15 research outputs found
USE OF VISUALIZATION IN DIGITAL FINANCIAL REPORTING: THE EFFECT OF SPARKLINE
Information visualization (InfoViz) is an essential component of decision support systems (DSS). Sparklines is a visualization tool. This study examines if Sparklines in digital financial reports aids novice investors and if so under what circumstances? Does it enhances decision-making performance and facilitates effective decision-making experience? Additionally, does it lowers decision making effort; reduces dilution effect from non-relevant data in financial reports and mitigates recency bias in using digital financial reports?
The hypothesis is guided by the theory of Proximity Compatibility Principle and the Theory of Cognitive Fit. The research methodology for this study is a repeated measure, controlled laboratory based experiment. A pilot test was conducted in with a sample of forty undergraduate students from Gatton College of Business and Economics. The sample size for this study was 275 subjects.
The result revealed that there was significant effect of sparklines on decision making performance and it provides an incremental value over a tabular format. Sparklines makes an important contribution towards mitigating recency bias. The results also suggested that the irrelevant information cue in the shareholder’s report were not able to weaken the impact of relevant information in the audited financial data reported using sparklines. Sparklines increased the attention of the readers to the tables. Subjects performed the integrative tasks and spatial better when using Sparklines. For tasks such as symbolic tasks, Sparkline does not necessarily improve decision performance.
It was also found out that decision makers experience greater satisfaction when using sparklines. The overall cognitive load experienced by subjects was lower using sparklines when task demands are high (such as in a bankruptcy prediction task). Interestingly, the results indicate that there is no significant effect of sparkline on decision confidence and time. In conclusion, recall of facts and pattern among subjects was found superior with use of sparkline.
This study provides an empirical and justifiable basis for policy makers to make explicit recommendations about use of novel graphics such as sparkline in digital financial reports. Limitations of this study are noted
Wirelessly Sensing Open Parking Spaces : Accounting and Management of Parking Facility
Driving into a parking lot only to find that all of the parking spaces are taken is frustrating. It would be very beneficial toimplement a system that tracks the number of spaces left. Wireless sensors are well-fitted to do this task. Existing systemsperform this task in various ways, discussed at the beginning of the paper. Taking the shortcomings of these systems intoaccount, a new system based on signal strength is proposed and tested in a virtual environment
Wearables in medicine
Wearables as medical technologies are becoming an integral part of personal analytics, measuring physical status, recording physiological parameters, or informing schedule for medication. These continuously evolving technology platforms do not only promise to help people pursue a healthier life style, but also provide continuous medical data for actively tracking metabolic status, diagnosis, and treatment. Advances in the miniaturization of flexible electronics, electrochemical biosensors, microfluidics, and artificial intelligence algorithms have led to wearable devices that can generate real-time medical data within the Internet of things. These flexible devices can be configured to make conformal contact with epidermal, ocular, intracochlear, and dental interfaces to collect biochemical or electrophysiological signals. This article discusses consumer trends in wearable electronics, commercial and emerging devices, and fabrication methods. It also reviews real-time monitoring of vital signs using biosensors, stimuli-responsive materials for drug delivery, and closed-loop theranostic systems. It covers future challenges in augmented, virtual, and mixed reality, communication modes, energy management, displays, conformity, and data safety. The development of patient-oriented wearable technologies and their incorporation in randomized clinical trials will facilitate the design of safe and effective approaches
e-Commerce and taxation: Past, present and future
This paper examines the relation between security returns, industry-wide cash flows and accruals, and firm-specific cash flows and accruals during the period 1991-2001. We first replicate Ayers & Freeman’s (1997) findings that returns are significantly associated with industry earnings and this association begins and ends earlier than returns associated with firm earnings. We hypothesize the components of industry earnings - cash flows and accruals - are significantly associated with stock returns. Consistent with this hypothesis we find that both industry-wide cash flows and industry-wide accruals are significantly associated with returns and this association begins and ends earlier than returns associated with firm-specific cash flows and accruals. Contrary to expectations, we find no significant difference between industry-wide cash flows and industry-wide accruals. Finally, we hypothesize and find that industry-wide accruals are more value relevant than firm-specific accruals in years t and t+1. Collectively, these findings suggest industry-wide cash flows and accruals are value relevant; however, disaggregating industry earnings into its cash flow and accrual components is unnecessary as it does not provide incremental explanatory power beyond that of industry earnings. All data are publicly available from the sources identified in the paper
Assurance on the reliability of mobile payment system and its effects on its\u27 use: An empirical examination
In this paper, we decompose the CAPM equity beta for Coca-Cola and Pepsi (KOPEP) to show the industry component and the operating leverage and the financial leverage components for the period from 2004 to 2012. We compute the CAPM equity betas using a standard five year, sixty month, regression between returns for KOPEP using the S&P500 as the market index. We adjust for financial leverage using the Hamada (1969) methodology and we adjust for operating leverage using the degree of operating leverage (DOL). The average business beta for Coca-Cola is 0.1882 and the average business beta for Pepsico is 0.1369. Over the period of this analysis, Coca-Cola has had a business beta slightly higher than the business beta for Pepsico
Protecting Accounting Information Systems Using Machine Learning Based Intrusion Detection
The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we detect whether the network data is normal or an anomaly. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. A feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. The experimental findings revealed that the suggested method has a neglected false alarm rate, with the accuracy expected to be between 95% and 100%
Hybrid data mining to reduce false positive and false negative prediction in intrusion detection system
This paper proposes an approach of data mining machine learning methods for reducing the false positive and false negative predictions in existing Intrusion Detection Systems (IDS). It describes our proposal for building a confidential strong intelligent intrusion detection system which can save data and networks from potential attacks, having recognized movement or infringement regularly reported ahead or gathered midway. We have addressed different data mining methodologies and presented some recommended approaches which can be built together to enhance security of the system. The approach will reduce the overhead of administrators, who can be less concerned about the alerts as they have been already classified and filtered with less false positive and false negative alerts. Here we have made use of KDD-99 IDS dataset for details analysis of the procedures and algorithms which can be implemented