A Contingency Theory of Representational Complexity in Organizations
构建了一个正式模型，研究企业如何学习和使用环境表征，并识别出最优表征复杂性取决于环境复杂性、不确定性以及企业经验与知识，为简单与复杂表征孰优孰劣的争论提供了整合框架。
A long-standing question in the organizations literature is whether firms are better off by using simple or complex representations of their task environment. We address this question by developing a formal model of how firm performance depends on the process by which firms learn and use representations. Building on ideas from cognitive science, our model conceptualizes this process in terms of how firms construct a representation of the environment and then use that representation when making decisions. Our model identifies the optimal level of representational complexity as a function of (a) the environment’s complexity and uncertainty and (b) the firm’s experience and knowledge about the environment’s deep structure. We use this model to delineate the conditions under which firms should use simple versus complex representations; in doing so, we provide a coherent framework that integrates previous conflicting results on which type of representation leaves firms better off. Among other results, we show that the optimal representational complexity generally depends more on the firm’s knowledge about the environment than it does on the environment’s actual complexity. We also show that the relative advantage of heuristics vis-à-vis more complex representations critically depends on an unstated assumption of “informedness”: that managers can know what are the most relevant variables to pay attention to. We show that when this assumption does not hold, complex representations are usually better than simpler ones.