Information overload, choice deferral, and moderating role of need for cognition: Empirical evidence

ABSTRACT Choice deferral due to information overload is an undesirable result of competitive environments. The neoclassical maximization models predict that choice avoidance will not increase as more information is offered to consumers. The theories developed in the consumer behavior field predict that some properties of the environment may lead to behavioral effects and an increase in choice avoidance due to information overload. Based on stimuli generated experimentally and tested among 1,000 consumers, this empirical research provides evidence for the presence of behavioral effects due to information overload and reveals the different effects of increasing the number of options or the number of attributes. This study also finds that the need for cognition moderates these behavioral effects, and it proposes psychological processes that may trigger the effects observed.

When enough is not enough: information overload and metacognitive decisions to stop studying information

People are often exposed to more information than they can actually remember. Despite this frequent form of information overload, little is known about how much information people choose to remember. Using a novel “stop” paradigm, the current research examined whether and how people choose to stop receiving new—possibly overwhelming—information with the intent to maximize memory performance. Participants were presented with a long list of items and were rewarded for the number of correctly remembered words in a following free recall test. Critically, participants in a stop condition were provided with the option to stop the presentation of the remaining words at any time during the list, whereas participants in a control condition were presented with all items. Across five experiments, we found that participants tended to stop the presentation of the items to maximize the number of recalled items, but this decision ironically led to decreased memory performance relative to the control group. This pattern was consistent even after controlling for possible confounding factors (e.g., task demands). The results indicated a general, false belief that we can remember a larger number of items if we restrict the quantity of learning materials. These findings suggest people have an incomplete understanding of how we remember excessive amounts of information.

‘Stop spamming me!’: exploring information overload on Facebook

The problem of information overload on Facebook is exacerbating as users expand their networks. Growing quantity and increasingly poor quality of information on the Newsfeed may interfere with the hedonic experience of users resulting in frustration and dissatisfaction. In the long run, such developments threaten to undermine sustainability of the platform. To address these issues, our study adopts a grounded theory approach to explore the phenomenon of information overload on Facebook. We investigate main sources of information overload, identify strategies users adopt to deal with it as well as possible consequences. In-depth analysis of the phenomenon allows us to uncover individual peculiarities for identification of relevant information. Based on them we provide valuable recommendations for network providers.

Repeatedly experiencing information overload in a product customization: a case of sequential overchoice

The present study demonstrates how consumers can suffer from sequential overchoice. Customizing a tailor-made suit from combined-attribute choices (e.g., deciding on color and fabric in combination) leads to less satisfaction and less additional consumption than customizing it from single-attribute choices (e.g., deciding on color, then on fabric). The effect is mediated by information overload and moderated by consideration set size.

High value information in engineering organisations

The management of information in engineering organisations is facing a particular challenge in the ever-increasing volume of information. It has been recognised that an effective methodology is required to evaluate information in order to avoid information overload and to retain the right information for reuse. By using, as a starting point, a number of the current tools and techniques which attempt to obtain ‘the value’ of information, it is proposed that an assessment or filter mechanism for information is needed to be developed. This paper addresses this issue firstly by briefly reviewing the information overload problem, the definition of value, and related research work on the value of information in various areas. Then a “characteristic” based framework of information evaluation is introduced using the key characteristics identified from related work as an example. A Bayesian Network diagram method is introduced to the framework to build the linkage between the characteristics and information value in order to quantitatively calculate the quality and value of information. The training and verification process for the model is then described using 60 real engineering documents as a sample. The model gives a reasonable accurate result and the differences between the model calculation and training judgements are summarised as the potential causes are discussed. Finally, several further issues including the challenge of the framework and the implementations of this evaluation assessment method are raised.

Codification vs personalisation: A study of the information evaluation practice between aerospace and construction industries

In the emerging digital economy, the management of information in aerospace and construction organisations is facing a particular challenge due to the ever-increasing volume of information and the extensive use of information and communication technologies (ICTs). This paper addresses the problems of information overload and the value of information in both industries by providing some cross-disciplinary insights. In particular it identifies major issues and challenges in the current information evaluation practice in these two industries. Interviews were conducted to get a spectrum of industrial perspectives (director/strategic, project management and ICT/document management) on these issues in particular to information storage and retrieval strategies and the contrasting approaches to knowledge and information management of personalisation and codification. Industry feedback was collected by a follow-up workshop to strengthen the findings of the research. An information-handling agenda is outlined for the development of a future Information Evaluation Methodology (IEM) which could facilitate the practice of the codification of high-value information in order to support through-life knowledge and information management (K&IM) practice.

