0115 966 7955 Today's Opening Times 10:00 - 20:00 (BST)

Information Technology and Animation

Disclaimer: This dissertation has been submitted by a student. This is not an example of the work written by our professional dissertation writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.


Information technology (IT) plays a key role in creating knowledge [1] and supporting management towards decision making[2] and Visualization tools are computer applications that produce graphical representations that aid decision making. Visualization tools are used in IT based decision support systems (DSS) like the simple spreadsheets to complex computer-based systems like business intelligence system, enterprise resource management and reporting system, knowledge management systems, and expert systems; to help decision makers to solve structured ,unstructured and semi structured problems. In the digital era, decision makers have access to large amount of digital data which can be used by visual analytics software to support decision making. Well structured problem has clear path to solution but for solving ill structured problem, external representation of the data and the problem can reduce the effort in reaching an accurate solution[3]. Chief economist of Google Inc predicts that need for data visualization will be growing rapidly in the next few years. He writes. “the ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decade.... because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it… Managers need to be able to access and understand the data themselves [4].

Independent researchers have predicted high future demand for visualization tools. Gartner's research reports that in spite of global recession businesses are interested in investing in business intelligence (BI) platforms that are expanding their capabilities towards advanced data visualization, scorecards and interactive dashboards. It predicts BI market’s compound annual growth rate (CAGR) through 2012 to be 7.0% for stand-alone BI platforms ([5].

Review of the literature on computer graphics as decision aid reports that the format in which data is presented to decision makers is critical to provide information for making decision[6]. As per the dual coding theory, cognition consists of two sub systems: Visual and Verbal[7-9]. The theory specifies that when information is represented visually, the recall is easier and the human brain can process changes in shape, color and motion parallely. As per the Central Capacity theory humans have limited working memory [10] and visualization of data (e.g. chart, diagram, graph) activates the visual component of the short-term working memory (visual working memory) to hold the visual objects for immediate attention. Both the theory recommend visual data representation to provide information to the users for making decisions as visual information is easier to store and recall.

Visualization aids in perceptual information processing to identify exceptions, trends, patterns, relationship in the data (clusters, associations, causality etc), detect outliers and to summarize data perceptually [11]. Many complex business decision making need insight and insight is a sudden discovery of a solution to a problem which results in a subjectively catastrophic experience[12]. As competition in the knowledge economy gets fiercer, organizations are constantly trying to get new insights to gain competitive advantage. Effective visualization tools acts as external aids that supports thinking and building insight by providing graphs that represent information primarily through position, shape, color, size, location, movement and symbols, and that viewers decode that information by taking it in, organizing it, analyzing it and detecting patterns and structures perceptually[13]. Human brain is a powerful pattern-finding engine and effective graph make behavioral patterns, temporal trends, correlations, clusters, gaps, and outliers visible in seconds and data graphics should amplify cognition and complement what humans do well[14]. Effective visualization tools are of great importance in supporting decision making as it amplifies cognition, perceptual information processing and facilitates knowledge generation. When visualization tools are inadequate decision making performance is impaired.

A review of literature on computer graphics as decision aids has demonstrated that decision performance is effected by information presentation format[6]. Compared to static graph (SG) , Dynamic Graph (DG) facilitates in faster retrieval and recall of information [15, 16], information comprehension [16] required for decision making. One author writes “If learners are in control of the speed of animation and can view and review, stop and start, zoom in and out, and change orientation of parts and wholes of the animation at will, then the problems of veridical perception can be alleviated” [17]. However the use of dynamic interactive graph has increased in business decision making, its impact on decision making has not been explored extensively in the IS field. Both the IS literature and Accounting literature has emphasized the need for studying the role of presentation format in the efficiency and effectiveness of decision-makers’ decision quality in order to provide empirical evidence on the effect of presentation format on decision quality [18] [19].

