Until recently, scholars have customarily lumped multiple dimensions of environmental change into single constructs, and usually ascertained that the more the context changes, the more value firms derive from higher levels of exploration. In sync with more recent studies focusing on specific dimensions of change, in this paper we borrow theoretical elements from systems theory to examine the possibility that the reward to developing innovative product components may itself be eroded by implicit and yet burgeoning costs to fit the new component technology into existing architectures, thereby dampening system performance. Specifically, we theoretically assess how varying magnitudes of industry regulatory changes affect the optimum level of firm exploration, and propose—counterintuitively vis-à-vis past literature—that the more radical (i.e., competence destroying), as opposed to incremental (i.e., competence enhancing), these changes are, the more the optimum intensity of firm exploration recedes. Based on quantitative as well as qualitative empirical analyses from the Formula One racing industry, we precisely trace the observed performance outcomes back to the underlying logic of our theory, stressing that impaired capabilities to integrate the new component in the architecture redesign and time-based cognitive limitations both operate to inhibit the otherwise positive relationship between firm exploration and performance. In the end, we offer new insights to theory and practice

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

W hen considering the adaptive dynamics of organizations, it is important to account for the full set of adaptive mechanisms, including not only the possibility of learning and adaptation of a given behavior but also the internal selection over some population of routines and behaviors. In developing such a conceptual framework, it is necessary to distinguish between the underlying stable roots of behavior and the possibly adaptive expression of those underlying templates. Selection occurs over expressed behavior. As a result, plasticity, the capacity to adapt behavior, poses a trade-off as it offers the possibility of adaptive learning but at the same time mitigates the effectiveness of selection processes to identify more or less superior underlying roots of behavior. In addition, plasticity may mitigate the reliability with which practices are enacted. These issues are explored in the context of a computational model, which examines the interrelationship among processes of variation, selection, and plasticity.

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