Knowledge and information evaluation practice: an exploratory study in a construction firm

There are a number of challenges associated with managing knowledge and information in construction organizations delivering major capital assets. These include the ever-increasing volumes of information, losing people because of retirement or competitors, the continuously changing nature of information, lack of methods on eliciting useful knowledge, development of new information technologies and changes in management and innovation practices. Existing tools and methodologies for valuing intangible assets in fields such as engineering, project management and financial, accounting, do not address fully the issues associated with the valuation of information and knowledge. Information is rarely recorded in a way that a document can be valued, when either produced or subsequently retrieved and re-used. In addition there is a wealth of tacit personal knowledge which, if codified into documentary information, may prove to be very valuable to operators of the finished asset or future designers. This paper addresses the problem of information overload and identifies the differences between data, information and knowledge. An exploratory study was conducted with a leading construction consultant examining three perspectives (business, project management and document management) by structured interviews and specifically how to value information in practical terms. Major challenges in information management are identified. An through-life Information Evaluation methodology (IEM) is presented to reduce information overload and to make the information more valuable in the future.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Strategic Information Disclosure through Integrated Reporting : A study on OMXS30-listed companies’ compliance with the <IR> Framework content element Strategy and Resource Allocation

Background and problem – As a result of financial crises and the realization of a broader stakeholder network, recent decades have seen an increase in stakeholder demand for non- financial information in corporate reporting. This has led to a situation of information overload where separate financial and sustainability reports have developed in length and complexity interdependent of each other. Integrated reporting has been presented as a solution to this problematic situation. The question is whether the corporate world believe this to be the solution and if the development of corporate reporting is heading in this direction. Purpose - This thesis aims to examine and assess to what extent companies listed on the OMX Stockholm 30 (OMXS30), as per 2016-02-28, comply with the Strategic content element of the <IR> Framework and how this disclosure has developed since the framework’s pilot project and official release by using a self-constructed disclosure index based on its specific items. Methodology – The purpose was fulfilled through an analysis of 104 annual reports comprising 26 companies during the period of 2011-2014. The annual reports were assessed using a self-constructed disclosure index based on the <IR> Framework content element Strategy and Resource Allocation, where one point was given for each disclosed item. Analysis and conclusions – The study found that the OMXS30-listed companies to a large extent complies with the strategic content element of the <IR> Framework and that this compliance has seen a steady growth throughout the researched time span. There is still room for improvement however with a total average framework compliance of 84% for 2014. Although many items are being reported on, there are indications that companies generally miss out on the core values of Integrated reporting. 

Web 2.0 and its impact on knowledge and business organizations

Today, information overload and the lack of systems that enable locating employees with the right knowledge or skills are common challenges that large organisations face. This makes knowledge workers to re-invent the wheel and have problems to retrieve information from both internal and external resources. In addition, information is dynamically changing and ownership of data is moving from corporations to the individuals. However, there is a set of web based tools that may cause a major progress in the way people collaborate and share their knowledge. This article aims to analyse the impact of ‘Web 2.0’ on organisational knowledge strategies. A comprehensive literature review was done to present the academic background followed by a review of current ‘Web 2.0’ technologies and assessment of their strengths and weaknesses. As the framework of this study is oriented to business applications, the characteristics of the involved segments and tools were reviewed from an organisational point of view. Moreover, the ‘Enterprise 2.0’ paradigm does not only imply tools but also changes the way people collaborate, the way the work is done (processes) and finally impacts on other technologies. Finally, gaps in the literature in this area are outlined.

Sistema de recomendação e personalização de televisão

Com a expansão da Televisão Digital e a convergência entre os meios de difusão convencionais e a televisão sobre IP, o número de canais disponíveis tem aumentado de forma gradual colocando o espectador numa situação de difícil escolha quanto ao programa a visionar. Sobrecarregados com uma grande quantidade de programas e informação associada, muitos espectadores desistem sistematicamente de ver um programa e tendem a efectuar zapping entre diversos canais ou a assistir sempre aos mesmos programas ou canais. Diante deste problema de sobrecarga de informação, os sistemas de recomendação apresentam-se como uma solução. Nesta tese pretende estudar-se algumas das soluções existentes dos sistemas de recomendação de televisão e desenvolver uma aplicação que permita a recomendação de um conjunto de programas que representem potencial interesse ao espectador. São abordados os principais conceitos da área dos algoritmos de recomendação e apresentados alguns dos sistemas de recomendação de programas de televisão desenvolvidos até à data. Para realizar as recomendações foram desenvolvidos dois algoritmos baseados respectivamente em técnicas de filtragem colaborativa e de filtragem de conteúdo. Estes algoritmos permitem através do cálculo da similaridade entre itens ou utilizadores realizar a predição da classificação que um utilizador atribuiria a um determinado item (programa de televisão, filme, etc.). Desta forma é possível avaliar o nível de potencial interesse que o utilizador terá em relação ao respectivo item. Os conjuntos de dados que descrevem as características dos programas (título, género, actores, etc.) são armazenados de acordo com a norma TV-Anytime. Esta norma de descrição de conteúdo multimédia apresenta a vantagem de ser especificamente vocacionada para conteúdo audiovisual e está disponível livremente. O conjunto de recomendações obtidas é apresentado ao utilizador através da interacção com uma aplicação Web que permite a integração de todos os componentes do sistema. Para validação do trabalho foi considerado um dataset de teste designado de htrec2011-movielens-2k e cujo conteúdo corresponde a um conjunto de filmes classificados por diversos utilizadores num ambiente real. Este conjunto de filmes possui, para além da classificações atribuídas pelos utilizadores, um conjunto de dados que descrevem o género, directores, realizadores e país de origem. Para validação final do trabalho foram realizados diversos testes dos quais o mais relevante correspondeu à avaliação da distância entre predições e valores reais e cujo objectivo é classificar a capacidade dos algoritmos desenvolvidos preverem com precisão as classificações que os utilizadores atribuiriam aos itens analisados.

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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.