Index
Satya Banerjee Dr.
(National Institute of Fashion Technology, India)
Sanjay Mohapatra Dr.
(Xavier Institute of Management, India)
M. Bharati Dr.
(Veer Surendra Sai University of Technology, India)
ISBN: 978-1-80262-634-6, eISBN: 978-1-80262-633-9
Publication date: 14 February 2022
This content is currently only available as a PDF
Citation
Banerjee, S., Mohapatra, S. and Bharati, M. (2022), "Index", AI in Fashion Industry, Emerald Publishing Limited, Leeds, pp. 173-176. https://doi.org/10.1108/978-1-80262-633-920221008
Publisher
:Emerald Publishing Limited
Copyright © 2022 Satya Banerjee, Sanjay Mohapatra and M. Bharati. Published under exclusive licence by Emerald Publishing Limited
INDEX
Adaptive neuro-fuzzy inference system (ANFIS)
, 30–31
Aesthetic experience
, 32–34
Agglomeration schedule
, 9–10
Alexa
, 73–74
Algorithms
, 34
Allen Solly
, 105–106
Amazon
, 46–47, 122
American Psychological Association (APA)
, 52–53
Ancestry search
, 56–57
Anokhee
, 105–106
Artificial Intelligence (AI)
, 2–3, 10–11
fashion forecasting
, 31–32, 72, 76
Stylumia. See Stylumia
Artificial Neural Networks (ANN)
, 2–3
Baidu
, 35–36, 73–74
Bandwagon effect
, 17
BIBA Apparels Pvt. Ltd.
, 105–106
Binary coding
, 124
Bombay Selection
, 105–106
Boolean connectors
, 57
Brands
, 41–42, 75–76
Burberry
, 34–35, 83
Buying behaviour
, 24–25
Celebrity
, 24
Cheap clothing
, 22–23
Class differentiation
, 22–23
Click-through rates (CTRs)
, 79
Clothing parsing
, 78–79
Cluster analysis
, 113–114
Coding
, 108, 112
Content analysis
, 78
Cortana
, 73–74
COVID-19
, 105–106, 128
Co-word selection
, 56–57
Critical discourse analysis (CDA)
, 78
Customer satisfaction
, 126
Debenhams
, 34–35
Deep Learning (DL)
, 2–3, 30–31, 75–76
Delphi method
, 30–31
Dendrogram
, 9–10
Design thinking
, 32–34
Digital Object Identifiers (DOIs)
, 55
Dress code
, 21
EBSCO
, 55
E-commerce
, 34, 36, 80
E-forecasting
, 4–5
Electronic Word of Mouth (e-WOM)
, 42
Elle
, 2
Ethnic wear market, India
, 104–106
Euclidean distance matrix
, 113
Fabindia
, 105–106
Facebook
, 3, 37, 107
emergence and rise of
, 42–43
Indian Kurti. See Indian Kurti
qualitative data
, 9–10
social media engagement and
, 43–44
Facebook Messenger
, 109–110
Fashion auxiliary services
, 29
Fashion e-forecasting
, 76–83
Fashion icons
, 24
Fashion identity
, 65, 69, 89
conceptual model of
, 68
intelligence
, 76–83
Fashion industry
acceptance process
, 24
age and
, 21
business perspective
, 25–27
buying behaviour
, 24–25
cognitive perspective
, 17–19
consumer spendings
, 1
deep discounting
, 1
defining
, 16–17
developments
, 2–3
forecasting
, 2–3, 27, 30
gender and
, 20
identity and
, 22–24
intelligence
, 34–36
investment in
, 1
low shelf life products
, 1–2
magazines
, 2
methodological contributions
, 7
politics and
, 21
practical implications
, 7–9
psychological perspective
, 17–19
research design and methodology
, 9–11
research gaps
, 4–5
research objectives
, 5
sex impulse and
, 20–21
social media and
, 41–42
sociological perspective
, 17–19
theoretical contributions
, 6–7
tools
, 2
Fashion information
, 81–82
Fashion intelligence. See Stylumia
Fashion Intelligence Technology (FIT)
, 93–94
FashionUnited
, 35–36
Fast fashion
, 22–23
FBB
, 105–106
Forbes report
, 25–26
Forecast errors
, 125
Forecasting
, 2, 79–80
applications
, 3
Artificial Intelligence (AI)
, 31–32, 72, 76
consumer scan
, 29
contemporary approach
, 30–31
internet-based fashion
, 3
machine vision era
, 31–32
scanning
, 28
short-term
, 3
social media
, 29
Framework building
, 65
Fused Business
, 3
Gaussian mixture model
, 31–32
Gender
, 20
Glamour
