Cross-impact analysis (CIA), as a means of futures research, reveals the characteristic role of a variable in relation to all other variables within a system and identifies those variables that play a significant role in the development of the system in the future. Systematic description of all potential interactions between a given set of variables and the assessment of the strength of these interactions are the main steps of the analysis. A critical weakness of CIA is that it does not incorporate the time impact into the analysis. In reality, an event (or a variable) affects another one with a time lag and knowing the time relationship between events is no less important than knowing the causal relationship. In this paper, we propose a complementary approach to CIA including the time impact. The proposed approach begins by identifying the time lags in which the initial causal impact between each pair of variables emerges. Next, the cross-impact matrix is revised and in order to determine the role of each variable these revised impacts are weighted by time. An illustrative example is included to demonstrate the proposed approach. (c) 2006 Elsevier Inc. All rights reserved.