Adjusted Goodness of Fit Index (AGFI)

, 129, 168

Adoption intention

, 157

Age

impact of age on usage of e-payment following five null hypotheses

, 92

analyze influence of demographic characteristics on e-payment based on

, 89–97

effect on SEPS model

, 174–177

Agenda 2030

, 9

Analysis of variance (ANOVA)

, 41

Analytical Hierarchy Process (AHP)

, 191

method

, 41–42

Attitude (ATT)

, 30

Automated-teller-machine (ATM)

, 5

Autonomy to Use Technology (AUTT)

, 134, 157, 174, 178–179, 186, 190

validation of AUTT through CFA

, 134–135

Average Variance Extracted (AVE)

, 144

Bartlett Test of Sphericity

, 126

Bartlett’s measure tests

, 125–126

Behavioural intention (BI)

, 39

Bharat Interface for Money (BHIM)

, 35–36

Bibliometric analysis (see also Confirmatory factor analysis (CFA))

, 23–25, 28

influential authors

, 26–27

influential countries

, 27

influential keywords

, 27–28

influential sources

, 28

phase

, 25

results of

, 26–28

Brundtland report (see United Nations World Commission on Environment and Development (1987))

Capital market

, 1–2

Cash on delivery (COD)

, 39

Chi-square test

, 115

Chi-square value normalized by degrees of freedom (CMIN/df)

, 144

Clear theory conceptualization

, 70

Co-authorship visualization

, 26–27

Comma-separated values (CSV)

, 25

Communication

, 140

benefits offered by e-payment

, 84–85

Comparative Fit Index (CFI)

, 129, 168

Competition, Confidence, Cost, and Convenience (4C)

, 6

Confirmatory analysis

, 69–70

Confirmatory factor analysis (CFA) (see also Bibliometric analysis)

, 14, 32, 70, 116, 144, 155–156, 162, 186

validation of AUTT through

, 134–135

validation of e-payment system factors through

, 129–143

validation of FSI through

, 137–138

validation of PIS through

, 143

validation of TC through

, 138–140

validation of TCP through

, 141

Consumer demographics

, 58–61

Coverage, Confidence, Cost, Convenience and Convergence (5C)

, 6

Cronbach’s reliability statistics of all statements

, 122–125

Cronbach’s α

, 118

Data extraction

, 25

Data reduction technique

, 126

Databases

, 191

selection

, 25

Demographic dossier of respondents

, 74–77

Demographics

, 161–162

Demonetization’

, 32–33

Department of Investment and Public Asset Management (Dipam)

, 3–4

Descriptive analysis of online payment

, 78–89

Descriptive approach

, 68

in e-payment system

, 68

Descriptive statistics

, 115

Diagnostic approach in e-payment system

, 68

Digital banking and payment (DBP)

, 38

Digital India

, 6

project

, 3–4

Digital payment (DP)

, 24–25, 53

Digital payment systems (DPSs)

, 32–33

Digital transactions

, 5

Digital wallets (DWs)

, 36

Digitalization

, 73

Diversity in customers

, 74

E-payment (EP)

, 5–6, 9–10, 29, 78–79, 186

impact of age on usage of e-payment following five null hypotheses

, 92

barriers faced using

, 83

barriers in acceptance of

, 84

based on gender, age, education and income, analyze influence of demographic characteristics on

, 89–97

communication benefits offered by

, 84–85

impact of education on usage of e-payment following five null hypotheses

, 92–97

features make e-payment as financial innovation

, 87–88

feedback of respondents on

, 79–80

gateway

, 6

impact of gender on usage of EP following five null hypotheses

, 91–92

impact of income on usage of e-payment following five null hypotheses

, 97

innovative features of financial service of e-payment

, 87

innovative features of financial technology of e-payment

, 85

preference for e-payment over other modes of payment

, 80–83

preference for payment mode

, 79

quality of

, 89

reasons for use of

, 80

reasons resist use of

, 89

E-payment system (EPS)

, 2–4, 23–24, 53, 73, 113, 115, 155–157, 186

AUTT

, 132–135

Cronbach’s reliability statistics of all statements

, 122–125

and data security

, 52

descriptive and diagnostic approach in

, 68

and e-commerce

, 44–51

Eigen value

, 126

and financial services

, 44–51

and financial technology

, 51

FSI

, 136–138

identification of factor structure of E-payment system through EFA

, 118–129

and network security

, 52

perception of users towards

, 102

PIE

, 131–132

PIS

, 142–143

predictive approach in

, 68–71

prescriptive approach in

, 71

TC

, 138–140

TCP

, 140–141

total variance

, 126

validation of e-payment system factors through CFA

, 129–143

validation of explored factors for

, 143–144

validity test for sample adequacy

, 125–126

Ease of use (EOU)

, 31, 46, 51, 157–158

Economic sustainability

, 8

Education

analyze influence of demographic characteristics on e-payment based on

, 89–97

impact of education on usage of e-payment following five null hypotheses

, 92–97

effect on SEPS Model

, 177–178

Eigen Value

, 126

Electronic commerce

, 27–28

Electronic Commerce Research and Applications (ECRA)

