Big Data Needs Small Data: Exploring Digital Adaptability of Restaurants in the context of Covid-19 in Boston
Name
Shi-h_shi-smarchs-architecture-2023-thesis.pdf
Description
Thesis PDF
Size
78.26 MB
Format
Adobe PDF
Checksum (MD5)
c63142915d8d7b6f7930dd33002b775b
Author(s)
Shi, Huiwen
Advisor(s)
Mazereeuw, Miho
Date Issued
February 2023
Publisher
Massachusetts Institute of Technology
Abstract
Using a combination of quantitative and qualitative research methods, this research explores the relationship between digital engagement levels reflected through online reviews with restaurant digital adaptability in the context of the Covid-19 pandemic in Boston.
First, the project scraped 523,348 reviews for the 3,325 restaurants in Boston from 2004 to 2022, Feb. Using K-means clustering to analyze time series data based on positive review quantities, restaurants are clustered into four typologies for digital engagement level. Second, three neighborhoods are selected as study areas for different distributions of store typologies. Finally, site visits and interviews were conducted with store owners/managers among sampled neighborhoods. The findings reveal that the clustering result based on Yelp.com reflects restaurants' digitalization strategies. Second, it identifies that business digital adaptability is crucial for restaurants’ business resilience regardless of business type. Last but not least, the research discovers the limitation of using a single-sourced user-generated dataset due to market segmentation, identifying the necessity of ground-truthing exercises to validate the quality of the data.
MIT Department
Massachusetts Institute of Technology. Department of Architecture
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In Copyright - Educational Use Permitted
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