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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.

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.

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.

Information overload have become a major problem due to sudden rise in the Internet data and users visiting websites. The recommendation system is a technique of information filtering to predict the preferences that users may like. The main target of recommendation system is to provide useful information to users and solve the large-scale problem of information overload where users are not able to get correct results. Different products have different ratings given by many users. On most websites, each product has some labels or phrases, which are used to identify the content of a product called as tags. In practice, users generally look at the ratings and tags attached to the item to decide whether they should buy it or not. The proposed approach generates recommendations using collaborative filtering by utilizing users’ tags and ratings information. The proposed approach predicts rating of products to the target user based on target user’s profile and other similar users. The proposed approach adjusts the predicted ratings of recommended items using standard deviation of items’ ratings.

New technologies and fast expansion of internet have made access to information easier for learners in web-based education. E-learning environments today are used largely to handcraft training courses about thoughtfully selected topics for delivery to learners registered for those courses. E-learning environments are online so it is not mandatory for learners and instructors to meet in the same place and these e-learning environments play an important role in self-learning and self-training of learners. Educational organizations nowadays adapting e-learning platforms for their learners so as they can gradually get benefited and enhance their learning capabilities. The recommender system helps the learners to overcome information overload problem as learners are recommended e-learning resources according to their habits, likes & dislikes, learning styles, interest area and their level of knowledge.The paper presents a novel approach, a framework for building architecture for a recommender system that integrates tacit knowledge in e-learning environment so as to enhance the learning potential of learners. The proposed approach is based on Nonaka and Takeuchi’s knowledge based SECI model of learning that twirl around inter-transformation of two forms of knowledge that are explicit and tacit. The proposed framework adapts SECI model and contains four modules, first a learner module which contains learners’ personal information, their learning style and their interest area that is uses to identify learners learning preferences and activities. Second a domain module which contains all the information for a particular area and holds the knowledge about the set of course structure. Third an e-assessment module that consists of three blocks named diagnostic assessment, formative assessment and revision module, the main intention of is to provide more benefits to the learners to improve their knowledge. The last module is recommendation module that helps to generate the recommendation of suitable learning module to the learners, founded on their learning style and their level of knowledge.

Disclosure as a regulatory technique faces strong criticisms. However, regulators keep relying on this technique to remedy market failures. Obesity is not an exception. Ultra-processed food dominates our diet, despite being a main cause of overweight and obesity. Overweight and obesity, in turn, are major risk factors for many chronic diseases, such as diabetes and cancer. Nutrition labels are a type of disclosure. The empirical evidence of their effectiveness is mixed. This might be because nutrition labels have to deal with consumers' lack of literacy and/or information overload. Regulatory simplification can be an alternative to enhance label's efficacy. However, current criticisms of disclosure also reach its simplified forms, predicting that simpler labels will not have an effect on consumer behavior. Chile's new food labeling law incorporates a warning-signs model to alert consumers of foods and drinks containing sugar, fat, sodium, and calories levels above a certain threshold. This paper provides an exploratory impact assessment of this law, casting doubt on the skeptics' view of the inefficacy of simplified disclosures. I mostly base this analysis on information available online (i.e., impact assessment surveys; newspaper articles; stakeholder presentations). However, I also conducted field interviews with key actors to complement, and corroborate, the information available on the internet. The Chilean experience is still recent; however, it shows that simpler disclosures can have a significant effect on consumer behavior. This is a positive (or descriptive) claim. Although generalizations are hard to make, consumers tend to prefer products with no (or fewer) labels. Accordingly, only one year after the new labeling law was enacted, more than 1,500 products have modified their composition. This is despite warning labels appearing to impact only well-educated consumers. This may be causing an externality on less-educated consumers in some markets, probably a "positive" one, in many cases. On a normative level, the impact of the new labeling regulation on the nutritional value of food is still unclear. The strategies to reach the safe harbors may not only decrease the intake of ingredients subject to control but also the nutritional value of food. Further research should provide a more specific snapshot of how particular markets reacted to the new labeling law. In addition, consumers might be drawing incorrect inferences from the warning labels. The main objective of this article is to show that consumers are not indifferent or unreactive to food warning labels. Precisely because of this, law-makers should be careful in designing regulations that consider not only how consumers will respond to the warning-labels, but also how industry participants will react to supply products with no (or fewer) labels.

