Fred Collopy’s Research

“In theory there is no difference between theory and practice.
In practice there is.”
variously attributed

"Now I am no believer in the worth of any mere taste for art
that cannot produce what it professes to appreciate."
George Bernard Shaw
Preface to the Second Volume of Plays:
Pleasant and Unpleasant

My research is a blend of thinking and doing, of the empirical and the constructive.


On How to Manage by Designing
With our 2002 workshop on Managing as Designing we initiated a dialogue that brought a broad diversity of designers together with management scholars to explore how practices from design could inform management. This workshop resulted in a documentary film, a book, a series of ongoing international gatherings, and major changes in the curricula of Weatherhead and other management schools.
	Richard J. Boland, Jr. and Fred Collopy, “Design Matters for Management,” from Managing as Designing, Stanford University Press, 2002, 3-18. Fred Collopy, “I Think With My Hands: On Balancing the Intuitive and Analytic in Designing,” in R. J. Boland, Jr. and F. Collopy [eds.], Managing as Designing, Stanford University Press, 2004, 164-168. Richard J. Boland, Jr. and Fred Collopy, “Toward a Design Vocabulary for Management,” in R. J. Boland, Jr. and F. Collopy [eds.], Managing as Designing, Stanford University Press, 2004, 265-276. Richard J. Boland, Jr., Fred Collopy, Kalle Lyytinen and Youngjin Yoo, “Managing as Designing: Lessons for Organization Leaders from the Practice of Frank O. Ghery,” Design Issues, 24 (2008), 10-25.
On Representing Financial Data
Using the cycle model, an abstract representation of the operations, financing, and capabilities of a firm, we developed and tested a program for viewing basic accounting and financial information visually and dynamically. In experiments we have found that subjects using it are able to identify which firms are likely to go bankrupt more accurately than those using spreadsheets or conventional graphs.
Demonstration software and a paper describing the experiments are available. Lin Zhao, Julia Grant, Fred Collopy and Richard J. Boland, Jr., “Dynamic Representation of Financial Ratio Data: Designs and Empirical Results.” Richard J. Boland, Jr., Fred Collopy, Julia Grant, and Lin Zhao, “Virtual Prototyping of Financial Flows as a Form of Management Control,” in Brandon and Kocaturk [eds.], Virtual Futures for Design, Construction and Procurement, Wiley Blackwell Publishing, 2008.
On Visual Instrument Design
I designed and programmed Imager, a performance instrument that provides real-time control of color, form, and motion of abstract images so that I can play with abstract images as musicians play with sounds.
	Fred Collopy, “Color, Form, and Motion: Dimensions of a Musical Art of Light,” Leonardo, Vol. 33, No. 5, 2000, 355-360. Fred Collopy and Robert M. Fuhrer, “A Visual Programming Language for Expressing Visual Rhythms,” Journal of Visual Programming Languages, 12, 2001, 283-297. Fred Collopy, Robert M. Fuhrer, and David Jameson, “Visual Music in a Visual Programming Language,” IEEE Symposium on Visual Languages, (1999), 111-118. Fred Collopy, “Improvisational Lumia: Playing Along with Musicians,” Leonardo, Vol. 34, No. 4, 2001, 353.
On the Use of Information and Information Systems
In a series of lab studies and retrospective field research, we found that competitor-oriented objectives and some uses of competitor-oriented information can be detrimental to profits. J. Scott Armstrong and Fred Collopy, “Competitor Orientation: Effects of Objectives and Information on Managerial Decisions and Profitability,” Journal of Marketing Research, 33 (1996), 188-199. J. Scott Armstrong and Fred Collopy, “The Profitability of Winning,” Chief Executive, June, 1994, 61-63. I identified systematic biases (regression to mean) in managers' self-assessments of the amount of time they spent using computers. At least then, heavy users thought they used them less than they actually did; while light users perceived their use as greater than it was. Fred Collopy, “Bias in Retrospective Self-Reports of Time Use: An Empirical Study of Computer Users,” Management Science, 42 (1996), 758-767. Fred Collopy, “White Collar Computing: A Field Study Using Automated Logging,” Proceedings of the Twenty-First Annual Hawaii International Conference on System Sciences,, vol. IV (1988), 236-244. We defined and tested nine principles to support the design of user-tailorable technologies. Matt Germonprez, Dirk Hovorka, and Fred Collopy, “A Theory of Tailorable Technology Design,” Journal of the Association for Information Systems, Vol. 8 (2007) Issue 6, Article 21.
