The strategic management literature is unclear about how firm and industry effects influence performance, and the analysis of longitudinal data therein continues to be problematic. The authors analyze longitudinal data using hierarchical linear modeling to illustrate a random coefficients modeling alternative for examining firm performance. This approach allows researchers to explicitly model different conceptual approaches to testing change in performance over time and model predictor variables and cross-level interactions at multiple levels of analysis, and it also allows for investigation of time series errors. The results have implications for the strategic management field's goal of understanding multilevel determinants of firm performance over time.
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