer's negative affect is influenced by perceived threat and perceived information overload during the COVID-19 pandemic., This study investigated consumers’ information-avoidance behavior in the context of a public health emergency—the COVID-19 pandemic in China. Guided by the stimulus-organism-response paradigm, it proposes a model for exploring the effects of external stimuli (perceived threat and perceived information overload) related to COVID-19 on consumers’ internal states (sadness, anxiety, and cognitive dissonance) and their subsequent behavioral intentions to avoid health information and engage in preventive behaviors. With a survey sample (N = 721), we empirically examined the proposed model and tested the hypotheses. The results indicate that sadness, anxiety, and cognitive dissonance, which were a result of perceived threat and perceived information overload, had heterogeneous effects on information avoidance. Anxiety and cognitive dissonance increased information avoidance intention, while sadness decreased information avoidance intention. Moreover, information avoidance predicted a reluctance on the part of consumers to engage in preventive behaviors during the COVID-19 pandemic. These findings not only contribute to the information behavior literature and extend the concept of information avoidance to a public health emergency context, but also yield practical insights for global pandemic control.

Whether the general belief in a just world (GBJW) can protect individual emotions during a major disaster is a matter of debate. This study conducted two experiments to explore this question during the COVID-19 epidemic. Experiment 1 (N = 92, Mage = 22.52, 48.91% was male) manipulated the focus regarding the COVID-19 epidemic to investigate the impact of this focus on participants' emotions. The results showed that compared with the nonepidemic focus group, the epidemic focus group had higher negative emotions and lower positive emotions. Experiment 2 (N = 200, Mage = 23.91, 49% was male) manipulated the epidemic focus and GBJW to investigate their effects on the participants' emotions. The results showed that high levels of GBJW reduced negative emotions and increased positive emotions regardless of whether the participants were focused on the epidemic. This study expands the influence of the GBJW on individual emotions and finds that the GBJW can protect individuals' emotions when they face a major social disaster. These findings imply that controlling people's intake of information on the epidemic can avoid their suffering from the vicarious traumatization caused by epidemic-related information overload and that improving the public's GBJW protects their mental health during an epidemic.

The growing state of research on recycled aggregate (R-A) concrete presents academics with an information overload that may obstruct effective research and academic collaboration. Scientometric analysis has the potential to quantitatively analyze the various aspects of the extensive literature using a suitable software tool. This study adopted a scientometric analysis approach along with the traditional review of the present state of research on R-A concrete. The bibliographic data was retrieved from the Scopus database comprising 2180 documents and analyzed. The publication sources, keywords, authors, articles, and countries were evaluated from the bibliographic data. Also, the most crucial areas of R-A concrete have been highlighted and examined. The practical use of R-A concrete is examined, as are improving strategies. The scientometric analysis revealed that the leading authors in the relevant field are De Brito J. and Poon C.S., based on the number of publications and citations, respectively. It was concluded from the literature review that R-A concrete exhibits inferior mechanical and durability performance due to the weak mechanical characteristics of R–As. By employing improvement techniques like mixing process modification, pre-coating the R–As, and addition of admixtures can help improve R-A concrete performance. The large-scale applications of R-A concrete are limited due to its inferior mechanical and durability properties, increased cost of improvement techniques, and lack of standards for the processing of R–As, as well as the production and mix design of R-A concrete. Finally, new research has been suggested in light of the gaps in the current study.

With the launch of academic search engines such as Google Scholar, Microsoft Academic, and Scopus, researchers are using them to access scholarly materials on a large scale without making any payment. In this optimal research environment, academic libraries or community databases have naturally experienced a rapid increase in volume; however, the excessive quantitative growth of information, or information overload, acts as a double-edged sword that prevents researchers from finding relevant prior studies or researchers with similar interests. Existing keyword and rule-based search systems carry the risk of recommending research literature with a high citation frequency rather than recommending contextually similar documents. Therefore, this study proposes a semi-supervised semantic-based research literature and researcher recommendation system using LDA and BERT. Since a semi-supervised method is used, research literature can be embedded based on contextual and classification information, and the global topic information of the research literature can also be captured. In addition, a research literature information extractor system has been implemented, which comprises classification network of our model and an explainable keywords extractor system implemented using BERT’s self-attention structure. Based on the experimental results, it can be confirmed that the proposed study model shows better performance than other baselines. This model can help users to find information and contextual matching data quickly and accurately from the database. In addition, when implementing an academic library and community database, such a powerful research-related recommendation system and research literature information extractor are expected to have an enormous effect.

