Uncertain risk: assessing open data signals

Purpose Open data resources contain few signals for assessing their suitability for data analytics. The purpose of this paper is to characterize the uncertainty experienced by open data consumers with a framework based on economic theory. Design/methodology/approach Drawing on information asymmetry theory about market exchanges, this paper investigates the practical challenges faced by data consumers seeking to reuse open data. An inductive qualitative analysis of over 2,900 questions asked between 2013 and 2018 on an internet forum identified how a community of 15,000 open data consumers expressed uncertainty about data sources. Findings Open data consumers asked direct questions that expressed uncertainty about the availability, interoperability and interpretation of data resources. Questions focused on future value and some requests were devoted to seeking data that matched known sources. The study proposes a data signal framework that explains uncertainty about open data within the context of control and visibility. Originality/value The proposed framework bridges digital government practice to information signaling theory. The empirical evidence substantiates market aspects of open data portals. This paper provided a needed case study of how data consumers experience uncertainty. The study integrates established theories about risk to improve the reuse of open data.

Lag Analysis of Fast fMRI Reveals Delayed Information Flow Between the Default Mode and Other Networks in Narcolepsy

Narcolepsy is a chronic neurological disease characterized by dysfunction of the hypocretin system in brain causing disruption in the wake-promoting system. In addition to sleep attacks and cataplexy, patients with narcolepsy commonly report cognitive symptoms while objective deficits in sustained attention and executive function have been observed. Prior resting-state functional magnetic resonance imaging (fMRI) studies in narcolepsy have reported decreased inter/intranetwork connectivity regarding the default mode network (DMN). Recently developed fast fMRI data acquisition allows more precise detection of brain signal propagation with a novel dynamic lag analysis. In this study, we used fast fMRI data to analyze dynamics of inter resting-state network (RSN) information signaling between narcolepsy type 1 patients (NT1, n = 23) and age- and sex-matched healthy controls (HC, n = 23). We investigated dynamic connectivity properties between positive and negative peaks and, furthermore, their anticorrelative (pos-neg) counterparts. The lag distributions were significantly (P < 0.005, familywise error rate corrected) altered in 24 RSN pairs in NT1. The DMN was involved in 83% of the altered RSN pairs. We conclude that narcolepsy type 1 is characterized with delayed and monotonic inter-RSN information flow especially involving anticorrelations, which are known to be characteristic behavior of the DMN regarding neurocognition.

Effect of earnings management on earnings predictability in information signaling perspective

This research is aimed to examine (1) effect of discretionary and innate accrual on earnings predictability (2) effect of market share and financial health on relationship between real earnings management and earnings predictability. This research use manufacture firms listed in Indonesian Stock Exchange 2003-2015 as research sample, with 2013-2014 as research period. Accrual earnings management is measured by discretionary and innate abnormal accrual. Real earnings management is measured by aggregate of abnormal cash flow of operation, abnormal production, abnormal discretionary expenses. As expected, discretionary accrual as opportunist act does not support earnings predictability, while innate accrual as information signaling of business model improves earnings predictability. Real earnings management as information signaling of market share and financial health improves earnings predictability as well. In general, earnings management as information signaling is more likely to communicate condition of firm and leads to informativeness of earnings.

Earnings Smoothing as Information Signaling or Garbling: A Review of Literature

Earnings smoothing, which refers to the action of managers managing earnings to reduce fluctuations of reported earnings, is a special type of earnings management because while earnings smoothing may be used to distort shareholders and creditors’ view of corporate actual performance, it may also serve as a tool to communicate corporate private information of future earnings to the aforementioned stakeholders. Hence, it comes to no surprise when prior literatures reveal that the studies on the role of earnings smoothing are divided into two streams: as information signaling and information garbling. This paper aims to review prior literatures, specifically on the role of earnings smoothing either as information signaling or garbling based on four themes: firm value, financing need, compensation contract and outsiders’ intervention. This paper reviews journal articles gathered from Web of Science database. Based on the shortcomings of prior literatures, this paper highlights avenue for future research.

Investor Heterogeneity, Auditor Choice, and Information Signaling

SUMMARY We develop a model of firms' auditor choices when presented with a heterogeneous group of investors. We show that firms' auditor choices in equilibrium depend on the composition of investors in the market. The signaling effect of choosing a high-quality auditor exists only when there is at least a certain proportion of sophisticated investors. If there is a sufficiently high proportion of sophisticated investors, then all firms will choose high-quality auditors. We also show that the overall audit quality in the market increases with an increasing proportion of sophisticated investors. When the audit market is differentiated and investors are heterogeneous, an increase in the penalty for firms that receive a qualified opinion will lead to a decrease in the overall audit quality in the market. Our conclusions remain valid even after taking audit fees, auditor quality change, and firm heterogeneity into consideration.

Architectures and Information Signaling Techniques for Cognitive Networks

The introduction of self-awareness, self-management, and self-healing into networks leads to a novel paradigm known as cognitive networking. This chapter overviews recent developments of this concept, discussing possible cognitive node structure and candidate cognitive network architecture. It explains the functionality of cognitive algorithms and discusses opportunities for potential optimization. Furthermore, the concept of cognitive information services is introduced. Information signaling techniques are then classified, reviewed in details, and compared among them. Finally, the performance of cognitive communication protocols is presented for a choice of examples.