Previous research so far in the IS domain has extensively studied 2D, 3D graph, combining 2D-3D graph and animation with low data volume that are easily comprehensible in tables or graph. Gaining insight from high volume of business data can be challenging because the high density of the data makes it difficult to view all the data at once. On a typical computer screen the high volume data cannot be seen at once, the data has to be sliced to visually represent it on screen. A study done on functional mechanisms of online product presentation and its effect on online shopping reported that interactivity of product presentations is a design feature that influence (1) the efficacy of the presentations; (2) consumers’ perceptions of the diagnosticity of websites, their perceptions of the compatibility between online shopping and physical shopping, and their shopping enjoyment derived from a particular online shopping experience jointly influence consumers’ attitudes toward shopping at a website; and (3) both consumers’ attitudes toward products and their attitudes toward shopping at a website contribute to their intentions to purchase the products displayed on the website[20].Thus Interactive display has a positive influence on decision making thus the first objective of this study is extend the work done in the past by studying whether the use of display which has bother interactivity and is dynamic impacts decision making, including reducing information overload when dealing with high volume of data. While indicating a positive l effect from the use of DID, on the basis of the theory of Cognitive Fit, which identifies that better performance results when the external representation corresponds to the nature of the task to be accomplished [21] this study argues that it is most effective when the qualities of the display corresponds to the salient features of the task. On Time is also an important component of information load[21] where information load is the volume of the data to be processed over the unit of time available for the task.

The second objective of this study is to examine weather and how the effect of visualization tools on decision taking may be contingent on the time constraints- available time available for information processing. A prior study on 2D graph Vs table to examine the joints effects of presentation format and color on decision accuracy and efficiency under different time constraints [22] concluded that under low time constraints (15 min), tabular reports are better for accurate decision making and 2D graphs are better for faster processing. The combination of Table and graph were better than graph alone for decision accuracy. Under High time constraint (5 min)s color coding led to improved decision making. Decision makers are sometimes pressed for time which can result in information overload, which is the point at which information processing demands exceed the information processing capacity of the individual. This study investigates the moderating effect of time constraints on DID effectiveness on decision making in terms of the quality of the decision that the decision makers are able to make. This study uses the term High Time constraint (HTC) task to characterize the task that needs to completed in an environment when the decision makers are under time pressure and to distinguish it from Low time constraint (LTC) task where the decision makers are not under any time pressure to complete the decision making task.

Next section contains the review of past work on DID and decision making done to arrive at the hypothesis on the overall impact of DID on decision making. The subsequent section presents a review of the previous litreature on the theory of Cognitive Fit which provides the foundation for our theories on the relationship between different task types and DID. Then the different task types with time constraints is explained along with the prediction about the moderating effects of task type on decision making. The research method for the proposed research is explained including the measurement of independent and dependent variables and the experimental design. Finally this paper concludes with the significance of this proposed study to both researchers and practioners.

Theory and Hypothesis.

The research model developed for this proposed study is shown in figure 1. The figure illustrates that DID enhances decision making. The effect of DID is more pronounced when the task is less time critical where the decision makers are not pressed for time and the effect of DID is limited when the task is time critical. The hypotheses supported by theory are developed below.

Dynamic Interactive Display

Businesses today are commonly using Dashboards, which is a combination of visualization tools that provide summarized and details reports of current status and alerts and creates situational awareness. The array of visualization tools that available today includes Tree Maps, [23], Node-Link diagram /Network diagram[24], Parallel coordinate Graph [25], Spark lines- Integrates text, and chart [26],Motion chart[27]. The present study focuses on visualization tools that provide dynamic and interactive display (DID) because in the recent time there has been staggering advances in visualization tools that are interactive and dynamic to support taking decisions and solving information-intensive problems in business. The DID chosen for this study is Google’s Motion Chart, which is a flash based visualization tool that delivers dynamic chart with high level of interactivity for multidimensional data[28].

