2026-09-18T20:48:09Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1451782022-08-30T03:30:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Palmer, Christopher
Paine, Fiona
Sloan School of Management
2022-08-29T16:38:24Z
2022-08-29T16:38:24Z
2022-05
2022-06-09T14:33:31.306Z
https://hdl.handle.net/1721.1/145178
This paper investigates the impact of firm data collection and analysis of collected data on the riskiness of firm cash flows. I use a scraped data set of the third party resources loaded on firms’ websites as a measure of firm data collection and analysis practices. I find that firm use of less effective web analytics is associated with an increase in the variance of sales, inventory, and both fixed and variable costs. This effect is despite a lack of change in the level of these variables. Looking at the effect of treatment on the treated, there is higher profit and sales variance during times of higher uncertainty. I use differences in web analytics technology and a change in their relative effectiveness as my identification strategy. As a case study of a large negative demand shock, I look at differences in firm reactions to COVID-19 based on their web analytics usage.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
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http://rightsstatements.org/page/InC-EDU/1.0/
Big Data and Firm Risk
Thesis
application/pdf
Master
Master of Science in Management Research
Publication
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<Abstract>This paper investigates the impact of firm data collection and analysis of collected data on the riskiness of firm cash flows. I use a scraped data set of the third party resources loaded on firms’ websites as a measure of firm data collection and analysis practices. I find that firm use of less effective web analytics is associated with an increase in the variance of sales, inventory, and both fixed and variable costs. This effect is despite a lack of change in the level of these variables. Looking at the effect of treatment on the treated, there is higher profit and sales variance during times of higher uncertainty. I use differences in web analytics technology and a change in their relative effectiveness as my identification strategy. As a case study of a large negative demand shock, I look at differences in firm reactions to COVID-19 based on their web analytics usage.</Abstract>
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