On the brink: Predicting business failure with mobile location-based checkins

Lei Wang, Ram Gopal, Ramesh Shankar, Joseph Pancras

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Abstract Mobile-enabled location-based services are generating a huge amount of customer checkin data every day. It is vital to understand how small businesses, like restaurants, use this real-time data to make better-informed business operation decisions in this mobile marketing era. Using data collected from Foursquare, a leading location-based service provider, and Yelp, we aim to find out the predictive power of customer checkins on business failure of restaurants in New York City by using several predictive modeling techniques, such as Neural Network, Logit model and K-nearest neighbor. Our findings are encouraging. The customer checkin data from both a focal restaurant and its neighbors have shown strong predictive power on business failure. Compared to the baseline model in which we only use business characteristic variables to predict failure, incorporating the checkin data captured from location-based services gives a remarkable improvement on predictive accuracy. Our findings provide the foundation for future studies on the predictive power of information obtained from location-based services on business operations.

Original languageEnglish (US)
Article number12604
Pages (from-to)3-13
Number of pages11
JournalDecision Support Systems
Volume76
DOIs
StatePublished - Jul 14 2015

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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