Organizational and political responses to strategic surprises such as the credit crunch in 2008 and the pandemic in 2020 are increasingly reliant on scientific insights. As a result, the accuracy of scientific models has become more critical, and models have become more complex to capture the real-world phenomena as best as they can. So much, so that appeals for simplification are beginning to surface. But unfortunately, simplification has its issues. Too simple models are so generic that they no longer accurately describe or predict real-world cause-effect relationships. On the other hand, too complex models are hard to generalize. Somewhere on the continuum between too simple and too complex lies the optimal model. In this article, the authors contribute to the ongoing discussion on model complexity by presenting a logical and systematic framework of simplification issues that may occur during the conceptualization and operationalization of variables, relationships, and model contexts. The framework was developed with the help of two cases, one from foresight, a relatively young discipline, and the other from the established discipline of innovation diffusion. Both disciplines have a widely accepted foundational predictive model that could use another look. The shared errors informed the simplification framework. The framework can help social scientists to detect possible oversimplification issues in literature reviews and inform their choices for either in- or decreases in model complexity.

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Organizational responses to strategic surprises like the credit crunch in 2008 and the pandemic in 2020, are increasingly reliant on scientific insights. As a result, model accuracy has become more critical than ever, and model complexity has increased to help us capture the real-world phenomena we study. So much so, that appeals for simplification are beginning to surface. But unfortunately, simplification has its issues.

Too simple models are so generic that they no longer accurately describe or predict real-world cause-effect relationships. On the other hand, too complex models are accurate for a specific context and are difficult to generalize. Somewhere on the continuum between too simple and too complex lies the optimal model.

In this article, the authors contribute to the ongoing discussion on model complexity by classifying the problems with mismatches between real-world and model complexity. They present a framework of four levels of model complexity and possible oversimplification problems.

The framework can help scholars within the social sciences to detect possible oversimplification from literature reviews and inform choices for either in- or decreases in model complexity.
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This dissertation was prompted by its author’s amazement that only a handful of financial experts had read the arrival of the 2009 recession in the subprime mortgage problems in the American housing market. Despite hefty confrontations in the media between investment experts during the years leading up to the recession, it took the fall of Lehmann Brothers for the world to become aware of the effects of the subprime crisis. Such myopia is exemplary for weak signals: the strategic phenomena detected in the environment or created during interpretation, that are distant to the perceiving top-manager’s frame of reference. If top-managers perceive weak signals early enough and interpret them accurately, they can increase the resilience of their company. If they don’t, their companies run high risks. In the case of the great recession, the correct perceiving top-managers betted against mortgage-backed securities, and the rest had to take drastic measures to survive a double-dip recession. Whether or not having insights into the effective perception of weak signals can make or break companies...@en

The effect of ambiguous and non-salient information on the managerial interpretive process is rarely investigated, although this information is considered to be the basis for strategic decision-making and requires a different process due to its ill-defined nature. This paper explores the process that outperforming managers used to detect and interpret weak signals. Interviews with 13 top-managers were analyzed using a grounded theory approach to allow the process to emerge. The findings confirmed earlier conclusions on perceptual filters that reduce the number and type of signals assessed. Contrary to earlier findings, the process was significantly altered to compensate for this loss in two ways. First, managers deliberately postponed their assessment of a weak signal by discussing it with people with distinct and second, some managers deliberately searched for signals that were unfamiliar and not fitting their mental model. The sample consisted of outperforming top managers, which raised questions about the generalizability of the findings in specific directions: are the alterations in the weak signal process exemplary for outperformers only, and how does its success compare to formal foresight methods. Finally, the findings have managerial relevance since they indicate how to detect and bring weak signals into the company before impact.

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The effect of ex-ante information such as emerging technologies on the interpretive process is rarely investigated, although this type of information is considered to be the basis for strategic decision-making and requires a different process due to its ill-defined nature.

This paper explores the ways outperforming managers perceive emerging change in their environment. Top managers were interviewed to determine the role of signals of emerging change in assessments of their company’s environment. The open interviews were analyzed using grounded theory. Contrary to scientific models of the market environment, as suggested in the strategic management literature, the results illustrate that top managers do not use compartmentalized models. Instead, the interviewees seem to acknowledge that their environment is highly uncertain and cannot be modeled as a mutually exclusive and complete set of compartments. As a result, they must remain open to new signals from various directions and sources. The explorative study is based on 13 interviews and serves as a starting point for further research into managerial perceptions of signals of emerging change. The findings have managerial utility for the early detection of emerging change in the company environment, thus allowing time to protect against, prepare for, or profit from change. The article’s scientific value is created by the approach which allows to infer how managers perceive their company’s environment, rather than choosing a usual method that almost necessarily leads towards compartmentalized models. It also raises theoretical questions about the role of cognition in complexity theory and its application in field studies.@en