Motion charts are dynamic. The graph is said to dynamic when the graph consists of series of single frames, each showing incremental changes in the position, brightness, shape, color of the variables; shown in a sequence which give the illusion of movement[29, 30]. Graph Dynamism consist of two types of change : position or form [31, 32]. Translation change refers to change in position (from one location to another) and transformation change refers to the change in form ( in size, shape, color, brightness)[33]. Motion chart reflect both translation change and transformation change. It is dynamic as it has several indicators which show incremental change in color, location and shape over time.

Graphical excellence consists of complex ideas communicated with clarity, precision and efficiency and are accurately perceived [14]. Animations are often not clear and complex or efficient ( too fast) to be accurately perceived and judicious use of interactivity may overcome this disadvantage [17]. Motion Chart provide high level of interactivity (i.e., the extent to which users can manipulate and transform the form and content of the graph in real time[34]. Interactivity is achieved when the users can control the speed of animation and can view and review, stop and start, can transform (change the way representation is rendered, such as zooming, panning or resizing), or manipulate (control the parameters during the process of image generation, i.e. filtering, visually encoding the variables based on color, shape).

Decision Making

Previous study on online shopping website has indicated that dynamic , interactive presentation of information has a positive effect on the shopping decisions ( intend to purchase, revisit the shopping website) of the consumers [20]. Decision making includes problem solving[2]. Problem solving involves mentally working to overcome obstacles that stand in reaching a goal and arriving at the solution to the problem involves identifying the problem, defining and representing the problem, formulating the strategy , organizing and reorganizing information, allocating resources, monitoring and evaluation[3]. Prior research has indicated that (1) task type, (2) individual characteristics, and (3) information presentation format have effects on cognitive processing for making decisions [6]. Decision accuracy, problem comprehension and satisfaction will be used to measure decision making performance. Decision accuracy is probably the most commonly used criterion for measuring decision making performance [22, 35-48]. It is suggested that compared to DG, Dynamic Interactive Graph should facilitate performance [49]. Therefore, this study proposes that DID can enable decision makers to be more accurate when evaluating high volume of data than Dynamic Display.

H1. Compared to Dynamic Display, DID increases users Decision Accuracy

Research in the Management field has not examined different types of visuals and its effect on graph comprehension in detail[6]. The effect of x-y and y-z relationship encoding on the time to comprehend information was studied with respect to2D and 3D line graph [50].

As per Pinker’s theory of Graph Comprehension[51], different types of display are suited for extracting different classes of information, primarily because of two contrasting types of encoding mechanisms governing the graph comprehension process: (1) automatic conceptual message lookup processes and bottom up processing, and (2) inferential and top-down encoding processes. Simplified Flow diagram of the graph comprehension process is shown in Figure 2.

Automatic conceptual message lookup is the acquisition of information using the bottom-up encoding processes where the required information is obtained from the graph easily from the graph by means of salient cues. For example the theory indicated that that trend in a line graph is encoded via the bottom-up encoding processes because the human eye can automatically extract the change perceptually. On the contrary, information that are encoded via the inferential and top-down encoding process , needs execution of deliberate and capacity-limited computations that requires effort and use of both short-term and long-term memory processing. Fulfilling comprehension tasks, such as making inferences and drawing conclusions about the data in the graph and selecting and organizing the information from the graph, requires integration of the retrieved and encoded new information. This study proposes that more scan and search operations are needed to visually locate and organize the new information represented in the dynamic graph for both conceptual message lookup process and inferential process. The DID possesses interactivity and hence retrieving the information from the graph will be easier for the users by manipulating the data using overview, zoom, filter features of the display. Therefore, in response to the comprehension task, DID will be better than dynamic display.

H2a. Compared to Dynamic Display, DID increases users graph comprehension for automatic conceptual message lookup process
H2b Compared to Dynamic Display, DID increases users graph comprehension for inferential and top-down encoding process
Next from the perspective of affective dimension, users form positive, neutral or negative attitude towards the perceived usefulness of the visualization tool in decision making. The study predicts that DID with interactivity and dynamic display of the multi dimensional data affects decision maker’s attitude regarding perceived usefulness of the tool more significantly in favor of the tool than dynamic graph.
H3 Users attitude towards the perceived usefulness of the DID display differ from their attitude towards the perceived usefulness of the dynamic display.

