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Our need to understand the impact of communication media on negotiation is growing as technological advances offer negotiators more communication options. As access to technologies such as computer chat and videoconferencing increases, negotiators are choosing to use or to avoid these media without knowing the impact of their choices on negotiations. This research assesses objective and subjective negotiation outcomes, such as profit and outcome satisfaction, across four communication media with varying levels of media richness (face‐to‐face, videoconference, telephone, and computer‐mediated communication). A conceptual framework is offered to illustrate how media richness impacts objective and subjective outcomes. Results suggest that media richness affects required bargaining time, outcome satisfaction and the desire for future negotiation interaction. Thus, the communication media for negotiations should be chosen with care.

Product design is increasingly recognized as a critical activity that has a significant impact on the performance of firms. Consequently, when firms undertake a new (existing) product design (redesign) activity, it is important to employ techniques that will generate optimal solutions. As optimal product design using conjoint analysis data is an NP-hard problem, heuristic techniques for its solution have been proposed. This research proposes the use of and evaluates the performance of Genetic Algorithms (GA), which is based on the principles of natural selection, as an alternative procedure for generating "good" (i.e., close to optimal) solutions for the product design problem. The paper focuses on (1) how GA can be applied to the product design problems, (2) determining the comparative performance of GA vis-à-vis the dynamic programming (DP) heuristic (Kohli and Krishnamurti [Kohli, R., R. Krishnamurti. 1987. A heuristic approach to product design. Management Sci. 33(12) 1523--1533.], [Kohli, R., R. Krishnamurti. 1989. Optimal product design using conjoint analysis: Computational complexity and algorithms. Eur. J. Oper. Res. 40 186--195.]) in generating solutions to the product design problems, (3) the sensitivity of the GA solutions to variations in parameter choices, and (4) generalizing the results of the dynamic programming heuristic to product designs involving attributes with varying number of levels and studying the impact of alternate attribute sequencing rules.marketing, product policy, conjoint analysis, heuristics, buyers' welfare

K-means, Kohonen, Monte Carlo simulation, neural networks, nonhierarchical clustering,