A Note on Interpersonal and Communication Skills for IS Professionals: Evidence of Positive Influence

A recent article by Byrd and Turner (2001) reported that interpersonal skills on the part of information systems personnel had a negative influence on the success of systems as measured by competitive advantage. Several reasons were forwarded to account for this unexpected result, including lack of richness in the measure of these skills, the use of strategic success measures, the true complexity of interpersonal relations within an organization, and the sample of CIOs who may have a bias in favor of technical skills. We address these concerns by incorporating a set of communication skills into the interpersonal skills set, sampling users for a different set of stakeholders, and employing a more complex model based on theories of expectation. The results indicate that the impacts of interpersonal skills on system success is not a simple function of the perceived level of the IS staff's skill proficiency but is also determined by the understood expectations of skill requirements.     

Single and Multiple Period Decision Models for Analysis of Quality and Quantity of Validation*

This paper investigates a single period model for the analysis of the impact of the quality of the validation effort. The single period model uses a Bayesian approach to find that validation is a critical point process. That model is then extended to allow for the uncertainty of the validation process to determine the quality of the underlying model. Some monotonicity results are developed for the model and investigated in light of the process being a critical point process. The model indicates that, consistent with comments from real world settings, the impact of the quality of the validation effort can be substantial. The paper also presents two multiperiod models of the impact of the quantity of the validation effort. In practice, the development of an expert system may follow a recurring multiperiod life cycle, where a prototype is built, the system is validated to determine how well it performs, and based on that performance, is either funded or not funded. The first multiple period model assumes that validation and funding occurs at each point in the PVF budget cycle. The model employs Bayesian revision of probabilities to update the prior probability of obtaining a model with an appropriate level of success. It is found that the critical point for multiperiod problems is different than that for single period problems. This model forms the basis of the second model. The second multiple period model extends the first by assuming that the quantity of validation can be varied. The more validation, the more likely that flaws in the model will be found. Thus, the more validation, the better the understanding of the level of performance of the model.     

Specifying Critical Inputs in a Genetic Algorithm-driven Decision Support System: An Automated Facility*

We present a simple scheme for the automated, iterative specification of the genetic mutation, crossover, and reproduction (usage) probabilities during run time for a specific genetic algorithm-driven tool. The tool is intended for supporting static scheduling decisions in flexible manufacturing systems. Using a randomly generated (base) test problem instance, we first assess the method by using it to determine the appropriate levels for specific types of mutation and crossover operators. The level for the third operator, reproduction, may then be inferred. We next report on its ability to choose one or more appropriate crossovers from a set of many such operators. Finally, we compare the method's performance with that of approaches suggested in prior research for the base problem and a number of other test problems. Our experimental findings within the specific scheduling domain studied suggest that the simple method could potentially be a valuable addition to any genetic algorithmbased decision support tool. It is, therefore, worthy of additional investigations.     

Structuring Conflict in Individual,Face-to-Face,and Computer-Mediated Group Decision Making: Carping Versus Objective Devil's Advocacy*

Researchers and practitioners have long been interested in the effects of cognitive conflict techniques on individual and group decision making. One widely used and studied technique, devil's advocacy (DA), has been found to enhance decision-making performance for both individuals and groups. Devil's advocacy begins with a recommended decision, followed by a critique of the decision that questions its assumptions. Researchers have not yet examined the effects of the objectivity of the devil's advocacy comments in computer-mediated environments. This paper reports the results of a laboratory experiment that focused on this question by comparing the effects of an objective, nonemotional DA to an emotional, “carping” DA within individuals and groups using either computer-mediated or face-to-face communication. In a manner consistent with prior research, both DA treatments were operationalized through the use of paper-based consulting reports. The results suggest that individuals and computer-mediated groups develop and consider more solution alternatives than face-to-face groups, and that subjects given the objective DA treatment produce higher quality decisions than those given the carping DA treatment. Face-to-face groups in the carping DA treatment considered the fewest alternative solutions in their decision-making process, reached the lowest solution quality, yet reached decision consensus in the fewest voting rounds. The practical implications of the results suggest that questioning statements made by a devil's advocate should be objective, regardless of group communication condition. Carping devil's advocacy appears to stifle group decision outcomes when groups are using face-to-face communication.