Theory of Cognitive Fit and Time constraints

The theory of Cognitive Fit suggest that a match between External representation and users’ tasks is important for the realization of positive results from the display format [52]. While DID generally influences decision making, the degree to which DID affects decision making varies contingent upon the task types being examined. Decision makers may be faced with different types of tasks. Tasks for Problem solving can be retrieval, Communication of facts, Comparison of alternatives, Trend analysis, Recognition and recall, Problem finding, Problem comprehension and Problem solving[36]. When time in money decision makers have to make decisions fast and thus decision task can be time critical. In prior studies terms ‘‘time pressure’’ and ‘‘time constraint’’ are most commonly used interchangeably [53]. Prior research so far has not investigated the time-constraint tasks that are best supported by DID. For this study High Time Constraint (HTC) tasks are those that force the decision makers to perform under high time pressure. It is different from Low time-constraint (LTC) task for which the decision makers are not under any time pressure to perform.

These types of task types moderate the degree to which DDI affects decision making. Research suggests for HTC task , decision makers accelerated their processing, are more selective in processing and instead of evaluating one alternative at a time (depth based) the decision makers concentrate on attribute based ( breadth wide) pattern of processing[54]. Dynamic Interactive display (DID) have features that lets the users manipulate the data and retrieve details on demand but DID are complex because it provides access to several layers of data which the users can slice and dice. Therefore when DID is used with HTC task , decision makers are unable to accelerate processing as the complexity of the graph makes the users experience information overload , which affects their performance negatively. However when DID is applied to LTC task, then the interactivity is useful because access to several layers of information and availability of time lets the decision makers process information in depth. Thus DID for HTC task does not contribute as much to decision making accuracy, problem comprehension and perceived usefulness as it does for LTC task.

H4. Increases in Decision accuracy, effected by DID, are more significant for LTC task than HTC task.
H5a. Increases in users graph comprehension for automatic conceptual message lookup process, effected by DID, are more significant for LTC task than HTC task.
H5b. Increases in graph comprehension for inferential and top-down encoding process, effected by DID, are more significant for LTC task than HTC task.
H6. The impacts of DID on perceived usefulness towards decision making are more
significant for LTC task than HTC task.

Research Method

The proposed study will employ a controlled laboratory experiment to empirically test the effects of DID on Decision making and the moderating effect of time-dependent task types to achieve a high degree of internal validity [55]. To simulate experimental display close to real experiences and to increase the generalizability of the findings, instead of developing a prototype of DID , we selected Motion Chart owned by Google Inc for the visualization tool to produce DID . Motion chart is a commercial product used in business.


Experimental Design

For this proposed study a within-subject factor along with a between-subject factor, 2X2 factorial design will be used. The within-subject factor, Display format, will have two levels: DID and Dynamic Graph. The between-subject factor, Task Type will have two levels: HTC and LTC (Refer fig 2). The within-subject design for the presentation format will enable control over individual differences like spatial ability[56], cognitive style, comprehension abilities, which could confound the results [6] and also economize on the number of participants required for this study.

Fig 2 2X2 Factorial Design
Because display format was a within-subject factor, different task type were employed for each display format. The different task will have the same difficulty level so that this study can control for difference in task difficulty to have an effect on decision performance. Different task will control for the learning effect that could happen by repeatedly making the same type of decision with same task using different display format under different time dependent task. The participants might not use the interactive features provided in the DID even though it is provided to them. To control for that, the task assigned to DID will require manipulation of the data to arrive at the correct solution and the participants with the correct result will be rewarded with Bonus gift certificate to motivate them to use the interactive features of DID to arrive at the optimum result. Prior use and practice with a display format has an effect on performance [57] hence to control for that this study will allow participants to have experience with the visualization tool by giving them a brief 15 mins hands on training with the product.