During the COVID-19 pandemic, unusual consumer behavior, such as hoarding toilet paper, was reported globally. We investigated this behavior when fears of consumer market disruptions started circulating, to capture human behavior in this unique situation. Based on the stimulus-organism-response (S-O-R) framework, we propose a structural model connecting exposure to online information sources (environmental stimuli) to two behavioral responses: unusual purchases and voluntary self-isolation. To test the proposed model, we collected data from 211 Finnish respondents via an online survey, and carried out analysis using PLS-SEM. We found a strong link between self-intention to self-isolate and intention to make unusual purchases, providing empirical evidence that the reported consumer behavior was directly linked to anticipated time spent in self-isolation. The results further revealed exposure to online information sources led to increased information overload and cyberchondria. Information overload was also a strong predictor of cyberchondria. Perceived severity of the situation and cyberchondria had significant impacts on people's intention to make unusual purchases and voluntarily self-isolate. Future research is needed to confirm the long-term effects of the pandemic on consumer and retail services., Highlights • We investigate people's purchase and isolation behavior during the COVID19 pandemic. • Information source exposure leads to information overload (IO) and cyberchondria. • IO had a negative effect on perceived severity, whereas cyberchondria had a positive impact. • Perceived severity (PS) positively affected intentions to self-isolate and unusual purchases. • Purchase self-efficacy reinforces the influence of PS on unusual purchases.

This paper experimentally investigates how different types of decision aids interact with increases in data load in a structured capital budgeting decision-making task. The experiment employed a 2x2 between-subjects design and was run in a course on management control systems with 136 master’s degree students at a German university. Subjects were tasked with reviewing investment proposals that contained differing amounts of information (low vs. high data load, i.e., irrelevant information cues in addition to those relevant for the decision). The second manipulation referred to the type of decision aid – either a detailed, rules-based capital budgeting guideline with clear cut-off rates, or the advice to employ generally accepted criteria for investment decision-making. The dependent variables investigated were perceived task complexity, decision accuracy, and decision confidence. Increases in data load led to an increase in perceived task complexity. There was only limited evidence for experimental conditions affecting decision accuracy. The group of subjects relying on the capital budgeting manual reported significantly higher decision confidence. Implications for practice on how to provide decision-makers with information for investment decision-making are derived.

Information overload has always been a challenge for business-people as well as professionals from other types of organizations. And today with search algorithms and artificial intelligence (AI) such an ever-present part of daily life and media consumption, the challenges in learning how to filter information for oneself for effective processing, interpretation and analysis have only increased. Several frameworks are presented that were created for instructing students to assist with addressing this. They were tested and refined over four years in a core MBA course focused on decision making and project-based work. They include ways to conceptualize the broad areas of information available for business decision making as well as how to identify information by thinking about who is producing it, why they are producing it and who their key customers are. Other frameworks presented deal with ways to identify pertinent information and how to process and work with it as part of a research investigation. These frameworks are presented as tools that can be used by business school instructors, but certainly have a broader application as useful guidelines for anyone hoping to be a better collector and processor of relevant information for decision making and project work.

Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

This paper reviews literature in the domain of information overload in accounting. The underlying psychological concepts of information load (as applied in accounting research) are summarized, and a framework to discuss findings in a structured way is proposed. This framework serves to make causes, consequences, and countermeasures transparent. Variables are further clustered into major categories from information processing research: input, process, and output. The main variables investigated are the characteristics of the information set, especially the number of information cues as an input variable; the experience of the decision-maker, the decision time, decision rule, and cue usage as process variables; and measures related to decision quality (i.e., accuracy, consensus, consistency) and related to self-insight (calibration, confidence, feeling of overload) as output variables. The contexts of the respective research papers are described, and the operationalization of variables detailed and compared. I employ the method of stylized facts to evaluate the strength of the links between variables (number of links, direction and significance of relationship). The findings can be summarized as follows: most articles focus on individual decision-making in the domain of external accounting, with financial distress predictions constituting a large part of these. Most papers focus on input and output variables with the underlying information processing receiving less attention. The effects observed are dependent on the type of information input and the task employed. Decision accuracy is likely to decrease once information load passes a certain threshold, while decision time and a feeling of overload increase with increasing information load. While experience increases decision accuracy, the results on decision time are conflicting. Most articles have not established a significant link between changes in information load and changes in decision confidence. Relative cue usage, consensus, consistency, and calibration decline with increasing information load. Based on these findings, implications for practice and future research are derived.