Online Consumer Reviews (OCRs) have become a very important touch point along the typical consumer decision-making journey, as they help consumers to evaluate products and services and to make better choices. In a context where consumers usually face information overload situations due to the huge number of OCRs available for each product or service, review helpfulness is identified as a sign of quality and diagnosticity. This paper explores the factors influencing review helpfulness adopting a new framework, the Consumer Voting Journey. Along this proposed journey, consumers face five stages before deciding if voting an online review as helpful, not helpful or not voting. In this paper, we focus on the three last stages: product consideration stage, review consideration stage and voting stage. A zero-inflated negative binomial regression model is used to explore first, the factors explaining the excess of OCRs with zero helpfulness votes and second, the count process explaining the number of yes helpful votes. The model explains the excess of zeros by using product order and review order probability variables, which reflect the probability of considering a specific product within a category and a specific review from a set of OCRs. The number of yes helpful votes received by OCRs is explained by review-specific and reviewer factors, including text content variables. The results evidence that not only review-specific and reviewer factors matter, but also that the order in which products and OCRs are displayed at a website has an important effect on review helpfulness. Furthermore, a model built from the perspective of the Consumer Voting Journey explains review helpfulness better than a traditional model in which only variables from the voting stage (review-specific and reviewer factors) are included.

As medical organizations modernize their operations by adopting modern electronic equipments, vast amount of Electronic Health Care Information are available through giant repositories. Health related information of a patient is multidimensional in nature since it is generated, gathered from variety of resources to include vital information like symptoms, disease, lab test results, various diagnostic results, complications, treatment schedules and prescriptions suggested by multiple specialists. Decision making in proceeding future treatments may be even complex; critical decision making do not rely on single patient health information. Such situations can be influenced by treatments and results experienced by similar patients. Visual Analytics (VA) is an emerging technology which shows significant promise in addressing information overload challenges. Visual Analytic process combines the strength of intelligent automatic data analysis coupled with visual perceptions aided by human interactions. This survey paper discusses on various research methodology adopted and results obtained using Visual Analytics framework in health care domain.

Within the EU investor protection framework, issuers are required to provide different investor groups with relevant information. Nevertheless, the issuers’ accounting regime seems to be inconsistent with this regulatory approach. Due to their ever increasing complexity, IAS/IFRS are not suitable to meet investors’ needs and can lead to an information overload, to which also sophisticated institutional investors are exposed. The article looks at the UK “strategic report” model and argues that the narrative component of financial reports can play a key role in rendering financial reports more informative and more suited to meeting information needs of different groups of users by diversifying information directed at sophisticated and unsophisticated investors. The article outlines the framework of a possible EU (more) harmonized framework in the area of narrative reporting, arguing that the ESMA can foster harmonization by providing guidelines on the enforcement of narrative reporting and (following the example of the Financial Reporting Council) promoting proactive activities aimed at developing guidance and recommendations on the contents and format of narrative reporting.

In recent years, corporate filings and their important sections have become thicker and longer. At the same time investors are swamped with information that is available through media, analysts and other sources creating information overload. Using several firm level proxies of information overload and several firm-level proxies of cost of equity capital, we examine in this paper whether information overload affects firms required rate of returns (i.e. financing costs). Based on a sample of 13418 firm-years during the 1993 to 2009 period, we find that information overload significantly and positively affects cost of equity capital. These results are robust to a series of validity checks.

Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system. “My dear, here we must process as much data as we can, just to stay in business. And if you wish to make a profit you must process at least twice as much data.” - Red Queen to Alice in Hedge-Fund-Land. Necessity is the mother of all invention/creation/innovation, but the often forgotten father is frustration. In this age of (Too Much) Information, it is imperative to uncover nuggets of knowledge (signal) from buckets of nonsense (noise). To aid in this effort to extract meaning from chaos and to gain a better understanding of the relationships between financial variables, we summarize the application of the theoretical results from (Kashyap 2016b) to microstructure studies. The central concept rests on a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions, and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We provide an empirical illustration using prices, volumes and volatilities across seven countries and three different continents. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing. In Indian mythology, it is believed that in each era, God takes on an avatar or reincarnation to fight the main source of evil in that epoch and to restore the balance between good and bad. In this age of too much information and complexity, perhaps the supreme being needs to be born as a data scientist, conceivably with an apt nickname, the Infoman. Until higher powers intervene and provide the ultimate solution to completely eliminate information overload, we have to make do with marginal methods, such as this composition, to reduce information. As we wait for the perfect solution, it is worth meditating upon what superior beings would do when faced with a complex situation, such as the one we are in. It is said that the Universe is but the Brahma's (Creator's) dream. Research (Effort/Struggle) can help us understand this world; Sleep (Ease/Peace of Mind) can help us create our own world. A lesson from close by and down under: We need to “Do Some Yoga and Sleep Like A Koala”.

In social broadcasting sites, users’ charitable content generation is affected by both their followees (to whose content they subscribe) and followers (people who subscribe to these users’ broadcasts). In this paper, we examine how reciprocity of followees affects social influence on users’ charitable content generation by leveraging an annual fundraising event on social media. We employ a latent instrumental variable approach which effectively controls for the endogeneity issue arising from followees’ charitable engagement. In addition, a field experiment is conducted to investigate the role of followers. We find that within a specific charitable topic, reciprocal relationship has a positive impact on social influence, and non-reciprocal relationship has a negative impact on social influence that drives content generation following the topic. Our findings suggest an effect of information overload caused by excessive messages generated by non-reciprocal nodes such as news media and nonprofit organizations. We also reveal that users usually generate charitable content out of their altruistic motivations. However, those users who include pictures in their previous charitable content are more likely to be driven by reputation-based motivations in generating charitable content.

The interaction between the two cognitive modes—the automatic, intuitive mode, and the conscious, analytic mode—has been a long-standing debate in the psychological fields. In a seminal paper, Louis and Sutton (1991) suggested that cognitive switches from the default automatic mode (“intuition”) to the conscious mode (“analysis”) could improve decisions. We develop a complementary theoretical perspective titled intuitive interruptions in the context of complex managerial decisions when analysis is the default mode. Combining hints in extant literature with decision thought experiments, we propose that a way for managerial decision makers to enjoy the complementarities between analysis and intuition is to allow intuitions to interrupt the default analytic processes of complex decision making—that is, to switch from the conscious, analytic mode to the automatic, intuitive mode, and then to switch back. Such intuitive interruptions focus and redirect subsequent analysis and consequently improve decision quality in situations of information overload and information insufficiency. Theoretical and practical implications are discussed.

As online information is increasingly tailored, or “personalised”, to the user, it has been praised by some as a pragmatic response to information overload, while criticised by others as creating an echo chamber that threatens deliberative democracy. The unsettling question is whether the latest wave of innovation in online content personalisation technologies has shifted decision-making power from humans to computers. The paper argues that a thorough understanding of personalisation technologies is necessary to critically evaluate their normative effect and impact on social values. It reflects on the differences between regulation by code and regulation by law, exploring how code affects individual and social autonomies, and considering whether meta-rules regulating code are appropriate. The aim of this paper is to detail the constitutive features of the digital world and elucidate how these create norms that regulate the Internet.

Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system. “My dear, here we must process as much data as we can, just to stay in business. And if you wish to make a profit you must process at least twice as much data.” - Red Queen to Alice in Hedge-Fund-Land. Necessity is the mother of all invention/creation/innovation, but the often forgotten father is frustration. In this age of (Too Much) Information, it is imperative to uncover nuggets of knowledge (signal) from buckets of nonsense (noise). To aid in this effort to extract meaning from chaos and to gain a better understanding of the relationships between financial variables, we summarize the application of the theoretical results from (Kashyap 2016b) to microstructure studies. The central concept rests on a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions, and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We provide an empirical illustration using prices, volumes and volatilities across seven countries and three different continents. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing. In Indian mythology, it is believed that in each era, God takes on an avatar or reincarnation to fight the main source of evil in that epoch and to restore the balance between good and bad. In this age of too much information and complexity, perhaps the supreme being needs to be born as a data scientist, conceivably with an apt nickname, the Infoman. Until higher powers intervene and provide the ultimate solution to completely eliminate information overload, we have to make do with marginal methods, such as this composition, to reduce information. As we wait for the perfect solution, it is worth meditating upon what superior beings would do when faced with a complex situation, such as the one we are in. It is said that the Universe is but the Brahma's (Creator's) dream. Research (Effort/Struggle) can help us understand this world; Sleep (Ease/Peace of Mind) can help us create our own world. A lesson from close by and down under: We need to “Do Some Yoga and Sleep Like A Koala”.

This paper constitutes a descriptive study of the online banking and financial behavior of Generation Y consumers based on 110 completed surveys. Topics covered in this study were drivers behind their choice of bank, online banking behavior, and influencers of financial decisions. Research findings suggest that Generation Y does not wade through information overload – instead, they resort to alternatives steps in product selection, such as, relying on a personal recommendation by a trusted influencer. Although generation Y spends much of their time online, survey findings indicate that college students are still learning how to use online banking, relying instead on their parents and the ATM for all their banking needs.

Recent years have witnessed a rapid increase of online data volume and the growing challenge of information overload for Web use and applications, thus information diversity is of great importance to both providers and users of search services. Based on the proposed diversity evaluation measure (namely, information coverage), two heuristic methods with corresponding algorithms are designed upon the greedy submodular idea. First, this paper proposes the CovC+S-Select algorithm, which possesses the characteristic of asymptotic optimality, to optimize information coverage by applying the strategy of simulated annealing. Then to accelerate the speed of CovC+S-Select algorithm, its fast approximation called FastCovC+S-Select has been introduced by utilizing the properties of information coverage. Furthermore, ample experiments have been conducted to show the effectiveness, efficiency and parameter robustness of proposed diversity-oriented extraction methods in this paper, along with comparative analyses revealing their performances advantageous over other related methods.

This paper analyses relationships between research and policy at European Union (EU) level about international migration by drawing from the perspectives of migration policy practitioners and their practices. By practitioners is meant those who seek to influence, shape or make migration policy at EU level including officials from EU institutions, national officials, academic researchers and representatives of international organisations, think tanks and NGOs. By practice is meant the socially recognised competence of practitioners. A particular focus is on the relationship between research and policy, or put another way, between the production of knowledge about international migration and the use of this knowledge in policy-shaping and policy-making. Questions include: how are relationships structured between researchers and policy-makers at EU level? How do policy-makers gather and process information? Is there risk of information overload and, if there is, how does this affect the relationship between researchers and policy-makers? Do policy-makers actually listen to researchers? If they do, what do they want to hear? And, do they actually hear it? Is there evidence that research has contributed to the development of shared understandings at EU level? If so, do these confirm or challenge existing policy orientations?

A simple model of memory load based on the order that information is searched in is introduced, and minimum memory load search orders are characterized. When tested in an experiment the model is found to track subjects’ choice errors exceptionally well. Together, these results indicate that in the type of rich search problems that are common in modern economic life, optimal sequential search is not feasible for humans because of the excessive memory load it requires. Nevertheless, that the model of memory load is found to explain subjects’ main systematic deviations from optimality suggests that a model of optimal sequential search subject to memory limitations could describe search behavior well. In addition, the model of memory load gives a structural explanation for previously elusive concepts such as choice error, information overload, and how heuristics are selected. Further, it can explain a variety of previously puzzling phenomena observed in rich search experiments, and oers a fully operational exclusion criterion of the type called for in the extended choice literature.

