scholarly journals Attention manipulation and information overload

2018 ◽  
Vol 2 (1) ◽  
pp. 78-106 ◽  
Author(s):  
PETRA PERSSON
Keyword(s):  
Product Design ◽  
Financial Literacy ◽  
Information Overload ◽  
Consumer Welfare ◽  
Relevant Information ◽  
Limited Attention ◽  
Disclosure Regulation ◽  
Consumer Attention ◽  
Disclosure Rules

AbstractLimits on consumer attention give firms incentives to manipulate prospective buyers’ allocation of attention. This paper models such attention manipulation and shows that it limits the ability of disclosure regulation to improve consumer welfare. Competitive information supply from firms competing for attention can reduce consumers’ knowledge by causing information overload. A single firm subjected to a disclosure mandate may deliberately induce such information overload to obfuscate financially relevant information or engage in product complexification to bound consumers’ financial literacy. Thus, disclosure rules that would improve welfare for agents without attention limitations can prove ineffective for consumers with limited attention. Obfuscation suggests a role for rules that mandate not only the content, but also the format of disclosure; however, even rules that mandate ‘easy-to-understand’ formats can be ineffective against complexification, which may call for regulation of product design.

scholarly journals Cascading Forgetting in Product Development Challenges and Evaluation

2019 ◽  
Vol 1 (1) ◽  
pp. 2527-2536
Author(s):  
Patricia Kügler ◽  
Claudia Schon ◽  
Benjamin Schleich ◽  
Steffen Staab ◽  
Sandro Wartzack
Keyword(s):  
Product Design ◽  
Information Overload ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Wear Behaviour ◽  
Combustion Engines ◽  
The Novel ◽  
Related Information ◽  
Intentional Forgetting ◽  
Novel Approach

AbstractVast amounts of information and knowledge is produced and stored within product design projects. Especially for reuse and adaptation there exists no suitable method for product designers to handle this information overload. Due to this, the selection of relevant information in a specific development situation is time-consuming and inefficient. To tackle this issue, the novel approach Intentional Forgetting (IF) is applied for product design, which aims to support reuse and adaptation by reducing the vast amount of information to the relevant. Within this contribution an IF-operator called Cascading Forgetting is introduced and evaluated, which was implemented for forgetting related information elements in ontology knowledge bases. For the evaluation the development process of a test-rig for studying friction and wear behaviour of the cam/tappet contact in combustion engines is analysed. Due to the interdisciplinary task of the evaluation and the characteristics of semantic model, challenges are discussed. In conclusion, the focus of the evaluation is to consider how reliable the Cascading Forgetting works and how intuitive ontology-based representations appear to engineers.

Information overload in business organizations and entrepreneurship: An analytical review of the literature

2021 ◽  
pp. 026638212110549
Author(s):  
CA(Dr.) Gaurav Bhambri
Keyword(s):  
Intelligent Agents ◽  
Information Overload ◽  
Personal Information ◽  
Management Strategies ◽  
Value Added ◽  
Relevant Information ◽  
Business Organizations ◽  
Professional Literature ◽  
Clear Cut ◽  
Paradoxical Situation

In this paper I analytically review the literature on the information overload problem, with special reference to the business organizations and entrepreneurship and the study mainly reveals that the problem of the information overload has been existed for many years, whereas in current years the problem has become more clearly recognized and experienced. A concern stressed in the literature is the paradoxical situation that most probably there is an abundance of information available and it is often difficult to obtain useful, and relevant information when it may be needed. Both perceptions and the actual effects of information overload have exacerbated by rapid advances made in the information and communication technology, whereas it is not clear cut as to whether Internet has worsened/improved the situation. Some solutions have put forward to reduce the information overload are a reduction in duplication of the information found in professional literature; the adoption of the personal information management strategies, along with the integration of software solutions such as push technology and intelligent agents; and the provision of value-added information. Main emphasis is placed on the technology as a tool and not driver, while increased in information literacy may provide key to reducing the information overload in organisations.

scholarly journals Academic SEO – increasing the visibility of research output

2019 ◽  
Author(s):  
Lisa Schilhan ◽  
Christian Kaier
Keyword(s):  
Information Overload ◽  
Research Output ◽  
Relevant Information ◽  
Search Algorithms ◽  
Theoretical Background ◽  
Data Set ◽  
Author Keywords ◽  
Fair Principles ◽  
Academic Search

