Optimizing operation research strategy for design intervention: A framework for goms selection rule

Abstract

Designers often refer to task analysis for mapping user’s activity while conceptualizing system design. Task analysis substantially optimizes task performance by addressing human error during system design. Goals, Operators, Methods, and Selection rule (GOMS) is widely used technique of task analysis to enhance usability, human performance and overall productivity of the system. Although widely used, a major constraint of GOMS is its inability to support designers to envisage the outcome of decision making for method selection while performing different operations to achieve the goal. This drawback of GOMS hinders efficiency of task analysis and thereby affects designers in optimizing design interventions for end users. The present study was conducted to address the issue of Decision making in GOMS. An integrated framework consisting of payoff matrix in GOMS has been proposed and validated. To evaluate the usability of proposed framework, five user experience experts were asked to evaluate two popular e commerce websites. The payoff matrix was evaluated using Laplace criterion of decision making under uncertainty rule of operation research strategy. The empirical evidence from the study indicated that selection rules for a method using human factors affects design decisions. The proposed framework reported here would support designers in their decision make process for an optimized design intervention.