Over the past two decades, the resource-based view of the firm (RBV) has emerged as one of the more influential paradigms from the field of strategic management. However, the theory has been subjected to a number of criticisms, particularly related to the use of methodologies in past research. Many RBV studies have tended to use averaged findings across broad industry samples. Approaches reliant on "averaging" methods will only uncover what is the case for the average, "representative" firm, and will not identify those unique, firm-specific assets that can result in sustained profitability. In order to examine the implications of methodological choice and the RBV, the subjective approach of Q methodology was used in a sample of music industry firms to identify a key resource set for the context of interest, identify strategic groups within the sample based on resource emphasis, and explore the ways in which managers use their resources to generate firm profits. A comparative approach examined resource outcomes by performance group, over multiple levels of analysis. The findings revealed (i) a number of relevant and new, context-specific resources from the music industry, (ii) the identification of three distinct clusters of firms that emerged from the sample based on resource preferences, firm characteristics, and managerial perceptions (iii) key resource findings that varied by level of analysis and by firm performance, and (iv) distinct processes through which the resources become valuable at the level of the firm--even when the same resources are considered. The outcomes of this thesis illustrate how methodological choice can affect findings when using the RBV to uncover important sources of advantage. Furthermore, the outcomes in this thesis point to the weaknesses of many past RBV studies that investigate the impact of resources and capabilities on firm performance, and remind scholars that a defining feature of the RBV is that its intention was to identify sources of advantage at the level of the firm. Moreover, the findings show that past RBV research using aggregated data across multi-industry samples can be misleading in its prescription to managers.

In designing inventories of wildland vegetation, two of the many basic methodological choices are: 1) whether data are collected, reduced, and stored in discrete classes or as continuous variables, and 2) whether data are gathered as general purpose variables to bear upon many questions, or as specific purpose variables optimized for only one type of prediction. The effects of these two choices on accuracy of vegetation inventories to predict plant community production were examined by comparing regression models built upon differing sets of independent variables "inventoried" from a common data base. Contrary to expectations, discrete variables of classified community types were better predictors of plant community production than the same vegetation data reduced as continuous variables by three ordination techniques. Substitution of specific purpose soil and vegetation variables thought to be especially relevant to production did not improve correlations from those of analogous general purpose variables. These results do not show the anticipated accuracy loss of general purpose inventory variables, but such findings cannot yet be generalized to other situations. Implications for the design of practical, extensive survey methods for wildland vegetation are briefly discussed.