In times of an ever-increasing information overload, Academic Search Engine Optimization (ASEO) supports findability of relevant information and contributes to the FAIR principles. It enhances efficiency in literature and data search and therefore plays an increasing role in the research lifecycle. ASEO is an important aspect to consider when preparing a scientific manuscript for publication. Authors can increase the visibility of their papers in library catalogues, databases, repositories and search engines with simple measures like choosing informative author keywords. The more (meta-)data these search algorithms can use, the higher the probability that a data set or paper will show up in a result list. ASEO enables search algorithms and readers to quickly and unambiguously identify relevant content, thus also helping institutions to increase research visibility. In addition, authors and publishers share an interest in describing content in a way that makes it easy to find it. Librarians, with their extensive knowledge and wealth of experience in literature research and metadata management such as keyword assignment, can provide valuable advice on the role of as correct and complete metadata as possible and on suitable keywords for search algorithms. For this reason, the Publication Services at Graz University Library have recently started offering training and workshops for authors. The presentation will provide an introduction into strategies to enhance visibility and findability of online content, such as research articles, with some theoretical background as well as practical examples.

Supporting Policy-Makers with Social Media Analysis Tools to Get Aware of Citizens' Opinions

2014 ◽  
pp. 121-156
Author(s):  
Timo Wandhöfer ◽  
Steve Taylor ◽  
Miriam Fernandez ◽  
Beccy Allen ◽  
Harith Alani ◽  
...  
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Behavior ◽  
Relevant Information ◽  
Media Analysis ◽  
Control Group ◽  
Data Sets ◽  
Online Forums ◽  
Policy Makers ◽  
Facebook Pages

The role of social media in politics has increased considerably. A particular challenge is how to deal with the deluge of information generated on social media: it is impractical to read lots of messages with the hope of finding useful information. In this chapter, the authors suggest an alternative approach: utilizing analysis software to extract the most relevant information of the discussions taking place. This chapter discusses the WeGov Toolbox as one concept for policy-makers to deal with the information overload on Social Media, and how it may be applied. Two complementary, in depth case studies were carried out to validate the usefulness of the analysis results of the WeGov Toolbox components' within its target audience's everyday life. Firstly, the authors used the “HeadsUp” forum, operated by the Hansard Society. Here, they were able to compare the key themes and opinions extracted automatically by the Toolbox to a control group of manually pre-analyzed data sets. In parallel, results of analyses based on four weeks' intensive monitoring on policy area-specific Facebook pages selected by German policy makers, as well as topics on Twitter globally and local, were assessed by taking into account their existing experience with content discussed and user behavior in their respective public spheres. The cases show that there are interesting applications for policy-makers to use the Toolbox in combination with online forums (blogs) and social networks, if behavioral user patterns will be considered and the framework will be refined.

Enhancing Citizens' Participation via Recommender Systems

2020 ◽  
pp. 624-650
Author(s):  
Luis Terán
Keyword(s):  
Decision Making ◽  
Semantic Web ◽  
Private Sector ◽  
Web 2.0 ◽  
Recommender Systems ◽  
Search Engines ◽  
Information Overload ◽  
Relevant Information ◽  
Research Topic ◽  
Government Services

With the introduction of Web 2.0, which includes users as content generators, finding relevant information is even more complex. To tackle this problem of information overload, a number of different techniques have been introduced, including search engines, Semantic Web, and recommender systems, among others. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. In this chapter, the use of recommender systems on eParticipation is presented. A brief description of the eGovernment Framework used and the participation levels that are proposed to enhance participation. The highest level of participation is known as eEmpowerment, where the decision-making is placed on the side of citizens. Finally, a set of examples for the different eParticipation types is presented to illustrate the use of recommender systems.

scholarly journals A Probability-Based Hybrid User Model for Recommendation System

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Hao ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Lin Gong ◽  
Bo Zhao
Keyword(s):  
Product Design ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
User Model ◽  
Information Communication ◽  
Design Cycle ◽  
Available Information ◽  
User Ratings ◽  
Content Based Filtering

