For large variance, this suggests that firms with the same values of capabilities/resources in _X may still realize different performance given other factors (e.g. environment) not pre-specified in _X . Alternatively, if each SBU possessed its own different _b ð _b Þ, then such structural heterogeneity could result in different realizations of performance while pursuing the same resource/capacity strategy. The hierarchical Bayesian RBV approach of Hansen et al. (2004) is an effective way of dealing with such structural heterogeneity in a continuous manner, but the approach requires multiple observations per SBU and somewhat ad hoc parametric assumptions concerning the forms of prior and hyper-prior distributions. Finally, level heterogeneity refers to different amounts of resources/capabilities ( _X i) possessed by each of the firms or SBU’s which also can lead to performance differences. Thus, one needs to identify the true source(s) of methodological heterogeneity in terms of a model form that can separate these various latent sources that can produce observed managerial heterogeneity.
The procedure proposed below will allow us to separate and identify these latent sources of methodological heterogeneity. At the managerial level, heterogeneity in performance may be observed, but the sources of it may be unclear or difficult to separate. For example, firms may show different levels in performance because they differ in terms of the capabilities they possess (level heterogeneity). Alternatively, they may have similar levels of capabilities, but may differ in terms of how well they exploit or utilize these capabilities to their advantage (structural heterogeneity). Or, there may be other unidentified sources of performance differences which transcend capabilities that are not included in the particular model (unexplained heterogeneity). The ways of identifying the sources of heterogeneity are therefore different and complementary.
Managerial heterogeneity considers the specific case of performance differences among rivals, yet only states that the competitors’ performances will differ. Methodological heterogeneity is defined more generally (i.e. not necessarily with respect solely to rival firms’ performances), and relates to different causes underlying managerial heterogeneity. Our proposed methodology has the capability to identify these different sources of heterogeneity in performance, which at the managerial level are not easily identified or separated.
We now describe the technical details of the proposed constrained latent structure regression procedure devised to accommodate these different sources of heterogeneity in the relationships between capabilities and performance according to the RBV. Latent structure or finite mixture models (รูปแบบผสมจำกัด) are utilized in statistics and psychometrics as a way to model structural heterogeneity. In particular, our goal is to empirically derive clusters or groups of firms derived from observed data and simultaneously obtain the relationships between firm capabilities and profitability per each derived cluster. Model selection heuristics are developed which identify the appropriate number of clusters or groups. The model framework accommodates user specified constraints regarding the positivity of the estimated coefficients. Posterior probabilities of firm membership in each derived cluster or group are simultaneously estimated as well. Note, the proposed methodology is sufficiently generalized to accommodate the examination of any designated resources and/or capabilities with any specified measurement of firm performance.
For large variance, this suggests that firms with the same values of capabilities/resources in _X may still realize different performance given other factors (e.g. environment) not pre-specified in _X . Alternatively, if each SBU possessed its own different _b ð _b Þ, then such structural heterogeneity could result in different realizations of performance while pursuing the same resource/capacity strategy. The hierarchical Bayesian RBV approach of Hansen et al. (2004) is an effective way of dealing with such structural heterogeneity in a continuous manner, but the approach requires multiple observations per SBU and somewhat ad hoc parametric assumptions concerning the forms of prior and hyper-prior distributions. Finally, level heterogeneity refers to different amounts of resources/capabilities ( _X i) possessed by each of the firms or SBU’s which also can lead to performance differences. Thus, one needs to identify the true source(s) of methodological heterogeneity in terms of a model form that can separate these various latent sources that can produce observed managerial heterogeneity.The procedure proposed below will allow us to separate and identify these latent sources of methodological heterogeneity. At the managerial level, heterogeneity in performance may be observed, but the sources of it may be unclear or difficult to separate. For example, firms may show different levels in performance because they differ in terms of the capabilities they possess (level heterogeneity). Alternatively, they may have similar levels of capabilities, but may differ in terms of how well they exploit or utilize these capabilities to their advantage (structural heterogeneity). Or, there may be other unidentified sources of performance differences which transcend capabilities that are not included in the particular model (unexplained heterogeneity). The ways of identifying the sources of heterogeneity are therefore different and complementary.Managerial heterogeneity considers the specific case of performance differences among rivals, yet only states that the competitors’ performances will differ. Methodological heterogeneity is defined more generally (i.e. not necessarily with respect solely to rival firms’ performances), and relates to different causes underlying managerial heterogeneity. Our proposed methodology has the capability to identify these different sources of heterogeneity in performance, which at the managerial level are not easily identified or separated.We now describe the technical details of the proposed constrained latent structure regression procedure devised to accommodate these different sources of heterogeneity in the relationships between capabilities and performance according to the RBV. Latent structure or finite mixture models (รูปแบบผสมจำกัด) are utilized in statistics and psychometrics as a way to model structural heterogeneity. In particular, our goal is to empirically derive clusters or groups of firms derived from observed data and simultaneously obtain the relationships between firm capabilities and profitability per each derived cluster. Model selection heuristics are developed which identify the appropriate number of clusters or groups. The model framework accommodates user specified constraints regarding the positivity of the estimated coefficients. Posterior probabilities of firm membership in each derived cluster or group are simultaneously estimated as well. Note, the proposed methodology is sufficiently generalized to accommodate the examination of any designated resources and/or capabilities with any specified measurement of firm performance.
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