عنوان فارسی مقاله:
تضعیف عدم تقارن اطلاعات از طریق تضمین پایداری: نقش حسابداران و سطوح اطمینان
عنوان انگلیسی مقاله:
Mitigating information asymmetry through sustainability assurance: The role of accountants and levels of assurance
سال انتشار : 2017
عنوان فارسی مقاله:
استخراج اطلاعات تجاری از نمودارها: آزمایش ردیابی چشم
عنوان انگلیسی مقاله:
Extracting business information from graphs: An eye tracking experiment
مقاله سال 2015
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بخشی از مقاله انگلیسی:
2. Theoretical framework
The descriptive and normative research questions in the introduction are not new. Starting from the seminal work of Buswell (1935), this literature is exponentially growing during the last years with the quick development of big data methods and visualization software (Shixia, Weiwei, Yingcai, & Mengchen, 2014). However, most of these studies follow a computer science approach (Diehl, Beck, & Burch, 2010; Wang, Chou, Su, & Tsai, 2007) and do not focus on providing and analyzing empirical evidence on the underlying cognitive procedures or the effectiveness of alternative visualization frames for a proper understanding of the information in a graph. The literature presents two experimental approaches to deal with these research questions. The first one is the ‘black box’ approach.Under this approach, experimental subjects receive the same information in different visualization frames. Then, these studies measure and compare a series of indicators of the quality of the decision making (accuracy of the answer to a question, time required to make the decision, etc.) among different frames. Although most studies apply psychological experiments, where subjects do not receive economic incentives, Arribas, Comeig, Urbano, and Vila (2014) provide an interesting example of the application of an incentivized economic experiment. They present statistical information using numerical and graphical representations and assess the impact of such statistical formats on both the optimality of market decisions and the time required to complete decision making. Although the ‘black box’ approach allows for analyzing the effectiveness of alternative visualization frames, this approach does not provide information on subjects' scanning patterns: order, number of times, and during how long subjects actually look at each specific element of the statistical chart, etc. A good understanding of the underlying cognitive processes and the establishment of evidence-based normative guidelines to improve data visualization requires this type of information and the consideration of alternative approaches. A proposal for this alternative is data collection using eye-tracking tools, which are able to provide real-time information on where a subject is locating her or his eyes and allows for the identification of actual visualization patterns. Eye movements may seem very stochastic but are indicators of higher-level cognitive processes (Conati & Maclaren, 2008). An eye-tracking methodology helps uncover subtle cognitive processing stages that are otherwise difficult to observe in visualization evaluation studies. Although completion time and accuracy on specific tasks may indicate that differences or problems exist, a deeper understanding of visual scanning strategies on information graphics may help to determine specific guidelines for designing graphs and selecting graph types for particular datasets and tasks. An eye-tracking methodology can help to observe these visual scanning strategies, providing richer information beyond that available from response time and accuracy-based methodologies (Goldberg & Helfman, 2011). However, the price for the information that sophisticated non-intrusive eyetrackers provide is the impossibility of running experiments with large samples able to support reliable statistical inference. For instance, sample size is 32 subjects in Goldberg and Helfman (2011) or 38 subjects in Burch, Heinrich, Konevtsova, Hoferlin, and Weiskopf (2011). This tradeoff between information accuracy and sample size is not a specific issue of eye trackin, but is a common feature of all neuroeconomic experiments, where the complexity and time consumption in data collection usually limits data analysis to descriptive analysis, precluding the application of more sophisticated inference methods or statistical hypothesis testing. The literature presents several examples of the application of eyetracking methods to analyze graph-scanning