Decision-making under time pressure with different information sources and performance-based financial incentives—Part 2
Introduction
In Part 1 [3], we provided the necessary background for this work and also described the construction and refinement of our experimental platform and an accompanying subject training software suite. Here we expand on the description of the platform, detail our experimental design, and present our results. In conjunction with three task complexity levels and a reward–penalty structure, specific time parameter settings enable us to operationalize the concepts of explicit and implicit time pressures. We describe this parameter choice process in Section 2. In Section 3, we review the goal of our exploratory research effort, describe our experimental design, and provide a rationale for both. Section 4 describes the construction of the reward/penalty mechanism. Section 5 sets forth our experimental findings. Section 6 provides concluding thoughts.
Section snippets
Determining critical control parameter settings
In order to determine three critical time bounds, namely, the Standard Rule Learning Time (SRLT), Standard Help Time (SHT), and Standard Decision-Making Time (SDMT), we used student volunteers. These volunteers participated in a training session prior to using the platform. The lab conditions and general environment were maintained as intended for the real experiment. The only exception was that when determining SRLT and SDMT, we disabled the system help facility (which we re-enabled when
The goal of the exploratory study and the study design
Our broad research goal was assessing whether our new, symbolic language held potential for adequately conveying abstract notions. Utilizing a hypothetical task, this exploratory study addressed the following question: In our experimental setting, is there any evidence that the proposed symbolic language (Image) is at least as good as the conventional Audio and Text alternatives? With the novelty of our symbolic language pitted against the familiarity of written and spoken English, there was a
Determining the reward/penalty structure
Table 6 presents our “per-decision” rewards and penalties. The table details a performance-based reward mechanism that provides a minimum of $9.00 and a maximum of $25.00 per session. These earnings include the show-up fee and assume that a subject makes a correct decision. The largest reward occurs when a subject makes the correct decision without system help and within 2 s (i.e., $0.37 per experiment).
Penalties were assessed when a subject made the wrong decision or made no decision.
Analyses of experimental findings
We collected experimental data on eight specific measures for all subjects within each of several pre-defined categories of experiments, such as, “all Text-Level 1-Normal experiments” (see Section 5.2 for a complete listing of categories). The measures used were:
- (1)
Average Earnings (AE): average of the monetary rewards earned (cents);
- (2)
Decision Accuracy Rate (DAR): percentage of correct decisions;
- (3)
Average Decision Time (ADT): average of the decision-making times (in seconds);
- (4)
Average Rule-learning
Concluding remarks
In Section 5, we examined a single source element, Mode, for trends, both in isolation and in the context of the remaining two elements—Speed and Complexity. (We repeated the analyses for these two source elements although we are not reporting our findings here). We also used the trends analysis rankings to draw aggregate inferences about all 27 information sources in terms of the four primary measures, as follows.
Consider the source, <Text, Level 1, Slower>, as an example. We have primary
Acknowledgements
The authors express their sincere thanks to the three anonymous reviewers for their critical comments that enabled significant improvements to this two-part manuscript.
Dr. James R. Marsden, the Shenkman Family Chair in e-Business, came to UConn in 1993 as Professor and Head, Department of Operations and Information Management, School of Business Administration, University of Connecticut. Dr. Marsden was part of a three-person concept development team that initiated and oversaw the development of the Connecticut Information Technology Institute and is currently serving as its Executive Director. He developed and implemented the Treibick Electronic Commerce
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Decision making under time pressure with different information sources and performance-based financial incentives - Part 1
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Dr. James R. Marsden, the Shenkman Family Chair in e-Business, came to UConn in 1993 as Professor and Head, Department of Operations and Information Management, School of Business Administration, University of Connecticut. Dr. Marsden was part of a three-person concept development team that initiated and oversaw the development of the Connecticut Information Technology Institute and is currently serving as its Executive Director. He developed and implemented the Treibick Electronic Commerce Initiative that is funded through a generous gift provided by Richard Treibick and the Treibick Family Foundation. Dr. Marsden also serves as Director of the OPIM/SBA MIS Research Lab and is a member of Advisory Board and Steering Committee of CIBER (Center for International Business Education and Research). He was a member of the edgelab development team and currently serves on the edgelab Steering Committee, which selects and resources projects and oversees operations. Dr. Marsden was a winner of the initial Chancellor's Award for IT Excellence and has a lengthy record in market innovation and analyses, economics of information, artificial intelligence, and production theory. His research work has appeared in Management Science; IEEE Transactions on Systems, Man, and Cybernetics; American Economic Review; Journal of Economic Theory; Journal of Political Economy; Computer Integrated Manufacturing Systems; Decision Support Systems; Journal of Management Information Systems, and numerous other academic journals. He was part of the IT Visioning and IT Planning Groups for the University and has played a leading role in developing the School of Business Administration as both a campus and national leader in IT education and research.
Professor Marsden received his AB (Phi Beta Kappa, James Scholar, Evans Scholar) degree from the University of Illinois and his MSc and PhD degrees from Purdue University. Having completed his J.D. while at the University of Kentucky, Jim has been admitted to both the Kentucky and Connecticut Bar. He is an Area Editor of Decision Support Systems and serves in a frequent external evaluator for major U.S. and international universities. He has held visiting positions at the University of York (England), University of Arizona, Purdue University, and the University of North Carolina. Jim was an Invited Lecturer at two NATO Advanced Study Institutes on Decision Support Systems and has given keynote addresses and university seminars throughout Europe and the Far East.
Ramakrishnan Pakath is an Associate Professor of Decision Science and Information Systems at the University of Kentucky. Ram holds the MSE (OR and IE) degree from The University of Texas at Austin and a PhD (Management-MIS) degree from Purdue University. His research focuses on (a) designing and evaluating adaptive problem processors, and (b) assessing information source impacts on system user performance. Dr. Pakath's research articles have appeared in such refereed forums as Decision Sciences, Decision Support Systems, European Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics, Information and Management, and Information Systems Research. He is author of the book Business Support Systems: An Introduction published by Copley, now in its second edition. Dr. Pakath has also contributed refereed material to a number of well-known books including Handbook of Industrial Engineering, Multimedia Technology and Applications, and Operations Research and Artificial Intelligence. He served as Director of the MIS Research Laboratory of the College of Business and Economics, University of Kentucky from 1993 to1997. He is an Associate Editor for Decision Support Systems and an Editorial Board Member of Journal of End User Computing and Management.
Kustim Wibowo is an Associate Professor in the MIS and Decision Sciences Department of the Eberly College of Business and IT, Indiana University of Pennsylvania. He received his PhD in MIS from the University of Kentucky. Dr. Wibowo also holds an MSc in Computer Science from Baylor University. His current research interests include: e-commerce and web security, information systems for educational technology, human resource information systems, and OLAP (OnLine Analytical Processing) for managerial decision support.
- 1
This author's work was supported by the Treibick Electronic Commerce Initiative at the University of Connecticut.
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Their work was supported by a grant from the MIS Research Laboratory Endowment at the University of Kentucky.
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Presently at MIS and Decision Sciences, Eberly College of Business, Indiana University of Pennsylvania, Indiana, PA 15701.