SEBI, the Indian securities market regulator had set a unique example for the entire world when they introduced mandatory IPO grading in India with effect from May 2007. Though the purpose of this grading was to provide retail investors with a ready-made assessment of the fundamental quality of the issuer of an IPO, so they could make a better and informed investment decision in an era of information overload, it faced severe criticism from all players of the market. Using a data set of 355 IPOs, inclusive of 131 graded IPOs, findings of this paper suggest that IPO grading does have strong positive effect on demand (oversubscription) and fairly strong negative effect on underpricing. This paper infers that NII and QIB are more benefitted from grading of IPOs than the retail investors.

SEBI, the Indian securities market regulator has set a unique example for the entire world by introducing IPO grading in India. The purpose of this grading was to provide retail investors with a ready-made assessment of the fundamental quality of the issuer of an IPO, so they could make a better and informed investment decision in an era of information overload. Grading of IPOs is subject to severe criticism. Critics question efficacy and effect of this process. Based on the study of 319 IPOs, inclusive of 97 graded IPOs, findings of this paper suggest that IPO grading do have fairly strong negative effect on underpricing.

In this paper, I will attempt to present a set of solutions to the issues related to the poor quality of legal writing. They will be focused around the benefits of online legal short form. In the first part, I will discuss the challenges posed by the information overload that we experience. I will also address the ongoing criticisms of law reviews and legalese. In the second part, I will evaluate the usefulness of abstracts and summaries. Finally, in the third part I will propose solutions, which on the immediate level will deal with legal writing itself, but also will have broader positive implications to the bar and other consumers of legal information.

Recommender systems — which filter information based on individual interests — represent one possible remedy for the problem of information overload. There are two major types of recommendation techniques — collaborative filtering and content-based filtering. While the content-based approach alleviates the “cold start” problem faced by collaborative filtering, its accuracy is generally lower. Therefore, a hybrid approach is commonly adopted. However, we find that the existing hybrid approaches are hampered by insufficient analysis of unstructured content features of recommended products and the neglects of heterogeneous individual preferences on product attributes. Therefore, we propose a new recommendation framework that utilizes latent semantic analysis to extract semantic features from unstructured text and employs multiple regression to identify unique product feature preferences for each user. By using a combined dataset from MovieLens and Microsoft Xbox, we develop a movie recommender as a proof of concept. The initial results represent a promising opportunity for combining behavioral studies and computer algorithms.

The paper analyses the problems of financial reporting disclosures by public companies, particularly in the context of the apparent contradiction between widespread claims of information overload on the one hand and continuing claims from users that they do not receive all the information they need on the other. It identifies how both market failures and regulatory failures contribute to current problems and argues that there are fundamental problems of financial reporting disclosure that no market or regulatory solutions can entirely remove. The paper includes a brief survey of some of the key findings of empirical research on disclosure.

This paper posits a scenario in which three famous information theorists meet each other in a bar and compare notes. The three include the celebrated information theorist Claude Shannon, the 18th century English statistician Thomas Bayes and the Harvard statistical linguist George Zipf. Each is famous for promoting a fundamental equation about the nature of human communication and informational updating. In the scenario they discover that with some modest mathematical transformations they can demonstrate that each of the equations, although based on entirely distinct phenomena in physics and linguistics, has the same basic structural form. This common structural form can be identified as a variation of the power law function. The remainder of the paper explores how such distinct phenomena share similar structural properties and why an understanding of these properties is important for the design and regulation of advanced information systems. The key organizing concept is the familiar metaphor in communication policy – the marketplace of ideas. As formalized in Title III the 1934 Communication Act, subsequent amendments and Commission rulings, regulatory policy has focused on trying the maximize the number of “voices” in the public sphere given the constraints of spectrum scarcity. Thus the traditional models of anti-trust HHI calculations, ownership caps, minority preferences and localism rulings have been designed to maximize diversity. But the regulatory logic is based on an industrial age notion of an extremely limited number of broadcast voices which is much less appropriate in an era when every broadband connection is as capable of broadcasting as of receiving. The paper argues that although information theory has traditionally been rather marginal in the domain of communication policy, it need not be. In promoting diversity it is challenging for evolving communication policy to meaningfully address not just the diversity of voices but the diversity of information.