strategies. For instance, Goldberg and Helfman (2011) present an illustrative eye-tracking study to compare how radial and linear graphs support value lookup tasks for both one and two data dimensions. This experiment presents linear and radial versions of bar, line, area, and scatter graphs to the participants, who complete each a counterbalanced series of tasks. Eye tracking also helps to classify error strategies and to support the establishment of improvement guidelines in the design of radial and linear graphs. As another example, Burch et al. (2011) apply eye tracking to identify visual exploration behaviors of participants solving a typical hierarchy exploration task by inspecting a static tree diagram. To uncover exploration strategies, they examine fixation points, duration, and saccades of participants' gaze trajectories. Huang (2013) highlights that graph esthetics usually come from common senses and personal intuitions—thus, their relevance to effectiveness is not clear—and conducts two eyetracking studies in an attempt to understand the underlying mechanism of edge crossings. These studies show that eye tracking is an effective method for gaining insights into how people read graphs and how the esthetics conditions human graph-reading behavior. However, the literature includes no previous examples in of the design and implementation of incentivized economic experiments with eye tracking: recording subjects' scanning strategy while they read basic statistical charts with information for a successful development of tasks with an economic incentive. The present study is a first attempt to fill this gap and get together the potentiality of eye-tracking methods with the reliability of incentivized economic experiments. To this end, experimental subjects observe simple horizontal bar charts with the information on the percentage of purchasers of ten movies. Then, participants answer a simple multiplechoice question (the percentage of sales of the fourth most purchased movie) using this information. The task has an economic incentive and those subjects who answer the question properly receive 2 euros. Graphs present the information in two different frames, defining the two treatments of the experiment. Treatment 1 does not provide the numeric value of sales percentage and this information only appears on the ax of the chart. Treatment 2 shows this numerical value beside the bars, making the information in the horizontal ax redundant. During graph reading, a non-intrusive eye tracker records subjects' scanning strategy. This type of eye tracker provided more accurate information (precise recording of eye movement almost at a pixel level) and has lower impact on the subjects. The structure of this study is as follows. This section summarizes the theoretical framework and Section 3 presents the economic experiment and the features of the eye tracker. Section 3 also describes the methodology used to analyze eye-tracking data, based on the definition of areas of interest and descriptive comparison of heat maps. Section 4 summarizes the results of the experiment and Section 5 presents a brief discussion of these results.
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کلمات کلیدی:
Process-Aware Systems: Second International Workshop, PAS 2015, ...https://books.google.com/books?isbn=9811010196Jian Cao, Xiao Liu, Kaijun Ren - 2016 - ComputersThey are proposed either to rank existing business process models for similarity ... business process recommendation technique, using graph mining technique to extract ... It isn't necessary to provide extra information in our recommendation.Intelligent Systems and Applications: Proceedings of the ...https://books.google.com/books?isbn=1614994846W.C.-C. Chu, H.-C. Chao, S.J.-H. Yang - 2015 - ComputersIn response to the rapidly growing ecology of global asset information and the increasing ... Therefore, based on by the hierarchical asset graph model, the matching ... [5] O. Levina, O. Holschke, and J. Rake-Revelant, Extracting business logic ...Intelligent Information and Database Systems: 8th Asian Conference, ...https://books.google.com/books?isbn=3662493810Ngoc-Thanh Nguyen, Bogdan Trawiński, Hamido Fujita - 2016 - ComputersPutrycz, E., Kark, A.W.: Recovering business rules from legacy source code for system ... Shao, J., Pound, C.J.: Extracting business rules from information systems. ... GraphViz – Graph Vizualization Software. http://www.graphviz.org Dykstra, ...
