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