Wang, Lei, Gopal, Ram D., Shankar, Ramesh and Pancras, Joseph (2022) Forecasting venue popularity on location-based services using interpretable machine learning. Production and Operations Management, 31 (7). pp. 2773-2788. doi:10.1111/poms.13727 ISSN 1059-1478.

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Abstract

Customers are increasingly utilizing location-based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location-based services, which then better facilitate companies’ operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best-performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word-of-mouth effect are the top three drivers for venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location-based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision making.

Item Type:	Journal Article
Subjects:	H Social Sciences > HF Commerce T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Social Sciences > Warwick Business School > Information Systems & Management Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Location-based services, Customer relations -- Marketing, Social media -- Forecasting, Customer relations -- Data processing, Mobile computing, Mobile geographic information systems, Internet marketing
Journal or Publication Title:	Production and Operations Management
Publisher:	Wiley-Blackwell Publishing Ltd.
ISSN:	1059-1478
Official Date:	July 2022
Dates:	Date Event July 2022 Published 9 April 2022 Available 27 March 2022 Accepted July 2021 Submitted
Volume:	31
Number:	7
Page Range:	pp. 2773-2788
DOI:	10.1111/poms.13727
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	This is the peer reviewed version of the following article: Wang, Lei, Gopal, Ram, Shankar, Ramesh and Pancras, Joseph (2022) Forecasting venue popularity on location-based services using interpretable machine learning. Production and Operations Management, which has been published in final form at https://doi.org/10.1111/poms.13727. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	30 March 2022

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