Friday, August 28, 2015

Ripples of Fear: The Diffusion of a Bank Panic

GREVE Henrich, KIM Ji-Yub (Jay), TEH Daphne
Ripples of Fear: The Diffusion of a Bank Panic American Sociological Review (forthcoming)

Community reactions against organizations can be driven by negative information being spread through a diffusion process, which is distinct from the diffusion of organizational practices. A classic example of selective diffusion of negative information is offered by bank panics, which involve widespread bank runs although a low proportion of banks experience a run. We develop theory on how organizational similarity, community similarity, and network proximity create selective diffusion paths specifically for resistance against organizations. Using data from the largest customer-driven bank panic in the U.S., we find strong effects of organizational and community similarity on the diffusion of bank runs. Runs on banks are more likely to diffuse across communities with similar ethnicity, national origin, religion, and wealth, and across banks that are structurally equivalent or have the same organizational form. We also find stronger influence from runs that are spatially proximate and in the same state.

Thursday, August 27, 2015

Winner of 2015 J. Richard Hackman Award

JANG Sujin
Winner of 2015 J. Richard Hackman Award
Dissertation that Most Significantly Advances the
Study of Groups

Wednesday, August 26, 2015

Price to Compete ... with Many: How to Identify Price Competition in High Dimensional Space

Read the working paper
INSEAD Working Paper 2015/62/TOM

We study price competition in markets with large numbers (in magnitude of hundreds or thousands) of potential competitors, using the hotel industry as a test bed. We address two methodological challenges: simultaneity bias and high dimensionality. Simultaneity bias arises from joint determination of prices in competitive markets. We propose an instrumental variable approach to address simultaneity bias in high dimensions. The novelty of the idea is to exploit online search and clickstream data to uncover demand shocks at a granular level, with sufficient variations both over time and across hotels in order to obtain valid instruments at a large scale. We then develop a methodology to identify relevant competitors in high dimensions combining the instrumental variable approach with high dimensional l-1 norm regularization. Our approach is in contrast to many existing applications of high dimensional statistical models that, using either regression or clustering techniques, are primarily concerned with correlation rather than causality. We apply this data-driven approach to identify price competition patterns in the New York City hotel market. We found: 1) engagement in competition-based revenue management is prevalent across branded and non-branded hotels and across all quality tiers. It explains 35.0% of the within-hotel price variation, an additional 12.0% on top of demand based factors; 2) Branded hotels are more capable of preserving geographical and quality boundaries than independent hotels. Also, budget hotels are more likely to cross geographical boundaries but less likely to cross quality boundaries than upper scale hotels. 3) Branded hotels are both more influential and more influenced in setting prices.

Tuesday, August 25, 2015

Subjectively biased objective functions

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EURO Journal on Decision Processes (forthcoming)

The maximization of an objective function is a cornerstone of OR/MS modeling. How can we integrate subjective values within these models without weakening their scientific objectivity? This paper proposes a methodological answer that maintains the objective function and relaxes the maximization principle. We introduce a class of biased models that combine an objective function with a “subjective” factor that biases the maximization of such a function. We present the main properties of these models as well as the axiomatic foundations that allow for the rigorous measurement of biasing factors. We invite OR/MS scholars to participate in the development of practical applications integrating ethical and sustainability values.

Closed-Loop Supply Chains for Photovoltaic Panels: A Case-Based Approach

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Journal of Industrial Ecology (forthcoming)

Photovoltaic (PV) waste is expected to significantly increase. However, legislation on producer responsibility for the collection and recovery of PV panels is limited to the European Union (EU) Waste Electrical and Electronic Equipment Directive Recast, which lays down design, collection, and recovery measures. Academic knowledge of closed-loop supply chains (CLSCs) for PV panels is scarce. We analyze the supply chain using multiple cases involving the main stakeholders in the design, production, collection, and recovery of PV panels. Our article answers two research questions: How does the PV supply chain operate, and what are critical factors affecting the reverse supply chain management of used panels? Our research seeks to fill the gap in the CLSC literature on PV panels, as well as to identify barriers and enablers for PV panel design, collection, and recycling.