As discussed earlier this study uses the term First, the pretest will be conducted with a pilot group that will be demographically similar to the experiment participants to determine the average time required by the subject to reach the optimum solution. Based on this average time the two limits will be chosen to arrive at the High time constraint and low time constraint condition. HTC task is a financial decision making task that will be given to the subjects which involve both bottom up encoding and inferential processing under the High Time constraint in an environment when the decision makers are under time pressure. When there is insufficient time to complete a task, decision performance becomes unpredictable[58] and to control for that the subjects in the HTC treatment will be given a time limit which will be close to the average time required to reach the optimum solution so that they get sufficient time. Low time constraint (LTC) task is a different financial decision making task that will be given to the subjects which involves both bottom up encoding and inferential processing where the decision makers are not under any time pressure to complete the decision making task. Half of the participants were assigned to Dynamic graph with HCT task and DID with LCT task and the other half in reverse order. Participants were assigned randomly to each condition to reduce potential extraneous effects in the experiment.


For the main experiment the participation will be voluntary and the subjects will be selected from the pool of graduate and undergraduate students taking courses in finance, economics or business in a large university. To motivate the subjects to participate in this study they will be offered gift certificates. to encourage their participation in the experiment. The participants will have to fill out a standard form about their demographics, if they have experience with Motion charts, number of years at the university. For the results to have external validity, this study proposes to conduct the experiment with handful of executives from the industry who are entrusted with the task of decision making to increase the generalizability of results.


The study will use the decision accuracy to measure the decision quality of the decision making performance. As discussed earlier this is a common measure used to measure decision making performance. To measure the problem comprehension this study will employ a validated comprehension study which has been used in prior studies on effects of information formats [59, 60]. To measure perceived usefulness of the display this study will use the validated survey used in prior studies to predict system usage [61].

Data Analysis

Cronbach’s Alpha will be used to assess the reliability of the constructs. A repeated measure ANOVA will be run to analyze the effects of information display on decision making. Separate t-test will be conducted to compare the mean difference between DID and dynamic display for each of the task type to examine the nature of interaction effects.
Significance of this Research

The proposed study can contribute to both theory and practice. By a controlled laboratory experiment , it will empirically test the impact of Dynamic Interactive Display (DID) in decision making performance for high volume of data. Although DID today are available in a number of areas like accounting and finance (Electronic financial statements using XBRL - eXtensible Business Reporting Language), geography (E.g. Active Maps), education (E.g. web based active textbook with animated interactive figures), medicine (E.g. Medical imaging), architecture (Floor plan, building plan) but the review of the literature suggests that the impact of DID has not been explored intensively in the IS field. This proposed study aims to provide empirical test of the theory of Cognitive Fit, in supporting that the impact of IT is limited which is contingent on whether a particular IT application, such as DID, is a good match with the requirement of processing high volume of data to complete a given task. The current study provides useful guidelines for design and use of dense, interactive visualizations towards effective business decision making. If Business want to want to improve decision making performance with high volume of data they can do it with DID. However, because there are so many visualization tools available for use that it is difficult to select the one that is useful for decision making under different time pressure. This proposed study will provide guideline for the usefulness of use DID for HTC and LTC task. These suggestions might help the decision makers and Visualization tool to enjoy the benefits of DID for the appropriate time-dependent task.

The proposed study in crucial to understand whether DID has positive effects on decision making and if yes then under what conditions. Interaction can becomes become a powerful tool where users need to filter and zoom on subset of data. There might be information overload and the users might feel lost in the data. There can be added cost in giving access to several levels of data. We also need to understand if providing interactive display is appropriate for tasks which need to be completed under high time pressure and requires processing of high volume of data. This proposed study attempts to imply that DID might not be usable for all decision making context and it is important to understand when it is the most appropriate and use it accordingly to support decision making.