عنوان فارسی مقاله:
مشارکت فکری در تجسم اطلاعات کسب و کار: یک چارچوب یکپارچه از قابلیت های تجسم و ابعاد هوش بصری
عنوان انگلیسی مقاله:
Business Information Visualization Intellectual Contributions: An Integrative Framework of Visualization Capabilities and Dimensions of Visual Intelligence
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بخشی از مقاله انگلیسی:
2. VISUALIZATION TERMINOLOGY
A number of terms related to visualization of data are available in the literature, such as Visualization (in general), Data Visualization, Information Visualization (often called InfoViz by practitioners), Scientific Visualization, Visual Analytics, and Business Visualization (often called BizViz by practitioners). Data Visualization emerged in the 1950s with the advent of computer graphics [8] and is defined as the science of visual representation of data. Scientific Visualization was used initially to refer to visualization as a part of a process of scientific computing [8] and focuses on processes for steering the data set and seeing the unseen, thereby enriching existing scientific methods [9]. Other variants and sub-types of data and scientific visualization were coined as well and are based on the type of representation they embody (Cartographic Visualization) or knowledge domain (Statistical Visualization). ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 5 Information Visualization has been coined in 1999 as the use of computer-supported interactive visual representations of abstract data to amplify cognition [10]. Typical examples of abstract data that has no inherent mapping to space are employee turnover statistics, bank branch deposit growth data or sales goals figures. Others suggested that “Information visualization utilizes computer graphics and interaction to assist humans in solving problems [11 p.58]” or define it as “…visual representations of the semantics, or meaning, of information. In contrast to scientific visualization, information visualization typically deals with nonnumeric, nonspatial, and high-dimensional data [12 p.12]” or as communication of abstract data through the use of interactive visual interfaces [13]. Visual Analytics is the science of analytical reasoning facilitated by interactive visual interface [14]. The initial domain driving the development of this discipline was homeland security but is currently being applied in security, health, commerce, transportation, energy, and personal applications. It is often described as dealing with complex data that enables detection of the expected and discovery of the unexpected [15]. BIV is an incarnation of visualization and is increasingly gaining researchers' and practitioners' attention [9]. While data visualization has been associated to BI from early static charting to interactive charting and dashboarding, BIV has been primarily presented through a representation and interaction lens or a decision making and task-orientation lens. A number of definitions exist for BIV. Tegarden defines it as “simply the use of visualization technologies to visualize business data or information (p.8)”. He also recognizes that “…business information has been visualized in the form of tables, outlines, pie charts, line graphs, and bar charts for a very long time. However, today business information visualization means the use of multidimensional graphics to represent business-related data or information[16 p.8].” Zhang offers a more detailed definition of BIV as "a process of creating appropriate computer-generated visual representations of large amounts of non-geometric managerial data for human problem-solving and decision-making support[9 p.4]." Card, Mackinlay and Shneiderman [10] definition could be applied to business context by defining BIV as the use of computer-supported interactive visual representations of abstract business data to amplify cognition. ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT 6 One could discern many similarities and differences among these various definitions. A clear thread among all of the definitions is the computer-supported interactive visualization of data. A temporal dimension can be observed, in that whereas earlier definitions focused on the development of visualization techniques and on the data understanding, the latter definitions focused more on the use of visualization, its impact, and, even, its role in behavioral understanding (for example, in security and business applications). We could offer a more comprehensive definition of BIV as the use of computer-supported interactive visual representations of business data to amplify cognition, achieve better data, business, and behavior understanding to improve decision making and business impact. The advantage of this definition is that it integrates the main features of previous definitions and brings forth the importance of understanding behavior, improving decision making and business impact, and explicitly highlights the importance of linking visualization to human cognition.
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کلمات کلیدی:
[PDF]Business Information Visualization for Decision-Making ... - CiteSeerXciteseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.1279&rep=rep1...pdfby P Zhang - Cited by 22 - Related articlesBusiness Information Visualization for Decision-Making Support. -- A Research Strategy ... theoretical and practical contributions. Literature Review and ... obvious physical model that can be used to represent the data that humans can ...[PDF]Business Information Visualization: A Visual ... - Semantic Scholarhttps://pdfs.semanticscholar.org/3da3/9a3e9bd8421ddbf978ff692cd4b708568a2e.pdfby D Bačić - Cited by 1 - Related articlesBusiness Information Visualization, Visual Intelligence, Business Intelligence, Decision Performance ... capabilities of BI tools contribute to the goal of ..... The perceptual, emotional and cognitive processes which ultimately lead to the choice of ...Dinko Bačić (Bacic) | Professional Profile - LinkedInhttps://www.linkedin.com/in/dinkobacicBusiness Information Visualization Intellectual Contributions: An Integrative Framework of Visualization Capabilities and Dimensions of Visual Intelligence.
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