SEBI, the Indian securities market regulator has set a unique example for the entire world by introducing IPO grading in India. The purpose of this grading was to provide retail investors with a ready-made assessment of the fundamental quality of the issuer of an IPO, so they could make a better and informed investment decision in an era of information overload. Grading of IPOs is subject to severe criticism. Critics question efficacy and effect of this process. Based on the study of 319 IPOs, inclusive of 97 graded IPOs, findings of this paper suggest that IPO grading does have strong positive effect on demand (over-subscription) and fairly strong negative effect on underpricing. This paper infers that NII and QIB are more benefited from grading of IPOs than the retail investors.

The prosperity of online shopping has led e-commerce vendors to provide increasingly rich information, particularly for experience products, to enhance consumers' shopping experience and satisfaction. However, there is little awareness that consumers may not be able to process all the information available because of human beings' limited information processing capacity. Online shoppers could be easily confused when facing rich information, particularly when the amount of information greatly exceeds their processing capacity. In contrast to previous research which has focused on the formatting of information or user interfaces to solve the information overload problem, this study explores a new solution based on the role of unconscious thought. Integrating information processing theory and the unconscious thought theory, the current study examines the different roles of information quantity, information quality and thought mode in consumers' decision satisfaction, in the presence of rich information. Our results show that unconscious thought moderates the relationship between information quality and consumer satisfaction towards their decision making when shopping experience products online, and is thus worthy of special attention in the design of e-commerce websites. The study contributes to both unconscious thought theory and information processing theory by exploring the interaction effect of the quantity and quality of information with thought mode in affecting the quality of purchasing decisions.

Assisting iterative, opportunistic and evolving visual sensemaking has been an important research topic as information overload continues. Such exploratory systems (ESs) maximize users’ information gain through learning and have been widely used in scientific discovery and decision making contexts. Although many ESs have been developed recently, there is a lack of general guidance on how to evaluate such systems. Researchers are facing challenges such as understanding the underlying cognitive learning process of these systems. In this paper, we propose an evaluation framework designed specifically for ESs. The new framework is built based on cognitive load theory (CLT) and takes the user as well as the system as the focus of evaluation. With the thorough examination of users’ cognitive process, carefully designed procedures detangle the interwoven, interactive process of ESs as well as the variability that comes with human subjects. To demonstrate the usage of the framework, we present an evaluation conducted on Newdle, a clustering-based ES for large news documents. This study was a successful use case of the framework. It revealed how and why clustering-based ES benefited (or not) users in a variety of information seeking tasks. We also report the different information seeking strategies observed from the users and summarize leverage points for designing clustering-based ES.

We evaluate the impact of timing on decision outcome, when both the timing and the relevant decision are chosen under uncertainty. Betting markets provide the testing ground, as we exploit an original dataset containing more than one million online bets on games of the Italian Major Soccer League. We find that individuals perform systematically better when they place their bets farther away from the game day. The better performance of early bettors holds controlling for (time-invariant) unobservable ability, learning during the season, and timing of the odds. We attribute this result to the increase of noisy information on game day, which hampers the capacity of late (non-professional) bettors to use very simple prediction methods, such as team rankings or last game results. We also find that more successful bettors tend to bet in advance, focus on a smaller set of events, and prefer events associated with smaller betting odds.