We examine whether concurrent quarterly reports help investors respond to earnings news when uncertainty is high. Although existing studies suggest that quarterly reports released concurrently with earnings depress trading due to information overload, 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 respond 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 in response to earnings news. We find the relation is stronger when firms face greater uncertainty and when firms include more discussion of revenue recognition. Our results suggest that concurrent quarterly reports are informative to investors when uncertainty is especially high.

Purpose The Australian and New Zealand governments have released smartphone-based apps to complement contact tracing in the event that they face a resurgence of COVID-19 infections. The apps form part of both countries’ policies to support a return to social and economic engagement following extended lockdowns. This paper aims to investigate the extent to which the two approaches are fit for purpose and compare their functional characteristics. Design/methodology/approach Using process mapping and analysis, this paper evaluates the potential of the two apps to improve the performance of existing contact tracing systems across a range of efficiency and effectiveness criteria with an emphasis on the framework proposed by Verrall (2020). Findings The Bluetooth-based Australian app appears likely to assist that country’s contact tracing system to perform more efficiently and effectively in the event of a resurgence of the virus and should increase confidence in re-engagement. The New Zealand QR code-based app, however, is not well-aligned with these objectives. Its interaction with a range of other regulations and obligations, combined with the inconvenience it imposes on its users, is likely to militate against its use. Bluetooth-based apps based on interactions between individuals likely better support these population-based objectives than QR code-based location-specific apps. Originality/value This paper provides an original and extensive analysis of the functionality and effectiveness of Australia and New Zealand’s official contact-tracing apps.

Recently, the rate of people using e-government sites has been increasing. It is expected that acquisition and delivery of government policies and administrative work through e-government sites will be developed consistently (Security News, 2018.03.05). However, the usage rate of government websites by old people is still low. Old people tend to visit public offices to acquire administrative information and seek an administrative help from public officials rather than using e-government sites. According to the 2017 report of Ministry of Public Administration and Security, 65% of administrative service used by people aged over 50 is performed by visiting public offices and the rate is 82.9% for people aged over 60. The use rate of e-government sites or administrative process via email is very low (people 50 +: 31.2% and 60+: 11.1%). The main reason is that old people tend to that believe offline visit rather than online process expedites the administrative service and they can get an appropriate help and guide from public officers. The rate of awareness and use of e- government sites for people aged over 60 is 62.4% and 54.3% respectively and the reason for such low rates is that old people are not familiar with the e-government sites and prefer offline administrative service to online service. Out of people aged over 50 who have not used e-government sites, less than 30% have a motive for e-government site use (Ministry of Public Administration and Security, 2017). Although uneasiness and low motive to use online administrative service by old people have not caused serious problems so far, a demand for old people to utilize online service is increasing with constant developments in online administrative service due to the advent of sophisticated knowledge based society and regional inequity. Online administrative service is also important for younger people because administrative services such as tuition loans, aid for living expenses, and support fund for younger people are processed online in e-government sites. Therefore, competence to understand and utilize public information in e-government sites is also required for younger people. A low level of online public information use results in problems such as a lack of access to and utilization of useful health information for young people as well as old people (Kim, Choi, & Park, 2018; Hardie, Kyanko, Busch, LoSasso, & Levin, 2011; Jiang, & Beaudoin, 2016). Previous research asserts that inappropriate health information or improper use of online health information may be a major cause of avoidance of health decision making and information overload, which eventually leads to negative behaviors including inaction or degraded health interest. Therefore, utilization of online public information is related to behaviors (e.g., healthy eating and observance of medical guide) as well as acquisition and comprehension of online public information. That is, inappropriate online public information affects citizen behavior and public relation of government for an effort to improve online public information negatively. Recently, according to Ministry of Environment, an inadequate way of providing information makes people feel confused and avoid information (Dailian, 18.03.12). If information is provided in an improper way, however valuable it may be, it causes confusion especially for those who are less able to process information in e-government sites. Old people are prone to a lack of online information use because they are less able to use e- government sites. On the contrary, more than 95% of people aged less than 40 who are more competent in computer technology utilize e-government sites, because they believe that e-government sites make it easy to process administrative inquiries anytime when they need. Although e-government sites are convenient, old people are reluctant to use them because of their lack of knowledge and experience of IT technology. Therefore, it is necessary for government to remove barriers to use e-government sites for old people. For young people, their lack of experience to process administrative inquiry, difficulty of administrative jargons, and complexity of administrative services may make them avoid to use e-government sites. In relation to this, recent research finds that infographics is a useful format of presenting huge online information for people understand and process information easily and in an effective way (Otten, Cheng, & Drewnowski, 2015). Recently, although more and more websites use simple ways to convey policy information such as card news, there are few studies to examine the effects of various information formats on information processing and the relationship between information format and psychological characteristics. In terms of the psychological characteristics of information processing, one of the reasons for reluctance to use online administration site may be related to the format of information provided by the site. People with low ability and low experience to use new technologies may have difficulties in processing information, and the difficulties of decision-making caused thereby can lead to decision-making avoidance (Anderson, 2003). The reason why decision making is difficult is that more information is entered than information that can be processed by individuals with different levels of information literacy and information processing strategies. Information overload is a situation in which more information is entered than information that can be handled by the user, making it difficult to handle cognitive and reactive processes such as judgment and decision making (Eppler & Mengis, 2004, p. 326). Information overload in the course of information acquisition or decision making can reduce information processing capabilities and reduce interest in achieving goals (David, 2011; Klapp, 1986). Anyone who lacks information on policy or administrative experience may experience information overload for information that is new or information about administrative procedures, and it is necessary to prevent them from avoiding decision-making or leading to non-action. In other words, it is necessary to study how people respond to different types of information presentation (e.g., text or inforgraphics) and how an information nudge strategy works, which will prevent decision-making avoidance and improve our understanding of how people accommodate their behavior as information is presented. In this study, we conducted research to identify how users respond to different types of information provided by the e-government sites and how user’s information processing tendency (visual vs. verbal preference) and information literacy moderate the relationship between information overload and user attitudes and responses to e-government sites.

