I Want You Back: The Interplay Between Legal Availability and Movie Piracy

Abstract

Although it is well known in the academic literature that anti-piracy measures can reduce the demand for pirated content, there are relatively few papers analyzing how legal availability impacts piracy. In this study, we answer two relevant research questions: (1) Does the availability of movies in legal digital channels reduce the demand for digital piracy? (2) Is the level of piracy prior to a movie’s release in a legal digital channel a reliable signal of legal demand after release? We answer these questions using a unique data set provided by a major motion picture studio. Our data contain 1520 catalog movies introduced to iTunes between April 2011 and April 2012. We find that iTunes availability leads to an 11.8% decrease in monthly piracy. We also find that pre-release piracy positively correlates with post-release electronic sell-through sales but not with video-on-demand sales.     

Testing alone is insufficient

Review of Economic Design - The fear of contracting a serious illness caused by a contagious disease limits economic activity even after reopening. Widespread testing alone will not alleviate this...     

Regulatory independence and thermal power plant performance: evidence from India

Journal of Regulatory Economics - We examine the role of regulatory independence on electricity generation performance in India. Electricity generation in India is exposed to several regulatory...     

Managing the retail operations in the COVID-19 pandemic: Evidence from Morocco

Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest-based prediction model with K-cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision-making model for BSE. From the analysis, the current retail performance index has been computed as “Average” level and the graph-theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning-based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events.