There is no longer any ambition from the lawmaker to present law in a way accessible to the ordinary citizen. The target for lawmakers and the intended user of law is the professional lawyer (judges and legal counsellors). However, not only the ordinary citizen, but also the modern practicing lawyer faces problems in accessing the content of law. In this presentation, I will use CISG as an illustration of problems consisting of information overload, blind spots and misleading structures. I claim that the law needs to be presented in a new way in order to facilitate practitioners’ understanding of the law.

Results have been mixed regarding whether, and how much, board of director connectedness is beneficial to firm value. Some prior research shows that overly busy directors are ineffective monitors, but these same “busy” directors can be valuable sources of information and other resources. For example, directors who are centrally located within a network can obtain information faster and those who are connected to other highly connected directors can access larger quantities of information. The information can take many forms including market trends, business innovations, and effective corporate practices and is available through these director network channels. However, increased information transfer speed (network centrality) and quantity (connections to highly-connected others) may not always balance out the negative effects of overcommitted directors, information overload, and the propagation of poor business practices. Using social network analysis, we investigate whether well-connected directors increase firm value and find that firms with well-connected directors have higher market value, after controlling for their operating cycle, investment opportunity sets and market competition. We also find that well-connected outside directors (independent) have a bigger impact on increasing firm value than well-connected inside directors.

How do firms respond to consumers' rational inattention? When a decision-maker’s attention is limited, her decisions depend on what she focuses on. This gives interested parties an incentive to manipulate not only the substance of communication but also the decision-maker's attention allocation. This paper models such attention manipulation. In its presence, competitive information supply can reduce the decision-maker’s knowledge by causing information overload. Further, a single information provider may deliberately induce information overload to conceal information. These findings, pertinent to consumer protection, suggest a role for rules that restrict communication, mandate not only the content but also the format of disclosure, and regulate product design.

This paper uses a Pragmatic theory of language (drawn from philosophy and linguistics) to diagnose the causes of excessive and inefficient financial disclosure and propose a regulatory solution. The diagnosis is that existing regulations are effective at encouraging firms to adhere to some, but not all, of the Maxims of Conversation identified by Pragmatics. Regulations encourage firms to disclose any information that might be relevant, but are ineffective in discouraging excessive disclosure of information that is of little relevance given what investors already know. This one-sidedness makes financial disclosures inefficient by limiting the ability for investors to infer that items the firm chooses not to disclose are not particularly important (an inference Pragmatic theorists call “implicature”). The solution is to encourage or require firms to supplement comprehensive disclosures with an “elevated” disclosure that is brief enough to force firms to be selective in choosing what information to include. Regulations can enhance implicature through rules that prohibit firms from elevating disclosures that are less newsworthy than disclosures that are not elevated, thereby enhancing the information conveyed to investors through implicature.

Online social communities have become an important communication channel for people to share and discover information. Pieces of information spread within the community via the underlying social network, from one individual to another. However, with the unprecedented ease and low cost of communication provided by online systems, information overload emerges as a negative factor that potentially threatens the effectiveness of communication. As such, traditional single-message based diffusion models, such as Independent Cascade Model (ICM), are inadequate for describing the role of information overload. We then proposed an extended version of ICM (EICM) that explicitly takes the message multiplicity into account, after examining the message exchange patterns observed from a real online social community. We extensively evaluated this new diffusion model and compared with standard ICM in addressing one fundamental algorithmic problem related to viral marketing: How to select a set of network nodes/individuals to facilitate information diffusion and maximize influence? The evaluation results obtained from using both real and simulated data sets show that our approach results in node-selection heuristics outperforming well-studied notions of various centrality measures based on the ICM. The study and findings presented in this research have important managerial implications. In particular, from a viral marketing perspective, information overload effect should be recognized in order for campaign managers to build advantages in their influence maximization decisions.

While allowing that transparency should be one of the tools employed to manage clinical researchers' conflicts of interest, this paper disagrees with the numerous recommendations that such information become part of the informed consent process. Empirical research in several areas suggests that the provision of this information to prospective trial participants will too often cause information overload; further, few research participants understand the relevance of the information to them once provided. Because many clinical trial participants do not have viable alternatives to trial participation, the information is not facilitating exit from participation. In short, we conclude that embedding this complicated information will ultimately do more harm than good for most recipients.