On Business, Demographic and Economic Forecasting
We developed “Rule-Based Forecasting,” an expert systems approach to improve the selection and combination of extrapolation forecasts and in integrate judgment with statistical methods. Fred Collopy and J. Scott Armstrong, “Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations,” Management Science, 38 (1992), 1394-1414. Fred Collopy and J. Scott Armstrong, “Toward Computer-Aided Forecasting Systems: Gathering, Coding, and Validating the Knowledge,” in George R. Widmeyer (ed.), DSS-899 Transactions: Ninth International Conference on Decision Support Systems, Institute of Management Science, (1989), pp. 103-119. Monica Adya, J. Scott Armstrong, Fred Collopy, and Miles Kennedy, “An Application of Rule-based Forecasting to a Situation Lacking Domain Knowledge,” International Journal of Forecasting, 16 (2000), 477-484. J. Scott Armstrong, Monica Adya, and Fred Collopy, “Rule-Based Forecasting: Using Expert and Domain Knowledge in the Extrapolation of Time Series”, Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong (ed.): Norwell, MA: Kluwer Academic Publishers, 2001, 259-282. Monica Adya, Fred Collopy, Miles Kennedy and J. Scott Armstrong, “Identifying Features of Time Series for Rule-Based Forecasting,” International Journal of Forecasting, 17 (2001), 143-157. J. S. Armstrong and F. Collopy, “Integration of Statistical Methods and Judgment for Time Series Forecasting: Principles from Empirical Research,” in G. Wright and P. Goodwin [eds.] Forecasting with Judgment, John Wiley & Sons, Ltd., (1998), 269-293. We found that machine learning techniques could improve on the estimates of experts for coefficients used in expert systems for forecasting and that neural networks could be effective under very particular conditions. Monica Adya, Fred Collopy, J. Scott Armstrong, and Miles Kennedy, “Automatic Identification of Time Series Features for Rule-Based Forecasting,” International Journal of Forecasting, 17 (2001), 143-157. William R. Foster, Fred Collopy and Lyle H. Ungar, “Neural Network Forecasting of Short, Noisy Time Series,” Computers in Chemical Engineering, 16 (1992), 293-297. We reviewed the approaches proposed for integrating statistical and judgmental forecasts and the empirical support for each. J. Scott Armstrong and Fred Collopy, “Integration of Statistical Methods and Judgment for Time Series Forecasting: Principles from Empirical Research,” in G. Wright and P. Goodwin (eds.), Forecasting with Judgment, John Wiley & Sons Ltd., 1998, 269-293. We found that of 48 studies that examined the use artificial neural networks to produce forecasts, only 22 were effectively validated and implemented. Of those, 18 supported the potential of neural nets for forecasting and prediction. Monica Adya and Fred Collopy, “How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation,” Journal of Forecasting, 17, 481-495. We proposed and tested the use of causal forces for the selection and weighing of extrapolation methods and then for decomposing time series. J. Scott Armstrong and Fred Collopy (1993), “Causal Forces: Structuring Knowledge for Time-series Extrapolation,” Journal of Forecasting, 12 (1998), 103-115. J. Scott Armstrong, Fred Collopy, and J. Thomas Yokum, “Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series,” International Journal of Forecasting, 21 (2005), 25-36. We found that the most widely used measures for assessing accuracy in forecasting studies are unreliable and unstable and proposed and evaluated the Relative Absolute Error (RAE) as an alternative. J. Scott Armstrong and Fred Collopy, “Error Measures for Generalizing about Forecasting Methods: Empirical Comparisons,” International Journal of Forecasting, 8 (1992), 69-80. Fred Collopy and J. Scott Armstrong, “Generalization and Communication Issues in the Use of Error Measures: A Reply,” International Journal of Forecasting, 8 (1992), 107-109. We found that prediction intervals are often asymmetric and proposed a method for modifying them. J. Scott Armstrong and Fred Collopy, “Identification of Asymmetric Prediction Intervals through Causal Forces,” Journal of Forecasting, 20 (2001), 273-283. We determined that the use of diffusion models to forecast information systems spending has produced errors that are greatly in excess of those resulting from the application of simple extrapolation methods. We also developed some important principles for conducting comparisons of forecasting methods. Fred Collopy, Monica Adya, and J. Scott Armstrong, “Principles for Examining Predictive Validity: The Case of Information Systems Spending Forecasts,” Information Systems Research, 5 (1994), 170-179.