Christina Fang

Publications38
h-index13
Citations1,597
Highly Influential Citations102
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Balancing Exploration and Exploitation Through Structural Design: The Isolation of Subgroups and Organizational Learning
TLDR
It is argued that an organization divided into semi-isolated subgroups may help strike this balance between exploration and exploitation in organizational learning, and it is found that moderate levels of cross-group linking lead to the highest equilibrium performance.
The Economics of Strategic Opportunity
As emphasized by Barney (1986), any explanation of superior profitability must account for why the resources supporting such profitability could have been acquired for a price below their rent…
Predicting the Next Big Thing: Success as a Signal of Poor Judgment
TLDR
It is shown that an accurate prediction about such an extreme event, e.g., a big hit, may in fact be an indication of poor rather than good forecasting ability.
When hubs forget, lie, and play favorites: : Interpersonal network structure, information distortion, and organizational learning
TLDR
Using simulation models, it is found that moderately hubby networks outperform both very hubby and democratic networks and that moderate amounts of information omission or misrepresentation can be surprisingly beneficial to performance, though the patterns of their effects are strikingly different.
Strategizing with Biases: Making Better Decisions Using the Mindspace Approach
This article introduces strategists to the Mindspace framework and explores its applications in strategic contexts. This framework consists of nine effective behavioral interventions that are…
Inferring Superior Capabilities from Sustained Superior Performance: A Bayesian Analysis
TLDR
It is argued that a Bayesian framework in which decision makers take into account the differences in cumulative advantage provides for a correct inference and shows, using both simulated and real performance data, that the Bayesian method gives rise to estimates relevant for the inference problem.
From T-Mazes to Labyrinths: Learning from Model-Based Feedback
TLDR
A formal model is created in which the actors develop a mental model of the value of stage-setting actions as a complex problem-solving task is repeated, and partial knowledge, either of particular states in the problem space or inefficient and circuitous routines through the space, is shown to be quite valuable.
Perspective - Chance Explanations in the Management Sciences
TLDR
The concept of a chance explanation is defined; the theoretical mechanisms by which random variation generates patterns at the macro level are described; how key empirical regularities in management can be explained by chance models are outlined; and the implications of chance models for theoretical integration, empirical testing, and management practice are discussed.
Organizational Learning as Credit Assignment: A Model and Two Experiments
TLDR
This work outlines a theoretical model of organizational learning curves and shows that this model accounts for existing empirical regularities related to the learning curves, variations in learning rates, and organizational adaptation to new environments.
The Near-Term Liability of Exploitation: Exploration and Exploitation in Multi-Stage Problems
TLDR
It is shown that search can lead to more robust actions in multistage decision problems than maximization, a benefit quite apart from its role in the updating of beliefs.
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