Consumers use online reviews to help make informed purchase decisions. This paper extends existing research by examining how content of online reviews influences perceptions of helpfulness by demonstrating how different emotions can influence helpfulness of both product and service online reviews beyond a valence-based approach using cognitive appraisal theory and attribution theory. This research contributes to existing knowledge regarding the theory of information processing, attribution theory, and cognitive appraisal theory of emotions. Using findings from this study, practitioners can make review websites more user-friendly which will help readers avoid information overload and make more informed purchase decisions.

This paper experimentally investigates structured investment decision-making with sequential information provision. Subjects were asked to rank investment alternatives using a scoring model. A mixed design with information provision as the within-subject variable and initial information load as the between-subjects variable was employed. A computer-based process tracing technology provided insights into the underlying information acquisition process – information cues were covered by boxes for the second step of the decision. Moving the cursor over a box uncovered the information. Duration and frequency of uncovering the different information cues was recorded in a database. The main variables of interest were decision accuracy, decision confidence, and measures characterizing the information acquisition process. The main findings were the following: the amount of information provided in the first step significantly impacted decision accuracy and decision confidence, which decreased for the high initial information load group. In addition, receiving a high number of information cues in the first step led to a reduction in the attention devoted to the information received in the second step. Confirmatory information search behavior could not be observed. Findings help further our understanding on how the amount and timing of information provision impact decision quality and confidence.

Logging -- used for system events and security breaches to describe more informational yet essential aspects of software features -- is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with $\sim$3 million lines of code and $\sim$4K log statements. Our tool successfully analyzes 99.22% of logging statements, increases log level distributions by $\sim$20%, and increases the focus of logs in bug fix contexts $\sim$83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels., Comment: 37 pages, 1 figure

Social media platforms are increasingly being used as important sources for obtaining various types of information in the current digital age. While an increasing number of studies have investigated the factors that influence user's news content sharing behavior, few have paid attention to the reposting latency of online news contents. Reposting latency refers to the delay of interval time between original post publish time and repost time. Reposting activity on social media is an important type of user feedback behavior to the message received. The speed of the response could reflect user's processing efficiency and capacity. This study examined the possible factors that may influence users' reposting latency of news contents on social media. In doing so, we employed a multilevel negative binomial model to examine the impacts of issue attention, temporal usage pattern, and information redundancy. Our findings show that multiple issues could distract user's attention, thus leading to the low reposting speed. We also found a distributed temporal usage pattern could help shorten reposting time, while information redundancy and information overload could increase the reposting latency of news contents on social media. The findings of this study can contribute to advancing the understanding of news consumption behavior on social media. The conclusions have the potential to help in explaining and further predicting the success of news diffusion.