Despite jury instructions designed to prevent jurors from commenting upon the trial, their deliberations or the process by which they reached a verdict, some have ignored these instructions and face the risk of prosecution under s8 of the Contempt of Court Act 1981. In the past few years following an explosion in the use of blogging, microblogging via mobile technologies (i.e. Twitter) and social networking sites such as Facebook and MySpace, there have been reports of juror impropriety and this has led to concerns being raised by many, including the Lord Chief Justice. This paper sets out to review a snap-shot study of Twitter, a widely-used microblogging tool that can be updated easily from a mobile phone and read widely by anyone with an Internet connection, even without being an account holder. The use of twitter, in particular by celebrities, has also become synonymous with updating the world at large on every single thought and movement of the account holder to the point of information overload. This study has limited validity, but aims to review whether jurors do indeed tweet about anything relating to their experience of jury service.

This paper suggests a reexamination of current governance structures, specifically those of unitary boards after the financial crisis of 2008. We suggest that the existing governance structures are based on an outdated paradigm of business, rooted in economics. We propose an alternative paradigm, a humanistic paradigm, which allows conceiving alternative, network oriented governance structures. As hierarchical firms grew larger and more complex, the risk of failure increased from biases, errors, and missing data in communication and control systems. These problems are exacerbated by information overload on senior managers, directors, and their respective regulators. In contrast to traditional corporate governance, network governance introduces a division of power via multiple boards checks and balances, and active stakeholder engagement (a humanistic alternative). We argue that those features could have prevented the stresses and failures of financial firms in 2008, since they were anticipated by both individuals within firms and external commentators. However, those exposed to risks possessed insufficient influence in either governing and/or regulating firms to take corrective action.

We present the Meme-to-Web model - an abstract agent-based model of cultural production and access, aimed at reproducing the dynamics of peer-based content production and retrieval. Agents generate and link cultural artifacts drawing on knowledge both existing and acquired during previous explorations, loosely mimicking the behavior of web surfers. We test three content access strategies: random walking, content searching with information overload, and a mix of the two. Results show a clear leveling effect of PageRank searches on the distribution of visits to artifacts, an effect also documented in related studies. At the same time the mediation performed by the PageRank in the access phase also affects content production and generates very different network shapes that share certain properties with real world web and blog networks.

With the increasing processing power and plummeting costs of multimedia technologies, our ability to ubiquitously access and disseminate information continues to become indefinitely easier. However, emerging research shows that we are struggling to process information as fast as it arrives. The problem of information overload is a significant one for contemporary organisations as it can adversely affect productivity, decision-making, and employee morale. To combat this problem, organisations often resort to investing in technical solutions such as business intelligence software or semantic technologies. While such technical approaches can certainly aid in making sense of information overload, less attention has been directed at understanding how social behaviours within inter-personal networks – the primary conduit of information – have evolved to deal with the surge of digital information. Using social network analysis and interview evidence from two information intensive firms, this study finds a small number of information specialists who emerge to filter useful information into and around the intra-organizational network. The article concludes with a discussion of the theoretical and practical implications of our findings.

Practitioners and researchers need to stay up-to-date with the latest advances in their fields, but the continual growth in the amount of literature available makes this task increasingly difficult. In this article, we describe the Citation-Sensitive In-Browser Summariser (CSIBS), a new research tool to help manage the literature browsing task. The design of CSIBS was based on a user requirements analysis which identified the information needs that biomedical researchers commonly encounter when browsing through academic literature. CSIBS supports researchers in their browsing tasks by presenting both a generic and a tailored preview about a citation at the point at which they encounter it. This information is aimed at helping the reader determine whether or not to invest the time in exploring the cited article further, thus alleviating information overload. Feedback from biomedical researchers indicates that CSIBS facilitates this relevance judgement task, and that the interface and previews are informative and easy to use.