Michael L. Williams

The impact of DSS use and information load on errors and decision quality

Abstract This paper uses a laboratory experiment to examine the effect of DSS use on the decision maker’s error patterns and decision quality. The DSS used in our experiments is the widely used Expert Choice (EC) implementation of the Analytic Hierarchy Process. Perhaps surprisingly, our experiments do not provide general support for the often tacit assumption that the use of a DSS such as EC improves decision quality. Rather, we find that, whereas a DSS can help decision makers develop a better understanding of the essence of a decision problem and can reduce logical error (especially if the information load is high), it is also susceptible to introducing accidental effects such as mechanical errors. In some cases, as in our study, the accidental errors may outweigh the benefits of using a DSS, leading to lower quality decisions.

A Meta-Analysis of Group Side Effects in Electronic Brainstorming: More Heads are Better than One

Electronic brainstorming (EBS) has been a focus of academic research since the 1980s. The results suggest that in most – but not all – cases, groups using EBS produce more ideas than groups using verbal brainstorming. In contrast, the results comparing groups using EBS to groups using nominal group brainstorming have been mixed: sometimes EBS groups produce more ideas, while in other cases, nominal groups produce more. This article examines the effects of group size on EBS, verbal brainstorming and nominal group brainstorming. We found that group size is a significant factor in predicting the performance of EBS relative to verbal brainstorming and nominal group brainstorming. As group size increases, the relative benefit of EBS increases. EBS groups outperform verbal groups when group size reaches four people. EBS groups outperform nominal groups when group size reaches 10 people.

The role of conditional release technologies and intelligent tutors in graduate management education

Learning management systems have come a long way since Sir Isaac Pitman initiated the first correspondence course in the early 1840s. Today, the demands of globalization call for new and innovative learning systems for enhancing graduate management education. One approach for meeting these challenges is through the increased use of learning management systems, in general, and conditional release technologies (CRT), in particular. The primary aim of CRTs is to distribute knowledge in small and more management learning packages based on student performance and characteristics. Intelligent tutors provide the vehicle for helping identify the appropriate material. The purpose of this paper is to highlight new developments in the use of CRTs and intelligent tutors in a program of graduate management education.

Enhancing the Blended Learning Experience Through Crowdsourcing: Applications to Management Education

The world of blended learning is now becoming the new norm throughout the management education universe. Under growing pressure from a variety of stakeholders, business educators are increasingly turning to the blended learning model as a vehicle to provide world-class education. The approach being adopted in many programs is to engage faculty and students in a virtual crowdsourcing learning experience via the blended learning model. Crowdsourcing as applied to business education is the process of connecting students and faculty with a broad-based group of both internal and external resources for the general purpose of problem solving, developing new skill sets, and measuring results. The purpose of this article is to identify the growing opportunities for crowdsourcing in management education and to highlight specific implementation strategies for helping facilitate this revolutionary learning process throughout the community of practice.