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The purpose of this paper is to explore how hubs’ social influence on social network decisions can cause the behavior of information cascades in a market.

The authors establish understanding of the fundamental mechanism of information cascades through a computational simulation approach.

Eigenvector centrality, betweenness centrality, and PageRank are statistically correlated with the occurrence of information cascades among agents; the hubs’ incorrect decisions in the early diffusion stage can significantly cause misled shift cascades; and the bridge role of hubs is more influential than their pivotal position role in the process of misled shift cascades.

This implication can be extendable in the field of marketing, sequential voting, and technology, or innovation adoption.

The purpose of this paper is to present the results of a pilot study involving high school teachers in natural sciences. The aim was to foster critical thinking about cascading hazards via the use of reasoned imagination. Cascading phenomena can lead to extreme catastrophes and are thus a challenge for disaster prevention and management.

Following a presentation listing some known cascading phenomena, the participants completed a questionnaire consisting of a blank hazard correlation matrix (HCM) and some open-ended questions. The HCM qualitatively described possible interactions between 16 different perils selected from a large spectrum of natural, technological and socio-economic hazards.

Most participants were able to describe cascading phenomena within the HCM by reducing them into sets of one-to-one interactions. Based on their experience and imagination, the participants foresaw additional interactions that were not discussed, never observed but are scientifically plausible. The majority of the respondents reported that they learnt something new and wanted to learn more about cascading hazards.

The HCM is especially effective in translating complex hazard scenarios into basic interactions and vice versa. Being imaginative (here via the use of reasoned imagination) and accessible, the HCM could be used as basis for transformative learning in the education of the public and of practitioners on the role of cascading hazards in catastrophes.

As user-generated content (UGC) is entering the news cycle alongside content captured by news professionals, it is important to detect misleading content as early as possible and avoid disseminating it. The purpose of this paper is to present an annotated dataset of 380 user-generated videos (UGVs), 200 debunked and 180 verified, along with 5,195 near-duplicate reposted versions of them, and a set of automatic verification experiments aimed to serve as a baseline for future comparisons.

The dataset was formed using a systematic process combining text search and near-duplicate video retrieval, followed by manual annotation using a set of journalism-inspired guidelines. Following the formation of the dataset, the automatic verification step was carried out using machine learning over a set of well-established features.

Analysis of the dataset shows distinctive patterns in the spread of verified vs debunked videos, and the application of state-of-the-art machine learning models shows that the dataset poses a particularly challenging problem to automatic methods.

Practical limitations constrained the current collection to three platforms: YouTube, Facebook and Twitter. Furthermore, there exists a wealth of information that can be drawn from the dataset analysis, which goes beyond the constraints of a single paper. Extension to other platforms and further analysis will be the object of subsequent research.

The dataset analysis indicates directions for future automatic video verification algorithms, and the dataset itself provides a challenging benchmark.

Having a carefully collected and labelled dataset of debunked and verified videos is an important resource both for developing effective disinformation-countering tools and for supporting media literacy activities.

Besides its importance as a unique benchmark for research in automatic verification, the analysis also allows a glimpse into the dissemination patterns of UGC, and possible telltale differences between fake and real content.

The purpose of this paper is to analyze media storytelling and rhetoric surrounding the credibility of the longstanding accounting practice of mark‐to‐market valuation.

The cascading storytelling model of progressive framing by the media of mark‐to‐market valuation was applied to story subsets of the three types of classic Aristotelian rhetorical appeals.

The authors found that the media blamed the accounting profession's mark‐to‐market valuation practices as substantive cause of recent corporate problems and declines in market values. In addition, the rhetorical framing of mark‐to‐market accounting practices in the media prompted the Financial Accounting Standards Board to a rush to judgment.

The paper is limited to the analysis of the storytelling included. Different results from other sources may provide another result.

The failure in the media to address the duality between the logos of accounting and the ethos of the media narratives exacerbated the cascading activation. Understanding this duality may provide a different lens in looking at information dissemination. This is not only relative to stakeholders in making more informed decisions but should also serve as a warning to the profession, to have more voice, to use a rhetorical strategy that can have more saliency in the public arena.

The paper examined storytelling as interplay of retrospective narrative, the presentness of living story, and the antenarratives shaping the future of not only the unfolding economic crisis, but the future of accounting itself. In terms of rhetoric, we extended the application of pathos, ethos, and logos by examining a cascading activation theory model. This is one of the few studies of antenarratives and how through cascade rhetoric the future is shaped.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

The purpose of this exploratory paper is to examine the lack of reliability of traditional neo-classical models and to argue that it is due to the hidden complexity and non-linearity that may operate at times in residential housing markets. As a result, market efficiency may be a special case, rather than the prevailing rule. An alternative framework that incorporates the higher order concepts of complexity – based on the non-linear, emergent behavior of multiple agents – is required to model discontinuities and imbalances in the housing markets.

The paper examines the building block concepts required to model the complexity of the housing market and analyzes their implications. These implications can be counter-intuitive and help explain the failure of policy makers to model the recent bust in global housing markets.

The paper finds that policy makers need to adopt an analytical framework that incorporates non-linearity, emergence and other building blocks of complexity in order to construct representative financial models that help understand systemic imbalances that may afflict residential housing markets.

This is the first paper to one's knowledge that argues that policy makers should adopt an alternative theoretical framework based on complexity concepts in order to create more effective financial models; such models should include indicators that provide early warning signals of potential discontinuities in housing markets.

We examine the role of financial and nonfinancial performance measures in managing revenues derived from life cycles of a type of intellectual property products − motion pictures.

Our study focuses on the first two markets in which audiences can watch a motion picture – the upstream theatrical market and the downstream home video market. We combine data collected from numerous public and proprietary sources and form a final sample of 654 motion pictures. Then we perform regression analysis on the data.

First, three measures of a movie’s performance in the theatrical market, opening box office revenue, peak rank, and weeks at the peak rank, have positive effects on subsequent revenues in the home video market. Second, the same set of performance measures also predicts the motion picture’s life span in the theatrical market. Third, when the actual life span of a motion picture in the theatrical market deviates from its predicted value, the total return on investment in the motion picture decreases.

We do not have data on other downstream markets related to motion pictures, such as pay-per-view and online video streaming.

This study suggests that the public and proprietary data can be used to inform managerial decisions regarding intellectual property product life cycles.

This is the first accounting study that directly examines life cycle revenues of intellectual property products. We also extend literature on revenue driver and revenue management research to the product level.

Looks at the 2000 Employment Research Unit Annual Conference held at the University of Cardiff in Wales on 6/7 September 2000. Spotlights the 76 or so presentations within and shows that these are in many, differing, areas across management research from: retail finance; precarious jobs and decisions; methodological lessons from feminism; call centre experience and disability discrimination. These and all points east and west are covered and laid out in a simple, abstract style, including, where applicable, references, endnotes and bibliography in an easy‐to‐follow manner. Summarizes each paper and also gives conclusions where needed, in a comfortable modern format.