Con siderable attention in the information systems and management science literature has focused on computer-based modeling environments, sometimes called integrated modeling environments or model management systems. This research has been primarily concerned with suggesting features/components of modeling environments such as improved executable modeling languages for model creation, integration, and data representation; specialized database systems for managing model data; and customized model-solver software. However, there has been little (if any) empirical guidance offered in the literature about the specific needs of business and industry for computer-based integrated modeling environments. Using a data set compiled from a national survey of modelers (analysts) and model users (decision makers), we empirically investigate the validity of several of the key assumptions of modeling environment research reported in the literature, and examine the relationships between the modeling factors: data complexity, model complexity, modeling intensity, modeler/user requirements, and need for computer-based integrated modeling environments in organizations. Our empirical analysis of the data set shows that practitioners rank automated access to model data and automated error checking (e.g., model syntax and semantics checking) high as desirable components in modeling environments. We find that users prefer to have modeling environments linked to their current modeling and modeling-support software systems.Our findings further suggest that a high percentage of modelers and users are dissatisfied with the software systems they are currently using to support their modeling activities. Finally, a covariance structure analysis of the modeling environment factors clearly shows that: (a) model complexity has a direct positive effect on modeling intensity; (b) data complexity has an insignificant direct effect on modeling intensity, but has a negative effect on modeler/user req...