, 2
Global Desi
, 105–106
Google
, 31–32
Google Books
, 57–58
Google Home
, 73–74
Google Maps
, 37
Google Scholar
, 55
Google trends
, 35–36
GQ
, 2
Harper’s Bazaar
, 2
Heuritech
, 35–36
Hierarchical clustering algorithm
, 9–10
H&M
, 105–106
Human Robot Interface (HRI)
, 2–3
IBM
, 31–32
Identity
, 22–24
Image processing
, 75–76
In-depth Interview (IDI)
, 97
Indian fashion industry
, 26
Indian Kurti
, 103–104
case methodology
, 107–108
case problem
, 106–107
cluster centres of
, 116
coding
, 112
data collection
, 110–112
dendrogram of
, 116
descriptive statistics of
, 114
ethnic wear market
, 104–106
result interpretation
, 117–118
sampling design
, 109–110
selection of attributes
, 108–109
selection of labels
, 109
statistical design
, 113–114
Instagram
, 3, 34–35, 93–94
Instyle
, 2
Inventory
, 125
ITC, India
, 27
JSTOR
, 55
Jwtintelligence
, 35–36
Keyword selection
, 56
K-means clustering algorithm
, 10
KNIME analytics
, 40–41
Kurta
, 104–105
Levis
, 105–106
Literature review
demonstration of
, 62
flow of
, 59
methodology of
, 51–61
reporting format and framework of
, 52–54
screening and filtering
, 60–61
shortlisting
, 60–61
sources and steps of
, 54–58
timelines of
, 58–60
Machine Learning (ML)
, 2–3, 75–76
Machine vision
, 31–32
MakerSights
, 34–35
Margins
, 124–125
Markdowns
, 125–126
planning
, 126
Marketing 4.0
, 44–47
Market Intelligence Technology (MIT)
, 93–94
Max
, 105–106
Meta-Analysis Reporting Standards (MARS)
, 52–53
Methodological Expectations of Cochrane Intervention Reviews (MECIR)
, 52–53
Method triangulation
, 97
Media exposure
, 23
Microsoft
, 31–32
Microsoft Excel Program
, 114–115
MOOSE
, 52–53
Myntra
, 93
Nalli
, 105–106
Natural Language Processing (NLP)
, 2–3, 75–76
Netnography
, 108, 110, 112, 123
Network analysis
, 78
Nextatlas
, 35–36
Non-functional demand
, 17
Nowcasting fashion
, 3
Pantaloons
, 105–106
Paris fashion week
, 34–35
Pepe Jeans
, 105–106
Pinterest
, 3, 34–35, 93–94
Platform text analytics
, 40–41
Point of Sale (POS)
, 29, 75–76
Politics
, 21
Preference proportion
, 108–109
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)
, 52–53
Pricing
, 125
Problem-solving
, 32–33
Product intelligence
, 82
Product life cycle
, 19
ProQuest
, 55
Purchase behaviour
, 24–25
Quick cluster
, 115
Real-time forecasting mechanism
, 106–107
Reference treeing
, 56–57
Relevancy
, 57
Reliance and Trends
, 105–106
Reporting format
, 52–54
Reverse coding
, 108
Scanning
, 28
Scopus
, 55
Seduction Principle
, 20–21
Self-identity
, 3–4, 22–23
Self-image
, 22–23
Sell-through rates
, 124
Semi-structured interviews
, 97
Sex impulse
, 20–21
Shifting erogenous zone
, 20–21
Short-range fashion forecasting
, 69, 71–72
Simple random sampling (SRS)
, 109–110
Siri
, 73–74
Snob effect
, 17
SOCH Studio
, 105–106
Social identity
, 3–4, 22–23
Social media
, 29, 34, 36, 77
facebook engagement and
, 43–44
fashion and
, 41–42
gratification
, 78
Web 2.0
, 36–41
Social networking sites (SNS)
, 80–81
SPSS 22.0
, 9–10
Stitch-fix
, 35–36
StyleSeek
, 79
Stylumia
, 10–11
case findings
, 98–100
case methodology
, 96–98
discussions and framework validation
, 98–100
genesis of
, 93–94
qualitative case methodology
, 94–96
Y-O-Y growth of
, 93–94
Systematic documentation
, 4
Text mining
, 78
Tiramisu
, 31–32
Tumblr
, 3, 34–35
Twitter
, 3, 83
Uber
, 46–47
U.S. Department of Commerce
, 26
Visual intelligence
, 75–76
Vogue
, 2
Walmart
, 93
Ward’s linkage
, 113–114
Web 1.0
, 36–37
Web 2.0
, 36, 41, 77
WhatsApp
, 104
Women’s wear, in India
, 105–106
Word of mouth (WOM)
, 41
YouTube
, 60–61, 96–97
Zappos
, 99–100
Zara
, 105–106, 122