Business Information Visualization: A Visual Intelligence-Based Framework

Business Intelligence aims to improve decision quality. Research on BI achieving this goal is inconclusive, yet BI is still one of the top priorities among CIOs and is an active IS research area. A better understanding of the relationship between human intelligence and BI capabilities may lead to more fruitful BI endeavors. This paper proposes a conceptual framework that links capabilities of Business Information Visualization, a key modern BI enabler, to non-verbal (visual) intelligence abilities, and suggests propositional guidelines of how BI could improve decision making by impacting these intelligence abilities. The paper demonstrates that there is strong research support to suggest such linkages. Better understanding of what human abilities are important for better decisions, and what specific BI capabilities are needed to support these abilities will help improve the design, deployment, and utilization of BI tools, and, hopefully ultimately, achieve more efficient and effective business decisions

The Role of Business Information Visualization in Knowledge Creation

Past research suggests that one of the reasons causing Business Intelligence (BI) systems to fall short of expectations could be that certain BI capabilities may not be appropriate for individuals’ or organizational challenges. Our research proposes the need to enhance our understanding of information and knowledge processes to address the issue. We evaluate the appropriateness of adopting the implications of information and knowledge processes to Business Information Visualization (BIV) as one of BI capabilities by exploring how data interaction and data representation reduce information-based challenges of uncertainty and complexity, thus enabling the creation of new insights. Formal model and resulting propositions are offered. Research implications suggest that effective and efficient insight generation can be achieved by deploying BI visualization capabilities only if those capabilities result in knowledge workers’ perception of lower information uncertainty and complexity. Larger implications of this study for BI, BIV and knowledge creation process are discussed

Making Graphic Management Models Smarter

In management, graphic models are an undervalued and poorly used form of theory-building and communication that should be treated with the same rigor that is commonly demanded of text. Graphic models are highly effective for depicting and explaining organizational complexity when designed appropriately. Using two mutual influence models in accountability as an example, we point out common flaws in modelling, and suggest guidelines on how to make graphic models smarter

Bias due to visual illusion in the graphical presentation of information: Working paper series--02-30

Graphical displays of information are among the most common decision aids available. Recent research has focused upon the effect that inappropriately prepared graphs may have on decision-making. This paper adds to the body of literature on graphical information presentation by demonstrating that a graph can result in biased information extraction due to visual illusion. The experimental results indicate that decision makers can fall prey to the so-called "Poggendorff illusion" and either systematically underestimate or systematically overestimate the values displayed on a cost-volume-profit graph. In addition, the results of this research suggest that the bias can be moderated by the inclusion of horizontal gridlines on graphs. The addition of horizontal gridlines also reduces the variance in some values decision makers extract from graphs. Accordingly, a graph preparation guideline should be followed to avoid the bias and high variance in information extraction - Horizontal gridlines should be included on all line graphs

The effect of visual illusions on the graphical display of information: Evidence of bias and mitigation: Working paper series--08-05

Graphical displays of business and accounting information are widely used as decision aids. Those who use Excel and other programs with graphics generation capabilities are faced with many options for organizing and displaying information in graphs in a manner that does not result in biased decision making or information extraction. This paper describes four graphs each of which exhibits characteristics associated with a certain known visual illusion. A controlled experiment was carried out to demonstrate the visual illusions resulted in systematically biased decision making. It is also proposed and validated that the bias can be mitigated by including properly formatted gridlines when preparing the graphs. The implication of this research is that that an important addition to the set of generally accepted graph preparation guidelines is to include properly formatted gridlines in graphs. Data Availability: Data are available from the author; however, request for data should specify the intended use

So fast so good : An analysis of answer quality and answer speed in community question answering sites

The authors investigate the interplay between answer quality and answer speed across question types in community question-answering sites (CQAs). The research questions addressed are the following: (a) How do answer quality and answer speed vary across question types? (b) How do the relationships between answer quality and answer speed vary across question types? (c) How do the best quality answers and the fastest answers differ in terms of answer quality and answer speed across question types? (d) How do trends in answer quality vary over time across question types? From the posting of 3,000 questions in six CQAs, 5,356 answers were harvested and analyzed. There was a significant difference in answer quality and answer speed across question types, and there were generally no significant relationships between answer quality and answer speed. The best quality answers had better overall answer quality than the fastest answers but generally took longer to arrive. In addition, although the trend in answer quality had been mostly random across all question types, the quality of answers appeared to improve gradually when given time. By highlighting the subtle nuances in answer quality and answer speed across question types, this study is an attempt to explore a territory of CQA research that has hitherto been relatively uncharted

Social Learning Systems: The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians’ interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN\u27s). This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS). The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience

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