With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

Computer-Supported Innovation for Modularized Product Design

2011 ◽  
Vol 341-342 ◽  
pp. 586-590
Author(s):  
Ming Piao Tsai ◽  
Peter Iming Shieh ◽  
Chin Hui Chuang
Keyword(s):  
Problem Solving ◽  
Product Design ◽  
Rapid Development ◽  
Modular Function ◽  
Relevant Information ◽  
Design Stage ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Knowledge Based ◽  
Use Of Knowledge

In the paper, a computer-supported model of the innovation design is presented, which is based on the synergy of modular function deployment (MFD), theory of inventive problem solving (TIPS, TRIZ) and case-based reasoning (CBR) to assist designer to engage in modularized product design. This approach involves gathering all relevant information about the customer voice and applying this information to drive the design of products or modules. So, it is starting from the MFD-Diagrams and continuing through the technical contradictions matrix in TRIZ during the conceptual design stage of new modules or parts. A CBR system is then adopted to quickly search some similar design cases for the references of inventive problem solving. Last, two reasoning mechanism are fabricated based on Protege and JESS expert tool, and a particular case of bicycle part design is studied through the use of knowledge-based prototype system to prove the effectiveness/efficiency of the computer-supported model. However, it will greatly enhance the competitive capability of companies trying to rapid development of the innovation design.

scholarly journals Combining Remote Sensing with Webdata and Machine Learning to Support Humanitarian Relief Work

2021 ◽  
Author(s):  
Jens Kersten ◽  
Malin Kopitzsch ◽  
Jan Bongard ◽  
Friederike Klan
Keyword(s):  
Social Media ◽  
Information Overload ◽  
Field Work ◽  
Relevant Information ◽  
Extraction Methods ◽  
Legal Aspects ◽  
Crisis Response ◽  
In Situ Data ◽  
Information Gaps

<p>Gathering, analyzing and disseminating up-to-date information related to incidents and disasters is key to disaster management and relief. Satellite imagery, geo-information, and in-situ data are the mainly used information sources to support decision making. However, limitations in data timeliness as well as in spatial and temporal resolution lead to systematic information gaps in current well-established satellite-based workflows. Citizen observations spread through social media channels, like Twitter, as well as freely available webdata, like WikiData or the GDELT database, are promising complementary sources of relevant information that might be utilized to fill these information gaps and to support in-situ data acquisition. Practical examples for this are impact assessments based on social media eyewitness reports, and the utilization of this information for the early tasking of satellite or drone-based image acquisitions.</p><p>The great potential, for instance of social media data analysis in crisis response, was investigated and demonstrated in various related research works. However, the barriers of utilizing webdata and appropriate information extraction methods for decision support in real-world scenarios are still high, for instance due to information overload, varying surrounding conditions, or issues related to limited field work infrastructures, trustworthiness, and legal aspects.</p><p>Within the current DLR research project "Data4Human", demand driven data services for humanitarian aid are developed. Among others, one project goal is to investigate the practical benefit of augmenting existing workflows of the involved partners (German Red Cross, World Food Programme, and Humanitarian Open Street Map) with social media (Twitter) and real-time global event database (GDELT) data. In this contribution, the general concepts, ideas and corresponding methods for webdata analysis are presented. State-of-the-art deep learning models are utilized to filter, classify and cluster the data to automatically identify potentially crisis-related data, to assess impacts, and to summarize and characterize the course of events, respectively. We present first practical findings and analysis results for the 2019 cyclones Idai and Kenneth.</p>

scholarly journals Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

2012 ◽  
pp. 684-705 ◽  
Author(s):  
Luis Terán ◽  
Andreas Ladner ◽  
Jan Fivaz ◽  
Stefani Gerber
Keyword(s):  
Recommender Systems ◽  
Information And Communication Technologies ◽  
Information Overload ◽  
Relevant Information ◽  
The Internet ◽  
Clustering Methods ◽  
Web Based ◽  
Information And Communication ◽  
Fuzzy Clustering Methods ◽  
Use Of The Internet

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user’s profile. This technique is mainly used in e-Commerce to suggest items that fit a customer’s purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens’ participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

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