recommendations to managers on which capabilities should receive additional investment in order to maximize financial performance. We contribute to the understanding of the performance effects of investing in capabilities in the RBV framework, which as noted above has been lacking, especially in the areas of IT capabilities.
An important contribution of our model is that we are able to discern the sources of managerial heterogeneity among firms in our sample. As noted earlier, performance heterogeneity among rival firms has usually been defined in managerial terms (Hoopes et al. , 2003; Peteraf, 1993), referring to firm capabilities and their ensuing effects on firm performance. Left unanswered in this definition is the root cause of performance heterogeneity. Our methodology allowed us to separate out and identify three different possible sources of managerial heterogeneity. We found, for example, that the four derived groups differed greatly in terms of their profitability performances, and in terms of which capabilities were most closely related to performance. We did not, however, find significant differences across the firms in terms of the actual levels of capabilities possessed. This finding suggests that structural heterogeneity rather than level heterogeneity is most prevalent in the particular sample studied. Since, industry effects were found to be insignificant, this finding seems to be valid across all industries included in this sample. This finding is important to managers: it suggests that the simple possession, or acquisition, of additional capabilities (either by investing in external acquisition or internal development) is not necessarily the path to improved performance. Rather, the better-performing firms (i.e. those in Groups 3 and 4) seem to be able to exploit and utilize the capabilities they have better than the other firms. We examined relationships with our derived latent groups and type of industry and strategic types. Surprisingly, there were no significant relationship found between these four latent groups and type of industry suggesting that this form of heterogeneity is endemic across different industries. We did find a highly significant relationship between these four latent groups and the Miles and Snow (1978) typology, although the mapping from one to the other was by no means clear with substantial mixing.
There are certain limitations to our study. It is unclear whether the groups we estimate are generalizable to other industries, or other countries or geographical regions, not included in our study. It is possible that another solution, not necessarily with four groups, may dominate our empirically-derived solution in terms of fit for a different sample. We certainly do not claim that structural rather than level or uncertainty heterogeneity will always be the dominant source of performance heterogeneity. The appealing feature of our methodology is that it could be applied to
any empirical sample including different kinds of industries (i.e. consumer-oriented, service-oriented, or inclusive of different geographical regions) to determine if heterogeneity exists there, and if so, what the sources seem to be empirically prevalent.
Extensions of this study could focus on identifying the specific groups found in other environmental contexts. Nevertheless, we believe the constrained latent structure regression methodology presented here can be successfully used in understanding strategic decision making and performance outcomes in a wide range of contexts.
Note
1. In this procedure, an SBU is classified as a prospector if the majority of responses to the 11-item scale correspond to the prospector answers. A similar rule is used to classify SBUs into the other three strategic types.