1. Shneiderman, B., Creating creativity: user interfaces for supporting innovation. ACM Transactions on Computer-Human Interaction (TOCHI), 2000. 7(1): p. 114-138.
2. Todd, P. and I. Benbasat, The impact of information technology on decision making: A cognitive perspective. Framing the domains of IT management—Projecting the future through the past, 2000.
3. Yahaya, A., Problem Solving. unspecified.
4. DeSanctis, G., COMPUTER GRAPHICS AS DECISION AIDS: DIRECTIONS FOR RESEARCH*. Decision Sciences, 1984. 15(4): p. 463-487.
5. Paivio, A., Dual coding theory: Retrospect and current status. Canadian Journal of psychology, 1991. 45(3): p. 255-287.
6. Sadoski, M. and A. Paivio, Imagery and text: A dual coding theory of reading and writing. 2001: Lawrence Erlbaum Associates.
7. Paivio, A., Mental representations. 1986: Clarendon Press Oxford [Oxfordshire.
8. Kahneman, D., Attention and effort. 1973: Prentice-Hall Englewood Cliffs, NJ.
9. Carpenter, P. and P. Shah, A model of the perceptual and conceptual processes in graph comprehension. JOURNAL OF EXPERIMENTAL PSYCHOLOGY APPLIED, 1998. 4: p. 75-100.
10. Weisberg, R., Metacognition and insight during problem solving: Comment on Metcalfe. Journal of Experimental Psychology: Learning, Memory, and Cognition, 1992. 18(2): p. 426-431.
11. Cleveland, W. and R. McGill, Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 1984: p. 531-554.
12. Tufte, E., The visual display of quantitative information. 1983. Envisioning information, 1990.
13. Rieber, L. and M. Boyce, The effects of computer animation on adult learning and retrieval tasks. Journal of Computer Based Instruction, 1990. 17(2): p. 46-52.
14. Morrison, J. and D. Vogel, The impacts of presentation visuals on persuasion. Information & Management, 1998. 33(3): p. 125-135.
15. Tversky, B., J. Morrison, and M. Betrancourt, Animation: can it facilitate? International Journal of Human Computer Studies, 2002. 57(4): p. 247-262.
16. Abdolmohammadi, M., J. Harris, and K. Smith, Government financial reporting on the Internet: The potential revolutionary effects of XBRL. Journal of Government Financial Management, 2002. 51(2): p. 24-31.
17. Debreceny, R. and G. Gray, The production and use of semantically rich accounting reports on the Internet: XML and XBRL. International Journal of Accounting Information Systems, 2001. 2(1): p. 47-74.
18. Vessey, I. and D. Galletta, Cognitive fit: An empirical study of information acquisition. Information Systems Research, 1991. 2(1): p. 63.
19. Eppler, M. and J. Mengis, The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 2004. 20(5): p. 325-344.
20. Shneiderman, B., Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on graphics (TOG), 1992. 11(1): p. 92-99.
21. Atallah, G., et al., Next Generation Network Management Technology. 1994.
22. Inselberg, A., et al. Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. 1990.
23. Tufte, E., Sparklines: Intense, Simple, Word-sized Graphics. 2004, May.
24. Rosling, H., A. Rosling-Ronnlund, and O. Rosling. New software brings statistics beyond the eye.
25. Roncarelli, R., The Computer animation dictionary. 1989: Springer New York.
26. Sundberg, P., Animation in CALL: Learning to think in the fourth dimension. Paper Presentation CALICO, 1998. 98.
27. Lowe, R., Selectivity in diagrams: Reading beyond the lines. Educational Psychology, 1994. 14(4): p. 467-491.
28. Lowe, R., Animation and learning: selective processing of information in dynamic graphics. Learning and Instruction, 2003. 13(2): p. 157-176.
29. Lowe, R., Extracting information from an animation during complex visual processing. European Journal of the Psychology of Education, 1999. 14: p. 225-244.
30. Kiousis, S., Interactivity: A concept explication. new media & society, 2002. 4(3): p. 355.
31. Jiang, Z. and I. Benbasat, Research Note Investigating the Influence of the Functional Mechanisms of Online Product Presentations. Information Systems Research, 2007. 18(4): p. 454.