, 33

Electronic payment (e-payment)

, 2–3

Electronic word-of-mouth (e-WOM)

, 39, 46, 51

Electronic-Clearing-Service (ECS)

, 5

Elimination process

, 116

Empirical analysis, methodology for

, 68–71

Environmental sustainability

, 8

Epistemology

, 57

Exploration

, 63

Exploratory factor analysis (EFA)

, 14, 32, 69, 116, 186

identification of factor structure of e-payment system through

, 118–129

Exploratory Principal Component Factor Analysis (EPCFA)

, 122

Exploratory research

, 63

Facilitating conditions (FC)

, 30, 46, 51

Factor-analysis-method

, 69–70, 118, 121

Feedback of respondents on e-payment

, 79–80

Financial innovation

, 6–7, 53–54, 115

analyze influence of demographic characteristics on e-payment based on gender, age, education and income

, 89–97

customer perception towards

, 73

demographic dossier of respondents

, 74–77

descriptive analysis of online payment

, 78–89

features make e-payment as

, 87–88

justification

, 73–74

justification

, 115–116

methodology for scale development

, 116–118

perception of users towards e-payment system

, 102

results

, 102–114

shopping e-payment generally used for

, 78–79

validation of e-payment system factors through CFA

, 129–143

validation of explored factors for e-payment system

, 143–144

Financial market

, 1–2, 23

bibliometric analysis

, 25–28

citation reporting

, 44

contribution

, 187

contribution of research work

, 18

descriptive and diagnostic approach in e-payment system

, 68

e-payment

, 5–6

financial innovation

, 6–7

framework and design in financial market research

, 57–58

future of financial market

, 44–53

justification

, 58–61

limitation and scope of work

, 191

managerial implication

, 187–191

methodology

, 24

methodology for empirical analysis

, 68–71

motivation for study and gap analysis

, 9–11

predictive approach in e-payment system

, 68–71

prescriptive approach in e-payment system

, 71

research design

, 63–68

research design and outcomes

, 12–14

research gaps

, 53–55

research insights

, 23–24

research-work organization

, 14–17

review of studies

, 28–44

review of studies undertaken abroad

, 37–44

review of studies undertaken in India

, 30–37

scope and objectives of study

, 11–12

SLR

, 24–25

sustainability

, 7–9

theoretical perspective

, 5–9

Financial service of e-payment, innovative features of

, 87

Financial sustainable innovation (FSI)

, 136–138, 157, 186, 190

validation of FSI Through CFA

, 137–138

Financial system

, 1–2, 23, 52–53

core of

, 2

Financial technology of e-payment, innovative features of

, 85

Food Sustainability

, 53–54

Gender

analyze influence of demographic characteristics on e-payment based on

, 89–97

effect on SEPS Model

, 170–174

impact of gender on usage of EP following five null hypotheses

, 91–92

Goodness of Fit Index (GFI)

, 129, 168

Goodness-of-Fit (GOF)

, 69–70, 129

Goverment-of-India

, 5–6

Government-to-person (G2P)

, 41

Hypothesis testing

, 14

Immediate-Payment-Service (IMPS)

, 5

Income

analyze influence of demographic characteristics on e-payment based on

, 89–97

effect on SEPS Model

, 179–180

impact of income on usage of e-payment following five null hypotheses

, 97

India

, 2–3

review of studies undertaken in

, 30–37

Innovate’

, 7

Innovative payment system

, 137

Intention to adopt (ITA)

, 157–158

ITA EPS

, 157–158

mobile payments

, 168–169

Intention to use (ITU)

, 30

ITU MPs

, 30

Internal consistency

, 118

estimation of reliability

, 118

estimation of validity

, 118

International Journal of Electronic Commerce (IJEC)

, 28, 33

Interpretive Structural Modelling (ISM)

, 191

Item statistics

, 118

ITU-quick-response-MP (ITU-QR-MP)

, 39

Jordan MP system (JoMoPay system)

, 38

Journal of Banking and Finance (JBF)

, 33

Journal of Money, Credit and Banking (JMCB)

, 33

JSON (database)

, 25

JSTOR (database)

, 191

Kaiser–Meyer–Olkin (KMO)

, 125

Keyword Search

, 25

Knowledge generation process

, 57

Literature review (LR)

, 12, 24

Majlis-Bandaraya-Shah-Alam-and-Majlis-Daerah-Kampar (MBSAMD)

, 43

Measure of Sampling Adequacy (MSA)

, 125

Meta-literature review

, 191

Ministry of Electronics & Information Technology (MeitY)

, 6

Mobile payments (MPs)

, 30

Mobile point-of-sale (MPOS)

, 42

Monte Carlo methods

, 66–67

Multi-factor model approach

, 143–144

National Capital Region (NCR)

, 36

National-Electronic Fund Transfer (NEFT)

, 5

New technology (NT)

, 7–8

Normed Fit Index (NFI)

, 129, 168

Offline-data-collection questionnaire

, 64

Online payment, descriptive analysis of

, 78–89

Online questionnaire

, 67

Ontology

, 57–58

Payer, purchase, physical technology and payment instrument (4p)

