eprintid: 10196692
rev_number: 13
eprint_status: archive
userid: 699
dir: disk0/10/19/66/92
datestamp: 2024-10-10 15:36:32
lastmod: 2024-10-10 15:36:32
status_changed: 2024-10-10 15:36:32
type: thesis
metadata_visibility: show
sword_depositor: 699
creators_name: Li, Xingyi
title: Unstructured Data in Digital Marketing and Supply Chain Management
ispublished: unpub
divisions: UCL
divisions: B04
divisions: F49
note: Copyright © The Author 2024. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).
abstract: The thesis focuses on the utilization of unstructured data in digital marketing and supply chain management. The first chapter, I explore the effect of expert opinions on consumer experience via the lens of consumer reviews in the restaurant industry, where the expert opinions are conveyed by Michelin stars. We apply two synthetic-control-based methods to estimate the effect of Michelin star changes on the sentiment and content of consumer reviews. We find that decreases in Michelin stars improve consumer review ratings, suggesting that the expectation effect of expert opinions is stronger than the reputation effect. In the second chapter, I move to explore how businesses adapt and respond to expert opinions. To do this, we analyze restaurants historical menus to explore how the restaurants responded
to Michelin star awards. We find that one of the reasons why restaurants with decreases in Michelin stars received higher star ratings after the star decrease is that they streamlined their menu structure and thereby improved the service quality. In the final chapter, we conduct a spend analysis of a procurement practice for manufacturers. We propose the three-component classification model to automate spend analysis and replicate the experts know-how. Using the spend data from Cranswick plc, a major food producer in the UK, we demonstrate improved accuracy of our methodology and superior performance compared to benchmark models.
date: 2024-09-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
thesis_award: Ph.D
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2310332
lyricists_name: Li, Xingyi
lyricists_id: XYLIX00
actors_name: Li, Xingyi
actors_id: XYLIX00
actors_role: owner
full_text_status: public
pages: 167
institution: UCL (University College London)
department: UCL School of Management
thesis_type: Doctoral
citation:        Li, Xingyi;      (2024)    Unstructured Data in Digital Marketing and Supply Chain Management.                   Doctoral thesis  (Ph.D), UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196692/2/PhD_Thesis.pdf