32. Remus, W., An empirical investigation of the impact of graphical and tabular data presentations on decision making. Management Science, 1984: p. 533-542.
33. Benbasat, I. and A. Dexter, An experimental evaluation of graphical and color-enhanced information presentation. Management Science, 1985: p. 1348-1364.
34. Benbasat, I. and A. Dexter, An investigation of the effectiveness of color and graphical information presentation under varying time constraints. MIS Quarterly, 1986: p. 59-83.
35. Lee, J., J. MacLachlan, and W. Wallace, The effects of 3D imagery on managerial data interpretation. MIS Quarterly, 1986: p. 257-269.
36. Amer, T., An experimental investigation of multi-cue financial information display and decision making. Journal of Information Systems, 1991. 5(2): p. 18-34.
37. Volmer, F., Effect of graphical presentations on insights into a company's financial position: an innovative educational approach to communicating financial information in financial reporting. Accounting Education, 1992. 1(2): p. 151-170.
38. Carey, J. and E. White, The effects of graphical versus numerical response on the accuracy of graph-based forecasts. Journal of Management, 1991. 17(1): p. 77.
39. Umanath, N. and I. Vessey, Multiattribute Data Presentation and Human Judgment: A Cognitive Fit Perspective*. Decision Sciences, 1994. 25(5-6): p. 795-824.
40. Montazemi, A., et al., On the effectiveness of decisional guidance. Decision Support Systems, 1996. 18(2): p. 181-198.
41. Dennis, A. and T. Carte, Using geographical information systems for decision making: Extending cognitive fit theory to map-based presentations. Information Systems Research, 1998. 9(2): p. 194.
42. Dull, R. and D. Tegarden, A comparison of three visual representations of complex multidimensional accounting information. Journal of Information Systems, 1999. 13(2): p. 117-131.
43. Vance Wilson, E. and I. Zigurs, Decisional guidance and end-user display choices. Accounting, Management and Information Technologies, 1999. 9(1): p. 49-75.
44. Jarvenpaa, S., The effect of task and graphical format congruence on information processing strategies and decision-making performance. Management Science, 1989. 35(3): p. 285-303.
45. Chan, S., The use of graphs as decision aids in relation to information overload and managerial decision quality. Journal of information science, 2001. 27(6): p. 417.
46. Ferguson, E. and M. Hegarty, Learning with real machines or diagrams: Application of knowledge to real-world problems. Cognition and Instruction, 1995. 13(1): p. 129-160.
47. Kumar, N. and I. Benbasat, The effect of anchoring and encoding on information representation: A comparison of 2D and 3D graphs. MIS Quarterly, 2004. 28(2): p. 255-281.
48. Pinker, S., A theory of graph comprehension. Artificial intelligence and the future of testing, 1990: p. 73-126.
49. Vessey, I., Cognitive Fit: A Theory-Based Analysis of the Graphs Versus Tables Literature*. Decision Sciences, 1991. 22(2): p. 219-240.
50. Maule, A. and O. Svenson, Time pressure and stress in human judgment and decision making. 1993: Plenum Press New York.
51. Johnson, E., J. Payne, and J. Bettman, Adapting to time constraints. Time pressure and stress in human judgment and decision making, 1993: p. 103–116.
52. Singleton, R., et al., Approaches to social research. 1999: Oxford University Press New York.
53. Large, A., et al., Effect of animation in enhancing descriptive and procedural texts in a multimedia learning environment. Journal of the American Society for Information Science, 1996. 47(6).
54. Rieber, L., Animation in computer-based instruction. Educational Technology Research and Development, 1990. 38(1): p. 77-86.

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Request Removal

If you are the original writer of this dissertation and no longer wish to have the dissertation published on the UK Essays website then please click on the link below to request removal:

More from UK Essays

Get help with your dissertation
Find out more
Build Time: 0.0040 Seconds