, 41

Payment and Settlement Act (2007)

, 5

Payment and Settlement Systems (PSS)

, 6

Payment information security (PIS)

, 137, 142–143, 157, 186, 190

validation of PIS through CFA

, 143

Payment infrastructure ecosystem (PIE)

, 131–132, 157, 178–179, 186, 190

validation of PIE through CFA

, 132

Payment system

, 147

Perceived ease of use (PEOU)

, 31, 46, 51, 157–158

Perceived risk (PR)

, 31, 46, 51

Perceived security (PS)

, 38, 46, 51, 169

Perceived usefulness (PU)

, 31, 46, 51, 157–158

Performance expectancy (PE)

, 30, 46, 51

Personal descriptive

, 74

Post-demonetization phase, India

, 3

Post-IT phase, India

, 3

Pre-IT phase, India

, 3

Predictive approach

, 68

in e-payment system

, 68–71

methodology for formulation of structural model

, 70–71

methodology for scale development

, 69

methodology for scale validation

, 69–70

Prescriptive approach

, 68

in e-payment system

, 71

Principal component analysis (PCA)

, 69, 116, 121–122

method

, 118

PubMed (databases)

, 25

Quality of e-payment

, 89

Questionnaire, design of

, 63–65

elements of survey questionnaire

, 64–65

R-matrix

, 125–126

Reliability, estimation of

, 118

Research design

, 63–68

data

, 67–68

data collection

, 67

design of questionnaire

, 63–65

exploratory research

, 63

and outcomes

, 12–14

population and sampling area

, 65

process

, 12

sample size

, 65–67

Research-work organization

, 14–17

Respondents, demographic dossier of

, 74–77

Review of literature

, 23–24

Root Mean Square Error of Approximation (RMSEA)

, 129, 144, 168

Scale development, methodology for

, 69, 116, 118

identification of factor structure of e-payment system through EFA

, 118–129

internal consistency and validity measurement

, 118

item evaluation and selection

, 116–118

scale refinement

, 116

Scale validation, methodology for

, 69–70

Scopus (database)

, 25–26

Segmentation Approach to Sustainability of E-payment System on Customer Perspective (SEPSCP)

, 186–187

Single-factor model approach

, 134, 137, 141, 143

Small and Medium Enterprises (SMEs)

, 38

Smart-Partial-Least-Square (Smart-PLS)

, 31

Social influence (SI)

, 30, 46, 51

Social sustainability

, 8

Standardized Root Mean Square Residual (SRMR)

, 129

Statistical package for social sciences (SPSS)

, 67–68

Structural equation modelling (SEM)

, 14, 31, 58, 61, 71, 129, 162, 164

approach

, 155–156

estimation of structural model of SEPS through

, 164–170

tests

, 70

Structural equation models (SE models)

, 70, 163

Structural model (SM)

, 155–156

estimation of structural model of SEPS through SEM

, 164–170

methodology for formulation of

, 70–71

Sustainability

, 7, 9, 53–54, 157, 187

estimation of structural model of SEPS through SEM

, 164–170

of financial innovation

, 155–156

justification of study

, 156–157

literature

, 9

measuring moderating effects of demographic characteristics on SEPS Model

, 170–180

research model and hypothesis development

, 157–162

SEM

, 162–164

variables/factors used in study

, 157

Sustainability of e-payment system (SEPS)

, 4–5, 11, 70, 164–165, 186–187

age effect on

, 174–177

education effect on

, 177–178

estimation of structural model of SEPS Through SEM

, 164–170

gender effect on

, 170–174

income effect on

, 179–180

marital status effect on

, 179

measuring moderating effects of demographic characteristics on SEPS model

, 170–180

type of family effect on

, 178–179

Sustainable development goals (SDGs)

, 158–159

Systematic literature review (SLR)

, 12, 14, 23–25

Technology

, 37

autonomy to use

, 132–135

validation of AUTT Through CFA

, 134–135

Technology Acceptance Model (TAM)

, 39

Technology Characteristics (TC)

, 138, 140, 157, 186, 190

validation of TC through CFA

, 138–140

Technology Communication Platform (TCP)

, 140–141, 157, 186, 190

validation of TCP through CFA

, 141

Total variance

, 126

Trust’

, 157

Unified-Theory-of-Acceptance-and-Use-of-Technology model (UTAUT model)

, 30, 38

United Arab of Emirates (UAE)

, 39, 164

United Nations (UNs)

, 9

United Nations Population Division (UNPD)

, 2

United Nations World Commission on Environment and Development (1987)

, 8

University of Ilorin (UI)

, 43

Usage behaviour (UB)

, 39

Valence-technology-affordances and-constraints-theory’ framework (Valence-TACT framework)

, 30

Validity

estimation of

, 118

estimation of reliability

, 118

estimation of validity

, 118

measurement

, 118

test for sample adequacy

, 125–126

Variable elimination process

, 67–68

Variables/factors used in study

, 157

VOSviewer software

, 25–26

Web of Science (database)

, 25, 191

World Summit

, 8