Dynamic Adaptive Strategy-Making Processes for Enhanced Strategic Responsiveness

The frequent reporting of major corporate incidents and restructuring efforts combined with the sobering impacts of financial crisis over the past decade has focused our attention on the importance ofstrategic responsivenessin turbulent business environments. The global business environment is exposed to potentially extreme economic events (e.g., Taleb, 2007), technological shifts (e.g., Bettis and Hitt, 1995), and natural phenomena (e.g., Meyer and Kunreuther, 2017) that create disruptive strategic discontinuities. Organizations are facing complex dynamic business contexts where risk events often are difficult to predict and therefore hard to manage and respond to. Various studies suggest that large companies have a 10 percent chance of losing more than 25 percent of their market value (in a single month) due to dropping demand, flawed products, or competitor moves without being able to recover the loss (e.g., Andersen and Schrøder, 2010). Hence, the life expectancy of established Fortune 500 firms is waning with only a fifty-sixty percent chance to survive in their current forms over the next decade (e.g., Goodburn, 2015). That is, the ability to make proper adaptive decisions and engage in effective responsive actions seems as important as ever in a risk landscape characterized by high uncertainty and a potential for extreme events. This has relevance for public policy-makers and corporate decision-makers alike as the ability to respond to the changing conditions affect corporate performance and thereby the wealth creating business activities that support society. To assess the implications of adaptive decision-making and responsive strategies, we develop a simple model of firms with heterogeneous response capabilities as they experience ongoing environmental changes and adjust business activities to achieve a better fit with the evolving context. As a common approach in social science studies, we introduce a (simple) generic 2 computational model to gain deeper insights into internal organizational processes that are hard to study empirically (e.g., Levinthal and March, 1981; March, 1988; Levinthal, 1997). We first establish the basic strategic responsiveness model and extend it subsequently to investigate the effects of more fine-grained strategy-making processes. Computational analyses have demonstrated how strategic responsiveness is associated with the favorable performance and risk outcomes consistently observed in empirical data of corporate performance collated across different industry contexts. This preliminary result can be derived as the consequence of a generic model that captures the adverse (penalty) effects of a poor strategic fit, or environmental mismatch, caused by failed adaptation linked to various irrational decision behaviors. The generic model is extended by introducing a more refined strategy-making process that incorporates decentralized experiential insights as the source of ongoing updates to the strategic projections made in the central planning process. The analyses show how this dynamic adaptive strategy-making approach is associated with enhanced performance outcomes and lower performance variance. The following frames the study in the extant management literature before the rationale of the generic strategic responsiveness model, and the logic of interactive strategy-making, are developed and explained in more detail. We then present the effects of the generic strategic responsiveness model where results are validated empirically by industry data before the model is extended to consider the favorable effects of interactive strategy-making. Finally, we use the analytical results to consider adaptive strategies discussing the implications for effective strategy-making processes.